system

Abstract

The system according to the embodiment aims to suggest tourist spots and shops based on the user's hobbies and preferences. [Solution] A system according to an embodiment includes a reception unit, an analysis unit, a proposal unit, and a route provision unit. The reception unit inputs a user's hobbies and preferences. The analysis unit analyzes the information input by the reception unit. The proposal unit proposes tourist spots and shops based on the results of the analysis by the analysis unit. The route provision unit provides a route between the spots and shops proposed by the proposal 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 do not adequately suggest tourist spots and shops based on the user's hobbies and preferences, and there is room for improvement.

[0005] The system according to the embodiment aims to suggest tourist spots and shops based on the user's hobbies and preferences. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a suggestion unit, and a route provision unit. The reception unit inputs the user's hobbies and preferences. The analysis unit analyzes the information input by the reception unit. The suggestion unit suggests tourist spots and shops based on the results of the analysis by the analysis unit. The route provision unit provides a route to the spots and shops suggested by the suggestion unit. [Effects of the Invention]

[0007] The system according to the embodiment can suggest tourist spots and shops based on the user's hobbies and preferences. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A tourism recommendation system according to an embodiment of the present invention is an app targeted at inbound foreign tourists. It uses AI to recommend tourist spots and shops based on the user's hobbies and preferences. In this tourism recommendation system, users input their hobbies and preferences into the app, and AI analyzes the input information to recommend the most suitable tourist spots and shops for the user. The recommended spots and shops are also provided with optimal routes based on the user's current location and mode of transportation. This allows the user to enjoy sightseeing tailored to their interests. For example, a user inputs information into the app, such as "I like historical places" or "I want to enjoy local cuisine." This information is then input into AI. The AI ​​then analyzes the input information and recommends the most suitable tourist spots and shops for the user. The AI ​​searches a database of tourist spots and shops based on the user's hobbies and preferences to make optimal recommendations. For example, a user who likes historical places is suggested local historical tourist spots, and a user who wants to enjoy local cuisine is suggested well-reviewed restaurants. Furthermore, the recommended spots and shops are also provided with optimal routes based on the user's current location and mode of transportation. For example, if a user travels on foot, the system will suggest spots and shops within walking distance, and if the user travels by car, it will suggest spots and shops within driving distance. This allows users to enjoy sightseeing efficiently. This system allows inbound foreign tourists to easily find tourist spots and shops that suit their hobbies and preferences, improving their travel satisfaction. It also contributes to revitalizing the local tourism and restaurant industries. As a result, the tourism suggestion system can improve the user's sightseeing experience by suggesting tourist spots and shops based on the user's hobbies and preferences and providing the optimal route.

[0029] The tourism suggestion system according to the embodiment includes a reception unit, an analysis unit, a suggestion unit, and a route provision unit. The reception unit inputs a user's hobbies and preferences. The user's hobbies and preferences include, but are not limited to, sports, music, and food. The reception unit can input information such as, for example, "I like historical places" or "I want to enjoy local cuisine." The analysis unit analyzes the information input by the reception unit. The analysis can be performed using, for example, but not limited to, data mining or statistical analysis. The analysis unit searches a database of tourist spots and shops based on the user's hobbies and preferences, and makes optimal suggestions. The suggestion unit suggests tourist spots and shops based on the results of the analysis by the analysis unit. The suggestions can be made based on, for example, user ratings and past data, but are not limited to, the suggestion unit can suggest local historical tourist spots to a user who likes historical places, and suggest well-reviewed restaurants to a user who wants to enjoy local cuisine. The route provision unit provides a route to the spots and shops suggested by the suggestion unit. The route provision may be, for example, the shortest route, a tourist route, or the like, but is not limited to these examples. For example, if the user is traveling on foot, the route providing unit may suggest spots and shops within walking distance, and if the user is traveling by car, may suggest spots and shops within driving distance. As a result, the tourism suggestion system according to the embodiment can improve the user's tourism experience by suggesting tourist spots and shops based on the user's hobbies and preferences and providing the optimal route.

[0030] The sightseeing suggestion system includes a history collection unit that collects the user's past behavioral history. The history collection unit collects the user's past behavioral history. The behavioral history includes, but is not limited to, for example, a visit history and a purchase history. For example, the history collection unit collects data on tourist spots that the user has visited in the past. The history collection unit can also collect data on restaurants that the user has used in the past. Furthermore, the history collection unit can collect data on events that the user has participated in in the past. In this way, the history collection unit can collect the user's past behavioral history and use it for analysis, thereby enabling more accurate suggestions. Some or all of the above-described processing in the history collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the history collection unit can input the user's past behavioral history into AI and have the AI ​​analyze the behavioral history.

