System

Abstract

An object of a system according to an embodiment is to enable a traveler to efficiently collect and present useful information at a travel destination.SOLUTION: A system according to an embodiment includes a reception unit, an acquisition unit, an analysis unit, and a provision unit. The reception unit receives a designation of a travel destination. The acquisition unit acquires information based on the travel destination designated by the reception unit. The analysis unit analyzes the information acquired by the acquisition unit and selects information useful for the traveler. The providing unit presents the information selected by the analyzing unit to the traveler.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem that it is difficult for travelers to efficiently collect useful information about their travel destinations.

[0005] The system according to the embodiment aims to enable travelers to efficiently collect and present useful information about their travel destinations. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an acquisition unit, an analysis unit, and a provision unit. The reception unit receives a travel destination designation. The acquisition unit acquires information based on the travel destination designated by the reception unit. The analysis unit analyzes the information acquired by the acquisition unit and selects information useful to the traveler. The provision unit presents the information selected by the analysis unit to the traveler. [Effects of the Invention]

[0007] The system according to the embodiment allows travelers to efficiently collect and present useful information about their travel destinations. [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) A travel information providing system according to an embodiment of the present invention automatically acquires and presents information to help travelers maximize their enjoyment of their travel destinations. When a traveler selects a travel destination, a generation AI automatically acquires information about the area, such as overviews of tourist attractions, restaurant ratings and reviews, feedback from other travelers, and recommendations from local residents. The system then analyzes the acquired information and selects and presents the most useful information for the traveler. For example, a traveler selects a travel destination, such as "I want to go to Tokyo." This information is input into the generation AI. The generation AI then analyzes the input information and automatically acquires information about the destination, such as overviews of tourist attractions, restaurant ratings and reviews, feedback from other travelers, and recommendations from local residents. For example, overviews of tourist attractions in Tokyo, such as Sensoji Temple and Tokyo Tower, are acquired. Restaurant ratings and reviews are also collected, providing useful information for travelers. The acquired information is then analyzed by the generation AI to select the most useful information for the traveler. For example, if a traveler is interested in food, restaurant ratings and reviews are prioritized and presented. The AI ​​also includes overviews of tourist attractions and recommendations from local residents. Finally, the selected information is presented to the traveler, allowing them to maximize their enjoyment at their travel destination. For example, before a traveler goes to Tokyo, they are presented with information such as overviews of Sensoji Temple and Tokyo Tower, information on highly rated restaurants, feedback from other travelers, and recommendations from local residents. This allows the travel information system to automatically obtain and present information that will help travelers maximize their enjoyment at their travel destination. For example, when a traveler specifies a travel destination, the generation AI automatically obtains overviews of local tourist attractions, restaurant ratings and reviews, feedback from other travelers, and recommendations from local residents. The obtained information is then analyzed, and the most useful information for the traveler is selected and presented. This allows the traveler to maximize their enjoyment at their travel destination.

[0029] A travel information providing system according to an embodiment includes a reception unit, an acquisition unit, an analysis unit, and a provision unit. The reception unit allows a traveler to specify a travel destination. The traveler may specify a travel destination by, for example, but not limited to, a place name, a tourist attraction name, or a specific activity. For example, the traveler may input "I want to go to Tokyo." The acquisition unit acquires information based on the travel destination specified by the reception unit. The acquisition unit acquires information by, for example, analyzing public databases on the Internet or posts on social media. For example, the acquisition unit acquires information such as summaries of tourist attractions, restaurant ratings and reviews, feedback from other travelers who have visited the destination, and recommendations from local residents. The analysis unit analyzes the information acquired by the acquisition unit and selects information useful to the traveler. For example, the analysis unit customizes the information based on the traveler's interests and preferences. For example, if the traveler is interested in food, restaurant ratings and reviews are preferentially presented. The provision unit presents the information selected by the analysis unit to the traveler. For example, the provision unit provides a user-friendly interface. For example, before a traveler goes to Tokyo, the traveler is presented with an overview of Sensoji Temple and Tokyo Tower, information on highly rated restaurants, feedback from other travelers, recommendations from local residents, etc. In this way, the travel information providing system according to the embodiment can automatically acquire and present information that will help travelers maximize their enjoyment at their travel destinations.

[0030] The acquisition unit can acquire information by analyzing public databases on the Internet or posts on social media. Public databases include, but are not limited to, databases of the Japan Tourism Agency and local tourist information sites. Posts on social media include, but are not limited to, Twitter (registered trademark), Instagram (registered trademark), and Facebook (registered trademark). The acquisition unit can acquire tourist attraction information from, for example, a database of the Japan Tourism Agency. The acquisition unit can also acquire tourist attraction information from local tourist information sites. Furthermore, the acquisition unit can analyze social media posts to acquire information useful to travelers. For example, Twitter posts can be analyzed to acquire information useful to travelers. This makes it possible to acquire information from various information sources on the Internet. Some or all of the above-described processing by the acquisition unit can be performed using, for example, AI, or without AI. For example, the acquisition unit can input social media posts into a generation AI and have the generation AI analyze the post content.

[0031] The analysis unit can customize information based on the traveler's interests and preferences. Methods for identifying a traveler's interests and preferences include, but are not limited to, past travel history, survey results, and social media activity. For example, the analysis unit can identify a traveler's interests and preferences based on past travel history. The analysis unit can also identify a traveler's interests and preferences based on survey results. Furthermore, the analysis unit can analyze social media activity to identify a traveler's interests and preferences. For example, information can be customized based on data on travel destinations the traveler has previously visited. This makes it possible to provide the most useful information to the traveler. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input data on a traveler's interests and preferences into the generation AI and have the generation AI customize the information.

[0032] The providing unit can provide an easy-to-use interface. Specific elements of an easy-to-use interface include, but are not limited to, usability test results, intuitive operability, and responsive design. The providing unit can, for example, design the interface based on the results of a usability test. The providing unit can also provide an interface that emphasizes intuitive operability. Furthermore, the providing unit can employ a responsive design to provide an interface that is easy to use on various devices. For example, the providing unit can provide an interface that is easy to use on smartphones and tablets. This allows the user to provide an interface that is easy to use. Some or all of the above-described processing in the providing unit can be performed, for example, using AI or without AI. For example, the providing unit can input the results of a usability test into a generation AI and have the generation AI execute the interface design.

[0033] The acquisition unit can acquire tourist attraction overviews, restaurant ratings or reviews, feedback from other travelers who have visited, and recommendations from local residents. Tourist attraction overviews include, but are not limited to, historical background, major attractions, and access methods. Restaurant ratings or reviews include, but are not limited to, ratings on review sites and user comments. Feedback includes, but are not limited to, survey results, user comments, and ratings. Local residents' recommendations include, but are not limited to, interview results, local blogs, and posts on social media. The acquisition unit, for example, acquires tourist attraction overviews. The acquisition unit can also acquire restaurant ratings and reviews. The acquisition unit can also acquire feedback from other travelers who have visited the tourist attraction. The acquisition unit can also acquire recommendations from local residents. This allows travelers to acquire information useful from multiple perspectives. Some or all of the above-described processing by the acquisition unit may be performed using, for example, AI, or without AI. For example, the acquisition unit can input a tourist attraction overview into the generation AI and cause the generation AI to acquire information.

[0034] The analysis unit can analyze the acquired information and select the most useful information for travelers. Specific criteria for the most useful information include, but are not limited to, traveler ratings, the latest information, and reliable information. For example, the analysis unit selects information that is highly rated by travelers. The analysis unit can also select the latest information. Furthermore, the analysis unit can select highly reliable information. For example, it preferentially selects information that is highly rated by travelers. This makes it possible to provide the most useful information for travelers. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input the acquired information into a generation AI and have the generation AI select the information.

