system

Abstract

The system according to the embodiment aims to automatically generate an itinerary according to a user's preferences and update it in real time. [Solution] The system according to the embodiment includes a reception unit, a generation unit, an update unit, a provision unit, and a management unit. The reception unit receives information on a user's attributes and preferences. The generation unit generates an appropriate itinerary based on the information received by the reception unit. The update unit instantly updates the itinerary generated by the generation unit in response to changes made by the user. The provision unit provides pre-travel information. The management unit manages post-travel archives.

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 technology has had the problem of making it difficult to automatically generate itineraries that meet a user's preferences and update them in real time.

[0005] The system according to the embodiment aims to automatically generate an itinerary according to a user's preferences and update it in real time. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a generation unit, an update unit, a provision unit, and a management unit. The reception unit receives information on a user's attributes and preferences. The generation unit generates an appropriate itinerary based on the information received by the reception unit. The update unit instantly updates the itinerary generated by the generation unit in response to changes made by the user. The provision unit provides information before the trip. The management unit manages archives after the trip. [Effects of the Invention]

[0007] The system according to the embodiment can automatically generate and update itineraries in real time according to the user's preferences. [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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. 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) In an embodiment of the present invention, a travel suggestion system automatically creates multiple itineraries with catchy titles after a user inputs their attributes, preferences, and images similar to the scenery they want to see. The system then arranges transportation, adjusts routes, and accommodations, and researches recommended tourist spots to create an itinerary that includes these. Furthermore, users can change parts of the itinerary or replace it with a better plan, and the itinerary is updated in real time to be executable. Once the plan is finalized, a list of preparations required before and on the day is displayed to prevent forgetting items. This system addresses the needs of users who are unsure about trip preparations, find travel agency plans expensive and inaccessible, or want to customize their trip but find research tedious. Before the trip, the system provides trivia about the destination and information on how to have fun. After the trip, the information is saved in a shareable archive, and new itineraries are suggested based on the satisfaction level of past trips. This makes travel even more enjoyable. For example, when a user inputs their attributes and preferences, the system generates an optimal itinerary based on that information. The system arranges transportation, adjusts routes, and books accommodations, and researches recommended tourist spots, creating an itinerary that includes all of these. Users can change parts of the itinerary or replace it with a better plan, and the itinerary is updated accordingly in real time to make it actionable. Once the plan is finalized, a list of preparations that need to be made before the trip and things that will be necessary on the day is displayed to prevent users from forgetting anything. Before the trip, the system provides trivia about the destination and information on how to have fun, and after the trip, this information is saved in an archive that can be shared, and new itineraries are suggested based on satisfaction with past trips. This allows the travel suggestion system to generate optimal itineraries based on user attributes and preferences, update them in real time, and provide and manage information before and after the trip.

[0029] A travel suggestion system according to an embodiment includes a reception unit, a generation unit, an update unit, a provision unit, and a management unit. The reception unit receives information about a user's attributes and preferences. The user's attribute and preference information includes, but is not limited to, age, gender, travel purpose, and activities of interest. The reception unit, for example, stores the information entered by the user in a database and provides it to the generation unit. The generation unit uses a generation AI to generate an optimal itinerary based on the information received by the reception unit. The generation AI creates an itinerary by combining transportation means, routes, accommodations, tourist spots, and other information based on the user's attribute and preference information. The generation unit generates an itinerary in response to, for example, a prompt from the generation AI saying, "Please generate an optimal itinerary based on the user's attributes and preferences." The update unit updates the itinerary in real time in response to user changes. For example, if the user makes changes to the itinerary, the update unit generates a new itinerary reflecting the changes. The update unit can also regenerate the itinerary based on the user's changes using the generation AI. The provision unit provides pre-travel information. The providing unit provides, for example, weather information, traffic information, and information on tourist attractions at the travel destination. The providing unit can also use the generation AI to provide optimal information based on the user's attributes and preferences. The management unit manages post-travel archives. The management unit, for example, stores travel photos, diaries, reviews, and the like so that the user can refer to them later. The management unit can also use the generation AI to suggest new itineraries based on the user's past travel history. As a result, the travel suggestion system according to the embodiment can generate optimal itineraries based on the user's attributes and preferences, update them in real time, and provide and manage information before and after the trip.