[0031] The tourism suggestion system includes a detail providing unit that provides detailed information about suggested spots and shops. The detail providing unit provides detailed information about the suggested spots and shops. The detailed information includes, but is not limited to, opening hours, prices, and reviews, for example. The detail providing unit provides, for example, the opening hours and prices of suggested tourist spots. The detail providing unit can also provide menus and reviews of suggested restaurants. Furthermore, the detail providing unit can also provide product information about suggested shopping spots. In this way, the detail providing unit can provide detailed information about suggested spots and shops, allowing the user to obtain more specific information. Some or all of the above-described processing in the detail providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the detail providing unit can input detailed information about suggested spots and shops into AI and have the AI ​​provide the detailed information.

[0032] The tourism suggestion system includes a feedback collection unit that collects user feedback. The feedback collection unit collects user feedback. The feedback includes, but is not limited to, questionnaires, reviews, and ratings, for example. The feedback collection unit, for example, collects reviews about tourist spots suggested by the user. The feedback collection unit can also collect ratings about restaurants suggested by the user. Furthermore, the feedback collection unit can also collect questionnaires about shopping spots suggested by the user. In this way, the feedback collection unit collects user feedback and reflects it in next suggestions, thereby improving the accuracy of suggestions. Some or all of the above-described processing in the feedback collection unit may be performed using, or without using, AI. For example, the feedback collection unit may input user feedback into AI and have the AI ​​analyze the feedback.

[0033] The reception unit can analyze the user's past input history and provide an optimal input interface. For example, the reception unit can automatically display hobbies and preferences that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest hobbies and preferences to be used during a specific time period based on the user's past input history. In this way, the reception unit can provide an optimal input interface by analyzing the user's past input history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past input history into AI and have the AI ​​analyze the input history.

[0034] The reception unit can customize input items based on the user's current interests when inputting hobbies and preferences. For example, the reception unit can suggest input items for related hobbies and preferences based on keywords recently searched by the user. The reception unit can also customize input items for related hobbies and preferences based on information about places the user has recently visited. Furthermore, the reception unit can suggest input items for related hobbies and preferences based on information about events the user has recently attended. This allows the reception unit to customize input items based on the user's current interests, enabling more appropriate input. Some or all of the above-described processing by the reception unit may be performed using, or without, AI. For example, the reception unit can input the user's current interest data into AI and have the AI ​​analyze the interests.

[0035] When inputting hobbies and preferences, the reception unit can prioritize displaying highly relevant input items in consideration of the user's geographical location information. For example, the reception unit can prioritize displaying input items related to tourist spots near the user's current location. The reception unit can also prioritize displaying input items related to restaurants near the user's current location. Furthermore, the reception unit can also prioritize displaying input items related to events near the user's current location. In this way, the reception unit can prioritize displaying highly relevant input items by considering the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's geographical location information to AI and have the AI ​​analyze the location information.

[0036] When inputting hobbies and preferences, the reception unit can analyze the user's social media activity and suggest related input items. For example, the reception unit can suggest input items for hobbies and preferences related to posts that the user has "liked" on social media. The reception unit can also suggest input items for hobbies and preferences related to accounts the user follows on social media. Furthermore, the reception unit can suggest input items for hobbies and preferences related to posts the user has shared on social media. In this way, the reception unit can suggest related input items by analyzing the user's social media activity. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media activity data into AI and have the AI ​​perform social media analysis.

[0037] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past behavioral history. The analysis unit can improve the accuracy of the analysis, for example, based on data on tourist spots visited by the user in the past. The analysis unit can also improve the accuracy of the analysis based on data on restaurants used by the user in the past. Furthermore, the analysis unit can improve the accuracy of the analysis based on data on events attended by the user in the past. In this way, the analysis unit improves the accuracy of the analysis by referring to the user's past behavioral history. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past behavioral history into AI and have the AI ​​analyze the behavioral history.

[0038] During analysis, the analysis unit can customize the analysis method based on the user's current interests. For example, the analysis unit customizes the analysis method based on keywords recently searched by the user. The analysis unit can also customize the analysis method based on information about places the user has recently visited. Furthermore, the analysis unit can also customize the analysis method based on information about events the user has recently attended. This allows the analysis unit to customize the analysis method based on the user's current interests, enabling more appropriate analysis. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input data about the user's current interests into AI and have the AI ​​analyze the interests.

[0039] During analysis, the analysis unit can improve the accuracy of the analysis by taking into account the user's geographical location information. The analysis unit can improve the accuracy of the analysis, for example, based on data on tourist spots near the user's current location. The analysis unit can also improve the accuracy of the analysis based on data on restaurants near the user's current location. The analysis unit can also improve the accuracy of the analysis based on data on events near the user's current location. In this way, the analysis unit improves the accuracy of the analysis by taking into account the user's geographical location information. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's geographical location information into AI and have the AI ​​analyze the location information.