[0035] The reception unit can analyze the user's past travel history and suggest travel destinations. Specific methods for acquiring past travel history include, but are not limited to, past reservation history and records of visited places. For example, the reception unit can suggest similar travel destinations based on data on travel destinations the user has visited in the past. The reception unit can also suggest highly rated travel destinations based on ratings of travel destinations the user has visited in the past. Furthermore, the reception unit can suggest travel destinations where similar activities can be enjoyed based on activities at travel destinations the user has visited in the past. For example, similar travel destinations can be suggested based on data on travel destinations the user has visited in the past. This makes it possible to suggest optimal travel destinations based on the user's past travel history. Some or all of the above-mentioned processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past travel history data into a generation AI and have the generation AI execute travel destination suggestions.

[0036] When specifying a travel destination, the reception unit can filter based on the user's current interests. Methods for identifying current interests include, but are not limited to, recent search history and social media activity. The reception unit can, for example, suggest travel destinations based on themes in which the user is currently interested (history, nature, art, etc.). The reception unit can also suggest travel destinations based on keywords recently searched by the user. The reception unit can also suggest travel destinations based on events or activities in which the user recently participated. For example, travel destinations can be suggested based on themes in which the user is currently interested. This makes it possible to suggest travel destinations based on the user's current interests. Some or all of the above-described processing in the reception unit may be performed using, or without, AI. For example, the reception unit can input data regarding the user's current interests into the generation AI and have the generation AI perform filtering.

[0037] When specifying a travel destination, the reception unit can select a reception means according to the user's input method. Specific types of input methods include, but are not limited to, voice input, text input, and image input. For example, when the user specifies the travel destination by voice, the reception unit can accept the input using voice recognition technology. Furthermore, when the user specifies the travel destination by text, the reception unit can also accept the input using text analysis technology. Furthermore, when the user specifies the travel destination by image, the reception unit can also accept the input using image recognition technology. For example, when the user specifies the travel destination by voice, the reception unit can accept the input using voice recognition technology. This makes it possible to select the optimal reception means according to the user's input method. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's input data to a generation AI and have the generation AI select the optimal reception means.

[0038] When a travel destination is specified, the reception unit can prioritize suggesting highly relevant travel destinations by taking into account the user's geographical location information. Specific methods for acquiring geographical location information include, but are not limited to, GPS data, location information services, etc. The reception unit, for example, prioritizes suggesting travel destinations close to the user's current location. The reception unit can also prioritize suggesting travel destinations that are easily accessible from the user's current location. Furthermore, the reception unit can also suggest optimal travel destinations by taking into account travel time from the user's current location. For example, the reception unit prioritizes suggesting travel destinations close to the user's current location. This makes it possible to suggest travel destinations based on the user's geographical location information. Some or all of the above-described processing by the reception unit may be performed using, or without, AI. For example, the reception unit can input the user's geographical location information into the generation AI and cause the generation AI to suggest highly relevant travel destinations.

[0039] When a travel destination is specified, the reception unit can analyze the user's social media activity and suggest related travel destinations. Specific methods for analyzing social media activity include, but are not limited to, analyzing post content and follower analysis. For example, the reception unit can suggest travel destinations visited by accounts the user follows on social media. The reception unit can also analyze the user's social media posts to suggest related travel destinations. Furthermore, the reception unit can suggest related travel destinations based on the activity of the user's friends on social media. For example, it can suggest travel destinations visited by accounts the user follows on social media. This makes it possible to suggest travel destinations based on the user's social media activity. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media activity data into a generation AI and cause the generation AI to suggest related travel destinations.

[0040] When a travel destination is specified, the reception unit can customize the suggestion method by reflecting the user's past feedback. Specific methods for acquiring past feedback include, but are not limited to, survey results, user comments, and ratings. For example, the reception unit can suggest places similar to travel destinations that the user has previously rated highly. The reception unit can also suggest travel destinations that the user has previously rated poorly, avoiding such destinations. Furthermore, the reception unit can customize the suggestion method based on the user's past feedback. For example, the reception unit can suggest places similar to travel destinations that the user has previously rated highly. This enables the suggestion method to be customized based on the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using, or without, AI. For example, the reception unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the suggestion method.

[0041] The acquisition unit may take into account the user's past search history when analyzing public databases on the Internet or posts on social media to acquire information. Specific methods for acquiring past search history include, but are not limited to, browser history and search engine logs. The acquisition unit may acquire relevant information based on, for example, keywords previously searched by the user. The acquisition unit may also acquire relevant information based on websites previously visited by the user. The acquisition unit may also analyze the user's past search history to acquire the most relevant information. For example, relevant information may be acquired based on keywords previously searched by the user. This makes it possible to acquire information based on the user's past search history. Some or all of the above-described processing by the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit may input the user's past search history data into the generation AI and cause the generation AI to acquire information.

[0042] When acquiring information, the acquisition unit can perform filtering based on the user's current travel plans and areas of interest. Specific methods for acquiring the current travel plans and areas of interest include, but are not limited to, travel reservation information, recent search history, etc. For example, the acquisition unit can prioritize acquiring information related to a travel destination currently planned by the user. The acquisition unit can also prioritize acquiring information related to areas of interest to the user (history, nature, art, etc.). Furthermore, the acquisition unit can acquire the most relevant information based on the user's current travel plans and areas of interest. For example, information related to the travel destination currently planned by the user can be prioritized. This makes it possible to acquire information based on the user's current travel plans and areas of interest. Some or all of the above-described processing in the acquisition unit may be performed using, or without, AI. For example, the acquisition unit can input data related to the user's current travel plans and areas of interest into the generation AI and have the generation AI perform filtering.

[0043] When acquiring information, the acquisition unit can select the optimal acquisition means depending on the user's input method. Specific selection criteria for the optimal acquisition means include, but are not limited to, voice recognition technology, text analysis technology, and image recognition technology. For example, when the user acquires information by voice, the acquisition unit can acquire the information using voice recognition technology. Furthermore, when the user acquires information by text, the acquisition unit can also acquire the information using text analysis technology. Furthermore, when the user acquires information by image, the acquisition unit can also acquire the information using image recognition technology. For example, when the user acquires information by voice, the acquisition unit acquires the information using voice recognition technology. This makes it possible to acquire optimal information depending on the user's input method. Some or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the user's input data to a generation AI and cause the generation AI to select the optimal acquisition means.

[0044] When acquiring information, the acquisition unit can prioritize acquiring highly relevant information taking into account the user's geographical location information. Specific criteria for highly relevant information include, but are not limited to, information that is geographically close and information related to the user's interests. For example, the acquisition unit prioritizes acquiring information close to the user's current location. The acquisition unit can also prioritize acquiring information that is easily accessible from the user's current location. Furthermore, the acquisition unit can acquire optimal information taking into account travel time from the user's current location. For example, information close to the user's current location is prioritized. This makes it possible to acquire information based on the user's geographical location information. Some or all of the above-described processing in the acquisition unit may be performed using, or without, AI. For example, the acquisition unit can input the user's geographical location information to the generation AI and cause the generation AI to acquire highly relevant information.

[0045] When acquiring information, the acquisition unit can analyze the user's social media activity and acquire related information. Specific methods for analyzing social media activity include, but are not limited to, analyzing posted content and followers, for example. The acquisition unit, for example, acquires information posted by accounts the user follows on social media. The acquisition unit can also analyze the user's social media posts to acquire related information. Furthermore, the acquisition unit can acquire related information by referring to the activities of the user's friends on social media. For example, it acquires information posted by accounts the user follows on social media. This makes it possible to acquire information based on the user's social media activity. Some or all of the above-described processing by the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit may input the user's social media activity data into the generation AI and cause the generation AI to acquire related information.