[0030] The reception unit can analyze the user's past travel history and automatically suggest appropriate attribute and preference information. For example, the reception unit can suggest similar travel destinations based on data on travel destinations the user has visited in the past. The reception unit can also automatically extract and suggest preferred activities from the user's past travel history. Furthermore, the reception unit can suggest similar accommodations based on data on accommodations the user has used in the past. This makes it possible to generate an itinerary that suits the user's preferences by suggesting optimal information based on the user's past travel history. Some or all of the above-mentioned processing in the reception unit may be performed using, or without, AI. For example, the reception unit can input the user's past travel data into the generation AI and have the generation AI suggest optimal attribute and preference information.

[0031] When inputting attribute and preference information, the reception unit can perform filtering based on the user's current living situation and areas of interest. For example, when the user inputs their current living situation, the reception unit can suggest related travel destinations based on that information. Furthermore, when the user inputs their areas of interest, the reception unit can also suggest related activities based on that information. Furthermore, the reception unit can filter unnecessary information and display only necessary information based on the user's current living situation and areas of interest. By filtering information based on the user's current living situation and areas of interest, more relevant information can be provided. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's living situation data to a generation AI and have the generation AI perform filtering.

[0032] When generating an itinerary, the generation unit can propose an appropriate itinerary based on the user's past travel history. For example, the generation unit can propose similar travel destinations based on data on travel destinations the user has visited in the past. The generation unit can also automatically extract and propose preferred activities from the user's past travel history. Furthermore, the generation unit can also propose similar accommodations based on data on accommodations the user has used in the past. In this way, an itinerary that suits the user's preferences can be generated by proposing an optimal itinerary based on the user's past travel history. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's past travel data into the generation AI and cause the generation AI to propose an optimal itinerary.

[0033] When generating an itinerary, the generation unit can customize the itinerary based on the user's current living situation and areas of interest. For example, when the user inputs their current living situation, the generation unit can suggest related travel destinations based on that information. Furthermore, when the user inputs their areas of interest, the generation unit can suggest related activities based on that information. Furthermore, the generation unit can filter unnecessary information and display only necessary information based on the user's current living situation and areas of interest. This allows the itinerary to be customized based on the user's current living situation and areas of interest, thereby providing a more relevant itinerary. Some or all of the above-described processing in the generation unit may be performed using, or without, AI. For example, the generation unit can input the user's living situation data into the generation AI and have the generation AI customize the itinerary.

[0034] When updating an itinerary, the update unit can analyze the user's past change history and suggest an appropriate update method. For example, the update unit can suggest similar changes based on data on itineraries changed by the user in the past. The update unit can also automatically extract and suggest preferred activities from the user's past change history. Furthermore, the update unit can suggest similar accommodations based on data on accommodations changed by the user in the past. This makes it possible to provide an itinerary that suits the user's preferences by suggesting an optimal update method based on the past change history. Some or all of the above-mentioned processing in the update unit may be performed using, for example, AI, or may be performed without using AI. For example, the update unit can input the user's past change data into a generation AI and have the generation AI suggest an optimal update method.

[0035] When updating an itinerary, the update unit can customize the update content based on the user's current living situation and areas of interest. For example, when the user inputs their current living situation, the update unit can suggest related travel destinations based on that information. Furthermore, when the user inputs their areas of interest, the update unit can suggest related activities based on that information. Furthermore, the update unit can filter unnecessary information and display only the necessary information based on the user's current living situation and areas of interest. This allows for customizing the update content based on the user's current living situation and areas of interest, thereby providing a more relevant itinerary. Some or all of the above-described processing in the update unit may be performed using, or without, AI. For example, the update unit can input the user's living situation data into a generation AI and have the generation AI customize the update content.

[0036] When providing information, the providing unit can analyze the user's past travel history and provide appropriate information. For example, the providing unit can provide information on similar travel destinations based on data on travel destinations the user has visited in the past. The providing unit can also provide information on preferred activities based on the user's past travel history. Furthermore, the providing unit can also provide information on similar accommodations based on data on accommodations the user has used in the past. This makes it possible to provide optimal information based on the user's past travel history and provide information that matches the user's preferences. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past travel data into a generation AI and cause the generation AI to provide optimal information.

[0037] The providing unit can customize information based on the user's current living situation and areas of interest when providing information. For example, when the user inputs their current living situation, the providing unit can provide information on related travel destinations based on the input. Furthermore, when the user inputs their areas of interest, the providing unit can also provide information on related activities based on the input. Furthermore, the providing unit can filter unnecessary information and display only necessary information based on the user's current living situation and areas of interest. This allows for more relevant information to be provided by customizing the information based on the user's current living situation and areas of interest. Some or all of the above-described processing in the providing unit may be performed using, or without, AI. For example, the providing unit can input the user's living situation data into a generating AI and have the generating AI customize the information.