[0040] During analysis, the analysis unit can analyze the user's social media activity and optimize the analysis method. The analysis unit can optimize the analysis method based on, for example, data related to posts that the user has "liked" on social media. The analysis unit can also optimize the analysis method based on data related to accounts the user follows on social media. The analysis unit can also optimize the analysis method based on data related to posts the user has shared on social media. In this way, the analysis unit can optimize the analysis method by analyzing the user's social media activity. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's social media activity data into AI and have the AI ​​perform social media analysis.

[0041] When making a suggestion, the suggestion unit can adjust the level of detail of the suggestion based on the importance of the tourist spot or shop. For example, the suggestion unit provides detailed information for a tourist spot with a high importance. The suggestion unit can also provide concise information for a tourist spot with a low importance. Furthermore, the suggestion unit can provide detailed menus and reviews for a shop with a high importance. This allows the suggestion unit to adjust the level of detail of the suggestion based on the importance of the tourist spot or shop, thereby enabling more appropriate suggestions. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input importance data of the tourist spot or shop into AI and have the AI ​​analyze the importance.

[0042] When making a suggestion, the suggestion unit can apply different suggestion algorithms depending on the category of the tourist spot or shop. For example, for historical tourist spots, the suggestion unit can apply a suggestion algorithm that emphasizes historical background and highlights. For restaurants, the suggestion unit can also apply a suggestion algorithm that emphasizes the type of cuisine or reputation. For shopping spots, the suggestion unit can also apply a suggestion algorithm that emphasizes the type of product or price range. This allows the suggestion unit to apply different suggestion algorithms depending on the category of the tourist spot or shop, enabling more appropriate suggestions. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input category data of tourist spots and shops into AI and have the AI ​​analyze the categories.

[0043] When making a suggestion, the suggestion unit can determine the priority of the suggestions based on the ratings of tourist spots and shops. For example, the suggestion unit can prioritize suggesting highly rated tourist spots. The suggestion unit can also prioritize suggesting highly rated restaurants. Furthermore, the suggestion unit can prioritize suggesting highly rated shopping spots. This allows the suggestion unit to determine the priority of suggestions based on the ratings of tourist spots and shops, enabling more appropriate suggestions. Some or all of the above-mentioned processing by the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input rating data of tourist spots and shops into AI and have the AI ​​analyze the ratings.

[0044] When making suggestions, the suggestion unit can adjust the order of suggestions based on the relevance of tourist spots and shops. For example, the suggestion unit may first suggest tourist spots that are most relevant to the user's hobbies and preferences. The suggestion unit may also first suggest restaurants that are most relevant to the user's hobbies and preferences. Furthermore, the suggestion unit may also first suggest shopping spots that are most relevant to the user's hobbies and preferences. This allows the suggestion unit to adjust the order of suggestions based on the relevance of tourist spots and shops, thereby enabling more appropriate suggestions. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit may input relevance data of tourist spots and shops into AI and have the AI ​​perform a relevance analysis.

[0045] When providing a route, the route providing unit can provide an optimal route by referring to the user's past movement history. The route providing unit can provide an optimal route, for example, based on routes the user has used in the past. The route providing unit can also provide a route that avoids congestion based on the user's past movement history. Furthermore, the route providing unit can analyze the user's past movement history and provide the most efficient route. In this way, the route providing unit can provide an optimal route by referring to the user's past movement history. Some or all of the above-described processing in the route providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the route providing unit can input the user's past movement history into AI and have the AI ​​analyze the movement history.

[0046] When providing a route, the route providing unit can customize the route based on the user's current means of transportation. For example, if the user is traveling on foot, the route providing unit can provide a route within a walking distance. Furthermore, if the user is traveling by car, the route providing unit can also provide a route within a car's distance. Furthermore, if the user is using public transportation, the route providing unit can also provide an optimal transfer route. In this way, the route providing unit can provide a more appropriate route by customizing the route based on the user's current means of transportation. Some or all of the above-described processing in the route providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the route providing unit can input data on the user's current means of transportation into AI and have the AI ​​analyze the means of transportation.

[0047] When providing a route, the route providing unit can provide an optimal route by taking into account the user's geographical location information. For example, the route providing unit can provide a route that includes tourist spots near the user's current location. The route providing unit can also provide a route that includes restaurants near the user's current location. Furthermore, the route providing unit can also provide a route that includes an event venue near the user's current location. In this way, the route providing unit can provide an optimal route by taking into account the user's geographical location information. Some or all of the above-described processing in the route providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the route providing unit can input the user's geographical location information into AI and have the AI ​​analyze the location information.

