System

Abstract

An object of a system according to an embodiment is to propose an optimal sightseeing route or staying place based on a user's preference or budget, and to provide multilingual support or local information.SOLUTION: A system includes a collection part, a proposal part, a translation part, a real-time translation part, a guide part, and an information provision part. The collection unit collects user preferences and budgets. The suggestion unit suggests a sightseeing route and a staying place based on the information collected by the collection unit. The translation unit supports a plurality of languages. The real-time translator supports on-site conversations. The guide unit provides content for learning about culture, lifestyle, and manners of the destination. The information providing unit provides weather information and disaster information in real time.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has made it difficult for travelers to find tourist routes and places to stay that suit their preferences and budget, and has also had issues with insufficient multilingual support and local information provision.

[0005] The system according to the embodiment aims to propose optimal sightseeing routes and places to stay based on the user's preferences and budget, and to provide multilingual support and local information. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, a suggestion unit, a translation unit, a real-time translation unit, a guide unit, and an information provision unit. The collection unit collects the user's preferences and budget. The suggestion unit suggests sightseeing routes and places to stay based on the information collected by the collection unit. The translation unit supports multiple languages. The real-time translation unit supports local conversations. The guide unit provides content that allows users to learn about the culture, lifestyle, and manners of the destination. The information provision unit provides real-time information on weather and disasters. [Effects of the Invention]

[0007] The system according to the embodiment can propose optimal sightseeing routes and places to stay based on the user's preferences and budget, and can provide multilingual support and local information. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate 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) An inbound tourism demand response system according to an embodiment of the present invention utilizes a generation AI to propose optimal sightseeing routes and destinations based on a user's preferences and budget, and provides multilingual support, real-time translation, cultural guides, and weather and disaster information. The inbound tourism demand response system collects the user's preferences and budget and then proposes sightseeing routes and destinations based on them. For example, if a user inputs, "Please suggest a sightseeing route from Tokyo to Kyoto," the generation AI proposes the optimal route based on the user's preferences and budget. Next, multilingual support is provided. For example, if a user inputs, "I was spoken to in English, but I would like to respond in Japanese," the generation AI translates in real time and provides the user with an appropriate response. Furthermore, content that allows users to learn about the destination's culture, lifestyle, and etiquette is provided. For example, if a user inputs, "I would like to know about the tea ceremony in Kyoto," the generation AI provides information on the history and etiquette of tea ceremony. Finally, weather, disaster, and local information is provided in real time. For example, if a user inputs, "I would like to know tomorrow's weather," the generation AI provides the latest weather information. This allows the inbound tourism demand response system to improve the user's travel experience. This allows the inbound demand response system to improve users' travel experiences by suggesting optimal sightseeing routes and places to stay based on users' preferences and budget, and providing multilingual support, real-time translation, cultural guides, and weather and disaster information. For example, users can be suggested sightseeing routes that suit their preferences and budget, and receive support for local conversation and cultural understanding. Furthermore, real-time information provision allows users to enjoy a safe and comfortable trip.

[0029] An inbound demand response system according to an embodiment includes a collection unit, a proposal unit, a translation unit, a real-time translation unit, a guide unit, and an information provision unit. The collection unit collects user preferences and budget. For example, the collection unit collects preferences and budget based on information input by the user. The collection unit can also estimate preferences and budget by analyzing the user's past travel history and social media activity using a generation AI. The proposal unit proposes sightseeing routes and destinations based on the information collected by the collection unit. For example, the proposal unit proposes optimal sightseeing routes and destinations based on the user's preferences and budget using a generation AI. The proposal unit can also apply different proposal algorithms depending on the category of the sightseeing route or destination. The translation unit supports multiple languages. For example, the translation unit translates text input by the user into multiple languages ​​using a generation AI. The translation unit can also estimate the user's emotions and adjust the translation expression based on the estimated emotions. The real-time translation unit supports conversations on-site. The real-time translation unit translates speech input by a user in real time, for example, using a generation AI. The real-time translation unit can also improve the accuracy of the translation based on the context of the conversation. The guide unit provides content that allows users to learn about the culture, lifestyle, and manners of the destination. The guide unit can provide information about the culture, lifestyle, and manners of the destination in response to questions input by a user, for example, using a generation AI. The guide unit can also estimate the user's emotions and adjust the way the guide is expressed based on the estimated emotions. The information provision unit provides weather and disaster information in real time. The information provision unit can provide the latest weather and disaster information in response to questions input by a user, for example, using a generation AI. The information provision unit can also provide optimal information taking into account the user's geographical location information. As a result, the inbound demand response system according to the embodiment can improve the user's travel experience by suggesting optimal sightseeing routes and places to stay based on the user's preferences and budget, and providing multilingual support, real-time translation, cultural guides, and weather and disaster information.

[0030] The collection unit can analyze the user's past travel history and select the optimal information collection method. For example, the collection unit can collect information on similar tourist destinations based on places the user has visited in the past. The collection unit can also analyze trends in preferred tourist destinations from the user's past travel history and collect information. The collection unit can also collect information on the optimal means of transportation by referring to transportation methods the user has used in the past. This enables more accurate information collection by selecting the optimal information collection method based on the user's past travel history. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the user's past travel history data into a generation AI and have the generation AI select the optimal information collection method.

[0031] When collecting information, the collection unit can filter the information based on the user's current interests. For example, the collection unit can prioritize collecting information about events and festivals in which the user is currently interested. The collection unit can also collect information about tourist spots related to a particular culture or history in which the user is interested. If the user is interested in the current season, the collection unit can also collect information about seasonal tourist spots. This allows for filtering information based on the user's current interests, thereby providing more relevant information. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input the user's current interests and interest data into the generation AI and have the generation AI perform information filtering.

[0032] When collecting information, the collection unit can select an appropriate collection means depending on the user's input method. For example, when the user uses voice input, the collection unit collects information using voice recognition technology. Furthermore, when the user uses text input, the collection unit can also collect information based on a keyword search. Furthermore, when the user uses image input, the collection unit can also collect related information using image recognition technology. This improves the efficiency of information collection by selecting the optimal collection means depending on the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's input data into a generation AI and have the generation AI select the optimal collection means.