[0038] When managing archives, the management unit can analyze the user's past travel history and suggest an appropriate management method. For example, the management unit can provide an archive of similar travel destinations based on data on travel destinations the user has visited in the past. The management unit can also provide an archive of preferred activities based on the user's past travel history. Furthermore, the management unit can provide an archive of similar accommodations based on data on accommodations the user has used in the past. This enables archive management that suits the user's preferences by suggesting an optimal management method based on the user's past travel history. Some or all of the above-described processing in the management unit can be performed using, for example, AI, or without AI. For example, the management unit can input the user's past travel data into a generation AI and have the generation AI suggest an optimal management method.

[0039] The management unit can customize the management content based on the user's current living situation and areas of interest during archive management. For example, when the user inputs their current living situation, the management unit can provide an archive of related travel destinations based on that information. The management unit can also provide an archive of related activities based on the user's areas of interest. Furthermore, the management unit can filter unnecessary information and display only the necessary information based on the user's current living situation and areas of interest. This enables more relevant archive management by customizing the management content based on the user's current living situation and areas of interest. Some or all of the above-described processing in the management unit can be performed using, or without, AI. For example, the management unit can input the user's living situation data into a generation AI and have the generation AI customize the management content.

[0040] The management unit can propose appropriate management content taking into account the user's geographical location information when managing archives. For example, when a user inputs their current location, the management unit provides an archive of nearby tourist spots. The management unit can also provide an archive of related accommodations based on the user's geographical location information. Furthermore, the management unit can also provide an archive of related activities based on the user's geographical location information. This enables more relevant archive management by proposing management content taking into account the geographical location information. Some or all of the above-described processing in the management unit may be performed using, or without, AI. For example, the management unit can input the user's geographical location data into a generation AI and have the generation AI propose optimal management content.

[0041] The management unit can analyze the user's social media activity and suggest relevant management content when managing the archive. For example, the management unit can analyze the user's social media posts and provide an archive of favorite travel destinations. The management unit can also provide an archive of favorite activities from the user's social media activity. Furthermore, the management unit can analyze the activity of the user's social media followers and provide related information. This enables more relevant archive management by analyzing social media activity and suggesting management content. Some or all of the above-mentioned processing in the management unit can be performed using, for example, AI, or without AI. For example, the management unit can input the user's social media data into a generation AI and have the generation AI suggest optimal management content.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The reception unit can monitor the user's health condition and suggest travel destinations and activities based on the user's health condition. For example, if the user is tired, it can suggest hot springs or spas where the user can relax. If the user is active, it can suggest itineraries that include hiking or sports activities. Furthermore, if the user has a specific health problem, it can suggest travel destinations and accommodations that address that problem. This makes it possible to provide the user with an optimal travel plan based on their health condition.

[0044] The update unit can obtain the user's real-time location information and dynamically update the itinerary based on the location information. For example, if the user arrives at the destination earlier than planned, nearby tourist attractions can be added. Also, if the user is stuck in traffic, an alternative route can be suggested. Furthermore, if the user stays in a particular location for a long time, activities related to that location can be suggested. This makes it possible to provide more flexible travel plans by dynamically updating the itinerary based on real-time location information.

[0045] The management unit can analyze the user's social media activity and suggest activities at travel destinations. For example, if the user has expressed interest in a particular tourist spot on social media, the management unit can suggest an itinerary that includes that spot. The management unit can also suggest places visited by influencers the user follows. Furthermore, the management unit can suggest events or festivals that the user may be interested in based on the content of the user's social media posts. This allows the management unit to provide a more personalized travel experience by suggesting activities at travel destinations based on social media activity.

[0046] The generation unit can analyze the user's past travel history and suggest meal plans for travel destinations. For example, it can suggest similar restaurants based on data on restaurants the user has visited in the past. It can also suggest new restaurants or cafes based on the user's past dining preferences. It can also suggest related events based on data on dining events or cooking classes the user has attended in the past. This makes it possible to provide a dining experience that suits the user's preferences by suggesting optimal meal plans based on the user's past travel history.