[0046] When acquiring information, the acquisition unit can customize the acquisition method by reflecting the user's past feedback. Specific acquisition methods for past feedback include, but are not limited to, survey results, user comments, and ratings. For example, the acquisition unit preferentially acquires information from information sources that the user has previously rated highly. The acquisition unit can also use an acquisition method that avoids information sources that the user has previously rated poorly. Furthermore, the acquisition unit can customize the acquisition method based on the user's past feedback. For example, the acquisition unit preferentially acquires information from information sources that the user has previously rated highly. This makes it possible to customize the information acquisition method based on the user's past feedback. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the acquisition method.

[0047] The analysis unit may take into account the user's past travel history when customizing information based on the traveler's interests and preferences. Specific methods for acquiring past travel history include, but are not limited to, past reservation history and records of visited places. The analysis unit may customize information based on data on travel destinations the user has visited in the past. The analysis unit may also customize information based on data on travel destinations the user has previously rated. The analysis unit may also analyze the user's past travel history and customize the most relevant information. For example, the analysis unit may customize information based on data on travel destinations the user has previously visited. This enables customization of information based on the user's past travel history. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input the user's past travel history data into the generation AI and have the generation AI customize the information.

[0048] When analyzing the acquired information, the analysis unit can apply different analysis algorithms depending on the category of the information. Specific methods for classifying information categories include, but are not limited to, tourist information, food and drink information, and transportation information. For example, the analysis unit applies an analysis algorithm dedicated to tourist attractions to information about tourist attractions. The analysis unit can also apply an analysis algorithm dedicated to restaurants to restaurant ratings and reviews. Furthermore, the analysis unit can apply an analysis algorithm dedicated to local residents to information recommended by local residents. For example, an analysis algorithm dedicated to tourist attractions is applied to information about tourist attractions. This enables optimal analysis according to the category of the information. Some or all of the above-mentioned processing by the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information category data to the generation AI and cause the generation AI to apply the analysis algorithm.

[0049] When analyzing acquired information, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. Specific methods for acquiring past analysis results include, but are not limited to, past analysis logs and user feedback. The analysis unit can improve the accuracy of the analysis by referring to, for example, analysis results that the user has previously rated highly. The analysis unit can also perform an analysis method that avoids analysis results that the user has previously rated poorly. Furthermore, the analysis unit can analyze the most relevant information based on the user's past analysis results. For example, the analysis unit can improve the accuracy of the analysis by referring to analysis results that the user has previously rated highly. This enables the accuracy of the analysis to be improved based on the user's past analysis results. Some or all of the above-described processing in the analysis unit can be performed, for example, using AI or without AI. For example, the analysis unit can input the user's past analysis result data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0050] When analyzing the acquired information, the analysis unit can determine the priority of analysis based on the time when the information was submitted. Specific methods for obtaining the time when the information was submitted include, but are not limited to, a log of the submission date and time, or before or after an event. The analysis unit, for example, prioritizes analyzing the most recent information. The analysis unit can also prioritize analyzing information within a period specified by the user. Furthermore, the analysis unit can prioritize analyzing the most relevant information based on the time when the information was submitted. For example, the most recent information is prioritized. This makes it possible to determine the priority of analysis based on the time when the information was submitted. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information submission time data to the generation AI and have the generation AI determine the priority of analysis.

[0051] When analyzing the acquired information, the analysis unit can adjust the order of analysis based on the relevance of the information. Specific criteria for the relevance of information include, but are not limited to, information related to the user's interests and information that is geographically close. For example, the analysis unit can prioritize analyzing information that is most relevant to the user's interests. The analysis unit can also prioritize analyzing information that is most relevant to the user's current travel plans. Furthermore, the analysis unit can perform the analysis in the most efficient order based on the relevance of the information. For example, the analysis unit can prioritize analyzing information that is most relevant to the user's interests. This makes it possible to adjust the order of analysis based on the relevance of the information. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information relevance data to the generation AI and cause the generation AI to adjust the order of analysis.

[0052] When analyzing the acquired information, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. Specific methods for acquiring the level of expertise include, but are not limited to, survey results, past usage history, etc. For example, if the user has technical expertise, the analysis unit can provide analysis results that use a lot of technical terms. Furthermore, if the user does not have technical expertise, the analysis unit can provide analysis results in simple language. Furthermore, the analysis unit can provide optimal analysis results according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit can provide analysis results that use a lot of technical terms. This makes it possible to provide analysis results that correspond to the user's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the user's level of expertise data into a generation AI and have the generation AI use technical terms.

[0053] When providing information, the providing unit can select the optimal providing method by referring to the user's past travel history. Specific methods for acquiring the past travel history include, but are not limited to, past reservation history and records of visited places. For example, the providing unit can prioritize the use of a providing method that the user has previously rated highly. The providing unit can also avoid providing methods that the user has previously rated poorly. Furthermore, the providing unit can select the most relevant providing method based on the user's past travel history. For example, the providing unit can prioritize the use of a providing method that the user has previously rated highly. This makes it possible to select the optimal information providing method based on the user's past travel history. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input the user's past travel history data into the generation AI and cause the generation AI to select the optimal providing method.

[0054] When providing information, the providing unit can customize the provided content according to the user's current travel plans. Specific methods for acquiring the current travel plans include, but are not limited to, travel reservation information, recent search history, etc. The providing unit, for example, prioritizes providing information related to the travel destination currently planned by the user. The providing unit can also provide the most relevant information based on the user's current travel plans. Furthermore, the providing unit can customize the optimal provided content based on the user's current travel plans. For example, information related to the travel destination currently planned by the user can be prioritized. This makes it possible to customize the information provided content based on the user's current travel plans. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's current travel plan data into the generation AI and have the generation AI customize the provided content.

[0055] The providing unit can improve the information providing method by reflecting user feedback when providing information. Specific methods of obtaining feedback include, but are not limited to, survey results, user comments, and ratings. For example, the providing unit prioritizes the use of a providing method that the user has previously rated highly. The providing unit can also avoid a providing method that the user has previously rated poorly. Furthermore, the providing unit can improve the optimal providing method based on user feedback. For example, the providing unit prioritizes the use of a providing method that the user has previously rated highly. This enables the information providing method to be improved based on user feedback. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input user feedback data into a generating AI and cause the generating AI to improve the providing method.

[0056] When providing information, the providing unit can select the optimal providing method by taking into account the user's geographical location information. Specific methods for acquiring geographical location information include, but are not limited to, GPS data and location information services. For example, the providing unit can prioritize providing information close to the user's current location. The providing unit can also prioritize providing information that is easily accessible from the user's current location. Furthermore, the providing unit can provide optimal information by taking into account travel time from the user's current location. For example, information close to the user's current location can be prioritized. This makes it possible to select the optimal information providing method based on the user's geographical location information. Some or all of the above-described processing in the providing unit may be performed using, or without, AI. For example, the providing unit can input the user's geographical location information to the generation AI and cause the generation AI to select the optimal providing method.

[0057] When providing information, the providing unit can analyze the user's social media activity and customize the provided content. Specific methods for analyzing social media activity include, but are not limited to, analyzing posted content and followers. For example, the providing unit can prioritize providing information posted by accounts the user follows on social media. The providing unit can also analyze the user's social media posts and provide related information. Furthermore, the providing unit can provide related information based on the activities of the user's friends on social media. For example, information posted by accounts the user follows on social media can be prioritized. This makes it possible to customize the information provided based on the user's social media activity. Some or all of the above-described processing by the providing unit may be performed using, or without, AI. For example, the providing unit can input the user's social media activity data into a generation AI and cause the generation AI to customize the provided content.