[0048] When providing a route, the route providing unit can analyze the user's social media activity and suggest a related route. For example, the route providing unit can suggest a route that includes tourist spots related to posts that the user has "liked" on social media. The route providing unit can also suggest a route that includes restaurants related to accounts the user follows on social media. Furthermore, the route providing unit can suggest a route that includes event venues related to posts the user has shared on social media. In this way, the route providing unit can suggest a related route by analyzing the user's social media activity. Some or all of the above-described processing in the route providing unit may be performed using, or without, AI. For example, the route providing unit can input the user's social media activity data into AI and have the AI ​​perform social media analysis.

[0049] When collecting history, the history collection unit can improve the accuracy of collection by referring to the user's past behavioral history. The history collection unit can improve the accuracy of collection, for example, based on data on tourist spots visited by the user in the past. The history collection unit can also improve the accuracy of collection based on data on restaurants used by the user in the past. Furthermore, the history collection unit can improve the accuracy of collection based on data on events attended by the user in the past. In this way, the history collection unit improves the accuracy of collection by referring to the user's past behavioral history. Some or all of the above-described processing in the history collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the history collection unit can input the user's past behavioral history into AI and have the AI ​​analyze the behavioral history.

[0050] When collecting history, the history collection unit can prioritize collecting highly relevant history by taking into account the user's geographical location information. For example, the history collection unit prioritizes collecting history of tourist spots near the user's current location. The history collection unit can also prioritize collecting history of restaurants near the user's current location. Furthermore, the history collection unit can also prioritize collecting history of event venues near the user's current location. In this way, the history collection unit can prioritize collecting highly relevant history by taking into account the user's geographical location information. Some or all of the above-described processing in the history collection unit may be performed using AI, for example, or may be performed without using AI. For example, the history collection unit can input the user's geographical location information into AI and have the AI ​​analyze the location information.

[0051] When providing detailed information, the detail providing unit can provide optimal information by referring to the user's past behavioral history. For example, the detail providing unit can provide optimal information based on data on tourist spots visited by the user in the past. The detail providing unit can also provide optimal information based on data on restaurants used by the user in the past. Furthermore, the detail providing unit can provide optimal information based on data on events attended by the user in the past. In this way, the detail providing unit can provide optimal information by referring to the user's past behavioral history. Some or all of the above-described processing in the detail providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the detail providing unit can input the user's past behavioral history into AI and have the AI ​​analyze the behavioral history.

[0052] When providing detailed information, the detail providing unit can provide optimal information by taking into account the user's geographical location information. For example, the detail providing unit can prioritize providing information about tourist spots near the user's current location. The detail providing unit can also prioritize providing information about restaurants near the user's current location. Furthermore, the detail providing unit can prioritize providing information about event venues near the user's current location. In this way, the detail providing unit can provide optimal information by taking into account the user's geographical location information. Some or all of the above-described processing in the detail providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the detail providing unit can input the user's geographical location information into AI and have the AI ​​analyze the location information.

[0053] When collecting feedback, the feedback collection unit can improve the accuracy of the collection by referring to the user's past feedback history. The feedback collection unit improves the accuracy of the collection, for example, based on data of feedback provided by the user in the past. The feedback collection unit can also improve the accuracy of the collection based on data of tourist spots that the user has previously rated. The feedback collection unit can also improve the accuracy of the collection based on data of restaurants that the user has previously rated. In this way, the feedback collection unit improves the accuracy of the collection by referring to the user's past feedback history. Some or all of the above-mentioned processing in the feedback collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback collection unit can input the user's past feedback history into AI and have the AI ​​analyze the feedback history.

[0054] When collecting feedback, the feedback collection unit can prioritize collecting highly relevant feedback by taking into account the user's geographical location information. For example, the feedback collection unit prioritizes collecting feedback about tourist spots near the user's current location. The feedback collection unit can also prioritize collecting feedback about restaurants near the user's current location. Furthermore, the feedback collection unit can also prioritize collecting feedback about event venues near the user's current location. In this way, the feedback collection unit can prioritize collecting highly relevant feedback by taking into account the user's geographical location information. Some or all of the above-described processing in the feedback collection unit may be performed using AI, for example, or may be performed without using AI. For example, the feedback collection unit can input the user's geographical location information into AI and have the AI ​​analyze the location information.

[0055] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0056] The sightseeing recommendation system may include a health monitoring unit that monitors the user's health condition. The health monitoring unit collects data such as the user's heart rate, number of steps, and sleep time, and provides the data to the analysis unit. The analysis unit evaluates the user's health condition based on this data and can cause the recommendation unit to suggest tourist spots and shops according to the user's health condition. For example, if the user is tired, the system can suggest tourist spots and spas where the user can relax. Also, if the user is active, the system can suggest hiking trails and sports facilities. This makes it possible to provide the user with an optimal sightseeing experience according to their health condition.

