system

Abstract

The system according to the embodiment aims to provide an optimal travel plan based on the user's emotions. [Solution] A system according to an embodiment includes a reception unit, an analysis unit, a generation unit, and a presentation unit. The reception unit receives input of a destination. The analysis unit analyzes emotions based on the destination received by the reception unit. The generation unit generates music that expresses the emotions analyzed by the analysis unit. The presentation unit presents a travel plan based on the music generated by the generation unit.

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 not adequately provided travel plans based on the user's emotions, and there is room for improvement.

[0005] The system according to the embodiment aims to provide an optimal travel plan based on the user's emotions. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a generation unit, and a presentation unit. The reception unit receives input of a destination. The analysis unit analyzes emotions based on the destination received by the reception unit. The generation unit generates music that expresses the emotions analyzed by the analysis unit. The presentation unit presents a travel plan based on the music generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide an optimal travel plan based on the user's emotions. [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 system according to an embodiment of the present invention generates music that expresses the emotions evoked at a destination when a user inputs the destination and presents a travel plan. In this system, a user inputs a destination, and a generation AI analyzes the emotions evoked at the destination and generates music that expresses them. The user listens to the generated music and selects music that matches the emotion they desire. Based on the selected music, the generation AI presents a travel plan that evokes that emotion. For example, when a user inputs a destination such as "Paris" or "Mount Fuji," the generation AI analyzes the emotions evoked at the destination and generates music that expresses them. The user listens to the generated music and selects music that matches the emotion they desire. Based on the selected music, the generation AI presents a travel plan that evokes that emotion. This allows the user to find a travel plan that suits them. This allows the system to generate music that expresses the emotions evoked at the destination and present a travel plan. For example, if a user desires to feel "adventurous," the generation AI generates music that expresses a sense of adventure and presents a travel plan that evokes that emotion. This allows the user to experience a new way of selecting travel plans.

[0029] The travel plan presentation system according to the embodiment includes a reception unit, an analysis unit, a generation unit, and a presentation unit. The reception unit allows a user to input a destination. The user can input a specific place name or tourist attraction, such as "Paris" or "Mount Fuji." The analysis unit analyzes emotions based on the destination accepted by the reception unit. The analysis unit, for example, uses a generation AI to analyze emotions felt at the destination. The generation unit generates music that expresses the emotions analyzed by the analysis unit. The generation unit, for example, uses the generation AI to generate music that expresses the analyzed emotions. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI. The presentation unit presents a travel plan based on the music generated by the generation unit. The presentation unit, for example, uses the generation AI to generate a travel plan based on selected music. In this way, the travel plan presentation system according to the embodiment allows a user to input a destination, generate music that expresses emotions felt at the destination, and present a travel plan.

[0030] The system includes a collection unit that collects reviews or social media posts. The collection unit collects reviews and social media posts. For example, the collection unit collects reviews and social media posts from a specific platform. For example, the collection unit can collect reviews and social media posts from travel sites. In this way, by collecting reviews and social media posts, sentiment analysis can be performed based on more information.

[0031] The system includes a selection unit that allows the user to select music. The selection unit allows the user to select generated music. The selection unit, for example, displays a list of generated music, allowing the user to select one. The selection unit, for example, provides an interface that allows the user to select music that matches the emotion the user wants to feel. This allows the user to select generated music, and a travel plan that matches the emotion the user wants to feel can be presented.

[0032] The system includes a data collection unit that collects data on tourist attractions, accommodations, and activities at travel destinations. The data collection unit collects data on tourist attractions, accommodations, and activities at travel destinations. The data collection unit collects data from, for example, travel sites and tourist guides. The data collection unit can collect, for example, information on tourist attractions, accommodations, and activities. By collecting detailed data on travel destinations, more specific travel plans can be presented.

[0033] The analysis unit can analyze reviews and social media posts related to the destination and identify emotions. The analysis unit, for example, analyzes reviews and social media posts and identifies emotions. The analysis unit, for example, uses natural language processing technology to extract emotions from reviews and social media posts. The analysis unit, for example, uses a sentiment analysis algorithm to identify emotions from reviews and social media posts. This allows for more accurate identification of emotions by analyzing reviews and social media posts.

[0034] The generation unit can generate music based on the identified emotion. The generation unit, for example, generates music based on the identified emotion. The generation unit, for example, uses a generation AI to generate music that expresses the identified emotion. The generation unit, for example, can adjust the tempo and melody of the music according to the emotion. In this way, by generating music based on the identified emotion, it is possible to provide music that matches the emotion the user desires to experience.

[0035] The presentation unit can generate a travel plan based on the selected music. The presentation unit generates a travel plan based on the selected music, for example. The presentation unit generates a travel plan based on the selected music, for example, using a generation AI. The presentation unit can suggest travel destinations and activities according to the emotions of the music, for example. By generating a travel plan based on the selected music, it is possible to provide a travel plan that matches the emotions the user desires to experience.

[0036] The reception unit can analyze the user's past travel history and suggest the optimal destination input method. The reception unit, for example, analyzes the user's past travel history and suggests the optimal destination input method. The reception unit, for example, automatically displays destinations that the user has frequently visited in the past as candidates. The reception unit, for example, preferentially suggests input methods (voice, text, etc.) that the user has used in the past. The reception unit, for example, predicts and suggests destinations related to specific seasons or events from the user's past travel history. This improves input efficiency by suggesting the optimal input method based on the user's past travel history.

[0037] The reception unit can present input candidates based on the user's current interests and concerns when a destination is input. For example, when a destination is input, the reception unit presents input candidates based on the user's current interests and concerns. For example, the reception unit suggests related destinations based on keywords recently searched by the user or websites recently visited by the user. For example, the reception unit analyzes the content of posts from accounts the user follows on social media and suggests related destinations. For example, the reception unit suggests related destinations based on events or activities the user recently participated in. In this way, by presenting input candidates based on the user's interests and concerns, more appropriate destinations can be suggested.

[0038] The reception unit can select the optimal input means depending on the user's input method when inputting a destination. For example, when inputting a destination, the reception unit selects the optimal input means depending on the user's input method (voice, text, image, etc.). For example, when the user inputs the destination by voice, the reception unit accurately recognizes the destination using voice recognition technology. For example, when the user inputs the destination using text, the reception unit provides an autocomplete function to assist the input. For example, when the user inputs the destination by uploading an image, the reception unit identifies the destination using image recognition technology. In this way, by selecting the optimal input means depending on the user's input method, the accuracy of input is improved.

[0039] The reception unit can prioritize presenting highly relevant destinations in consideration of the user's geographical location information when a destination is input. For example, the reception unit prioritizes presenting highly relevant destinations in consideration of the user's geographical location information when a destination is input. For example, the reception unit prioritizes displaying destinations that are close to the user's current location. For example, the reception unit prioritizes displaying destinations that are easy for the user to access from the user's current location. For example, the reception unit suggests the optimal destination in consideration of the user's mode of transportation from the current location. In this way, highly relevant destinations can be suggested by taking the user's geographical location information into consideration.

[0040] The reception unit can analyze the user's social media activity when a destination is input and suggest related destinations. For example, when a destination is input, the reception unit analyzes the user's social media activity and suggests related destinations. For example, the reception unit suggests related destinations based on places where the user has checked in on social media. For example, the reception unit analyzes the content of the user's social media posts and suggests related destinations. For example, the reception unit suggests related destinations by referring to the activity of the user's friends on social media. In this way, related destinations can be suggested by analyzing the user's social media activity.

[0041] The reception unit can customize the input method by reflecting the user's past feedback when inputting a destination. For example, the reception unit customizes the input method by reflecting the user's past feedback when inputting a destination. For example, the reception unit improves the input interface based on feedback provided by the user in the past. For example, the reception unit avoids input methods that the user has been dissatisfied with in the past and suggests an easier-to-use method. For example, the reception unit preferentially provides input methods that the user has preferred in the past. In this way, an easier-to-use input method can be provided by reflecting the user's past feedback.