[0058] When providing information, the providing unit can customize the providing method by reflecting the user's past feedback. Specific methods for acquiring past feedback include, but are not limited to, survey results, user comments, and ratings. For example, the providing unit prioritizes the use of a providing method that the user has previously rated highly. The providing unit can also avoid a providing method that the user has previously rated poorly. Furthermore, the providing unit can customize an optimal providing method based on the user's past feedback. For example, the providing unit prioritizes the use of a providing method that the user has previously rated highly. This enables the information providing method to be customized based on the user's past feedback. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past feedback data into the generating AI and cause the generating AI to customize the providing method.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The acquisition unit can also acquire information taking into account the user's past purchase history. For example, related information can be acquired based on travel guidebooks and related products purchased by the user in the past. Related information can also be acquired based on information on tickets and tours purchased by the user in the past. Furthermore, the acquisition unit can analyze the user's purchase history and acquire the most relevant information. This makes it possible to acquire information based on the user's purchase history.

[0061] The acquisition unit can also acquire information taking into account the user's current weather information. For example, if the weather at the user's travel destination is bad, it can prioritize acquiring information about activities that can be enjoyed indoors. On the other hand, if the weather is good, it can prioritize acquiring information about activities that can be enjoyed outdoors. Furthermore, it can also suggest optimal activities for the travel period based on the weather forecast. This makes it possible to acquire information based on weather information.

[0062] The reception unit can also monitor the user's current activity level and suggest travel destinations based on the activity level. For example, if the user is not getting enough exercise, it can suggest travel destinations where the user can enjoy active activities. Also, if the user is already active, it can suggest travel destinations where the user can relax. Furthermore, it can suggest optimal travel destinations based on the user's activity level. This makes it possible to suggest optimal travel destinations based on the user's activity level.

[0063] The acquisition unit can also acquire nearby event information taking into account the user's current location information. For example, it can prioritize acquisition of event information held in locations close to the user's current location. It can also prioritize acquisition of information about events in which the user is interested. Furthermore, it can acquire the most relevant event information based on the user's current location information. This makes it possible to acquire event information based on the user's location information.

[0064] The providing unit can also provide information taking into account the user's current device usage status. For example, if the user is using a smartphone, information optimized for the smartphone can be provided. If the user is using a tablet, information optimized for the tablet can be provided. Furthermore, if the user is using a desktop, information optimized for the desktop can be provided. This makes it possible to provide optimal information based on the user's device usage status.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: The reception unit receives the travel destination from the traveler. The traveler can specify the travel destination by, for example, a place name, a tourist spot name, a specific activity, etc. For example, the traveler may enter "I want to go to Tokyo." Step 2: The acquisition unit acquires information based on the travel destination specified by the reception unit. The acquisition unit acquires information, for example, by analyzing public databases on the Internet or posts on social media. For example, the acquisition unit acquires information such as overviews of tourist attractions, restaurant ratings and reviews, feedback from other travelers who have visited the destination, and recommendations from local residents. Step 3: The analysis unit analyzes the information acquired by the acquisition unit and selects information that is useful to the traveler. The analysis unit customizes the information based on the traveler's interests and preferences, for example. For example, if the traveler is interested in food, restaurant ratings and reviews will be presented preferentially. Step 4: The provider presents the information selected by the analyzer to the traveler. The provider provides, for example, a user-friendly interface. For example, before a traveler goes to Tokyo, the provider may present an overview of Sensoji Temple and Tokyo Tower, information on highly rated restaurants, feedback from other travelers, recommendations from local residents, and more.

[0067] (Example 2) A travel information providing system according to an embodiment of the present invention automatically acquires and presents information to help travelers maximize their enjoyment of their travel destinations. When a traveler selects a travel destination, a generation AI automatically acquires information about the area, such as overviews of tourist attractions, restaurant ratings and reviews, feedback from other travelers, and recommendations from local residents. The system then analyzes the acquired information and selects and presents the most useful information for the traveler. For example, a traveler selects a travel destination, such as "I want to go to Tokyo." This information is input into the generation AI. The generation AI then analyzes the input information and automatically acquires information about the destination, such as overviews of tourist attractions, restaurant ratings and reviews, feedback from other travelers, and recommendations from local residents. For example, overviews of tourist attractions in Tokyo, such as Sensoji Temple and Tokyo Tower, are acquired. Restaurant ratings and reviews are also collected, providing useful information for travelers. The acquired information is then analyzed by the generation AI to select the most useful information for the traveler. For example, if a traveler is interested in food, restaurant ratings and reviews are prioritized and presented. The AI ​​also includes overviews of tourist attractions and recommendations from local residents. Finally, the selected information is presented to the traveler, allowing them to maximize their enjoyment at their travel destination. For example, before a traveler goes to Tokyo, they are presented with information such as overviews of Sensoji Temple and Tokyo Tower, information on highly rated restaurants, feedback from other travelers, and recommendations from local residents. This allows the travel information system to automatically obtain and present information that will help travelers maximize their enjoyment at their travel destination. For example, when a traveler specifies a travel destination, the generation AI automatically obtains overviews of local tourist attractions, restaurant ratings and reviews, feedback from other travelers, and recommendations from local residents. The obtained information is then analyzed, and the most useful information for the traveler is selected and presented. This allows the traveler to maximize their enjoyment at their travel destination.

[0068] A travel information providing system according to an embodiment includes a reception unit, an acquisition unit, an analysis unit, and a provision unit. The reception unit allows a traveler to specify a travel destination. The traveler may specify a travel destination by, for example, but not limited to, a place name, a tourist attraction name, or a specific activity. For example, the traveler may input "I want to go to Tokyo." The acquisition unit acquires information based on the travel destination specified by the reception unit. The acquisition unit acquires information by, for example, analyzing public databases on the Internet or posts on social media. For example, the acquisition unit acquires information such as summaries of tourist attractions, restaurant ratings and reviews, feedback from other travelers who have visited the destination, and recommendations from local residents. The analysis unit analyzes the information acquired by the acquisition unit and selects information useful to the traveler. For example, the analysis unit customizes the information based on the traveler's interests and preferences. For example, if the traveler is interested in food, restaurant ratings and reviews are preferentially presented. The provision unit presents the information selected by the analysis unit to the traveler. For example, the provision unit provides a user-friendly interface. For example, before a traveler goes to Tokyo, the traveler is presented with an overview of Sensoji Temple and Tokyo Tower, information on highly rated restaurants, feedback from other travelers, recommendations from local residents, etc. In this way, the travel information providing system according to the embodiment can automatically acquire and present information that will help travelers maximize their enjoyment at their travel destinations.

[0069] The acquisition unit can acquire information by analyzing public databases on the Internet or posts on social media. Public databases include, but are not limited to, databases of the Japan Tourism Agency and local tourist information sites. Posts on social media include, but are not limited to, Twitter, Instagram, Facebook, and the like. The acquisition unit acquires tourist attraction information from, for example, a database of the Japan Tourism Agency. The acquisition unit can also acquire tourist attraction information from local tourist information sites. Furthermore, the acquisition unit can analyze social media posts to acquire information useful to travelers. For example, Twitter posts can be analyzed to acquire information useful to travelers. This makes it possible to acquire information from various sources on the Internet. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input social media posts into a generation AI and have the generation AI analyze the post content.

[0070] The analysis unit can customize information based on the traveler's interests and preferences. Methods for identifying a traveler's interests and preferences include, but are not limited to, past travel history, survey results, and social media activity. For example, the analysis unit can identify a traveler's interests and preferences based on past travel history. The analysis unit can also identify a traveler's interests and preferences based on survey results. Furthermore, the analysis unit can analyze social media activity to identify a traveler's interests and preferences. For example, information can be customized based on data on travel destinations the traveler has previously visited. This makes it possible to provide the most useful information to the traveler. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input data on a traveler's interests and preferences into the generation AI and have the generation AI customize the information.