[0047] The providing unit can acquire the user's current weather information and suggest activities at the travel destination based on the weather. For example, if it is raining, indoor activities that can be enjoyed can be suggested. If it is sunny, outdoor activities can be suggested. Furthermore, if extreme weather conditions are predicted, alternative travel destinations and activities can be suggested. This allows the user to minimize inconvenience during their trip by suggesting optimal activities based on current weather information.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The reception unit receives information about the user's attributes and preferences. The user's attributes and preferences include, for example, age, gender, purpose of travel, and activities of interest. The reception unit stores the information entered by the user in a database and provides it to the generation unit. Step 2: The generation unit uses the generation AI to generate an optimal itinerary based on the information received by the reception unit. The generation AI creates an itinerary by combining transportation methods, routes, accommodations, tourist spots, etc. based on the user's attributes and preferences. Step 3: The update unit updates the itinerary in real time according to the user's changes. If the user changes any part of the itinerary, a new itinerary is generated that reflects the changes. The update unit can also use the generation AI to regenerate the itinerary based on the user's changes. Step 4: The provider provides pre-trip information, such as weather information, traffic information, and tourist information for the travel destination. The provider can also use generation AI to provide optimal information based on the user's attributes and preferences. Step 5: The management unit manages the post-trip archive. The management unit stores travel photos, diaries, reviews, etc. for users to refer to later. The management unit can also use generative AI to suggest new itineraries based on the user's past travel history.

[0050] (Example 2) In an embodiment of the present invention, a travel suggestion system automatically creates multiple itineraries with catchy titles after a user inputs their attributes, preferences, and images similar to the scenery they want to see. The system then arranges transportation, adjusts routes, and accommodations, and researches recommended tourist spots to create an itinerary that includes these. Furthermore, users can change parts of the itinerary or replace it with a better plan, and the itinerary is updated in real time to be executable. Once the plan is finalized, a list of preparations required before and on the day is displayed to prevent forgetting items. This system addresses the needs of users who are unsure about trip preparations, find travel agency plans expensive and inaccessible, or want to customize their trip but find research tedious. Before the trip, the system provides trivia about the destination and information on how to have fun. After the trip, the information is saved in a shareable archive, and new itineraries are suggested based on the satisfaction level of past trips. This makes travel even more enjoyable. For example, when a user inputs their attributes and preferences, the system generates an optimal itinerary based on that information. The system arranges transportation, adjusts routes, and books accommodations, and researches recommended tourist spots, creating an itinerary that includes all of these. Users can change parts of the itinerary or replace it with a better plan, and the itinerary is updated accordingly in real time to make it actionable. Once the plan is finalized, a list of preparations that need to be made before the trip and things that will be necessary on the day is displayed to prevent users from forgetting anything. Before the trip, the system provides trivia about the destination and information on how to have fun, and after the trip, this information is saved in an archive that can be shared, and new itineraries are suggested based on satisfaction with past trips. This allows the travel suggestion system to generate optimal itineraries based on user attributes and preferences, update them in real time, and provide and manage information before and after the trip.

[0051] A travel suggestion system according to an embodiment includes a reception unit, a generation unit, an update unit, a provision unit, and a management unit. The reception unit receives information about a user's attributes and preferences. The user's attribute and preference information includes, but is not limited to, age, gender, travel purpose, and activities of interest. The reception unit, for example, stores the information entered by the user in a database and provides it to the generation unit. The generation unit uses a generation AI to generate an optimal itinerary based on the information received by the reception unit. The generation AI creates an itinerary by combining transportation means, routes, accommodations, tourist spots, and other information based on the user's attribute and preference information. The generation unit generates an itinerary in response to, for example, a prompt from the generation AI saying, "Please generate an optimal itinerary based on the user's attributes and preferences." The update unit updates the itinerary in real time in response to user changes. For example, if the user makes changes to the itinerary, the update unit generates a new itinerary reflecting the changes. The update unit can also regenerate the itinerary based on the user's changes using the generation AI. The provision unit provides pre-travel information. The providing unit provides, for example, weather information, traffic information, and information on tourist attractions at the travel destination. The providing unit can also use the generation AI to provide optimal information based on the user's attributes and preferences. The management unit manages post-travel archives. The management unit, for example, stores travel photos, diaries, reviews, and the like so that the user can refer to them later. The management unit can also use the generation AI to suggest new itineraries based on the user's past travel history. As a result, the travel suggestion system according to the embodiment can generate optimal itineraries based on the user's attributes and preferences, update them in real time, and provide and manage information before and after the trip.

[0052] The reception unit can estimate the user's emotions and adjust the input method for attribute and preference information based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can provide a simple interface and minimize input steps. Furthermore, if the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input to enable the user to quickly input attribute and preference information. This allows for more appropriate information input by adjusting the input method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using AI, for example, or without AI. For example, the reception unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0053] The reception unit can analyze the user's past travel history and automatically suggest appropriate attribute and preference information. For example, the reception unit can suggest similar travel destinations based on data on travel destinations the user has visited in the past. The reception unit can also automatically extract and suggest preferred activities from the user's past travel history. Furthermore, the reception unit can suggest similar accommodations based on data on accommodations the user has used in the past. This makes it possible to generate an itinerary that suits the user's preferences by suggesting optimal information based on the user's past travel history. Some or all of the above-mentioned processing in the reception unit may be performed using, or without, AI. For example, the reception unit can input the user's past travel data into the generation AI and have the generation AI suggest optimal attribute and preference information.