[0057] The tourism suggestion system can include a multilingual support unit that provides suggested content in multiple languages ​​based on the user's language setting. The multilingual support unit acquires the user's language setting from the reception unit and instructs the suggestion unit and detail providing unit to provide information in multiple languages. For example, if the user selects English, information about suggested tourist spots and shops will be displayed in English. Also, if the user selects Chinese, information can be provided in Chinese. Furthermore, the multilingual support unit allows the user to select multiple languages ​​and switch languages ​​as needed. This makes it possible to provide appropriate information to users who speak different languages.

[0058] The sightseeing recommendation system can include a budget management unit that adjusts the content of the recommendations taking into account the user's travel budget. The budget management unit obtains the travel budget set by the user from the reception unit and provides it to the analysis unit. The analysis unit suggests tourist spots and shops based on the budget. For example, if the user is planning a trip on a low budget, the analysis unit can suggest free or low-cost tourist spots and shops. Also, if the user is planning a trip on a high budget, the analysis unit can suggest high-end restaurants and luxury hotels. This makes it possible to provide the user with the optimal sightseeing experience according to their budget.

[0059] The sightseeing suggestion system can include a schedule management unit that manages the user's travel itinerary. The schedule management unit obtains the travel itinerary entered by the user from the reception unit and provides it to the analysis unit. The analysis unit suggests tourist spots and shops based on the itinerary. For example, if the user is planning a short trip, it can suggest a route that allows for efficient sightseeing. Also, if the user is planning a long trip, it can suggest a route that allows for sightseeing at a leisurely pace. This makes it possible to provide the user with an optimal sightseeing experience based on their travel itinerary.

[0060] The tourism suggestion system can analyze a user's past travel history and make new suggestions based on past travel destinations. For example, it can suggest similar tourist spots based on data on tourist spots that the user has visited in the past. It can also suggest restaurants of the same genre based on data on restaurants the user has used in the past. It can also suggest related events based on data on events the user has attended in the past. This allows it to make optimal suggestions based on the user's past travel history.

[0061] The sightseeing recommendation system can include a weather information unit that adjusts the recommendations based on the user's current weather information. The weather information unit acquires weather information for the user's current location and provides it to the analysis unit. The analysis unit makes recommendations for tourist spots and shops based on the weather information. For example, if it is raining, indoor tourist spots and shops can be suggested. If it is sunny, outdoor tourist spots and activities can be suggested. Furthermore, if the temperature is high, cool places and waterside spots can be suggested. This makes it possible to provide the user with an optimal sightseeing experience based on the current weather information.

[0062] The tourism suggestion system analyzes a user's social media activity and can suggest tourist spots and shops visited by the user's friends and followers. For example, it can suggest the same spots based on data on tourist spots visited by the user's friends. It can also suggest the same restaurants based on data on restaurants rated by the user's followers. It can also suggest related events based on data on events attended by the user's friends. This allows it to make optimal suggestions based on the user's social media activity.

[0063] The processing flow of the first embodiment will be briefly explained below.

[0064] Step 1: The reception unit inputs the user's hobbies and preferences. The user's hobbies and preferences include, but are not limited to, sports, music, and food. For example, the reception unit allows the user to input information such as "I like historical places" or "I want to enjoy local cuisine." Step 2: The analysis unit analyzes the information input by the reception unit. The analysis is performed by, for example, but not limited to, methods such as data mining and statistical analysis. For example, the analysis unit searches a database of tourist spots and shops based on the user's hobbies and preferences, and makes optimal suggestions. Step 3: The suggestion unit suggests tourist spots and shops based on the results of the analysis by the analysis unit. The suggestions are made based on, for example, user ratings and past data, but are not limited to, these examples. For example, the suggestion unit suggests local historical tourist spots to a user who likes historical places, and suggests well-reviewed restaurants to a user who wants to enjoy local cuisine. Step 4: The route providing unit provides the route to the spots and shops proposed by the suggestion unit. The route provision is performed, for example, by a method such as the shortest route or a tourist route, but is not limited to these examples. For example, if the user is traveling on foot, the route providing unit suggests spots and shops within a walking distance, and if the user is traveling by car, suggests spots and shops within a driving distance.

[0065] (Example 2) A tourism recommendation system according to an embodiment of the present invention is an app targeted at inbound foreign tourists. It uses AI to recommend tourist spots and shops based on the user's hobbies and preferences. In this tourism recommendation system, users input their hobbies and preferences into the app, and AI analyzes the input information to recommend the most suitable tourist spots and shops for the user. The recommended spots and shops are also provided with optimal routes based on the user's current location and mode of transportation. This allows the user to enjoy sightseeing tailored to their interests. For example, a user inputs information into the app, such as "I like historical places" or "I want to enjoy local cuisine." This information is then input into AI. The AI ​​then analyzes the input information and recommends the most suitable tourist spots and shops for the user. The AI ​​searches a database of tourist spots and shops based on the user's hobbies and preferences to make optimal recommendations. For example, a user who likes historical places is suggested local historical tourist spots, and a user who wants to enjoy local cuisine is suggested well-reviewed restaurants. Furthermore, the recommended spots and shops are also provided with optimal routes based on the user's current location and mode of transportation. For example, if a user travels on foot, the system will suggest spots and shops within walking distance, and if the user travels by car, it will suggest spots and shops within driving distance. This allows users to enjoy sightseeing efficiently. This system allows inbound foreign tourists to easily find tourist spots and shops that suit their hobbies and preferences, improving their travel satisfaction. It also contributes to revitalizing the local tourism and restaurant industries. As a result, the tourism suggestion system can improve the user's sightseeing experience by suggesting tourist spots and shops based on the user's hobbies and preferences and providing the optimal route.