[0042] During analysis, the analysis unit can improve the accuracy of the sentiment analysis by referring to past reviews and social media posts about the destination. The analysis unit, for example, analyzes past reviews about the destination to improve the accuracy of the sentiment analysis. The analysis unit, for example, analyzes social media posts about the destination to improve the accuracy of the sentiment analysis. The analysis unit, for example, refers to blogs and articles about the destination to improve the accuracy of the sentiment analysis. In this way, by referring to past reviews and social media posts, the accuracy of the sentiment analysis is improved.

[0043] During analysis, the analysis unit can apply different emotion analysis methods depending on the category of the destination. For example, the analysis unit applies an emotion analysis method that emphasizes a sense of relaxation to a destination with a natural landscape. For example, the analysis unit applies an emotion analysis method that emphasizes a sense of excitement to a destination with an urban tourism destination. For example, the analysis unit applies an emotion analysis method that emphasizes a sense of excitement to a destination with a cultural heritage site. In this way, by applying different emotion analysis methods depending on the category of the destination, the accuracy of the analysis is improved.

[0044] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past emotional data. The analysis unit improves the accuracy of the analysis, for example, based on emotional data from destinations the user has visited in the past. The analysis unit improves the accuracy of the analysis, for example, based on feedback the user has provided in the past. The analysis unit improves the accuracy of the analysis by referring to the user's past travel history, for example. In this way, the accuracy of the analysis is improved by referring to the user's past emotional data.

[0045] During analysis, the analysis unit can perform emotion analysis taking into account the geographical distribution of destinations. For example, if the destination is in an urban area, the analysis unit performs analysis taking into account emotions specific to the city. For example, if the destination is in a natural landscape, the analysis unit performs analysis taking into account emotions specific to nature. For example, if the destination is in a cultural heritage site, the analysis unit performs analysis taking into account emotions specific to the culture. In this way, by taking into account the geographical distribution of destinations, more accurate emotion analysis is possible.

[0046] During analysis, the analysis unit can improve the accuracy of sentiment analysis by referring to literature related to the destination. The analysis unit, for example, refers to academic papers related to the destination to improve the accuracy of sentiment analysis. The analysis unit, for example, refers to travel guides related to the destination to improve the accuracy of sentiment analysis. The analysis unit, for example, refers to history books related to the destination to improve the accuracy of sentiment analysis. In this way, by referring to related literature, the accuracy of sentiment analysis is improved.

[0047] During analysis, the analysis unit can perform sentiment analysis taking into account the market value of the destination. For example, the analysis unit performs sentiment analysis taking into account the tourism market value of the destination. For example, the analysis unit performs sentiment analysis taking into account the real estate market value of the destination. For example, the analysis unit performs sentiment analysis taking into account the economic impact of the destination. In this way, by taking market value into account, more realistic sentiment analysis becomes possible.

[0048] When generating music, the generation unit can adjust the genre and tempo of the music based on the characteristics of the destination. For example, for a destination with a natural landscape, the generation unit generates music with a genre and tempo that emphasizes a sense of relaxation. For example, for a destination with an urban tourism destination, the generation unit generates music with a genre and tempo that emphasizes a sense of excitement. For example, for a destination with a cultural heritage site, the generation unit generates music with a genre and tempo that emphasizes a sense of emotion. In this way, by adjusting the genre and tempo of the music based on the characteristics of the destination, more appropriate music can be generated.

[0049] When generating music, the generation unit can improve the accuracy of generation by referring to the user's past music preferences. The generation unit generates optimal music based on, for example, music genres that the user has previously liked. The generation unit generates optimal music based on, for example, the tempo of music that the user has previously listened to. The generation unit generates optimal music by referring to, for example, music that the user has previously rated. In this way, by referring to the user's past music preferences, the accuracy of generation is improved.

[0050] When generating music, the generation unit can adjust the atmosphere of the music according to the season and weather of the destination. For example, the generation unit generates music with a bright and refreshing atmosphere for a spring destination. For example, the generation unit generates music with a warm atmosphere for a winter destination. For example, the generation unit generates music with a calm atmosphere for a rainy day destination. In this way, by adjusting the atmosphere of the music according to the season and weather, more appropriate music can be generated.

[0051] When generating music, the generation unit can generate music taking into consideration the cultural background of the destination. For example, if the destination is France, the generation unit generates music that incorporates traditional French music. For example, if the destination is Japan, the generation unit generates music that incorporates traditional Japanese music. For example, if the destination is Brazil, the generation unit generates music that incorporates traditional Brazilian music. In this way, more appropriate music can be generated by taking the cultural background into consideration.

[0052] When generating music, the generation unit can determine the theme of the music by referring to the historical background of the destination. For example, if the destination is a historical city, the generation unit generates music that reflects that history. For example, if the destination is a battlefield, the generation unit generates music that reflects that history. For example, if the destination is an ancient ruin, the generation unit generates music that reflects that history. In this way, by referring to the historical background, more appropriate music can be generated.

[0053] When generating music, the generation unit can adjust the composition of the music based on tourist spots at the destination. For example, the generation unit generates music that reflects major tourist spots at the destination. For example, the generation unit generates music that reflects hidden tourist spots at the destination. For example, the generation unit generates music that reflects the atmosphere of tourist spots at the destination. In this way, by adjusting the composition of the music based on tourist spots, more appropriate music can be generated.

[0054] When presenting the plan, the presentation unit can suggest an optimal travel plan by referring to the user's past travel history. The presentation unit, for example, suggests an optimal travel plan based on places the user has visited in the past. The presentation unit, for example, suggests a travel plan that avoids crowds based on the user's past travel history. The presentation unit, for example, analyzes the user's past travel history and suggests the most efficient travel plan. In this way, by referring to the past travel history, a more appropriate travel plan can be suggested.

[0055] The presentation unit can customize the travel plan based on the user's current living situation when presenting the plan. For example, if the user is busy, the presentation unit suggests a short, enjoyable travel plan. For example, if the user wants to relax, the presentation unit suggests a relaxing travel plan. For example, if the user is seeking adventure, the presentation unit suggests an adventurous travel plan. In this way, by customizing the travel plan based on the user's current living situation, a more appropriate travel plan can be provided.

[0056] The presentation unit can improve the method of presenting a travel plan by reflecting user feedback when presenting the plan. The presentation unit improves the method of presenting a travel plan based on, for example, feedback provided by the user in the past. The presentation unit, for example, avoids travel plans that the user was dissatisfied with in the past and proposes plans that will provide more satisfaction. For example, the presentation unit preferentially presents travel plans that the user liked in the past. In this way, by reflecting user feedback, it is possible to provide travel plans that will provide more satisfaction.

[0057] When presenting the travel plan, the presentation unit can propose an optimal travel plan taking into consideration the user's geographical location information. The presentation unit, for example, prioritizes displaying travel plans that are close to the user's current location. The presentation unit, for example, prioritizes displaying travel plans that are easy to access from the user's current location. The presentation unit, for example, proposes an optimal travel plan taking into consideration the user's mode of transportation from the user's current location. In this way, by taking into consideration the geographical location information, a more appropriate travel plan can be proposed.

[0058] At the time of presentation, the presentation unit can analyze the user's social media activity and suggest a travel plan. For example, the presentation unit suggests a related travel plan based on the location where the user checked in on social media. For example, the presentation unit analyzes the content of the user's social media posts and suggests a related travel plan. For example, the presentation unit suggests a related travel plan based on the activity of the user's friends on social media. In this way, by analyzing social media activity, it is possible to suggest a more appropriate travel plan.