[0054] When inputting attribute and preference information, the reception unit can perform filtering based on the user's current living situation and areas of interest. For example, when the user inputs their current living situation, the reception unit can suggest related travel destinations based on that information. Furthermore, when the user inputs their areas of interest, the reception unit can also suggest related activities based on that information. Furthermore, the reception unit can filter unnecessary information and display only necessary information based on the user's current living situation and areas of interest. By filtering information based on the user's current living situation and areas of interest, more relevant information can be provided. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's living situation data to a generation AI and have the generation AI perform filtering.

[0055] The generation unit can estimate the user's emotions and adjust the itinerary presentation method based on the estimated user emotions. For example, if the user is relaxed, the generation unit can generate an itinerary that proceeds at a leisurely pace. Furthermore, if the user is in a hurry, the generation unit can generate an itinerary that emphasizes the shortest route. Furthermore, if the user is excited, the generation unit can generate an itinerary that adds visually stimulating effects. This allows for a more appropriate itinerary to be provided by adjusting the itinerary presentation method according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the generation unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the itinerary presentation method.

[0056] When generating an itinerary, the generation unit can propose an appropriate itinerary based on the user's past travel history. For example, the generation unit can propose similar travel destinations based on data on travel destinations the user has visited in the past. The generation unit can also automatically extract and propose preferred activities from the user's past travel history. Furthermore, the generation unit can also propose similar accommodations based on data on accommodations the user has used in the past. In this way, an itinerary that suits the user's preferences can be generated by proposing an optimal itinerary based on the user's past travel history. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's past travel data into the generation AI and cause the generation AI to propose an optimal itinerary.

[0057] When generating an itinerary, the generation unit can customize the itinerary based on the user's current living situation and areas of interest. For example, when the user inputs their current living situation, the generation unit can suggest related travel destinations based on that information. Furthermore, when the user inputs their areas of interest, the generation unit can suggest related activities based on that information. Furthermore, the generation unit can filter unnecessary information and display only necessary information based on the user's current living situation and areas of interest. This allows the itinerary to be customized based on the user's current living situation and areas of interest, thereby providing a more relevant itinerary. Some or all of the above-described processing in the generation unit may be performed using, or without, AI. For example, the generation unit can input the user's living situation data into the generation AI and have the generation AI customize the itinerary.

[0058] The update unit can estimate the user's emotions and adjust the itinerary update method based on the estimated user emotions. For example, if the user is relaxed, the update unit can generate an itinerary that proceeds at a leisurely pace. Furthermore, if the user is in a hurry, the update unit can generate an itinerary that emphasizes the shortest route. Furthermore, if the user is excited, the update unit can generate an itinerary that adds visually stimulating effects. This allows for adjusting the itinerary update method according to the user's emotions, thereby providing a more appropriate itinerary. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the update unit can be performed using, for example, an AI, or without an AI. For example, the update unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the itinerary update method.

[0059] When updating an itinerary, the update unit can analyze the user's past change history and suggest an appropriate update method. For example, the update unit can suggest similar changes based on data on itineraries changed by the user in the past. The update unit can also automatically extract and suggest preferred activities from the user's past change history. Furthermore, the update unit can suggest similar accommodations based on data on accommodations changed by the user in the past. This makes it possible to provide an itinerary that suits the user's preferences by suggesting an optimal update method based on the past change history. Some or all of the above-mentioned processing in the update unit may be performed using, for example, AI, or may be performed without using AI. For example, the update unit can input the user's past change data into a generation AI and have the generation AI suggest an optimal update method.

[0060] When updating an itinerary, the update unit can customize the update content based on the user's current living situation and areas of interest. For example, when the user inputs their current living situation, the update unit can suggest related travel destinations based on that information. Furthermore, when the user inputs their areas of interest, the update unit can suggest related activities based on that information. Furthermore, the update unit can filter unnecessary information and display only the necessary information based on the user's current living situation and areas of interest. This allows for customizing the update content based on the user's current living situation and areas of interest, thereby providing a more relevant itinerary. Some or all of the above-described processing in the update unit may be performed using, or without, AI. For example, the update unit can input the user's living situation data into a generation AI and have the generation AI customize the update content.