[0033] When making a suggestion, the suggestion unit can adjust the level of detail of the suggestion based on the importance of the tourist route and the destination. For example, the suggestion unit provides detailed information about major tourist destinations. The suggestion unit can also provide concise information about minor tourist destinations. The suggestion unit can also provide detailed accommodation information according to the importance of the destination. This allows the suggestion to be optimal for the user by adjusting the level of detail of the suggestion based on the importance of the tourist route and the destination. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input importance data about the tourist route and the destination to the generation AI and cause the generation AI to adjust the level of detail of the suggestion.

[0034] When making suggestions, the suggestion unit can apply different suggestion algorithms depending on the category of the tourist route or the destination. For example, in the case of a historical tourist destination, the suggestion unit makes suggestions that emphasize historical background and attractions. In addition, in the case of a natural tourist destination, the suggestion unit can make suggestions that emphasize natural beauty and activities. In addition, in the case of an urban tourist destination, the suggestion unit can make suggestions that emphasize shopping and gourmet information. In this way, applying different suggestion algorithms depending on the category of the tourist route or the destination enables more appropriate suggestions. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input category data of the tourist route or the destination into the generation AI and cause the generation AI to apply different suggestion algorithms.

[0035] When making a suggestion, the suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results. For example, the suggestion unit makes similar suggestions by referring to suggestions that the user has previously given high ratings. The suggestion unit can also analyze the user's past suggestion results, understand the user's preference trends, and make suggestions. The suggestion unit can also make new suggestions by avoiding suggestions that the user has previously expressed dissatisfaction with. In this way, the accuracy of the suggestion is improved by referring to the user's past suggestion results. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's past suggestion result data into the generation AI and cause the generation AI to improve the accuracy of the suggestions.

[0036] The translation unit can improve the accuracy of the translation based on the context during translation. The translation unit, for example, takes into account the context of the conversation to perform an appropriate translation. The translation unit can also perform a natural translation by taking into account the context of the sentence. The translation unit can also perform a specialized translation by taking into account the context related to a specific theme. This enables a more natural translation by improving the accuracy of the translation based on the context. Some or all of the above-mentioned processing in the translation unit may be performed using, for example, AI, or may be performed without using AI. For example, the translation unit can input context data into a generation AI and have the generation AI improve the accuracy of the translation.

[0037] The translation unit can improve the accuracy of translation by referring to the user's past translation history during translation. For example, the translation unit refers to translations the user has used in the past and performs a similar translation. The translation unit can also analyze the user's past translation history and reflect preferred expressions. The translation unit can also perform a new translation by avoiding translations that the user has previously expressed dissatisfaction with. In this way, by referring to the user's past translation history, the accuracy of the translation is improved. Some or all of the above-mentioned processing in the translation unit may be performed using, for example, AI, or may be performed without using AI. For example, the translation unit can input the user's past translation history data into the generation AI and have the generation AI improve the accuracy of the translation.

[0038] The translation unit can take into account the nuances between different languages ​​when translating. The translation unit can, for example, take into account cultural differences between languages ​​to perform appropriate translation. The translation unit can also take into account differences in expressions between languages ​​to perform natural translation. The translation unit can also take into account differences in nuance between languages ​​to perform accurate translation. This enables more accurate translation by taking into account the nuances between different languages. Some or all of the above-mentioned processing in the translation unit can be performed using, for example, AI, or can be performed without using AI. For example, the translation unit can input nuance data between languages ​​into the generation AI and have the generation AI improve the accuracy of the translation.

[0039] The real-time translation unit can improve the accuracy of the translation based on the context of the conversation during real-time translation. The real-time translation unit, for example, performs appropriate real-time translation by taking into account the context of the conversation. The real-time translation unit can also perform specialized real-time translation based on the theme of the conversation. The real-time translation unit can also perform natural real-time translation by taking into account the flow of the conversation. This enables more natural real-time translation by improving the accuracy of the translation based on the context of the conversation. Some or all of the above-mentioned processing in the real-time translation unit may be performed using, for example, AI, or may be performed without using AI. For example, the real-time translation unit can input conversation context data into a generation AI and have the generation AI improve the accuracy of the translation.

[0040] The real-time translation unit can improve the accuracy of translation during real-time translation by referring to the user's past conversation history. The real-time translation unit, for example, refers to conversational expressions used by the user in the past to perform similar real-time translation. The real-time translation unit can also analyze the user's past conversation history and reflect preferred expressions. The real-time translation unit can also perform new real-time translations that avoid conversational expressions that the user has previously expressed dissatisfaction with. In this way, by referring to the user's past conversation history, the accuracy of real-time translation is improved. Some or all of the above-described processing in the real-time translation unit may be performed using, for example, AI, or may be performed without using AI. For example, the real-time translation unit can input the user's past conversation history data into a generation AI and have the generation AI improve the accuracy of the translation.

[0041] The real-time translation unit can perform translation taking into account nuances between different languages ​​during real-time translation. The real-time translation unit can perform appropriate real-time translation, for example, taking into account cultural differences between languages. The real-time translation unit can also perform natural real-time translation taking into account differences in expressions between languages. The real-time translation unit can also perform accurate real-time translation taking into account differences in nuance between languages. This enables more accurate real-time translation by taking into account nuances between different languages. Some or all of the above-mentioned processing in the real-time translation unit may be performed using, for example, AI, or may be performed without using AI. For example, the real-time translation unit can input nuance data between languages ​​into the generation AI and have the generation AI improve the accuracy of the translation.

[0042] The guide unit can adjust the level of detail of the culture and lifestyle habits of the destination when providing the guide. For example, the guide unit provides detailed information about major cultures and lifestyle habits. The guide unit can also provide concise information about minor cultures and lifestyle habits. The guide unit can also provide detailed explanations about important manners and customs of the destination. By adjusting the level of detail of the culture and lifestyle habits of the destination, the optimal guide for the user can be provided. Some or all of the above-mentioned processing in the guide unit may be performed using, for example, AI, or may be performed without using AI. For example, the guide unit can input data about the culture and lifestyle habits of the destination into the generation AI and cause the generation AI to adjust the level of detail of the guide.