[0071] The providing unit can provide an easy-to-use interface. Specific elements of an easy-to-use interface include, but are not limited to, usability test results, intuitive operability, and responsive design. The providing unit can, for example, design the interface based on the results of a usability test. The providing unit can also provide an interface that emphasizes intuitive operability. Furthermore, the providing unit can employ a responsive design to provide an interface that is easy to use on various devices. For example, the providing unit can provide an interface that is easy to use on smartphones and tablets. This allows the user to provide an interface that is easy to use. Some or all of the above-described processing in the providing unit can be performed, for example, using AI or without AI. For example, the providing unit can input the results of a usability test into a generation AI and have the generation AI execute the interface design.

[0072] The acquisition unit can acquire tourist attraction overviews, restaurant ratings or reviews, feedback from other travelers who have visited, and recommendations from local residents. Tourist attraction overviews include, but are not limited to, historical background, major attractions, and access methods. Restaurant ratings or reviews include, but are not limited to, ratings on review sites and user comments. Feedback includes, but are not limited to, survey results, user comments, and ratings. Local residents' recommendations include, but are not limited to, interview results, local blogs, and posts on social media. The acquisition unit, for example, acquires tourist attraction overviews. The acquisition unit can also acquire restaurant ratings and reviews. The acquisition unit can also acquire feedback from other travelers who have visited the tourist attraction. The acquisition unit can also acquire recommendations from local residents. This allows travelers to acquire information useful from multiple perspectives. Some or all of the above-described processing by the acquisition unit may be performed using, for example, AI, or without AI. For example, the acquisition unit can input a tourist attraction overview into the generation AI and cause the generation AI to acquire information.

[0073] The analysis unit can analyze the acquired information and select the most useful information for travelers. Specific criteria for the most useful information include, but are not limited to, traveler ratings, the latest information, and reliable information. For example, the analysis unit selects information that is highly rated by travelers. The analysis unit can also select the latest information. Furthermore, the analysis unit can select highly reliable information. For example, it preferentially selects information that is highly rated by travelers. This makes it possible to provide the most useful information for travelers. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input the acquired information into a generation AI and have the generation AI select the information.

[0074] The reception unit can estimate the user's emotions and suggest travel destinations based on the estimated user emotions. Methods for estimating the user's emotions include, but are not limited to, facial expression recognition, voice analysis, and text analysis. For example, if the user is feeling stressed, the reception unit can suggest a relaxing travel destination. Furthermore, if the user is excited, the reception unit can suggest a travel destination where the user can enjoy active activities. Furthermore, if the user is sad, the reception unit can suggest a soothing travel destination. For example, if the user is feeling stressed, the reception unit can suggest a relaxing travel destination. This makes it possible to suggest travel destinations based on the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the reception unit may be performed using an AI, for example, or without an AI. For example, the reception unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0075] The reception unit can analyze the user's past travel history and suggest travel destinations. Specific methods for acquiring past travel history include, but are not limited to, past reservation history and records of visited places. For example, the reception unit can suggest similar travel destinations based on data on travel destinations the user has visited in the past. The reception unit can also suggest highly rated travel destinations based on ratings of travel destinations the user has visited in the past. Furthermore, the reception unit can suggest travel destinations where similar activities can be enjoyed based on activities at travel destinations the user has visited in the past. For example, similar travel destinations can be suggested based on data on travel destinations the user has visited in the past. This makes it possible to suggest optimal travel destinations based on the user's past travel history. Some or all of the above-mentioned processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past travel history data into a generation AI and have the generation AI execute travel destination suggestions.

[0076] When specifying a travel destination, the reception unit can filter based on the user's current interests. Methods for identifying current interests include, but are not limited to, recent search history and social media activity. The reception unit can, for example, suggest travel destinations based on themes in which the user is currently interested (history, nature, art, etc.). The reception unit can also suggest travel destinations based on keywords recently searched by the user. The reception unit can also suggest travel destinations based on events or activities in which the user recently participated. For example, travel destinations can be suggested based on themes in which the user is currently interested. This makes it possible to suggest travel destinations based on the user's current interests. Some or all of the above-described processing in the reception unit may be performed using, or without, AI. For example, the reception unit can input data regarding the user's current interests into the generation AI and have the generation AI perform filtering.

[0077] When specifying a travel destination, the reception unit can select a reception means according to the user's input method. Specific types of input methods include, but are not limited to, voice input, text input, and image input. For example, when the user specifies the travel destination by voice, the reception unit can accept the input using voice recognition technology. Furthermore, when the user specifies the travel destination by text, the reception unit can also accept the input using text analysis technology. Furthermore, when the user specifies the travel destination by image, the reception unit can also accept the input using image recognition technology. For example, when the user specifies the travel destination by voice, the reception unit can accept the input using voice recognition technology. This makes it possible to select the optimal reception means according to the user's input method. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's input data to a generation AI and have the generation AI select the optimal reception means.

[0078] The reception unit can estimate the user's emotions and prioritize travel destinations based on the estimated user emotions. Methods for prioritizing travel destinations based on emotions include, but are not limited to, emotion scores and destination ratings. For example, if the user wants to relax, the reception unit can prioritize relaxing travel destinations. Furthermore, if the user wants to be active, the reception unit can prioritize travel destinations offering a wide range of activities. Furthermore, if the user wants a cultural experience, the reception unit can prioritize cultural tourist spots. For example, if the user wants to relax, the reception unit prioritizes relaxing travel destinations. This enables the prioritization of travel destinations based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or without AI. For example, the reception unit can input the user's emotion data into the generation AI and have the generation AI determine priorities based on emotions.

[0079] When a travel destination is specified, the reception unit can prioritize suggesting highly relevant travel destinations by taking into account the user's geographical location information. Specific methods for acquiring geographical location information include, but are not limited to, GPS data, location information services, etc. The reception unit, for example, prioritizes suggesting travel destinations close to the user's current location. The reception unit can also prioritize suggesting travel destinations that are easily accessible from the user's current location. Furthermore, the reception unit can also suggest optimal travel destinations by taking into account travel time from the user's current location. For example, the reception unit prioritizes suggesting travel destinations close to the user's current location. This makes it possible to suggest travel destinations based on the user's geographical location information. Some or all of the above-described processing by the reception unit may be performed using, or without, AI. For example, the reception unit can input the user's geographical location information into the generation AI and cause the generation AI to suggest highly relevant travel destinations.

[0080] When a travel destination is specified, the reception unit can analyze the user's social media activity and suggest related travel destinations. Specific methods for analyzing social media activity include, but are not limited to, analyzing post content and follower analysis. For example, the reception unit can suggest travel destinations visited by accounts the user follows on social media. The reception unit can also analyze the user's social media posts to suggest related travel destinations. Furthermore, the reception unit can suggest related travel destinations based on the activity of the user's friends on social media. For example, it can suggest travel destinations visited by accounts the user follows on social media. This makes it possible to suggest travel destinations based on the user's social media activity. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media activity data into a generation AI and cause the generation AI to suggest related travel destinations.

[0081] When a travel destination is specified, the reception unit can customize the suggestion method by reflecting the user's past feedback. Specific methods for acquiring past feedback include, but are not limited to, survey results, user comments, and ratings. For example, the reception unit can suggest places similar to travel destinations that the user has previously rated highly. The reception unit can also suggest travel destinations that the user has previously rated poorly, avoiding such destinations. Furthermore, the reception unit can customize the suggestion method based on the user's past feedback. For example, the reception unit can suggest places similar to travel destinations that the user has previously rated highly. This enables the suggestion method to be customized based on the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using, or without, AI. For example, the reception unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the suggestion method.