[0059] The presentation unit can customize the travel plan by reflecting the user's past feedback when presenting the plan. The presentation unit customizes the travel plan, for example, based on feedback provided by the user in the past. The presentation unit, for example, avoids travel plans that the user was dissatisfied with in the past and proposes plans that will provide more satisfaction. For example, the presentation unit preferentially presents travel plans that the user liked in the past. In this way, by reflecting past feedback, it is possible to provide a travel plan that will provide more satisfaction.

[0060] During collection, the collection unit can select collection targets based on the characteristics of the destination. For example, for a natural scenery destination, the collection unit prioritizes collecting reviews and posts about nature. For example, for an urban tourism destination, the collection unit prioritizes collecting reviews and posts about cities. For example, for a cultural heritage destination, the collection unit prioritizes collecting reviews and posts about culture. This allows for more appropriate data to be collected by selecting collection targets based on the characteristics of the destination.

[0061] When collecting, the collection unit can improve the accuracy of the collection by referring to the user's past reviews and posts. The collection unit improves the accuracy of the collection, for example, based on reviews posted by the user in the past. The collection unit improves the accuracy of the collection, for example, based on reviews rated by the user in the past. The collection unit improves the accuracy of the collection, for example, by referring to the user's past posting history. In this way, the accuracy of the collection is improved by referring to the past reviews and posts.

[0062] The collection unit can collect data while taking into consideration the geographical distribution of destinations. For example, if the destination is in an urban area, the collection unit prioritizes collecting city-specific reviews and posts. For example, if the destination is in a natural landscape, the collection unit prioritizes collecting nature-specific reviews and posts. For example, if the destination is in a cultural heritage site, the collection unit prioritizes collecting culture-specific reviews and posts. This allows for more appropriate data to be collected by taking into consideration the geographical distribution.

[0063] When collecting, the collection unit can improve the accuracy of the collection by referring to literature related to the destination. The collection unit, for example, refers to academic papers related to the destination to improve the accuracy of the collection. The collection unit, for example, refers to travel guides related to the destination to improve the accuracy of the collection. The collection unit, for example, refers to history books related to the destination to improve the accuracy of the collection. In this way, by referring to related literature, the accuracy of the collection is improved.

[0064] When making a selection, the selection unit can suggest optimal music by referring to the user's past music preferences. The selection unit suggests optimal music, for example, based on music genres that the user has liked in the past. The selection unit suggests optimal music, for example, based on the tempo of music that the user has listened to in the past. The selection unit suggests optimal music by referring to music that the user has rated in the past. In this way, more appropriate music can be suggested by referring to the user's past music preferences.

[0065] When making a selection, the selection unit can customize music based on the user's current mood and situation. For example, if the user wants to relax, the selection unit suggests music that emphasizes a sense of relaxation. For example, if the user is excited, the selection unit suggests music that emphasizes a sense of excitement. For example, if the user is feeling stressed, the selection unit suggests music aimed at reducing stress. In this way, by customizing music based on the user's current mood and situation, more appropriate music can be suggested.

[0066] When making a selection, the selection unit can suggest optimal music taking into consideration the user's geographical location information. For example, the selection unit suggests music that is suitable for the user to listen to in a location close to the user's current location. For example, the selection unit suggests music that suits the climate and environment of the user's current location. For example, the selection unit suggests music that suits the culture and climate of the user's current location. In this way, more appropriate music can be suggested by taking into consideration the geographical location information.

[0067] When making a selection, the selection unit can analyze the user's social media activity to suggest music. For example, the selection unit can suggest related music based on music the user has shared on social media. For example, the selection unit can analyze the content of the user's social media posts to suggest related music. For example, the selection unit can refer to the activity of the user's friends on social media to suggest related music. In this way, more appropriate music can be suggested by analyzing social media activity.

[0068] When collecting data, the data collection unit can select collection targets based on the characteristics of the destination. For example, for a destination with natural scenery, the data collection unit prioritizes collecting data related to nature. For example, for a destination with urban tourism, the data collection unit prioritizes collecting data related to cities. For example, for a destination with cultural heritage, the data collection unit prioritizes collecting data related to culture. In this way, by selecting collection targets based on the characteristics of the destination, more appropriate data can be collected.

[0069] When collecting data, the data collection unit can improve the accuracy of the collection by referring to the user's past travel history. The data collection unit improves the accuracy of the collection, for example, based on data on places the user has visited in the past. The data collection unit improves the accuracy of the collection, for example, based on data on tourist spots, accommodations, and activities that the user has rated in the past. The data collection unit improves the accuracy of the collection, for example, by referring to the user's past travel history. In this way, the accuracy of the collection is improved by referring to the past travel history.

[0070] The data collection unit can collect data while taking into account the geographical distribution of destinations. For example, if the destination is in an urban area, the data collection unit prioritizes collecting city-specific data. For example, if the destination is in a natural landscape, the data collection unit prioritizes collecting nature-specific data. For example, if the destination is in a cultural heritage site, the data collection unit prioritizes collecting culture-specific data. This allows for more appropriate data to be collected by taking into account the geographical distribution.

[0071] When collecting data, the data collection unit can improve the accuracy of the collection by referring to literature related to the destination. The data collection unit, for example, refers to academic papers related to the destination to improve the accuracy of the collection. The data collection unit, for example, refers to travel guides related to the destination to improve the accuracy of the collection. The data collection unit, for example, refers to history books related to the destination to improve the accuracy of the collection. In this way, by referring to related literature, the accuracy of the collection is improved.

[0072] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0073] The analysis unit can analyze the user's past travel history and improve the accuracy of emotion analysis based on the obtained emotion data. For example, emotion data from places the user has visited in the past can be collected and reflected in the analysis. This enables analysis that takes into account the emotions the user has experienced in the past. The emotion analysis algorithm can also be adjusted based on feedback provided by the user in the past. Furthermore, emotion data related to specific seasons or events can be extracted from the user's past travel history and used in the analysis.

[0074] The selection unit can suggest optimal music by taking into account the user's past music preferences. For example, the selection unit can suggest optimal music based on the music genres that the user has liked in the past. The selection unit can suggest optimal music based on the tempo of music that the user has listened to in the past. Furthermore, the selection unit can suggest optimal music by taking into account music that the user has rated in the past. In this way, more appropriate music can be suggested by taking into account the user's past music preferences.

[0075] The analysis unit can apply different emotion analysis methods depending on the category of the destination. For example, an emotion analysis method that emphasizes a sense of relaxation can be applied to a natural scenery destination. An emotion analysis method that emphasizes a sense of excitement can be applied to an urban tourism destination. Furthermore, an emotion analysis method that emphasizes a sense of excitement can be applied to a cultural heritage destination. In this way, applying different emotion analysis methods depending on the category of the destination improves the accuracy of the analysis.

[0076] When generating music, the generation unit can adjust the genre and tempo of the music based on the characteristics of the destination. For example, for a destination with a natural landscape, music with a genre and tempo that emphasizes a relaxing feel can be generated. For a destination with an urban tourism destination, music with a genre and tempo that emphasizes an exciting feel can be generated. Furthermore, for a destination with a cultural heritage site, music with a genre and tempo that emphasizes an emotional feel can be generated. In this way, by adjusting the genre and tempo of the music based on the characteristics of the destination, more appropriate music can be generated.

[0077] When presenting the plan, the presentation unit can suggest the most suitable travel plan by referring to the user's past travel history. For example, the presentation unit can suggest the most suitable travel plan based on the places the user has visited in the past. The presentation unit can also suggest a travel plan that avoids crowds based on the user's past travel history. Furthermore, the presentation unit can analyze the user's past travel history and suggest the most efficient travel plan. This makes it possible to suggest a more suitable travel plan by referring to the user's past travel history.