[0043] When providing a guide, the guide unit can improve the accuracy of the guide by referring to the user's past guide usage history. For example, the guide unit can refer to guides that the user has previously given high ratings and provide a similar guide. The guide unit can also analyze the user's past guide usage history and provide a guide that the user prefers. The guide unit can also provide a new guide that avoids guides that the user has previously expressed dissatisfaction with. In this way, by referring to the user's past guide usage history, the accuracy of the guide is improved. Some or all of the above-mentioned processing in the guide unit may be performed using, for example, AI, or may be performed without using AI. For example, the guide unit can input the user's past guide usage history data into a generation AI and have the generation AI improve the accuracy of the guide.

[0044] When providing guidance, the guide unit can update the guide content to reflect the latest information about the destination. The guide unit, for example, provides guidance that reflects the latest event information about the destination. The guide unit can also provide guidance that reflects the latest tourist spot information about the destination. The guide unit can also provide guidance that reflects the latest traffic information about the destination. In this way, by reflecting the latest information about the destination, the latest guidance is always provided. Some or all of the above-mentioned processing in the guide unit may be performed using AI, for example, or may be performed without using AI. For example, the guide unit can input the latest information data about the destination into the generation AI and have the generation AI update the guide content.

[0045] The information providing unit can adjust the level of detail of weather and disaster information when providing information. For example, the information providing unit provides detailed information for serious weather and disaster information. The information providing unit can also provide concise information for minor weather and disaster information. The information providing unit can also prioritize providing weather and disaster information related to the user's current location. In this way, by adjusting the level of detail of weather and disaster information, optimal information is provided to the user. Some or all of the above-described processing in the information providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the information providing unit can input weather and disaster information data to the generation AI and cause the generation AI to adjust the level of detail of the information.

[0046] When providing information, the information providing unit can improve the accuracy of the information provided by referring to the user's past information provision history. For example, the information providing unit can provide similar information by referring to information provision methods that the user has previously rated highly. The information providing unit can also analyze the user's past information provision history and provide preferred information. The information providing unit can also provide new information by avoiding information provision methods that the user has previously expressed dissatisfaction with. In this way, the accuracy of information provided is improved by referring to the user's past information provision history. Some or all of the above-described processing in the information providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the information providing unit can input the user's past information provision history data into the generation AI and cause the generation AI to improve the accuracy of information provided.

[0047] The information providing unit can update the content when providing information by reflecting the latest information. The information providing unit, for example, provides information by reflecting the latest weather information. The information providing unit can also provide information by reflecting the latest disaster information. The information providing unit can also provide information by reflecting the latest local information. In this way, by reflecting the latest information, the latest information is always provided. Some or all of the above-mentioned processing in the information providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the information providing unit can input the latest information data to the generation AI and cause the generation AI to update the information content.

[0048] When providing information, the information providing unit can provide optimal information by taking into account the user's geographical location information. For example, when the user is in their current location, the information providing unit can prioritize providing weather information for that area. Furthermore, when the user is traveling, the information providing unit can prioritize providing local disaster information. Furthermore, when the user requests information about a specific area, the information providing unit can provide information related to that area. In this way, more relevant information can be provided by taking the user's geographical location information into consideration. Some or all of the above-described processing in the information providing unit can be performed, for example, using AI or without AI. For example, the information providing unit can input the user's geographical location information data into the generation AI and cause the generation AI to provide optimal information.

[0049] When providing information, the information providing unit can analyze the user's social media activity and provide related information. The information providing unit can provide information based on, for example, the content posted by accounts the user follows on social media. The information providing unit can also analyze the content posted by the user on social media and provide related information. The information providing unit can also provide information based on the content posted by the user's friends. In this way, by analyzing the user's social media activity, more relevant information can be provided. Some or all of the above-described processing in the information providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the information providing unit can input the user's social media activity data into the generation AI and cause the generation AI to provide related information.

[0050] The information providing unit can customize the information providing method by reflecting the user's past feedback when providing information. For example, the information providing unit preferentially uses information providing methods that the user has previously rated highly. The information providing unit can also reflect the user's preferred information providing method based on the user's past feedback. The information providing unit can also provide a new information providing method by avoiding information providing methods that the user has previously expressed dissatisfaction with. In this way, a more appropriate information providing method is provided by reflecting the user's past feedback. Some or all of the above-described processing in the information providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the information providing unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the information providing method.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] The inbound demand response system can further include a health management unit that monitors the user's health condition. The health management unit collects data such as the user's heart rate, number of steps, and sleep status to support health management during travel. The health management unit can also suggest appropriate rest areas and healthy meals based on the user's health condition. Furthermore, the health management unit can provide information on the nearest medical institution if the user complains of feeling unwell. This allows users to manage their health with peace of mind even while traveling.

[0053] The suggestion unit can analyze the user's past travel history and select the optimal information collection method. For example, it can collect information on similar tourist spots based on places the user has visited in the past. The suggestion unit can also analyze trends in preferred tourist spots from the user's past travel history and collect information. The suggestion unit can also collect information on the optimal means of transportation by referring to the means of transportation the user has used in the past. This allows for more accurate information collection by selecting the optimal information collection method based on the user's past travel history.

[0054] When collecting information, the collection unit can filter the information based on the user's current interests. For example, the collection unit can prioritize collecting information about events and festivals in which the user is currently interested. The collection unit can also collect information about tourist spots related to a particular culture or history in which the user is interested. If the user is interested in the current season, the collection unit can also collect information about seasonal tourist spots. This allows the information to be filtered based on the user's current interests, making it possible to provide more relevant information.

[0055] When collecting information, the collection unit can select an appropriate collection means depending on the user's input method. For example, if the user uses voice input, the collection unit can collect information using voice recognition technology. If the user uses text input, the collection unit can also collect information based on keyword search. If the user uses image input, the collection unit can also collect related information using image recognition technology. This improves the efficiency of information collection by selecting the optimal collection means depending on the user's input method.

[0056] When making a suggestion, the suggestion unit can adjust the level of detail of the suggestion based on the importance of the tourist route and the destination. For example, detailed information is provided for major tourist destinations. The suggestion unit can also provide concise information for minor tourist destinations. The suggestion unit can also provide detailed accommodation information depending on the importance of the destination. This allows the suggestion to be optimal for the user by adjusting the level of detail of the suggestion based on the importance of the tourist route and the destination.