[0061] The providing unit can estimate the user's emotions and adjust the information provision method based on the estimated user's emotions. For example, if the user is relaxed, the providing unit can provide information that progresses at a leisurely pace. If the user is in a hurry, the providing unit can also provide information that focuses on the main points. Furthermore, if the user is excited, the providing unit can also provide information that adds visually stimulating effects. This allows for adjusting the information provision method according to the user's emotions, thereby providing more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the information provision method.

[0062] When providing information, the providing unit can analyze the user's past travel history and provide appropriate information. For example, the providing unit can provide information on similar travel destinations based on data on travel destinations the user has visited in the past. The providing unit can also provide information on preferred activities based on the user's past travel history. Furthermore, the providing unit can also provide information on similar accommodations based on data on accommodations the user has used in the past. This makes it possible to provide optimal information based on the user's past travel history and provide information that matches the user's preferences. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past travel data into a generation AI and cause the generation AI to provide optimal information.

[0063] The providing unit can customize information based on the user's current living situation and areas of interest when providing information. For example, when the user inputs their current living situation, the providing unit can provide information on related travel destinations based on the input. Furthermore, when the user inputs their areas of interest, the providing unit can also provide information on related activities based on the input. Furthermore, the providing unit can filter unnecessary information and display only necessary information based on the user's current living situation and areas of interest. This allows for more relevant information to be provided by customizing the information based on the user's current living situation and areas of interest. Some or all of the above-described processing in the providing unit may be performed using, or without, AI. For example, the providing unit can input the user's living situation data into a generating AI and have the generating AI customize the information.

[0064] The management unit can estimate the user's emotions and adjust the archive management method based on the estimated user emotions. For example, if the user is relaxed, the management unit can provide an archive that progresses at a leisurely pace. If the user is in a hurry, the management unit can also provide an archive that focuses on the main points. Furthermore, if the user is excited, the management unit can also provide an archive with visually stimulating effects. This enables more appropriate archive management by adjusting the archive management method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the management unit can be performed using AI, for example, or without AI. For example, the management unit can input the user's emotion data into the generation AI and have the generation AI adjust the archive management method.

[0065] When managing archives, the management unit can analyze the user's past travel history and suggest an appropriate management method. For example, the management unit can provide an archive of similar travel destinations based on data on travel destinations the user has visited in the past. The management unit can also provide an archive of preferred activities based on the user's past travel history. Furthermore, the management unit can provide an archive of similar accommodations based on data on accommodations the user has used in the past. This enables archive management that suits the user's preferences by suggesting an optimal management method based on the user's past travel history. Some or all of the above-described processing in the management unit can be performed using, for example, AI, or without AI. For example, the management unit can input the user's past travel data into a generation AI and have the generation AI suggest an optimal management method.

[0066] The management unit can customize the management content based on the user's current living situation and areas of interest during archive management. For example, when the user inputs their current living situation, the management unit can provide an archive of related travel destinations based on that information. The management unit can also provide an archive of related activities based on the user's areas of interest. Furthermore, the management unit can filter unnecessary information and display only the necessary information based on the user's current living situation and areas of interest. This enables more relevant archive management by customizing the management content based on the user's current living situation and areas of interest. Some or all of the above-described processing in the management unit can be performed using, or without, AI. For example, the management unit can input the user's living situation data into a generation AI and have the generation AI customize the management content.

[0067] The management unit can estimate the user's emotions and prioritize the content to be managed based on the estimated user emotions. For example, if the user is feeling stressed, the management unit prioritizes managing important information. The management unit can also manage detailed information if the user is relaxed. Furthermore, if the user is in a hurry, the management unit can prioritize managing minimal information. This enables more appropriate archive management by prioritizing the content to be managed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the management unit can be performed using, for example, an AI, or without an AI. For example, the management unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of the content to be managed.

[0068] The management unit can propose appropriate management content taking into account the user's geographical location information when managing archives. For example, when a user inputs their current location, the management unit provides an archive of nearby tourist spots. The management unit can also provide an archive of related accommodations based on the user's geographical location information. Furthermore, the management unit can also provide an archive of related activities based on the user's geographical location information. This enables more relevant archive management by proposing management content taking into account the geographical location information. Some or all of the above-described processing in the management unit may be performed using, or without, AI. For example, the management unit can input the user's geographical location data into a generation AI and have the generation AI propose optimal management content.