[0078] When collecting, the collection unit can select the collection target based on the characteristics of the destination. For example, for a natural landscape destination, it can prioritize collecting reviews and posts about nature. For an urban tourism destination, it can prioritize collecting reviews and posts about the city. Furthermore, for a cultural heritage destination, it can prioritize collecting reviews and posts about culture. In this way, by selecting the collection target based on the characteristics of the destination, it is possible to collect more appropriate data.

[0079] The processing flow of the first embodiment will be briefly explained below.

[0080] Step 1: The user inputs a destination into the reception unit. For example, the user can input a specific place name or tourist spot such as "Paris" or "Mount Fuji." Step 2: The analysis unit analyzes emotions based on the destination accepted by the acceptance unit. The analysis unit analyzes emotions obtained at the destination using, for example, a generation AI. Step 3: The generator generates music that expresses the emotion analyzed by the analyzer. For example, the generator uses a generation AI to generate music that expresses the emotion analyzed. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI. Step 4: The presentation unit presents the travel plan based on the music generated by the generation unit. The presentation unit generates the travel plan based on the selected music using, for example, a generation AI.

[0081] (Example 2) A system according to an embodiment of the present invention generates music that expresses the emotions evoked at a destination when a user inputs the destination and presents a travel plan. In this system, a user inputs a destination, and a generation AI analyzes the emotions evoked at the destination and generates music that expresses them. The user listens to the generated music and selects music that matches the emotion they desire. Based on the selected music, the generation AI presents a travel plan that evokes that emotion. For example, when a user inputs a destination such as "Paris" or "Mount Fuji," the generation AI analyzes the emotions evoked at the destination and generates music that expresses them. The user listens to the generated music and selects music that matches the emotion they desire. Based on the selected music, the generation AI presents a travel plan that evokes that emotion. This allows the user to find a travel plan that suits them. This allows the system to generate music that expresses the emotions evoked at the destination and present a travel plan. For example, if a user desires to feel "adventurous," the generation AI generates music that expresses a sense of adventure and presents a travel plan that evokes that emotion. This allows the user to experience a new way of selecting travel plans.

[0082] The travel plan presentation system according to the embodiment includes a reception unit, an analysis unit, a generation unit, and a presentation unit. The reception unit allows a user to input a destination. The user can input a specific place name or tourist attraction, such as "Paris" or "Mount Fuji." The analysis unit analyzes emotions based on the destination accepted by the reception unit. The analysis unit, for example, uses a generation AI to analyze emotions felt at the destination. The generation unit generates music that expresses the emotions analyzed by the analysis unit. The generation unit, for example, uses the generation AI to generate music that expresses the analyzed emotions. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI. The presentation unit presents a travel plan based on the music generated by the generation unit. The presentation unit, for example, uses the generation AI to generate a travel plan based on selected music. In this way, the travel plan presentation system according to the embodiment allows a user to input a destination, generate music that expresses emotions felt at the destination, and present a travel plan.

[0083] The system includes a collection unit that collects reviews or social media posts. The collection unit collects reviews and social media posts. For example, the collection unit collects reviews and social media posts from a specific platform. For example, the collection unit can collect reviews and social media posts from travel sites. In this way, by collecting reviews and social media posts, sentiment analysis can be performed based on more information.

[0084] The system includes a selection unit that allows the user to select music. The selection unit allows the user to select generated music. The selection unit, for example, displays a list of generated music, allowing the user to select one. The selection unit, for example, provides an interface that allows the user to select music that matches the emotion the user wants to feel. This allows the user to select generated music, and a travel plan that matches the emotion the user wants to feel can be presented.

[0085] The system includes a data collection unit that collects data on tourist attractions, accommodations, and activities at travel destinations. The data collection unit collects data on tourist attractions, accommodations, and activities at travel destinations. The data collection unit collects data from, for example, travel sites and tourist guides. The data collection unit can collect, for example, information on tourist attractions, accommodations, and activities. By collecting detailed data on travel destinations, more specific travel plans can be presented.

[0086] The analysis unit can analyze reviews and social media posts related to the destination and identify emotions. The analysis unit, for example, analyzes reviews and social media posts and identifies emotions. The analysis unit, for example, uses natural language processing technology to extract emotions from reviews and social media posts. The analysis unit, for example, uses a sentiment analysis algorithm to identify emotions from reviews and social media posts. This allows for more accurate identification of emotions by analyzing reviews and social media posts.

[0087] The generation unit can generate music based on the identified emotion. The generation unit, for example, generates music based on the identified emotion. The generation unit, for example, uses a generation AI to generate music that expresses the identified emotion. The generation unit, for example, can adjust the tempo and melody of the music according to the emotion. In this way, by generating music based on the identified emotion, it is possible to provide music that matches the emotion the user desires to experience.

[0088] The presentation unit can generate a travel plan based on the selected music. The presentation unit generates a travel plan based on the selected music, for example. The presentation unit generates a travel plan based on the selected music, for example, using a generation AI. The presentation unit can suggest travel destinations and activities according to the emotions of the music, for example. By generating a travel plan based on the selected music, it is possible to provide a travel plan that matches the emotions the user desires to experience.

[0089] The reception unit can estimate the user's emotions and customize the destination input method based on the estimated user emotions. For example, the reception unit estimates the user's emotions and customizes the destination input method based on the estimated user emotions. For example, when the user is feeling stressed, the reception unit provides a simple interface and minimizes input steps. For example, when the user is relaxed, the reception unit provides detailed input options and suggests a customizable input method. For example, when the user is in a hurry, the reception unit prioritizes voice input to enable the user to quickly input a destination. In this way, by customizing the destination input method according to the user's emotions, a more user-friendly interface can be provided.

[0090] The reception unit can analyze the user's past travel history and suggest the optimal destination input method. The reception unit, for example, analyzes the user's past travel history and suggests the optimal destination input method. The reception unit, for example, automatically displays destinations that the user has frequently visited in the past as candidates. The reception unit, for example, preferentially suggests input methods (voice, text, etc.) that the user has used in the past. The reception unit, for example, predicts and suggests destinations related to specific seasons or events from the user's past travel history. This improves input efficiency by suggesting the optimal input method based on the user's past travel history.

[0091] The reception unit can present input candidates based on the user's current interests and concerns when a destination is input. For example, when a destination is input, the reception unit presents input candidates based on the user's current interests and concerns. For example, the reception unit suggests related destinations based on keywords recently searched by the user or websites recently visited by the user. For example, the reception unit analyzes the content of posts from accounts the user follows on social media and suggests related destinations. For example, the reception unit suggests related destinations based on events or activities the user recently participated in. In this way, by presenting input candidates based on the user's interests and concerns, more appropriate destinations can be suggested.

[0092] The reception unit can select the optimal input means depending on the user's input method when inputting a destination. For example, when inputting a destination, the reception unit selects the optimal input means depending on the user's input method (voice, text, image, etc.). For example, when the user inputs the destination by voice, the reception unit accurately recognizes the destination using voice recognition technology. For example, when the user inputs the destination using text, the reception unit provides an autocomplete function to assist the input. For example, when the user inputs the destination by uploading an image, the reception unit identifies the destination using image recognition technology. In this way, by selecting the optimal input means depending on the user's input method, the accuracy of input is improved.

[0093] The reception unit can estimate the user's emotions and determine the priority of input destinations based on the estimated user's emotions. The reception unit, for example, estimates the user's emotions and determines the priority of input destinations based on the estimated user's emotions. For example, if the user wants to relax, the reception unit preferentially displays relaxing destinations. For example, if the user is seeking adventure, the reception unit preferentially displays adventurous destinations. For example, if the user is seeking a cultural experience, the reception unit preferentially displays cultural destinations. In this way, by determining the priority of destinations based on the user's emotions, more appropriate destinations can be suggested.