[0057] When making suggestions, the suggestion unit can apply different suggestion algorithms depending on the category of tourist route or destination. For example, in the case of a historical tourist destination, the suggestion unit can make suggestions that emphasize the historical background and highlights. In addition, in the case of a natural tourist destination, the suggestion unit can make suggestions that emphasize the beauty of nature and activities. In addition, in the case of an urban tourist destination, the suggestion unit can make suggestions that emphasize shopping and gourmet information. In this way, by applying different suggestion algorithms depending on the category of tourist route or destination, more appropriate suggestions can be made.

[0058] When making a suggestion, the suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results. For example, the suggestion unit can make similar suggestions by referring to suggestions that the user has given high ratings to in the past. The suggestion unit can also analyze the user's past suggestion results, understand preferences, and make suggestions. The suggestion unit can also make new suggestions by avoiding suggestions that the user has expressed dissatisfaction with in the past. In this way, the accuracy of the suggestion can be improved by referring to the user's past suggestion results.

[0059] The processing flow of the first embodiment will be briefly explained below.

[0060] Step 1: The collection unit collects the user's preferences and budget. For example, the collection unit collects preferences and budget based on information entered by the user. The collection unit can also use generative AI to analyze the user's past travel history and social media activity to estimate preferences and budget. Step 2: The suggestion unit suggests sightseeing routes and places to stay based on the information collected by the collection unit. For example, the suggestion unit uses a generation AI to suggest optimal sightseeing routes and places to stay based on the user's preferences and budget. The suggestion unit can also apply different suggestion algorithms depending on the category of sightseeing routes and places to stay. Step 3: The translation unit supports multiple languages. For example, the translation unit uses generative AI to translate the text entered by the user into multiple languages. The translation unit can also estimate the user's emotions and adjust the translation expression based on the estimated emotions. Step 4: The real-time translation unit supports on-site conversations. The real-time translation unit translates user-input speech in real time, for example, using generative AI. The real-time translation unit can also improve the accuracy of the translation based on the context of the conversation. Step 5: The guide unit provides content that allows users to learn about the culture, lifestyle, and manners of the destination. For example, the guide unit uses a generative AI to provide information about the culture, lifestyle, and manners of the destination in response to questions entered by the user. The guide unit can also estimate the user's emotions and adjust the way the guide is expressed based on the estimated emotions. Step 6: The information provider provides real-time weather and disaster information. For example, the information provider uses a generation AI to provide the latest weather and disaster information in response to questions entered by the user. The information provider can also provide optimal information taking into account the user's geographical location information.

[0061] (Example 2) An inbound tourism demand response system according to an embodiment of the present invention utilizes a generation AI to propose optimal sightseeing routes and destinations based on a user's preferences and budget, and provides multilingual support, real-time translation, cultural guides, and weather and disaster information. The inbound tourism demand response system collects the user's preferences and budget and then proposes sightseeing routes and destinations based on them. For example, if a user inputs, "Please suggest a sightseeing route from Tokyo to Kyoto," the generation AI proposes the optimal route based on the user's preferences and budget. Next, multilingual support is provided. For example, if a user inputs, "I was spoken to in English, but I would like to respond in Japanese," the generation AI translates in real time and provides the user with an appropriate response. Furthermore, content that allows users to learn about the destination's culture, lifestyle, and etiquette is provided. For example, if a user inputs, "I would like to know about the tea ceremony in Kyoto," the generation AI provides information on the history and etiquette of tea ceremony. Finally, weather, disaster, and local information is provided in real time. For example, if a user inputs, "I would like to know tomorrow's weather," the generation AI provides the latest weather information. This allows the inbound tourism demand response system to improve the user's travel experience. This allows the inbound demand response system to improve users' travel experiences by suggesting optimal sightseeing routes and places to stay based on users' preferences and budget, and providing multilingual support, real-time translation, cultural guides, and weather and disaster information. For example, users can be suggested sightseeing routes that suit their preferences and budget, and receive support for local conversation and cultural understanding. Furthermore, real-time information provision allows users to enjoy a safe and comfortable trip.

[0062] An inbound demand response system according to an embodiment includes a collection unit, a proposal unit, a translation unit, a real-time translation unit, a guide unit, and an information provision unit. The collection unit collects user preferences and budget. For example, the collection unit collects preferences and budget based on information input by the user. The collection unit can also estimate preferences and budget by analyzing the user's past travel history and social media activity using a generation AI. The proposal unit proposes sightseeing routes and destinations based on the information collected by the collection unit. For example, the proposal unit proposes optimal sightseeing routes and destinations based on the user's preferences and budget using a generation AI. The proposal unit can also apply different proposal algorithms depending on the category of the sightseeing route or destination. The translation unit supports multiple languages. For example, the translation unit translates text input by the user into multiple languages ​​using a generation AI. The translation unit can also estimate the user's emotions and adjust the translation expression based on the estimated emotions. The real-time translation unit supports conversations on-site. The real-time translation unit translates speech input by a user in real time, for example, using a generation AI. The real-time translation unit can also improve the accuracy of the translation based on the context of the conversation. The guide unit provides content that allows users to learn about the culture, lifestyle, and manners of the destination. The guide unit can provide information about the culture, lifestyle, and manners of the destination in response to questions input by a user, for example, using a generation AI. The guide unit can also estimate the user's emotions and adjust the way the guide is expressed based on the estimated emotions. The information provision unit provides weather and disaster information in real time. The information provision unit can provide the latest weather and disaster information in response to questions input by a user, for example, using a generation AI. The information provision unit can also provide optimal information taking into account the user's geographical location information. As a result, the inbound demand response system according to the embodiment can improve the user's travel experience by suggesting optimal sightseeing routes and places to stay based on the user's preferences and budget, and providing multilingual support, real-time translation, cultural guides, and weather and disaster information.