[0082] The acquisition unit can estimate the user's emotion and adjust the timing of information acquisition based on the estimated user's emotion. Specific methods for adjusting the timing of information acquisition include, but are not limited to, the user's emotion score, before or after a specific event, etc. For example, the acquisition unit acquires information slowly when the user is relaxed. Furthermore, the acquisition unit can also acquire information quickly when the user is in a hurry. Furthermore, the acquisition unit can acquire information in real time when the user is excited. For example, when the user is relaxed, the acquisition unit acquires information slowly. This enables adjustment of the timing of information acquisition based on the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the acquisition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the acquisition unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the timing of information acquisition.

[0083] The acquisition unit may take into account the user's past search history when analyzing public databases on the Internet or posts on social media to acquire information. Specific methods for acquiring past search history include, but are not limited to, browser history and search engine logs. The acquisition unit may acquire relevant information based on, for example, keywords previously searched by the user. The acquisition unit may also acquire relevant information based on websites previously visited by the user. The acquisition unit may also analyze the user's past search history to acquire the most relevant information. For example, relevant information may be acquired based on keywords previously searched by the user. This makes it possible to acquire information based on the user's past search history. Some or all of the above-described processing by the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit may input the user's past search history data into the generation AI and cause the generation AI to acquire information.

[0084] When acquiring information, the acquisition unit can perform filtering based on the user's current travel plans and areas of interest. Specific methods for acquiring the current travel plans and areas of interest include, but are not limited to, travel reservation information, recent search history, etc. For example, the acquisition unit can prioritize acquiring information related to a travel destination currently planned by the user. The acquisition unit can also prioritize acquiring information related to areas of interest to the user (history, nature, art, etc.). Furthermore, the acquisition unit can acquire the most relevant information based on the user's current travel plans and areas of interest. For example, information related to the travel destination currently planned by the user can be prioritized. This makes it possible to acquire information based on the user's current travel plans and areas of interest. Some or all of the above-described processing in the acquisition unit may be performed using, or without, AI. For example, the acquisition unit can input data related to the user's current travel plans and areas of interest into the generation AI and have the generation AI perform filtering.

[0085] When acquiring information, the acquisition unit can select the optimal acquisition means depending on the user's input method. Specific selection criteria for the optimal acquisition means include, but are not limited to, voice recognition technology, text analysis technology, and image recognition technology. For example, when the user acquires information by voice, the acquisition unit can acquire the information using voice recognition technology. Furthermore, when the user acquires information by text, the acquisition unit can also acquire the information using text analysis technology. Furthermore, when the user acquires information by image, the acquisition unit can also acquire the information using image recognition technology. For example, when the user acquires information by voice, the acquisition unit acquires the information using voice recognition technology. This makes it possible to acquire optimal information depending on the user's input method. Some or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the user's input data to a generation AI and cause the generation AI to select the optimal acquisition means.

[0086] The acquisition unit can estimate the user's emotions and determine the priority of information to be acquired based on the estimated user emotions. Specific methods for determining the priority of information include, but are not limited to, emotion scores and information importance. For example, when the user is relaxed, the acquisition unit prioritizes acquiring information that helps the user relax. Furthermore, when the user is in a hurry, the acquisition unit can prioritize acquiring information that can be acquired quickly. Furthermore, when the user is excited, the acquisition unit can prioritize acquiring visually stimulating information. For example, when the user is relaxed, the acquisition unit prioritizes acquiring information that helps the user relax. This enables the priority of information to be determined based on 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 acquisition unit may be performed using, for example, an AI. For example, the acquisition unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of information.

[0087] When acquiring information, the acquisition unit can prioritize acquiring highly relevant information taking into account the user's geographical location information. Specific criteria for highly relevant information include, but are not limited to, information that is geographically close and information related to the user's interests. For example, the acquisition unit prioritizes acquiring information close to the user's current location. The acquisition unit can also prioritize acquiring information that is easily accessible from the user's current location. Furthermore, the acquisition unit can acquire optimal information taking into account travel time from the user's current location. For example, information close to the user's current location is prioritized. This makes it possible to acquire information based on the user's geographical location information. Some or all of the above-described processing in the acquisition unit may be performed using, or without, AI. For example, the acquisition unit can input the user's geographical location information to the generation AI and cause the generation AI to acquire highly relevant information.

[0088] When acquiring information, the acquisition unit can analyze the user's social media activity and acquire related information. Specific methods for analyzing social media activity include, but are not limited to, analyzing posted content and followers, for example. The acquisition unit, for example, acquires information posted by accounts the user follows on social media. The acquisition unit can also analyze the user's social media posts to acquire related information. Furthermore, the acquisition unit can acquire related information by referring to the activities of the user's friends on social media. For example, it acquires information posted by accounts the user follows on social media. This makes it possible to acquire information based on the user's social media activity. Some or all of the above-described processing by the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit may input the user's social media activity data into the generation AI and cause the generation AI to acquire related information.

[0089] When acquiring information, the acquisition unit can customize the acquisition method by reflecting the user's past feedback. Specific acquisition methods for past feedback include, but are not limited to, survey results, user comments, and ratings. For example, the acquisition unit preferentially acquires information from information sources that the user has previously rated highly. The acquisition unit can also use an acquisition method that avoids information sources that the user has previously rated poorly. Furthermore, the acquisition unit can customize the acquisition method based on the user's past feedback. For example, the acquisition unit preferentially acquires information from information sources that the user has previously rated highly. This makes it possible to customize the information acquisition method based on the user's past feedback. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the acquisition method.

[0090] The analysis unit can estimate the user's emotions and adjust the information analysis method based on the estimated user emotions. Specific methods for adjusting the information analysis method include, but are not limited to, emotion scores and selection of an analysis algorithm. For example, if the user is relaxed, the analysis unit analyzes information at a leisurely pace. Furthermore, if the user is in a hurry, the analysis unit can analyze information quickly. Furthermore, if the user is excited, the analysis unit can analyze information with visually stimulating effects added. For example, if the user is relaxed, the analysis unit analyzes information at a leisurely pace. This enables adjustment of the information analysis method based on the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using AI, or may be performed without AI. For example, the analysis unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the information analysis method.

[0091] The analysis unit may take into account the user's past travel history when customizing information based on the traveler's interests and preferences. Specific methods for acquiring past travel history include, but are not limited to, past reservation history and records of visited places. The analysis unit may customize information based on data on travel destinations the user has visited in the past. The analysis unit may also customize information based on data on travel destinations the user has previously rated. The analysis unit may also analyze the user's past travel history and customize the most relevant information. For example, the analysis unit may customize information based on data on travel destinations the user has previously visited. This enables customization of information based on the user's past travel history. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input the user's past travel history data into the generation AI and have the generation AI customize the information.

[0092] When analyzing the acquired information, the analysis unit can apply different analysis algorithms depending on the category of the information. Specific methods for classifying information categories include, but are not limited to, tourist information, food and drink information, and transportation information. For example, the analysis unit applies an analysis algorithm dedicated to tourist attractions to information about tourist attractions. The analysis unit can also apply an analysis algorithm dedicated to restaurants to restaurant ratings and reviews. Furthermore, the analysis unit can apply an analysis algorithm dedicated to local residents to information recommended by local residents. For example, an analysis algorithm dedicated to tourist attractions is applied to information about tourist attractions. This enables optimal analysis according to the category of the information. Some or all of the above-mentioned processing by the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information category data to the generation AI and cause the generation AI to apply the analysis algorithm.