[0094] The reception unit can prioritize presenting highly relevant destinations in consideration of the user's geographical location information when a destination is input. For example, the reception unit prioritizes presenting highly relevant destinations in consideration of the user's geographical location information when a destination is input. For example, the reception unit prioritizes displaying destinations that are close to the user's current location. For example, the reception unit prioritizes displaying destinations that are easy for the user to access from the user's current location. For example, the reception unit suggests the optimal destination in consideration of the user's mode of transportation from the current location. In this way, highly relevant destinations can be suggested by taking the user's geographical location information into consideration.

[0095] The reception unit can analyze the user's social media activity when a destination is input and suggest related destinations. For example, when a destination is input, the reception unit analyzes the user's social media activity and suggests related destinations. For example, the reception unit suggests related destinations based on places where the user has checked in on social media. For example, the reception unit analyzes the content of the user's social media posts and suggests related destinations. For example, the reception unit suggests related destinations by referring to the activity of the user's friends on social media. In this way, related destinations can be suggested by analyzing the user's social media activity.

[0096] The reception unit can customize the input method by reflecting the user's past feedback when inputting a destination. For example, the reception unit customizes the input method by reflecting the user's past feedback when inputting a destination. For example, the reception unit improves the input interface based on feedback provided by the user in the past. For example, the reception unit avoids input methods that the user has been dissatisfied with in the past and suggests an easier-to-use method. For example, the reception unit preferentially provides input methods that the user has preferred in the past. In this way, an easier-to-use input method can be provided by reflecting the user's past feedback.

[0097] The analysis unit can estimate the user's emotion and adjust the emotion analysis algorithm based on the estimated user's emotion. For example, the analysis unit estimates the user's emotion and adjusts the emotion analysis algorithm based on the estimated user's emotion. For example, if the user is relaxed, the analysis unit applies an algorithm that emphasizes a sense of relaxation. For example, if the user is excited, the analysis unit applies an algorithm that emphasizes a sense of excitement. For example, if the user is feeling stressed, the analysis unit applies an algorithm aimed at reducing stress. In this way, by adjusting the emotion analysis algorithm based on the user's emotion, the accuracy of the analysis is improved.

[0098] During analysis, the analysis unit can improve the accuracy of the sentiment analysis by referring to past reviews and social media posts about the destination. The analysis unit, for example, analyzes past reviews about the destination to improve the accuracy of the sentiment analysis. The analysis unit, for example, analyzes social media posts about the destination to improve the accuracy of the sentiment analysis. The analysis unit, for example, refers to blogs and articles about the destination to improve the accuracy of the sentiment analysis. In this way, by referring to past reviews and social media posts, the accuracy of the sentiment analysis is improved.

[0099] During analysis, the analysis unit can apply different emotion analysis methods depending on the category of the destination. For example, the analysis unit applies an emotion analysis method that emphasizes a sense of relaxation to a destination with a natural landscape. For example, the analysis unit applies an emotion analysis method that emphasizes a sense of excitement to a destination with an urban tourism destination. For example, the analysis unit applies an emotion analysis method that emphasizes a sense of excitement to a destination with a cultural heritage site. In this way, by applying different emotion analysis methods depending on the category of the destination, the accuracy of the analysis is improved.

[0100] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past emotional data. The analysis unit improves the accuracy of the analysis, for example, based on emotional data from destinations the user has visited in the past. The analysis unit improves the accuracy of the analysis, for example, based on feedback the user has provided in the past. The analysis unit improves the accuracy of the analysis by referring to the user's past travel history, for example. In this way, the accuracy of the analysis is improved by referring to the user's past emotional data.

[0101] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user's emotions. The analysis unit, for example, estimates the user's emotions and adjusts the display method of the analysis results based on the estimated user's emotions. For example, if the user is relaxed, the analysis unit provides a display method that emphasizes a sense of relaxation. For example, if the user is excited, the analysis unit provides a display method that emphasizes a sense of excitement. For example, if the user is feeling stressed, the analysis unit provides a display method aimed at reducing stress. In this way, by adjusting the display method of the analysis results based on the user's emotions, more appropriate information can be provided.

[0102] During analysis, the analysis unit can perform emotion analysis taking into account the geographical distribution of destinations. For example, if the destination is in an urban area, the analysis unit performs analysis taking into account emotions specific to the city. For example, if the destination is in a natural landscape, the analysis unit performs analysis taking into account emotions specific to nature. For example, if the destination is in a cultural heritage site, the analysis unit performs analysis taking into account emotions specific to the culture. In this way, by taking into account the geographical distribution of destinations, more accurate emotion analysis is possible.

[0103] During analysis, the analysis unit can improve the accuracy of sentiment analysis by referring to literature related to the destination. The analysis unit, for example, refers to academic papers related to the destination to improve the accuracy of sentiment analysis. The analysis unit, for example, refers to travel guides related to the destination to improve the accuracy of sentiment analysis. The analysis unit, for example, refers to history books related to the destination to improve the accuracy of sentiment analysis. In this way, by referring to related literature, the accuracy of sentiment analysis is improved.

[0104] During analysis, the analysis unit can perform sentiment analysis taking into account the market value of the destination. For example, the analysis unit performs sentiment analysis taking into account the tourism market value of the destination. For example, the analysis unit performs sentiment analysis taking into account the real estate market value of the destination. For example, the analysis unit performs sentiment analysis taking into account the economic impact of the destination. In this way, by taking market value into account, more realistic sentiment analysis becomes possible.

[0105] The generation unit can estimate the user's emotion and adjust the music generation algorithm based on the estimated user's emotion. For example, the generation unit estimates the user's emotion and adjusts the music generation algorithm based on the estimated user's emotion. For example, when the user is relaxed, the generation unit applies an algorithm that generates music that emphasizes a sense of relaxation. For example, when the user is excited, the generation unit applies an algorithm that generates music that emphasizes a sense of excitement. For example, when the user is feeling stressed, the generation unit applies an algorithm that generates music aimed at reducing stress. In this way, by adjusting the music generation algorithm based on the user's emotion, more appropriate music can be generated.

[0106] When generating music, the generation unit can adjust the genre and tempo of the music based on the characteristics of the destination. For example, for a destination with a natural landscape, the generation unit generates music with a genre and tempo that emphasizes a sense of relaxation. For example, for a destination with an urban tourism destination, the generation unit generates music with a genre and tempo that emphasizes a sense of excitement. For example, for a destination with a cultural heritage site, the generation unit generates music with a genre and tempo that emphasizes a sense of emotion. In this way, by adjusting the genre and tempo of the music based on the characteristics of the destination, more appropriate music can be generated.

[0107] When generating music, the generation unit can improve the accuracy of generation by referring to the user's past music preferences. The generation unit generates optimal music based on, for example, music genres that the user has previously liked. The generation unit generates optimal music based on, for example, the tempo of music that the user has previously listened to. The generation unit generates optimal music by referring to, for example, music that the user has previously rated. In this way, by referring to the user's past music preferences, the accuracy of generation is improved.

[0108] When generating music, the generation unit can adjust the atmosphere of the music according to the season and weather of the destination. For example, the generation unit generates music with a bright and refreshing atmosphere for a spring destination. For example, the generation unit generates music with a warm atmosphere for a winter destination. For example, the generation unit generates music with a calm atmosphere for a rainy day destination. In this way, by adjusting the atmosphere of the music according to the season and weather, more appropriate music can be generated.

[0109] The generation unit can estimate the user's emotion and adjust the length of the music to be generated based on the estimated user's emotion. For example, if the user is relaxed, the generation unit generates longer music. For example, if the user is in a hurry, the generation unit generates shorter music. For example, if the user is excited, the generation unit generates music of an appropriate length. In this way, more appropriate music can be generated by adjusting the length of the music based on the user's emotion.