[0069] The management unit can analyze the user's social media activity and suggest relevant management content when managing the archive. For example, the management unit can analyze the user's social media posts and provide an archive of favorite travel destinations. The management unit can also provide an archive of favorite activities from the user's social media activity. Furthermore, the management unit can analyze the activity of the user's social media followers and provide related information. This enables more relevant archive management by analyzing social media activity and suggesting management content. Some or all of the above-mentioned processing in the management unit can be performed using, for example, AI, or without AI. For example, the management unit can input the user's social media data into a generation AI and have the generation AI suggest optimal management content. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, generation unit, update unit, provision unit, and management unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit receives information on the user's attributes and preferences via the control unit 46A of the smart device 14. The generation unit generates an optimal itinerary using a generation AI via the specific processing unit 290 of the data processing device 12. The update unit updates the itinerary in real time in response to user changes via the specific processing unit 290 of the data processing device 12. The provision unit provides pre-travel information via the control unit 46A of the smart device 14. The management unit manages post-travel archives via the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, generation unit, update unit, provision unit, and management unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit receives information on the user's attributes and preferences via the control unit 46A of the smart glasses 214. The generation unit generates an optimal itinerary using a generation AI via the specific processing unit 290 of the data processing device 12. The update unit updates the itinerary in real time in response to user changes via the specific processing unit 290 of the data processing device 12. The provision unit provides pre-travel information via the control unit 46A of the smart glasses 214. The management unit manages post-travel archives via the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, generation unit, update unit, provision unit, and management unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit receives information on the user's attributes and preferences via the control unit 46A of the headset type terminal 314. The generation unit generates an optimal itinerary using a generation AI via the specific processing unit 290 of the data processing device 12. The update unit updates the itinerary in real time in response to user changes via the specific processing unit 290 of the data processing device 12. The provision unit provides pre-travel information via the control unit 46A of the headset type terminal 314. The management unit manages post-travel archives via the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, generation unit, update unit, provision unit, and management unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit receives information on the user's attributes and preferences via the control unit 46A of the robot 414. The generation unit generates an optimal itinerary using a generation AI via the specific processing unit 290 of the data processing device 12. The update unit updates the itinerary in real time in response to changes made by the user via the specific processing unit 290 of the data processing device 12. The provision unit provides pre-travel information via the control unit 46A of the robot 414. The management unit manages post-travel archives via the specific processing unit 290 of the data processing device 12.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] The reception unit can monitor the user's health condition and suggest travel destinations and activities based on the user's health condition. For example, if the user is tired, it can suggest hot springs or spas where the user can relax. If the user is active, it can suggest itineraries that include hiking or sports activities. Furthermore, if the user has a specific health problem, it can suggest travel destinations and accommodations that address that problem. This makes it possible to provide the user with an optimal travel plan based on their health condition.

[0072] The generation unit can estimate the user's emotions and set a theme for the itinerary based on the estimated emotions. For example, if the user is feeling stressed, a relaxation-themed itinerary can be generated. If the user is feeling adventurous, an adventure-themed itinerary can be generated. Furthermore, if the user is feeling romantic, an itinerary including romantic date spots can be generated. In this way, by setting a theme according to the user's emotions, a more personalized travel experience can be provided.

[0073] The update unit can obtain the user's real-time location information and dynamically update the itinerary based on the location information. For example, if the user arrives at the destination earlier than planned, nearby tourist attractions can be added. Also, if the user is stuck in traffic, an alternative route can be suggested. Furthermore, if the user stays in a particular location for a long time, activities related to that location can be suggested. This makes it possible to provide more flexible travel plans by dynamically updating the itinerary based on real-time location information.

[0074] The providing unit can estimate the user's emotions and adjust the way information is displayed based on the estimated emotions. For example, if the user is relaxed, the information can be displayed using calm colors and slow animations. If the user is excited, the information can be displayed using vivid colors and dynamic animations. Furthermore, if the user is concentrating, the information can be displayed using a simple and intuitive interface. This makes it possible to provide information more effectively by adjusting the way information is displayed according to the user's emotions.

[0075] The management unit can analyze the user's social media activity and suggest activities at travel destinations. For example, if the user has expressed interest in a particular tourist spot on social media, the management unit can suggest an itinerary that includes that spot. The management unit can also suggest places visited by influencers the user follows. Furthermore, the management unit can suggest events or festivals that the user may be interested in based on the content of the user's social media posts. This allows the management unit to provide a more personalized travel experience by suggesting activities at travel destinations based on social media activity.

[0076] The reception unit can estimate the user's emotions and adjust the design of the input interface based on the estimated emotions. For example, if the user is feeling stressed, a simple and intuitive interface can be provided. Alternatively, if the user is relaxed, detailed customization options can be provided. Furthermore, if the user is in a hurry, voice input or quick selection options can be provided. This allows for smoother information input by providing an input interface that corresponds to the user's emotions.