[0066] The tourism suggestion system according to the embodiment includes a reception unit, an analysis unit, a suggestion unit, and a route provision unit. The reception unit inputs a user's hobbies and preferences. The user's hobbies and preferences include, but are not limited to, sports, music, and food. The reception unit can input information such as, for example, "I like historical places" or "I want to enjoy local cuisine." The analysis unit analyzes the information input by the reception unit. The analysis can be performed using, for example, but not limited to, data mining or statistical analysis. The analysis unit searches a database of tourist spots and shops based on the user's hobbies and preferences, and makes optimal suggestions. The suggestion unit suggests tourist spots and shops based on the results of the analysis by the analysis unit. The suggestions can be made based on, for example, user ratings and past data, but are not limited to, the suggestion unit can suggest local historical tourist spots to a user who likes historical places, and suggest well-reviewed restaurants to a user who wants to enjoy local cuisine. The route provision unit provides a route to the spots and shops suggested by the suggestion unit. The route provision may be, for example, the shortest route, a tourist route, or the like, but is not limited to these examples. For example, if the user is traveling on foot, the route providing unit may suggest spots and shops within walking distance, and if the user is traveling by car, may suggest spots and shops within driving distance. As a result, the tourism suggestion system according to the embodiment can improve the user's tourism experience by suggesting tourist spots and shops based on the user's hobbies and preferences and providing the optimal route.

[0067] The sightseeing suggestion system includes a history collection unit that collects the user's past behavioral history. The history collection unit collects the user's past behavioral history. The behavioral history includes, but is not limited to, for example, a visit history and a purchase history. For example, the history collection unit collects data on tourist spots that the user has visited in the past. The history collection unit can also collect data on restaurants that the user has used in the past. Furthermore, the history collection unit can collect data on events that the user has participated in in the past. In this way, the history collection unit can collect the user's past behavioral history and use it for analysis, thereby enabling more accurate suggestions. Some or all of the above-described processing in the history collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the history collection unit can input the user's past behavioral history into AI and have the AI ​​analyze the behavioral history.

[0068] The tourism suggestion system includes a detail providing unit that provides detailed information about suggested spots and shops. The detail providing unit provides detailed information about the suggested spots and shops. The detailed information includes, but is not limited to, opening hours, prices, and reviews, for example. The detail providing unit provides, for example, the opening hours and prices of suggested tourist spots. The detail providing unit can also provide menus and reviews of suggested restaurants. Furthermore, the detail providing unit can also provide product information about suggested shopping spots. In this way, the detail providing unit can provide detailed information about suggested spots and shops, allowing the user to obtain more specific information. Some or all of the above-described processing in the detail providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the detail providing unit can input detailed information about suggested spots and shops into AI and have the AI ​​provide the detailed information.

[0069] The tourism suggestion system includes a feedback collection unit that collects user feedback. The feedback collection unit collects user feedback. The feedback includes, but is not limited to, questionnaires, reviews, and ratings, for example. The feedback collection unit, for example, collects reviews about tourist spots suggested by the user. The feedback collection unit can also collect ratings about restaurants suggested by the user. Furthermore, the feedback collection unit can also collect questionnaires about shopping spots suggested by the user. In this way, the feedback collection unit collects user feedback and reflects it in next suggestions, thereby improving the accuracy of suggestions. Some or all of the above-described processing in the feedback collection unit may be performed using, or without using, AI. For example, the feedback collection unit may input user feedback into AI and have the AI ​​analyze the feedback.

[0070] The reception unit can analyze the user's emotions and adjust the input method for hobbies and preferences based on the analyzed user emotions. For example, if the user is feeling stressed, the reception unit can provide a simple interface and minimize input steps. Furthermore, if the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input to enable quick input of hobbies and preferences. This allows the reception unit to improve the user's input experience by adjusting the input method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using AI, or without AI. For example, the reception unit can input the user's emotion data into the generation AI and have the generation AI perform emotion analysis.