[0110] When generating music, the generation unit can generate music taking into consideration the cultural background of the destination. For example, if the destination is France, the generation unit generates music that incorporates traditional French music. For example, if the destination is Japan, the generation unit generates music that incorporates traditional Japanese music. For example, if the destination is Brazil, the generation unit generates music that incorporates traditional Brazilian music. In this way, more appropriate music can be generated by taking the cultural background into consideration.

[0111] When generating music, the generation unit can determine the theme of the music by referring to the historical background of the destination. For example, if the destination is a historical city, the generation unit generates music that reflects that history. For example, if the destination is a battlefield, the generation unit generates music that reflects that history. For example, if the destination is an ancient ruin, the generation unit generates music that reflects that history. In this way, by referring to the historical background, more appropriate music can be generated.

[0112] When generating music, the generation unit can adjust the composition of the music based on tourist spots at the destination. For example, the generation unit generates music that reflects major tourist spots at the destination. For example, the generation unit generates music that reflects hidden tourist spots at the destination. For example, the generation unit generates music that reflects the atmosphere of tourist spots at the destination. In this way, by adjusting the composition of the music based on tourist spots, more appropriate music can be generated.

[0113] The presentation unit can estimate the user's emotions and adjust the presentation method of the travel plan based on the estimated user emotions. The presentation unit, for example, estimates the user's emotions and adjusts the presentation method of the travel plan based on the estimated user emotions. For example, if the user is relaxed, the presentation unit presents a travel plan that emphasizes a sense of relaxation. For example, if the user is excited, the presentation unit presents a travel plan that emphasizes a sense of excitement. For example, if the user is stressed, the presentation unit presents a travel plan aimed at reducing stress. In this way, by adjusting the presentation method of the travel plan based on the user's emotions, a more appropriate travel plan can be provided.

[0114] When presenting the plan, the presentation unit can suggest an optimal travel plan by referring to the user's past travel history. The presentation unit, for example, suggests an optimal travel plan based on places the user has visited in the past. The presentation unit, for example, suggests a travel plan that avoids crowds based on the user's past travel history. The presentation unit, for example, analyzes the user's past travel history and suggests the most efficient travel plan. In this way, by referring to the past travel history, a more appropriate travel plan can be suggested.

[0115] The presentation unit can customize the travel plan based on the user's current living situation when presenting the plan. For example, if the user is busy, the presentation unit suggests a short, enjoyable travel plan. For example, if the user wants to relax, the presentation unit suggests a relaxing travel plan. For example, if the user is seeking adventure, the presentation unit suggests an adventurous travel plan. In this way, by customizing the travel plan based on the user's current living situation, a more appropriate travel plan can be provided.

[0116] The presentation unit can improve the method of presenting a travel plan by reflecting user feedback when presenting the plan. The presentation unit improves the method of presenting a travel plan based on, for example, feedback provided by the user in the past. The presentation unit, for example, avoids travel plans that the user was dissatisfied with in the past and proposes plans that will provide more satisfaction. For example, the presentation unit preferentially presents travel plans that the user liked in the past. In this way, by reflecting user feedback, it is possible to provide travel plans that will provide more satisfaction.

[0117] The presentation unit can estimate the user's emotions and prioritize the travel plans based on the estimated user emotions. For example, the presentation unit estimates the user's emotions and prioritizes the travel plans based on the estimated user emotions. For example, if the user wants to relax, the presentation unit prioritizes displaying relaxing travel plans. For example, if the user is seeking adventure, the presentation unit prioritizes displaying adventurous travel plans. For example, if the user is seeking a cultural experience, the presentation unit prioritizes displaying cultural travel plans. In this way, by prioritizing the travel plans based on the user's emotions, more appropriate travel plans can be provided.

[0118] When presenting the travel plan, the presentation unit can propose an optimal travel plan taking into consideration the user's geographical location information. The presentation unit, for example, prioritizes displaying travel plans that are close to the user's current location. The presentation unit, for example, prioritizes displaying travel plans that are easy to access from the user's current location. The presentation unit, for example, proposes an optimal travel plan taking into consideration the user's mode of transportation from the user's current location. In this way, by taking into consideration the geographical location information, a more appropriate travel plan can be proposed.

[0119] At the time of presentation, the presentation unit can analyze the user's social media activity and suggest a travel plan. For example, the presentation unit suggests a related travel plan based on the location where the user checked in on social media. For example, the presentation unit analyzes the content of the user's social media posts and suggests a related travel plan. For example, the presentation unit suggests a related travel plan based on the activity of the user's friends on social media. In this way, by analyzing social media activity, it is possible to suggest a more appropriate travel plan.

[0120] The presentation unit can customize the travel plan by reflecting the user's past feedback when presenting the plan. The presentation unit customizes the travel plan, for example, based on feedback provided by the user in the past. The presentation unit, for example, avoids travel plans that the user was dissatisfied with in the past and proposes plans that will provide more satisfaction. For example, the presentation unit preferentially presents travel plans that the user liked in the past. In this way, by reflecting past feedback, it is possible to provide a travel plan that will provide more satisfaction.

[0121] The collection unit can estimate the user's emotions and adjust the method for collecting reviews and SNS posts based on the estimated user's emotions. The collection unit, for example, estimates the user's emotions and adjusts the method for collecting reviews and SNS posts based on the estimated user's emotions. For example, when the user is relaxed, the collection unit preferentially collects reviews and posts that emphasize a sense of relaxation. For example, when the user is excited, the collection unit preferentially collects reviews and posts that emphasize an sense of excitement. For example, when the user is stressed, the collection unit preferentially collects reviews and posts aimed at reducing stress. In this way, by adjusting the collection method based on the user's emotions, more appropriate reviews and posts can be collected.

[0122] During collection, the collection unit can select collection targets based on the characteristics of the destination. For example, for a natural scenery destination, the collection unit prioritizes collecting reviews and posts about nature. For example, for an urban tourism destination, the collection unit prioritizes collecting reviews and posts about cities. For example, for a cultural heritage destination, the collection unit prioritizes collecting reviews and posts about culture. This allows for more appropriate data to be collected by selecting collection targets based on the characteristics of the destination.

[0123] When collecting, the collection unit can improve the accuracy of the collection by referring to the user's past reviews and posts. The collection unit improves the accuracy of the collection, for example, based on reviews posted by the user in the past. The collection unit improves the accuracy of the collection, for example, based on reviews rated by the user in the past. The collection unit improves the accuracy of the collection, for example, by referring to the user's past posting history. In this way, the accuracy of the collection is improved by referring to the past reviews and posts.

[0124] The collection unit can estimate the user's emotions and determine the priority of reviews and posts to be collected based on the estimated user's emotions. The collection unit, for example, estimates the user's emotions and determines the priority of reviews and posts to be collected based on the estimated user's emotions. For example, when the user is relaxed, the collection unit preferentially collects reviews and posts that emphasize a sense of relaxation. For example, when the user is excited, the collection unit preferentially collects reviews and posts that emphasize an sense of excitement. For example, when the user is feeling stressed, the collection unit preferentially collects reviews and posts aimed at reducing stress. In this way, by determining the priority based on the user's emotions, more appropriate reviews and posts can be collected.

[0125] The collection unit can collect data while taking into consideration the geographical distribution of destinations. For example, if the destination is in an urban area, the collection unit prioritizes collecting city-specific reviews and posts. For example, if the destination is in a natural landscape, the collection unit prioritizes collecting nature-specific reviews and posts. For example, if the destination is in a cultural heritage site, the collection unit prioritizes collecting culture-specific reviews and posts. This allows for more appropriate data to be collected by taking into consideration the geographical distribution.

[0126] When collecting, the collection unit can improve the accuracy of the collection by referring to literature related to the destination. The collection unit, for example, refers to academic papers related to the destination to improve the accuracy of the collection. The collection unit, for example, refers to travel guides related to the destination to improve the accuracy of the collection. The collection unit, for example, refers to history books related to the destination to improve the accuracy of the collection. In this way, by referring to related literature, the accuracy of the collection is improved.