[0077] The generation unit can analyze the user's past travel history and suggest meal plans for travel destinations. For example, it can suggest similar restaurants based on data on restaurants the user has visited in the past. It can also suggest new restaurants or cafes based on the user's past dining preferences. It can also suggest related events based on data on dining events or cooking classes the user has attended in the past. This makes it possible to provide a dining experience that suits the user's preferences by suggesting optimal meal plans based on the user's past travel history.

[0078] The update unit can estimate the user's emotions and suggest changes to the itinerary based on the estimated emotions. For example, if the user is tired, the update unit can suggest increasing rest time. If the user is excited, the update unit can suggest adding an activity. Furthermore, if the user is stressed, the update unit can suggest adding a place where the user can relax. In this way, it is possible to provide a more comfortable travel experience by suggesting changes to the itinerary based on the user's emotions.

[0079] The providing unit can acquire the user's current weather information and suggest activities at the travel destination based on the weather. For example, if it is raining, indoor activities that can be enjoyed can be suggested. If it is sunny, outdoor activities can be suggested. Furthermore, if extreme weather conditions are predicted, alternative travel destinations and activities can be suggested. This allows the user to minimize inconvenience during their trip by suggesting optimal activities based on current weather information.

[0080] The management unit can estimate the user's emotions and adjust the post-trip feedback collection method based on the estimated emotions. For example, if the user is relaxed, a detailed feedback form can be provided. If the user is in a hurry, a simple evaluation form can be provided. Furthermore, if the user is excited, an option to upload photos or videos can be provided. This allows for more accurate feedback to be obtained by providing a feedback collection method that corresponds to the user's emotions.

[0081] The processing flow of the second embodiment will be briefly explained below.

[0082] Step 1: The reception unit receives information about the user's attributes and preferences. The user's attributes and preferences include, for example, age, gender, purpose of travel, and activities of interest. The reception unit stores the information entered by the user in a database and provides it to the generation unit. Step 2: The generation unit uses the generation AI to generate an optimal itinerary based on the information received by the reception unit. The generation AI creates an itinerary by combining transportation methods, routes, accommodations, tourist spots, etc. based on the user's attributes and preferences. Step 3: The update unit updates the itinerary in real time according to the user's changes. If the user changes any part of the itinerary, a new itinerary is generated that reflects the changes. The update unit can also use the generation AI to regenerate the itinerary based on the user's changes. Step 4: The provider provides pre-trip information, such as weather information, traffic information, and tourist information for the travel destination. The provider can also use generation AI to provide optimal information based on the user's attributes and preferences. Step 5: The management unit manages the post-trip archive. The management unit stores travel photos, diaries, reviews, etc. for users to refer to later. The management unit can also use generative AI to suggest new itineraries based on the user's past travel history.

[0083] 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.

[0084] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0085] 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.

[0086] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0087] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0088] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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).

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0097] 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. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0098] 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.

[0099] 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.

[0100] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0101] 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.

[0102] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0103] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0104] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0113] 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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0114] 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.

[0115] 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.

[0116] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0117] 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.

[0118] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0119] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0130] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. 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 the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0131] 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.

[0132] 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.

[0133] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0134] 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.

[0135] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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."

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] [Explanation of symbols]

[0155] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives information about a user's attributes and preferences; a generation unit that generates an appropriate itinerary based on the information received by the reception unit; an update unit that instantly updates the itinerary generated by the generation unit in response to changes made by a user; a provision department that provides pre-travel information; A management unit that manages archives after the trip. A system characterized by:

2. The reception unit Estimate the user's emotions and adjust the input method for attribute and preference information based on the estimated user emotions.

2. The system of claim 1.

3. The reception unit Analyzes a user's past travel history and automatically suggests relevant attributes and preferences 2. The system of claim 1.

4. The reception unit Filtering demographic and preference information based on the user's current life situation and interests 2. The system of claim 1.

5. The generation unit Inferring user emotions and adjusting the way the itinerary is presented based on the estimated user emotions 2. The system of claim 1.

6. The generation unit When generating an itinerary, suggest appropriate itineraries based on the user's past travel history 2. The system of claim 1.

7. The generation unit Customize the itinerary based on the user's current life situation and interests at the time of itinerary generation 2. The system of claim 1.

8. The update unit Inferring user emotions and adjusting the itinerary update method based on the estimated user emotions 2. The system of claim 1.

9. The update unit When updating an itinerary, the system analyzes the user's past change history and suggests the appropriate update method.

2. The system of claim 1.

10. The update unit When updating an itinerary, customize the update based on the user's current life situation and interests 2. The system of claim 1.

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