[0063] The collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit can collect information during a time when the user is able to relax. Furthermore, if the user is excited, the collection unit can immediately start collecting information and quickly provide suggestions. Furthermore, if the user is tired, the collection unit can collect information after the user has rested. This allows information to be collected at a more appropriate time by adjusting the timing of information collection 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 may be, for example, 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-described processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0064] The collection unit can analyze the user's past travel history and select the optimal information collection method. For example, the collection unit can collect information on similar tourist destinations based on places the user has visited in the past. The collection unit can also analyze trends in preferred tourist destinations from the user's past travel history and collect information. The collection unit can also collect information on the optimal means of transportation by referring to transportation methods the user has used in the past. This enables more accurate information collection by selecting the optimal information collection method based on the user's past travel history. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the user's past travel history data into a generation AI and have the generation AI select the optimal information collection method.

[0065] When collecting information, the collection unit can filter the information based on the user's current interests. For example, the collection unit can prioritize collecting information about events and festivals in which the user is currently interested. The collection unit can also collect information about tourist spots related to a particular culture or history in which the user is interested. If the user is interested in the current season, the collection unit can also collect information about seasonal tourist spots. This allows for filtering information based on the user's current interests, thereby providing more relevant information. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input the user's current interests and interest data into the generation AI and have the generation AI perform information filtering.

[0066] When collecting information, the collection unit can select an appropriate collection means depending on the user's input method. For example, when the user uses voice input, the collection unit collects information using voice recognition technology. Furthermore, when the user uses text input, the collection unit can also collect information based on a keyword search. Furthermore, when the user uses image input, the collection unit can also collect related information using image recognition technology. This improves the efficiency of information collection by selecting the optimal collection means depending on the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's input data into a generation AI and have the generation AI select the optimal collection means.

[0067] The suggestion unit can estimate the user's emotions and adjust the way the suggestions are expressed based on the estimated user emotions. For example, if the user is relaxed, the suggestion unit can provide a suggestion with detailed explanations. If the user is in a hurry, the suggestion unit can provide a concise and to-the-point suggestion. If the user is excited, the suggestion unit can provide a visually appealing suggestion. This enables more appropriate suggestions by adjusting the way the suggestions are expressed 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-described processing in the suggestion unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the suggestion unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the way the suggestions are expressed.

[0068] When making a suggestion, the suggestion unit can adjust the level of detail of the suggestion based on the importance of the tourist route and the destination. For example, the suggestion unit provides detailed information about major tourist destinations. The suggestion unit can also provide concise information about minor tourist destinations. The suggestion unit can also provide detailed accommodation information according to the importance of the destination. This allows the suggestion to be optimal for the user by adjusting the level of detail of the suggestion based on the importance of the tourist route and the destination. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input importance data about the tourist route and the destination to the generation AI and cause the generation AI to adjust the level of detail of the suggestion.

[0069] When making suggestions, the suggestion unit can apply different suggestion algorithms depending on the category of the tourist route or the destination. For example, in the case of a historical tourist destination, the suggestion unit makes suggestions that emphasize historical background and attractions. In addition, in the case of a natural tourist destination, the suggestion unit can make suggestions that emphasize natural beauty and activities. In addition, in the case of an urban tourist destination, the suggestion unit can make suggestions that emphasize shopping and gourmet information. In this way, applying different suggestion algorithms depending on the category of the tourist route or the destination enables more appropriate suggestions. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input category data of the tourist route or the destination into the generation AI and cause the generation AI to apply different suggestion algorithms.

[0070] When making a suggestion, the suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results. For example, the suggestion unit makes similar suggestions by referring to suggestions that the user has previously given high ratings. The suggestion unit can also analyze the user's past suggestion results, understand the user's preference trends, and make suggestions. The suggestion unit can also make new suggestions by avoiding suggestions that the user has previously expressed dissatisfaction with. In this way, the accuracy of the suggestion is improved by referring to the user's past suggestion results. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's past suggestion result data into the generation AI and cause the generation AI to improve the accuracy of the suggestions.

[0071] The translation unit can estimate the user's emotions and adjust the translation expression based on the estimated user emotions. For example, if the user is relaxed, the translation unit can provide a careful and detailed translation. If the user is in a hurry, the translation unit can also provide a concise and to-the-point translation. If the user is excited, the translation unit can also provide a visually appealing translation. This allows for more appropriate translation by adjusting the translation expression 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-described processing in the translation unit can be performed using AI, for example, or without AI. For example, the translation unit can input the user's emotion data into the generation AI and have the generation AI adjust the translation expression.

[0072] The translation unit can improve the accuracy of the translation based on the context during translation. The translation unit, for example, takes into account the context of the conversation to perform an appropriate translation. The translation unit can also perform a natural translation by taking into account the context of the sentence. The translation unit can also perform a specialized translation by taking into account the context related to a specific theme. This enables a more natural translation by improving the accuracy of the translation based on the context. Some or all of the above-mentioned processing in the translation unit may be performed using, for example, AI, or may be performed without using AI. For example, the translation unit can input context data into a generation AI and have the generation AI improve the accuracy of the translation.

[0073] The translation unit can improve the accuracy of translation by referring to the user's past translation history during translation. For example, the translation unit refers to translations the user has used in the past and performs a similar translation. The translation unit can also analyze the user's past translation history and reflect preferred expressions. The translation unit can also perform a new translation by avoiding translations that the user has previously expressed dissatisfaction with. In this way, by referring to the user's past translation history, the accuracy of the translation is improved. Some or all of the above-mentioned processing in the translation unit may be performed using, for example, AI, or may be performed without using AI. For example, the translation unit can input the user's past translation history data into the generation AI and have the generation AI improve the accuracy of the translation.

[0074] The translation unit can take into account the nuances between different languages ​​when translating. The translation unit can, for example, take into account cultural differences between languages ​​to perform appropriate translation. The translation unit can also take into account differences in expressions between languages ​​to perform natural translation. The translation unit can also take into account differences in nuance between languages ​​to perform accurate translation. This enables more accurate translation by taking into account the nuances between different languages. Some or all of the above-mentioned processing in the translation unit can be performed using, for example, AI, or can be performed without using AI. For example, the translation unit can input nuance data between languages ​​into the generation AI and have the generation AI improve the accuracy of the translation.