[0127] The selection unit can estimate the user's emotions and adjust the music selection method based on the estimated user's emotions. For example, the selection unit estimates the user's emotions and adjusts the music selection method based on the estimated user's emotions. For example, if the user is relaxed, the selection unit preferentially suggests music that emphasizes a sense of relaxation. For example, if the user is excited, the selection unit preferentially suggests music that emphasizes a sense of excitement. For example, if the user is feeling stressed, the selection unit preferentially suggests music intended to reduce stress. In this way, by adjusting the selection method based on the user's emotions, more appropriate music can be suggested.

[0128] When making a selection, the selection unit can suggest optimal music by referring to the user's past music preferences. The selection unit suggests optimal music, for example, based on music genres that the user has liked in the past. The selection unit suggests optimal music, for example, based on the tempo of music that the user has listened to in the past. The selection unit suggests optimal music by referring to music that the user has rated in the past. In this way, more appropriate music can be suggested by referring to the user's past music preferences.

[0129] When making a selection, the selection unit can customize music based on the user's current mood and situation. For example, if the user wants to relax, the selection unit suggests music that emphasizes a sense of relaxation. For example, if the user is excited, the selection unit suggests music that emphasizes a sense of excitement. For example, if the user is feeling stressed, the selection unit suggests music aimed at reducing stress. In this way, by customizing music based on the user's current mood and situation, more appropriate music can be suggested.

[0130] The selection unit can estimate the user's emotions and determine the priority of music to be selected based on the estimated user emotions. The selection unit, for example, estimates the user's emotions and determines the priority of music to be selected based on the estimated user emotions. For example, if the user wants to relax, the selection unit preferentially displays music that emphasizes a sense of relaxation. For example, if the user is excited, the selection unit preferentially displays music that emphasizes a sense of excitement. For example, if the user is feeling stressed, the selection unit preferentially displays music intended to reduce stress. In this way, by determining the priority of music based on the user's emotions, more appropriate music can be suggested.

[0131] When making a selection, the selection unit can suggest optimal music taking into consideration the user's geographical location information. For example, the selection unit suggests music that is suitable for the user to listen to in a location close to the user's current location. For example, the selection unit suggests music that suits the climate and environment of the user's current location. For example, the selection unit suggests music that suits the culture and climate of the user's current location. In this way, more appropriate music can be suggested by taking into consideration the geographical location information.

[0132] When making a selection, the selection unit can analyze the user's social media activity to suggest music. For example, the selection unit can suggest related music based on music the user has shared on social media. For example, the selection unit can analyze the content of the user's social media posts to suggest related music. For example, the selection unit can refer to the activity of the user's friends on social media to suggest related music. In this way, more appropriate music can be suggested by analyzing social media activity.

[0133] The data collection unit can estimate the user's emotions and adjust the data collection method for tourist attractions, accommodations, and activities based on the estimated user's emotions. The data collection unit, for example, estimates the user's emotions and adjusts the data collection method for tourist attractions, accommodations, and activities based on the estimated user's emotions. For example, if the user is relaxed, the data collection unit prioritizes collecting data on tourist attractions, accommodations, and activities that emphasize a sense of relaxation. For example, if the user is excited, the data collection unit prioritizes collecting data on tourist attractions, accommodations, and activities that emphasize a sense of excitement. For example, if the user is stressed, the data collection unit prioritizes collecting data on tourist attractions, accommodations, and activities that aim to reduce stress. In this way, by adjusting the data collection method based on the user's emotions, more appropriate data can be collected.

[0134] When collecting data, the data collection unit can select collection targets based on the characteristics of the destination. For example, for a destination with natural scenery, the data collection unit prioritizes collecting data related to nature. For example, for a destination with urban tourism, the data collection unit prioritizes collecting data related to cities. For example, for a destination with cultural heritage, the data collection unit prioritizes collecting data related to culture. In this way, by selecting collection targets based on the characteristics of the destination, more appropriate data can be collected.

[0135] When collecting data, the data collection unit can improve the accuracy of the collection by referring to the user's past travel history. The data collection unit improves the accuracy of the collection, for example, based on data on places the user has visited in the past. The data collection unit improves the accuracy of the collection, for example, based on data on tourist spots, accommodations, and activities that the user has rated in the past. The data collection unit improves the accuracy of the collection, for example, by referring to the user's past travel history. In this way, the accuracy of the collection is improved by referring to the past travel history.

[0136] The data collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user's emotions. The data collection unit, for example, estimates the user's emotions and determines the priority of data to be collected based on the estimated user's emotions. For example, if the user is relaxed, the data collection unit prioritizes collecting data that emphasizes a sense of relaxation. For example, if the user is excited, the data collection unit prioritizes collecting data that emphasizes a sense of excitement. For example, if the user is feeling stressed, the data collection unit prioritizes collecting data aimed at reducing stress. In this way, by determining the priority of data based on the user's emotions, more appropriate data can be collected.

[0137] The data collection unit can collect data while taking into account the geographical distribution of destinations. For example, if the destination is in an urban area, the data collection unit prioritizes collecting city-specific data. For example, if the destination is in a natural landscape, the data collection unit prioritizes collecting nature-specific data. For example, if the destination is in a cultural heritage site, the data collection unit prioritizes collecting culture-specific data. This allows for more appropriate data to be collected by taking into account the geographical distribution.

[0138] When collecting data, the data collection unit can improve the accuracy of the collection by referring to literature related to the destination. The data collection unit, for example, refers to academic papers related to the destination to improve the accuracy of the collection. The data collection unit, for example, refers to travel guides related to the destination to improve the accuracy of the collection. The data collection unit, for example, refers to history books related to the destination to improve the accuracy of the collection. In this way, by referring to related literature, the accuracy of the collection is improved. === Hard Collateral 1-1 === Each of the multiple elements, including the reception unit, analysis unit, generation unit, presentation unit, collection unit, selection unit, and data collection 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 reception device 38 of the smart device 14, and the user inputs a destination. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes emotions based on the destination. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates music that expresses the analyzed emotions. The presentation unit is realized, for example, by the output device 40 of the smart device 14, and presents a travel plan based on the generated music. The collection unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and collects reviews and social media posts. The selection unit is realized, for example, by the reception device 38 of the smart device 14, and selects the generated music. The data collection unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and collects data on tourist attractions, accommodations, and activities at the travel destination. === Hard Collateral 1-2 === Each of the multiple elements, including the above-described reception unit, analysis unit, generation unit, presentation unit, collection unit, selection unit, and data collection 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 microphone 238 of the smart glasses 214, and the user inputs a destination. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes emotions based on the destination. The generation unit is realized by the specific processing unit 290 of the data processing device 12, and generates music that expresses the analyzed emotions. The presentation unit is realized by the speaker 240 of the smart glasses 214, and presents a travel plan based on the generated music. The collection unit is realized by the specific processing unit 290 of the data processing device 12, and collects reviews and social media posts. The selection unit is realized by the microphone 238 of the smart glasses 214, and the user selects the generated music. The data collection unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and collects data on tourist spots, accommodations, and activities at travel destinations. === Hard Collateral 1-3 === Each of the multiple elements including the above-described reception unit, analysis unit, generation unit, presentation unit, collection unit, selection unit, and data collection 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 microphone 238 of the headset-type terminal 314, and the user inputs a destination. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes emotions based on the destination. The generation unit is realized by the specific processing unit 290 of the data processing device 12, and generates music that expresses the analyzed emotions. The presentation unit is realized by the speaker 240 of the headset-type terminal 314, and presents a travel plan based on the generated music. The collection unit is realized by the specific processing unit 290 of the data processing device 12, and collects reviews and social media posts. The selection unit is realized by the microphone 238 of the headset-type terminal 314, and the user selects the generated music. The data collection unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and collects data on tourist spots, accommodations, and activities at travel destinations. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, presentation unit, collection unit, selection unit, and data collection 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 microphone 238 of the robot 414, and the user inputs a destination. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes emotions based on the destination. The generation unit is realized by the specific processing unit 290 of the data processing device 12, and generates music that expresses the analyzed emotions. The presentation unit is realized by the speaker 240 of the robot 414, and presents a travel plan based on the generated music. The collection unit is realized by the specific processing unit 290 of the data processing device 12, and collects reviews and social media posts. The selection unit is realized by the microphone 238 of the robot 414, and the user selects the generated music. The data collection unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and collects data on tourist spots, accommodations, and activities at travel destinations.