[0071] The reception unit can analyze the user's past input history and provide an optimal input interface. For example, the reception unit can automatically display hobbies and preferences that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest hobbies and preferences to be used during a specific time period based on the user's past input history. In this way, the reception unit can provide an optimal input interface by analyzing the user's past input history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past input history into AI and have the AI ​​analyze the input history.

[0072] The reception unit can customize input items based on the user's current interests when inputting hobbies and preferences. For example, the reception unit can suggest input items for related hobbies and preferences based on keywords recently searched by the user. The reception unit can also customize input items for related hobbies and preferences based on information about places the user has recently visited. Furthermore, the reception unit can suggest input items for related hobbies and preferences based on information about events the user has recently attended. This allows the reception unit to customize input items based on the user's current interests, enabling more appropriate input. Some or all of the above-described processing by the reception unit may be performed using, or without, AI. For example, the reception unit can input the user's current interest data into AI and have the AI ​​analyze the interests.

[0073] The reception unit can estimate the user's emotions and prioritize input items based on the estimated user emotions. For example, if the user is excited, the reception unit can prioritize entertainment-related input items. Furthermore, if the user is relaxed, the reception unit can prioritize relaxation-related input items. Furthermore, if the user is tired, the reception unit can prioritize rest-related input items. This allows the reception unit to prioritize input items according to the user's emotions, thereby improving the user's input experience. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the reception unit may be performed using AI, or may be performed without AI. For example, the reception unit can input the user's emotion data into the generation AI and have the generation AI perform emotion analysis.

[0074] When inputting hobbies and preferences, the reception unit can prioritize displaying highly relevant input items in consideration of the user's geographical location information. For example, the reception unit can prioritize displaying input items related to tourist spots near the user's current location. The reception unit can also prioritize displaying input items related to restaurants near the user's current location. Furthermore, the reception unit can also prioritize displaying input items related to events near the user's current location. In this way, the reception unit can prioritize displaying highly relevant input items by considering the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's geographical location information to AI and have the AI ​​analyze the location information.

[0075] When inputting hobbies and preferences, the reception unit can analyze the user's social media activity and suggest related input items. For example, the reception unit can suggest input items for hobbies and preferences related to posts that the user has "liked" on social media. The reception unit can also suggest input items for hobbies and preferences related to accounts the user follows on social media. Furthermore, the reception unit can suggest input items for hobbies and preferences related to posts the user has shared on social media. In this way, the reception unit can suggest related input items by analyzing the user's social media activity. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media activity data into AI and have the AI ​​perform social media analysis.

[0076] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated user emotions. For example, if the user is relaxed, the analysis unit can perform a detailed analysis and generate more suggestions. If the user is in a hurry, the analysis unit can also perform a quick analysis and prioritize the most important suggestions. Furthermore, if the user is excited, the analysis unit can prioritize entertainment-related suggestions. This allows the analysis unit to adjust the analysis algorithm according to the user's emotions, enabling more appropriate analysis. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI perform emotion analysis.

[0077] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past behavioral history. The analysis unit can improve the accuracy of the analysis, for example, based on data on tourist spots visited by the user in the past. The analysis unit can also improve the accuracy of the analysis based on data on restaurants used by the user in the past. Furthermore, the analysis unit can improve the accuracy of the analysis based on data on events attended by the user in the past. In this way, the analysis unit improves the accuracy of the analysis by referring to the user's past behavioral history. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past behavioral history into AI and have the AI ​​analyze the behavioral history.

[0078] During analysis, the analysis unit can customize the analysis method based on the user's current interests. For example, the analysis unit customizes the analysis method based on keywords recently searched by the user. The analysis unit can also customize the analysis method based on information about places the user has recently visited. Furthermore, the analysis unit can also customize the analysis method based on information about events the user has recently attended. This allows the analysis unit to customize the analysis method based on the user's current interests, enabling more appropriate analysis. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input data about the user's current interests into AI and have the AI ​​analyze the interests.

[0079] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the analysis unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that focuses on the main points. This allows the analysis unit to adjust the display method of the analysis results according to the user's emotions, enabling more appropriate display. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI perform emotion analysis.

[0080] During analysis, the analysis unit can improve the accuracy of the analysis by taking into account the user's geographical location information. The analysis unit can improve the accuracy of the analysis, for example, based on data on tourist spots near the user's current location. The analysis unit can also improve the accuracy of the analysis based on data on restaurants near the user's current location. The analysis unit can also improve the accuracy of the analysis based on data on events near the user's current location. In this way, the analysis unit improves the accuracy of the analysis by taking into account the user's geographical location information. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's geographical location information into AI and have the AI ​​analyze the location information.