[0075] The real-time translation unit can estimate the user's emotions and adjust the expression method of the real-time translation based on the estimated user's emotions. For example, if the user is relaxed, the real-time translation unit can provide a careful and detailed real-time translation. If the user is in a hurry, the real-time translation unit can also provide a concise and to-the-point real-time translation. If the user is excited, the real-time translation unit can also provide a visually appealing real-time translation. This enables more appropriate real-time translation by adjusting the expression method of the real-time translation 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 real-time translation unit can be performed using, for example, AI, or without AI. For example, the real-time translation unit can input the user's emotion data into the generation AI and have the generation AI adjust the expression method of the real-time translation.

[0076] The real-time translation unit can improve the accuracy of the translation based on the context of the conversation during real-time translation. The real-time translation unit, for example, performs appropriate real-time translation by taking into account the context of the conversation. The real-time translation unit can also perform specialized real-time translation based on the theme of the conversation. The real-time translation unit can also perform natural real-time translation by taking into account the flow of the conversation. This enables more natural real-time translation by improving the accuracy of the translation based on the context of the conversation. Some or all of the above-mentioned processing in the real-time translation unit may be performed using, for example, AI, or may be performed without using AI. For example, the real-time translation unit can input conversation context data into a generation AI and have the generation AI improve the accuracy of the translation.

[0077] The real-time translation unit can improve the accuracy of translation during real-time translation by referring to the user's past conversation history. The real-time translation unit, for example, refers to conversational expressions used by the user in the past to perform similar real-time translation. The real-time translation unit can also analyze the user's past conversation history and reflect preferred expressions. The real-time translation unit can also perform new real-time translations that avoid conversational expressions that the user has previously expressed dissatisfaction with. In this way, by referring to the user's past conversation history, the accuracy of real-time translation is improved. Some or all of the above-described processing in the real-time translation unit may be performed using, for example, AI, or may be performed without using AI. For example, the real-time translation unit can input the user's past conversation history data into a generation AI and have the generation AI improve the accuracy of the translation.

[0078] The real-time translation unit can perform translation taking into account nuances between different languages ​​during real-time translation. The real-time translation unit can perform appropriate real-time translation, for example, taking into account cultural differences between languages. The real-time translation unit can also perform natural real-time translation taking into account differences in expressions between languages. The real-time translation unit can also perform accurate real-time translation taking into account differences in nuance between languages. This enables more accurate real-time translation by taking into account nuances between different languages. Some or all of the above-mentioned processing in the real-time translation unit may be performed using, for example, AI, or may be performed without using AI. For example, the real-time translation unit can input nuance data between languages ​​into the generation AI and have the generation AI improve the accuracy of the translation.

[0079] The guide unit can estimate the user's emotions and adjust the way the guide is presented based on the estimated user's emotions. For example, if the user is relaxed, the guide unit can provide a guide with detailed explanations. If the user is in a hurry, the guide unit can also provide a concise and to-the-point guide. If the user is excited, the guide unit can also provide a visually appealing guide. By adjusting the way the guide is presented based on the user's emotions, more appropriate guidance can be provided. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, 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-described processing in the guide unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the guide unit can input the user's emotion data into the generation AI and have the generation AI adjust the way the guide is presented.

[0080] The guide unit can adjust the level of detail of the culture and lifestyle habits of the destination when providing the guide. For example, the guide unit provides detailed information about major cultures and lifestyle habits. The guide unit can also provide concise information about minor cultures and lifestyle habits. The guide unit can also provide detailed explanations about important manners and customs of the destination. By adjusting the level of detail of the culture and lifestyle habits of the destination, the optimal guide for the user can be provided. Some or all of the above-mentioned processing in the guide unit may be performed using, for example, AI, or may be performed without using AI. For example, the guide unit can input data about the culture and lifestyle habits of the destination into the generation AI and cause the generation AI to adjust the level of detail of the guide.

[0081] When providing a guide, the guide unit can improve the accuracy of the guide by referring to the user's past guide usage history. For example, the guide unit can refer to guides that the user has previously given high ratings and provide a similar guide. The guide unit can also analyze the user's past guide usage history and provide a guide that the user prefers. The guide unit can also provide a new guide that avoids guides that the user has previously expressed dissatisfaction with. In this way, by referring to the user's past guide usage history, the accuracy of the guide is improved. Some or all of the above-mentioned processing in the guide unit may be performed using, for example, AI, or may be performed without using AI. For example, the guide unit can input the user's past guide usage history data into a generation AI and have the generation AI improve the accuracy of the guide.

[0082] When providing guidance, the guide unit can update the guide content to reflect the latest information about the destination. The guide unit, for example, provides guidance that reflects the latest event information about the destination. The guide unit can also provide guidance that reflects the latest tourist spot information about the destination. The guide unit can also provide guidance that reflects the latest traffic information about the destination. In this way, by reflecting the latest information about the destination, the latest guidance is always provided. Some or all of the above-mentioned processing in the guide unit may be performed using AI, for example, or may be performed without using AI. For example, the guide unit can input the latest information data about the destination into the generation AI and have the generation AI update the guide content.

[0083] The information providing unit can estimate the user's emotions and adjust the way information is presented based on the estimated user's emotions. For example, when the user is relaxed, the information providing unit can provide detailed information. When the user is in a hurry, the information providing unit can also provide concise, to-the-point information. When the user is excited, the information providing unit can also provide visually appealing information. This allows more appropriate information to be provided by adjusting the way information is presented 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 information providing unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the information providing unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the way information is presented.

[0084] The information providing unit can adjust the level of detail of weather and disaster information when providing information. For example, the information providing unit provides detailed information for serious weather and disaster information. The information providing unit can also provide concise information for minor weather and disaster information. The information providing unit can also prioritize providing weather and disaster information related to the user's current location. In this way, by adjusting the level of detail of weather and disaster information, optimal information is provided to the user. Some or all of the above-described processing in the information providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the information providing unit can input weather and disaster information data to the generation AI and cause the generation AI to adjust the level of detail of the information.