[0139] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0140] The analysis unit can analyze the user's past travel history and improve the accuracy of emotion analysis based on the obtained emotion data. For example, emotion data from places the user has visited in the past can be collected and reflected in the analysis. This enables analysis that takes into account the emotions the user has experienced in the past. The emotion analysis algorithm can also be adjusted based on feedback provided by the user in the past. Furthermore, emotion data related to specific seasons or events can be extracted from the user's past travel history and used in the analysis.

[0141] The collection unit can estimate the user's emotions and adjust the method for collecting reviews and SNS posts based on the estimated user's emotions. For example, if the user is relaxed, reviews and posts that emphasize a sense of relaxation are preferentially collected. If the user is excited, reviews and posts that emphasize an sense of excitement are preferentially collected. Furthermore, if the user is feeling stressed, reviews and posts aimed at stress reduction can also be preferentially collected. In this way, by adjusting the collection method based on the user's emotions, more appropriate reviews and posts can be collected.

[0142] The selection unit can suggest optimal music by taking into account the user's past music preferences. For example, the selection unit can suggest optimal music based on the music genres that the user has liked in the past. The selection unit can suggest optimal music based on the tempo of music that the user has listened to in the past. Furthermore, the selection unit can suggest optimal music by taking into account music that the user has rated in the past. In this way, more appropriate music can be suggested by taking into account the user's past music preferences.

[0143] The data collection unit can estimate the user's emotions and adjust the data collection method for tourist attractions, accommodations, and activities based on the estimated user's emotions. For example, if the user is relaxed, data on tourist attractions, accommodations, and activities that emphasize a sense of relaxation is preferentially collected. If the user is excited, data on tourist attractions, accommodations, and activities that emphasize a sense of excitement is preferentially collected. Furthermore, if the user is feeling stressed, data on tourist attractions, accommodations, and activities aimed at stress reduction can also be preferentially collected. In this way, by adjusting the data collection method based on the user's emotions, more appropriate data can be collected.

[0144] The presentation unit can estimate the user's emotions and adjust the way in which the travel plan is presented based on the estimated user emotions. For example, if the user is relaxed, the presentation unit can present a travel plan that emphasizes the feeling of relaxation. If the user is excited, the presentation unit can present a travel plan that emphasizes the feeling of excitement. Furthermore, if the user is feeling stressed, the presentation unit can present a travel plan aimed at reducing stress. In this way, by adjusting the way in which the travel plan is presented based on the user's emotions, it is possible to provide a more appropriate travel plan.

[0145] The analysis unit can apply different emotion analysis methods depending on the category of the destination. For example, an emotion analysis method that emphasizes a sense of relaxation can be applied to a natural scenery destination. An emotion analysis method that emphasizes a sense of excitement can be applied to an urban tourism destination. Furthermore, an emotion analysis method that emphasizes a sense of excitement can be applied to a cultural heritage destination. In this way, applying different emotion analysis methods depending on the category of the destination improves the accuracy of the analysis.

[0146] When generating music, the generation unit can adjust the genre and tempo of the music based on the characteristics of the destination. For example, for a destination with a natural landscape, music with a genre and tempo that emphasizes a relaxing feel can be generated. For a destination with an urban tourism destination, music with a genre and tempo that emphasizes an exciting feel can be generated. Furthermore, for a destination with a cultural heritage site, music with a genre and tempo that emphasizes an emotional feel can be generated. In this way, by adjusting the genre and tempo of the music based on the characteristics of the destination, more appropriate music can be generated.

[0147] When presenting the plan, the presentation unit can suggest the most suitable travel plan by referring to the user's past travel history. For example, the presentation unit can suggest the most suitable travel plan based on the places the user has visited in the past. The presentation unit can also suggest a travel plan that avoids crowds based on the user's past travel history. Furthermore, the presentation unit can analyze the user's past travel history and suggest the most efficient travel plan. This makes it possible to suggest a more suitable travel plan by referring to the user's past travel history.

[0148] When collecting, the collection unit can select the collection target based on the characteristics of the destination. For example, for a natural landscape destination, it can prioritize collecting reviews and posts about nature. For an urban tourism destination, it can prioritize collecting reviews and posts about the city. Furthermore, for a cultural heritage destination, it can prioritize collecting reviews and posts about culture. In this way, by selecting the collection target based on the characteristics of the destination, it is possible to collect more appropriate data.

[0149] The selection unit can estimate the user's emotions and adjust the music selection method based on the estimated user's emotions. For example, if the user is relaxed, music that emphasizes a sense of relaxation can be preferentially suggested. If the user is excited, music that emphasizes a sense of excitement can be preferentially suggested. Furthermore, if the user is feeling stressed, music intended to reduce stress can be preferentially suggested. In this way, by adjusting the selection method based on the user's emotions, more appropriate music can be suggested.

[0150] The processing flow of the second embodiment will be briefly explained below.

[0151] Step 1: The user inputs a destination into the reception unit. For example, the user can input a specific place name or tourist spot such as "Paris" or "Mount Fuji." Step 2: The analysis unit analyzes emotions based on the destination accepted by the acceptance unit. The analysis unit analyzes emotions obtained at the destination using, for example, a generation AI. Step 3: The generator generates music that expresses the emotion analyzed by the analyzer. For example, the generator uses a generation AI to generate music that expresses the emotion analyzed. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI. Step 4: The presentation unit presents the travel plan based on the music generated by the generation unit. The presentation unit generates the travel plan based on the selected music using, for example, a generation AI.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0156] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0157] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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).

[0162] 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.

[0163] 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.

[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 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.

[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 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.

[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 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.

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

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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).

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0188] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0189] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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).

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] 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).

[0209] 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.

[0210] 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."

[0211] 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.

[0212] 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.

[0213] 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.

[0214] 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.

[0215] 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.

[0216] 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.

[0217] 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.

[0218] 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.

[0219] 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.

[0220] 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.

[0221] 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.

[0222] 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.

[0223] [Explanation of symbols]

[0224] 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 an input of a destination; an analysis unit that analyzes emotions based on the destination accepted by the acceptance unit; a generation unit that generates music that expresses the emotion analyzed by the analysis unit; a presentation unit that presents a travel plan based on the music generated by the generation unit. A system characterized by:

2. Equipped with a collection section to collect reviews or social media posts 2. The system of claim 1.

3. A selection section is provided for allowing the user to select music.

2. The system of claim 1.

4. Equipped with a data collection unit that collects data on tourist destinations, accommodations, and activities at travel destinations 2. The system of claim 1.

5. The analysis unit Analyze reviews and social media posts related to your destination to identify sentiment 2. The system of claim 1.

6. The generation unit Generate music based on identified emotions 2. The system of claim 1.

7. The presentation unit Generate itineraries based on music selections 2. The system of claim 1.

8. The reception unit Inferring user emotions and customizing the destination input method based on the estimated user emotions 2. The system of claim 1.

9. The reception unit Analyzes the user's past travel history and suggests the best way to enter a destination 2. The system of claim 1.

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