[0093] When analyzing acquired information, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. Specific methods for acquiring past analysis results include, but are not limited to, past analysis logs and user feedback. The analysis unit can improve the accuracy of the analysis by referring to, for example, analysis results that the user has previously rated highly. The analysis unit can also perform an analysis method that avoids analysis results that the user has previously rated poorly. Furthermore, the analysis unit can analyze the most relevant information based on the user's past analysis results. For example, the analysis unit can improve the accuracy of the analysis by referring to analysis results that the user has previously rated highly. This enables the accuracy of the analysis to be improved based on the user's past analysis results. Some or all of the above-described processing in the analysis unit can be performed, for example, using AI or without AI. For example, the analysis unit can input the user's past analysis result data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0094] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. Specific methods for adjusting the display method of the analysis results include, but are not limited to, emotion scores and selection of a display format. For example, if the user is relaxed, the analysis unit can display the analysis results at a leisurely pace. Furthermore, if the user is in a hurry, the analysis unit can display the analysis results quickly. Furthermore, if the user is excited, the analysis unit can display the analysis results with visually stimulating effects. For example, if the user is relaxed, the analysis results can be displayed at a leisurely pace. This enables adjustment of the display method of the analysis results based on the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit may be performed using, for example, an AI. For example, the analysis unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the display method of the analysis results.

[0095] When analyzing the acquired information, the analysis unit can determine the priority of analysis based on the time when the information was submitted. Specific methods for obtaining the time when the information was submitted include, but are not limited to, a log of the submission date and time, or before or after an event. The analysis unit, for example, prioritizes analyzing the most recent information. The analysis unit can also prioritize analyzing information within a period specified by the user. Furthermore, the analysis unit can prioritize analyzing the most relevant information based on the time when the information was submitted. For example, the most recent information is prioritized. This makes it possible to determine the priority of analysis based on the time when the information was submitted. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information submission time data to the generation AI and have the generation AI determine the priority of analysis.

[0096] When analyzing the acquired information, the analysis unit can adjust the order of analysis based on the relevance of the information. Specific criteria for the relevance of information include, but are not limited to, information related to the user's interests and information that is geographically close. For example, the analysis unit can prioritize analyzing information that is most relevant to the user's interests. The analysis unit can also prioritize analyzing information that is most relevant to the user's current travel plans. Furthermore, the analysis unit can perform the analysis in the most efficient order based on the relevance of the information. For example, the analysis unit can prioritize analyzing information that is most relevant to the user's interests. This makes it possible to adjust the order of analysis based on the relevance of the information. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information relevance data to the generation AI and cause the generation AI to adjust the order of analysis.

[0097] When analyzing the acquired information, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. Specific methods for acquiring the level of expertise include, but are not limited to, survey results, past usage history, etc. For example, if the user has technical expertise, the analysis unit can provide analysis results that use a lot of technical terms. Furthermore, if the user does not have technical expertise, the analysis unit can provide analysis results in simple language. Furthermore, the analysis unit can provide optimal analysis results according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit can provide analysis results that use a lot of technical terms. This makes it possible to provide analysis results that correspond to the user's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the user's level of expertise data into a generation AI and have the generation AI use technical terms.

[0098] The providing unit can estimate the user's emotions and adjust the information provision method based on the estimated user's emotions. Specific methods for adjusting the information provision method include, but are not limited to, emotion scores and selection of a delivery format. For example, if the user is relaxed, the providing unit can provide information at a leisurely pace. Furthermore, if the user is in a hurry, the providing unit can also provide information quickly. Furthermore, if the user is excited, the providing unit can provide information with visually stimulating effects. For example, if the user is relaxed, the information can be provided at a leisurely pace. This enables adjustment of the information provision method based on the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, 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 providing unit may be performed using, for example, an AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the information provision method.

[0099] When providing information, the providing unit can select the optimal providing method by referring to the user's past travel history. Specific methods for acquiring the past travel history include, but are not limited to, past reservation history and records of visited places. For example, the providing unit can prioritize the use of a providing method that the user has previously rated highly. The providing unit can also avoid providing methods that the user has previously rated poorly. Furthermore, the providing unit can select the most relevant providing method based on the user's past travel history. For example, the providing unit can prioritize the use of a providing method that the user has previously rated highly. This makes it possible to select the optimal information providing method based on the user's past travel history. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input the user's past travel history data into the generation AI and cause the generation AI to select the optimal providing method.

[0100] When providing information, the providing unit can customize the provided content according to the user's current travel plans. Specific methods for acquiring the current travel plans include, but are not limited to, travel reservation information, recent search history, etc. The providing unit, for example, prioritizes providing information related to the travel destination currently planned by the user. The providing unit can also provide the most relevant information based on the user's current travel plans. Furthermore, the providing unit can customize the optimal provided content based on the user's current travel plans. For example, information related to the travel destination currently planned by the user can be prioritized. This makes it possible to customize the information provided content based on the user's current travel plans. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's current travel plan data into the generation AI and have the generation AI customize the provided content.

[0101] The providing unit can improve the information providing method by reflecting user feedback when providing information. Specific methods of obtaining feedback include, but are not limited to, survey results, user comments, and ratings. For example, the providing unit prioritizes the use of a providing method that the user has previously rated highly. The providing unit can also avoid a providing method that the user has previously rated poorly. Furthermore, the providing unit can improve the optimal providing method based on user feedback. For example, the providing unit prioritizes the use of a providing method that the user has previously rated highly. This enables the information providing method to be improved based on user feedback. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input user feedback data into a generating AI and cause the generating AI to improve the providing method.

[0102] The providing unit can estimate the user's emotions and determine the order in which information is provided based on the estimated user emotions. Specific methods for determining the order in which information is provided include, but are not limited to, emotion scores and information importance. For example, if the user is relaxed, the providing unit can prioritize providing relaxing information. Furthermore, if the user is in a hurry, the providing unit can prioritize providing information that can be provided quickly. Furthermore, if the user is excited, the providing unit can prioritize providing visually stimulating information. For example, if the user is relaxed, the providing unit prioritizes providing relaxing information. This enables the order in which information is provided to be determined based on the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI. For example, the providing unit can input the user's emotion data into the generation AI and have the generation AI determine the order in which information is provided.

[0103] When providing information, the providing unit can select the optimal providing method by taking into account the user's geographical location information. Specific methods for acquiring geographical location information include, but are not limited to, GPS data and location information services. For example, the providing unit can prioritize providing information close to the user's current location. The providing unit can also prioritize providing information that is easily accessible from the user's current location. Furthermore, the providing unit can provide optimal information by taking into account travel time from the user's current location. For example, information close to the user's current location can be prioritized. This makes it possible to select the optimal information providing method based on the user's geographical location information. Some or all of the above-described processing in the providing unit may be performed using, or without, AI. For example, the providing unit can input the user's geographical location information to the generation AI and cause the generation AI to select the optimal providing method.

[0104] When providing information, the providing unit can analyze the user's social media activity and customize the provided content. Specific methods for analyzing social media activity include, but are not limited to, analyzing posted content and followers. For example, the providing unit can prioritize providing information posted by accounts the user follows on social media. The providing unit can also analyze the user's social media posts and provide related information. Furthermore, the providing unit can provide related information based on the activities of the user's friends on social media. For example, information posted by accounts the user follows on social media can be prioritized. This makes it possible to customize the information provided based on the user's social media activity. Some or all of the above-described processing by the providing unit may be performed using, or without, AI. For example, the providing unit can input the user's social media activity data into a generation AI and cause the generation AI to customize the provided content.