[0085] When providing information, the information providing unit can improve the accuracy of the information provided by referring to the user's past information provision history. For example, the information providing unit can provide similar information by referring to information provision methods that the user has previously rated highly. The information providing unit can also analyze the user's past information provision history and provide preferred information. The information providing unit can also provide new information by avoiding information provision methods that the user has previously expressed dissatisfaction with. In this way, the accuracy of information provided is improved by referring to the user's past information provision history. Some or all of the above-described processing in the information providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the information providing unit can input the user's past information provision history data into the generation AI and cause the generation AI to improve the accuracy of information provided.

[0086] The information providing unit can update the content when providing information by reflecting the latest information. The information providing unit, for example, provides information by reflecting the latest weather information. The information providing unit can also provide information by reflecting the latest disaster information. The information providing unit can also provide information by reflecting the latest local information. In this way, by reflecting the latest information, the latest information is always provided. Some or all of the above-mentioned processing in the information providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the information providing unit can input the latest information data to the generation AI and cause the generation AI to update the information content.

[0087] The information providing unit can estimate the user's emotions and determine the priority of information provision based on the estimated user's emotions. For example, when the user is relaxed, the information providing unit can prioritize providing detailed information. Furthermore, when the user is in a hurry, the information providing unit can prioritize providing concise information. Furthermore, when the user is excited, the information providing unit can prioritize providing visually appealing information. In this way, by determining the priority of information provision according to the user's emotions, more appropriate information can be provided. Emotion estimation is realized using an emotion estimation function, for example, using 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 information providing unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the information providing unit can input the user's emotion data into the generation AI and cause the generation AI to determine the priority of information provision.

[0088] When providing information, the information providing unit can provide optimal information by taking into account the user's geographical location information. For example, when the user is in their current location, the information providing unit can prioritize providing weather information for that area. Furthermore, when the user is traveling, the information providing unit can prioritize providing local disaster information. Furthermore, when the user requests information about a specific area, the information providing unit can provide information related to that area. In this way, more relevant information can be provided by taking the user's geographical location information into consideration. Some or all of the above-described processing in the information providing unit can be performed, for example, using AI or without AI. For example, the information providing unit can input the user's geographical location information data into the generation AI and cause the generation AI to provide optimal information.

[0089] When providing information, the information providing unit can analyze the user's social media activity and provide related information. The information providing unit can provide information based on, for example, the content posted by accounts the user follows on social media. The information providing unit can also analyze the content posted by the user on social media and provide related information. The information providing unit can also provide information based on the content posted by the user's friends. In this way, by analyzing the user's social media activity, more relevant information can be provided. Some or all of the above-described processing in the information providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the information providing unit can input the user's social media activity data into the generation AI and cause the generation AI to provide related information.

[0090] The information providing unit can customize the information providing method by reflecting the user's past feedback when providing information. For example, the information providing unit preferentially uses information providing methods that the user has previously rated highly. The information providing unit can also reflect the user's preferred information providing method based on the user's past feedback. The information providing unit can also provide a new information providing method by avoiding information providing methods that the user has previously expressed dissatisfaction with. In this way, a more appropriate information providing method is provided by reflecting the user's past feedback. Some or all of the above-described processing in the information providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the information providing unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the information providing method. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, suggestion unit, translation unit, real-time translation unit, guide unit, and information provision unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and uses generation AI to suggest sightseeing routes and places to stay based on the user's preferences and budget. The translation unit is realized, for example, by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12, and supports multiple languages. The real-time translation unit is realized, for example, by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12, and supports conversations on the spot. The guide unit is realized, for example, by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12, and provides content that allows users to learn about the culture, lifestyle, and manners of the destination. The information providing unit is realized by, for example, the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12, and provides information on weather and disasters in real time. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, suggestion unit, translation unit, real-time translation unit, guide unit, and information provision unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and uses generation AI to suggest sightseeing routes and places to stay based on the user's preferences and budget. The translation unit is realized, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12, and supports multiple languages. The real-time translation unit is realized, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12, and supports local conversations. The guide unit is realized, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12, and provides content that allows users to learn about the culture, lifestyle, and manners of the destination. The information providing unit is realized by, for example, the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12, and provides information on weather and disasters in real time. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, suggestion unit, translation unit, real-time translation unit, guide unit, and information provision unit, is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing device 12. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and uses a generation AI to suggest sightseeing routes and places to stay based on the user's preferences and budget. The translation unit is realized, for example, by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing device 12, and supports multiple languages. The real-time translation unit is realized, for example, by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing device 12, and supports conversations on the spot. The guide unit is realized, for example, by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing device 12, and provides content that allows users to learn about the culture, lifestyle, and manners of the destination. The information providing unit is realized by, for example, the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing device 12, and provides weather and disaster information in real time. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, suggestion unit, translation unit, real-time translation unit, guide unit, and information provision unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and uses a generative AI to suggest sightseeing routes and places to stay based on the user's preferences and budget. The translation unit is realized, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12, and supports multiple languages. The real-time translation unit is realized, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12, and supports conversations on the spot. The guide unit is realized, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12, and provides content that allows users to learn about the culture, lifestyle, and manners of the destination. The information providing unit is realized by, for example, the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12, and provides information on weather and disasters in real time.

[0091] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0092] The inbound demand response system can further include a health management unit that monitors the user's health condition. The health management unit collects data such as the user's heart rate, number of steps, and sleep status to support health management during travel. The health management unit can also suggest appropriate rest areas and healthy meals based on the user's health condition. Furthermore, the health management unit can provide information on the nearest medical institution if the user complains of feeling unwell. This allows users to manage their health with peace of mind even while traveling.

[0093] The collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated user's emotions. For example, if the user is feeling stressed, information collection can be performed during a time when the user is able to relax. Furthermore, if the user is excited, the collection unit can immediately start information collection and quickly make suggestions. Furthermore, if the user is tired, the collection unit can collect information after the user has rested. In this way, by adjusting the timing of information collection according to the user's emotions, information can be collected at a more appropriate time.

[0094] The suggestion unit can analyze the user's past travel history and select the optimal information collection method. For example, it can collect information on similar tourist spots based on places the user has visited in the past. The suggestion unit can also analyze trends in preferred tourist spots from the user's past travel history and collect information. The suggestion unit can also collect information on the optimal means of transportation by referring to the means of transportation the user has used in the past. This allows for more accurate information collection by selecting the optimal information collection method based on the user's past travel history.

[0095] When collecting information, the collection unit can filter the information based on the user's current interests. For example, the collection unit can prioritize collecting information about events and festivals in which the user is currently interested. The collection unit can also collect information about tourist spots related to a particular culture or history in which the user is interested. If the user is interested in the current season, the collection unit can also collect information about seasonal tourist spots. This allows the information to be filtered based on the user's current interests, making it possible to provide more relevant information.

[0096] When collecting information, the collection unit can select an appropriate collection means depending on the user's input method. For example, if the user uses voice input, the collection unit can collect information using voice recognition technology. If the user uses text input, the collection unit can also collect information based on keyword search. If the user uses image input, the collection unit can also collect related information using image recognition technology. This improves the efficiency of information collection by selecting the optimal collection means depending on the user's input method.

[0097] The suggestion unit can estimate the user's emotions and adjust the way suggestions are expressed based on the estimated user's emotions. For example, if the user is relaxed, the suggestion unit can provide suggestions that include detailed explanations. If the user is in a hurry, the suggestion unit can also provide concise and to-the-point suggestions. If the user is excited, the suggestion unit can also provide visually appealing suggestions. This allows for more appropriate suggestions to be made by adjusting the way suggestions are expressed depending on the user's emotions.

[0098] When making a suggestion, the suggestion unit can adjust the level of detail of the suggestion based on the importance of the tourist route and the destination. For example, detailed information is provided for major tourist destinations. The suggestion unit can also provide concise information for minor tourist destinations. The suggestion unit can also provide detailed accommodation information depending on the importance of the destination. This allows the suggestion to be optimal for the user by adjusting the level of detail of the suggestion based on the importance of the tourist route and the destination.

[0099] When making suggestions, the suggestion unit can apply different suggestion algorithms depending on the category of tourist route or destination. For example, in the case of a historical tourist destination, the suggestion unit can make suggestions that emphasize the historical background and highlights. In addition, in the case of a natural tourist destination, the suggestion unit can make suggestions that emphasize the beauty of nature and activities. In addition, in the case of an urban tourist destination, the suggestion unit can make suggestions that emphasize shopping and gourmet information. In this way, by applying different suggestion algorithms depending on the category of tourist route or destination, more appropriate suggestions can be made.

[0100] When making a suggestion, the suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results. For example, the suggestion unit can make similar suggestions by referring to suggestions that the user has given high ratings to in the past. The suggestion unit can also analyze the user's past suggestion results, understand preferences, and make suggestions. The suggestion unit can also make new suggestions by avoiding suggestions that the user has expressed dissatisfaction with in the past. In this way, the accuracy of the suggestion can be improved by referring to the user's past suggestion results.

[0101] The translation unit can estimate the user's emotions and adjust the translation expression style based on the estimated user's emotions. For example, if the user is relaxed, the translation unit can provide a careful and detailed translation. If the user is in a hurry, the translation unit can also provide a concise and to-the-point translation. If the user is excited, the translation unit can also provide a visually appealing translation. This allows for more appropriate translation by adjusting the translation expression style according to the user's emotions.

[0102] The processing flow of the second embodiment will be briefly explained below.

[0103] Step 1: The collection unit collects the user's preferences and budget. For example, the collection unit collects preferences and budget based on information entered by the user. The collection unit can also use generative AI to analyze the user's past travel history and social media activity to estimate preferences and budget. Step 2: The suggestion unit suggests sightseeing routes and places to stay based on the information collected by the collection unit. For example, the suggestion unit uses a generation AI to suggest optimal sightseeing routes and places to stay based on the user's preferences and budget. The suggestion unit can also apply different suggestion algorithms depending on the category of sightseeing routes and places to stay. Step 3: The translation unit supports multiple languages. For example, the translation unit uses generative AI to translate the text entered by the user into multiple languages. The translation unit can also estimate the user's emotions and adjust the translation expression based on the estimated emotions. Step 4: The real-time translation unit supports on-site conversations. The real-time translation unit translates user-input speech in real time, for example, using generative AI. The real-time translation unit can also improve the accuracy of the translation based on the context of the conversation. Step 5: The guide unit provides content that allows users to learn about the culture, lifestyle, and manners of the destination. For example, the guide unit uses a generative AI to provide information about the culture, lifestyle, and manners of the destination in response to questions entered by the user. The guide unit can also estimate the user's emotions and adjust the way the guide is expressed based on the estimated emotions. Step 6: The information provider provides real-time weather and disaster information. For example, the information provider uses a generation AI to provide the latest weather and disaster information in response to questions entered by the user. The information provider can also provide optimal information taking into account the user's geographical location information.

[0104] 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.

[0105] 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 generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0106] 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.

[0107] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0108] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0109] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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).

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0122] 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.

[0123] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0124] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0125] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0138] 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.

[0139] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0140] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0141] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0155] 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.

[0156] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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."

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] [Explanation of symbols]

[0176] 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 collection unit that collects user preferences and budgets; a suggestion unit that suggests sightseeing routes and places to stay based on the information collected by the collection unit; A translation department that supports multiple languages; Real-time translation to support local conversations, The guide section provides content that teaches you about the culture, lifestyle, and etiquette of your destination, An information providing unit that provides weather and disaster information in real time. A system characterized by:

2. The collecting unit Estimate the user's emotions and adjust the timing of information collection based on the estimated user emotions.

2. The system of claim 1.

3. The collecting unit Analyze the user's past travel history and select the optimal information gathering method 2. The system of claim 1.

4. The collecting unit At the time of collection, filtering based on the user's current interests or concerns 2. The system of claim 1.

5. The collecting unit When collecting information, select the appropriate collection method depending on the user's input method.

2. The system of claim 1.

6. The proposal unit Inferring user emotions and adjusting the presentation of suggestions based on the estimated user emotions 2. The system of claim 1.

7. The proposal unit When making suggestions, adjust the level of detail of the suggestions based on the importance of the sightseeing route and the places to stay.

2. The system of claim 1.

8. The proposal unit When making suggestions, different suggestion algorithms are applied depending on the category of tourist route or destination.

2. The system of claim 1.

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