[0105] When providing information, the providing unit can customize the providing method by reflecting the user's past feedback. Specific methods for acquiring past feedback include, but are not limited to, survey results, user comments, and ratings. For example, the providing unit prioritizes the use of a providing method that the user has previously rated highly. The providing unit can also avoid a providing method that the user has previously rated poorly. Furthermore, the providing unit can customize an optimal providing method based on the user's past feedback. For example, the providing unit prioritizes the use of a providing method that the user has previously rated highly. This enables the information providing method to be customized based on the user's past feedback. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past feedback data into the generating AI and cause the generating AI to customize the providing method. === Hard Collateral 1-1 === Each of the multiple elements, including the above-mentioned reception unit, acquisition unit, analysis unit, and provision unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14 and provides an interface for travelers to specify their travel destinations. The acquisition unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and acquires information by analyzing public databases on the Internet and posts on social media. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the acquired information to select information useful to travelers. The provision unit is realized, for example, by the control unit 46A of the smart device 14 and presents the selected information to travelers through a user-friendly interface. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned reception unit, acquisition unit, analysis unit, and provision unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 and provides an interface for travelers to specify their travel destinations. The acquisition unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and acquires information by analyzing public databases on the Internet and posts on social media. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the acquired information to select information useful to travelers. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214 and presents the selected information to travelers through a user-friendly interface. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, acquisition unit, analysis unit, and provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset type terminal 314 and provides an interface for travelers to specify their travel destinations. The acquisition unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and acquires information by analyzing public databases on the Internet and posts on social media. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the acquired information to select information useful to travelers. The provision unit is realized, for example, by the control unit 46A of the headset type terminal 314 and presents the selected information to travelers through a user-friendly interface. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, acquisition unit, analysis unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and provides an interface for travelers to specify their travel destinations. The acquisition unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and acquires information by analyzing public databases on the Internet and posts on social media. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the acquired information to select information useful to travelers. The provision unit is realized, for example, by the control unit 46A of the robot 414 and presents the selected information to travelers through a user-friendly interface.

[0106] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0107] The reception unit can also monitor the user's current health condition and suggest travel destinations based on the health condition. For example, if the user is tired, it can suggest travel destinations where the user can relax. If the user is active, it can suggest travel destinations with plenty of activities. Furthermore, if the user has a specific health problem, it can suggest travel destinations that address that problem. This makes it possible to suggest optimal travel destinations based on the user's health condition.

[0108] The acquisition unit can also acquire information taking into account the user's past purchase history. For example, related information can be acquired based on travel guidebooks and related products purchased by the user in the past. Related information can also be acquired based on information on tickets and tours purchased by the user in the past. Furthermore, the acquisition unit can analyze the user's purchase history and acquire the most relevant information. This makes it possible to acquire information based on the user's purchase history.

[0109] The analysis unit can also estimate the user's emotions and customize information based on the estimated user emotions. For example, if the user is excited, information about active activities can be prioritized for customization. If the user wants to relax, information about places where they can relax can be prioritized for customization. Furthermore, if the user is sad, information about places that have a healing effect can be customized. This makes it possible to customize information according to the user's emotions.

[0110] The providing unit can also estimate the user's emotions and adjust the method of providing information based on the estimated user's emotions. For example, if the user is relaxed, the information can be provided at a slow pace. If the user is in a hurry, the information can be provided quickly. Furthermore, if the user is excited, the information can be provided with visually stimulating effects. This makes it possible to adjust the method of providing information based on the user's emotions.

[0111] The acquisition unit can also acquire information taking into account the user's current weather information. For example, if the weather at the user's travel destination is bad, it can prioritize acquiring information about activities that can be enjoyed indoors. On the other hand, if the weather is good, it can prioritize acquiring information about activities that can be enjoyed outdoors. Furthermore, it can also suggest optimal activities for the travel period based on the weather forecast. This makes it possible to acquire information based on weather information.

[0112] The analysis unit can also estimate the user's emotions and adjust the information analysis method based on the estimated user's emotions. For example, if the user is relaxed, the analysis unit can analyze information at a slow pace. If the user is in a hurry, the analysis unit can analyze information quickly. Furthermore, if the user is excited, the analysis unit can analyze information with visually stimulating effects. This makes it possible to adjust the information analysis method based on the user's emotions.

[0113] The reception unit can also monitor the user's current activity level and suggest travel destinations based on the activity level. For example, if the user is not getting enough exercise, it can suggest travel destinations where the user can enjoy active activities. Also, if the user is already active, it can suggest travel destinations where the user can relax. Furthermore, it can suggest optimal travel destinations based on the user's activity level. This makes it possible to suggest optimal travel destinations based on the user's activity level.

[0114] The acquisition unit can also acquire nearby event information taking into account the user's current location information. For example, it can prioritize acquisition of event information held in locations close to the user's current location. It can also prioritize acquisition of information about events in which the user is interested. Furthermore, it can acquire the most relevant event information based on the user's current location information. This makes it possible to acquire event information based on the user's location information.

[0115] The analysis unit can also estimate the user's emotions and adjust the display method of the analysis results based on the estimated user's emotions. For example, if the user is relaxed, the analysis results can be displayed at a slow pace. If the user is in a hurry, the analysis results can be displayed quickly. Furthermore, if the user is excited, the analysis results can be displayed with visually stimulating effects. This makes it possible to adjust the display method of the analysis results based on the user's emotions.

[0116] The providing unit can also provide information taking into account the user's current device usage status. For example, if the user is using a smartphone, information optimized for the smartphone can be provided. If the user is using a tablet, information optimized for the tablet can be provided. Furthermore, if the user is using a desktop, information optimized for the desktop can be provided. This makes it possible to provide optimal information based on the user's device usage status.

[0117] The processing flow of the second embodiment will be briefly explained below.

[0118] Step 1: The reception unit receives the travel destination from the traveler. The traveler can specify the travel destination by, for example, a place name, a tourist spot name, a specific activity, etc. For example, the traveler may enter "I want to go to Tokyo." Step 2: The acquisition unit acquires information based on the travel destination specified by the reception unit. The acquisition unit acquires information, for example, by analyzing public databases on the Internet or posts on social media. For example, the acquisition unit acquires information such as overviews of tourist attractions, restaurant ratings and reviews, feedback from other travelers who have visited the destination, and recommendations from local residents. Step 3: The analysis unit analyzes the information acquired by the acquisition unit and selects information that is useful to the traveler. The analysis unit customizes the information based on the traveler's interests and preferences, for example. For example, if the traveler is interested in food, restaurant ratings and reviews will be presented preferentially. Step 4: The provider presents the information selected by the analyzer to the traveler. The provider provides, for example, a user-friendly interface. For example, before a traveler goes to Tokyo, the provider may present an overview of Sensoji Temple and Tokyo Tower, information on highly rated restaurants, feedback from other travelers, recommendations from local residents, and more.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0124] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0140] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0156] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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).

[0176] 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.

[0177] 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."

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] [Explanation of symbols]

[0191] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives designation of a travel destination; an acquisition unit that acquires information based on the travel destination designated by the reception unit; an analysis unit that analyzes the information acquired by the acquisition unit and selects information that is useful to travelers; a providing unit that presents the information selected by the analysis unit to the traveler. A system characterized by:

2. The acquisition unit Obtain information by analyzing public databases on the Internet or posts on social media 2. The system of claim 1.

3. The analysis unit Customize information based on traveler interests and preferences 2. The system of claim 1.

4. The providing unit Provide an easy-to-use interface 2. The system of claim 1.

5. The acquisition unit Get overviews of attractions, restaurant ratings or reviews, feedback from other travelers who have visited, and recommendations from locals 2. The system of claim 1.

6. The analysis unit Analyze the acquired information and select the most useful information for travelers 2. The system of claim 1.

7. The reception unit Estimate the user's emotions and suggest travel destinations based on the estimated user emotions 2. The system of claim 1.

8. The reception unit Analyzes the user's past travel history and suggests travel destinations 2. The system of claim 1.

Images