[0081] During analysis, the analysis unit can analyze the user's social media activity and optimize the analysis method. The analysis unit can optimize the analysis method based on, for example, data related to posts that the user has "liked" on social media. The analysis unit can also optimize the analysis method based on data related to accounts the user follows on social media. The analysis unit can also optimize the analysis method based on data related to posts the user has shared on social media. In this way, the analysis unit can optimize the analysis method by analyzing the user's social media activity. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's social media activity data into AI and have the AI ​​perform social media analysis.

[0082] The suggestion unit can estimate the user's emotions and adjust the way suggestions are expressed based on the estimated user emotions. For example, if the user is relaxed, the suggestion unit can provide detailed suggestions and multiple options. Furthermore, if the user is in a hurry, the suggestion unit can prioritize the most important suggestions and express them concisely. Furthermore, if the user is excited, the suggestion unit can emphasize entertainment-related suggestions. This allows the suggestion unit to adjust the way suggestions are expressed based on the user's emotions, enabling more appropriate suggestions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion unit can be performed using, for example, an AI. For example, the suggestion unit can input the user's emotion data into the generation AI and have the generation AI perform emotion analysis.

[0083] When making a suggestion, the suggestion unit can adjust the level of detail of the suggestion based on the importance of the tourist spot or shop. For example, the suggestion unit provides detailed information for a tourist spot with a high importance. The suggestion unit can also provide concise information for a tourist spot with a low importance. Furthermore, the suggestion unit can provide detailed menus and reviews for a shop with a high importance. This allows the suggestion unit to adjust the level of detail of the suggestion based on the importance of the tourist spot or shop, thereby enabling more appropriate suggestions. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input importance data of the tourist spot or shop into AI and have the AI ​​analyze the importance.

[0084] When making a suggestion, the suggestion unit can apply different suggestion algorithms depending on the category of the tourist spot or shop. For example, for historical tourist spots, the suggestion unit can apply a suggestion algorithm that emphasizes historical background and highlights. For restaurants, the suggestion unit can also apply a suggestion algorithm that emphasizes the type of cuisine or reputation. For shopping spots, the suggestion unit can also apply a suggestion algorithm that emphasizes the type of product or price range. This allows the suggestion unit to apply different suggestion algorithms depending on the category of the tourist spot or shop, enabling more appropriate suggestions. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input category data of tourist spots and shops into AI and have the AI ​​analyze the categories.

[0085] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated user emotions. For example, if the user is relaxed, the suggestion unit can provide detailed suggestions and multiple options. Furthermore, if the user is in a hurry, the suggestion unit can prioritize the most important suggestions and express them concisely. Furthermore, if the user is excited, the suggestion unit can emphasize entertainment-related suggestions. This allows the suggestion unit to adjust the length of the suggestions according to the user's emotions, thereby enabling more appropriate suggestions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion unit can be performed using, for example, an AI. For example, the suggestion unit can input the user's emotion data into the generation AI and have the generation AI perform emotion analysis.

[0086] When making a suggestion, the suggestion unit can determine the priority of the suggestions based on the ratings of tourist spots and shops. For example, the suggestion unit can prioritize suggesting highly rated tourist spots. The suggestion unit can also prioritize suggesting highly rated restaurants. Furthermore, the suggestion unit can prioritize suggesting highly rated shopping spots. This allows the suggestion unit to determine the priority of suggestions based on the ratings of tourist spots and shops, enabling more appropriate suggestions. Some or all of the above-mentioned processing by the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input rating data of tourist spots and shops into AI and have the AI ​​analyze the ratings.

[0087] When making suggestions, the suggestion unit can adjust the order of suggestions based on the relevance of tourist spots and shops. For example, the suggestion unit may first suggest tourist spots that are most relevant to the user's hobbies and preferences. The suggestion unit may also first suggest restaurants that are most relevant to the user's hobbies and preferences. Furthermore, the suggestion unit may also first suggest shopping spots that are most relevant to the user's hobbies and preferences. This allows the suggestion unit to adjust the order of suggestions based on the relevance of tourist spots and shops, thereby enabling more appropriate suggestions. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit may input relevance data of tourist spots and shops into AI and have the AI ​​perform a relevance analysis.

[0088] The route providing unit can estimate the user's emotions and adjust the route display method based on the estimated user emotions. For example, if the user is nervous, the route providing unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the route providing unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the route providing unit can provide a display method that focuses on the main points. This allows the route providing unit to adjust the route display method according to the user's emotions, enabling a more appropriate display. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the route providing unit may be performed using AI, or may be performed without AI. For example, the route providing unit can input the user's emotion data into the generation AI and have the generation AI perform emotion analysis.

[0089] When providing a route, the route providing unit can provide an optimal route by referring to the user's past movement history. The route providing unit can provide an optimal route, for example, based on routes the user has used in the past. The route providing unit can also provide a route that avoids congestion based on the user's past movement history. Furthermore, the route providing unit can analyze the user's past movement history and provide the most efficient route. In this way, the route providing unit can provide an optimal route by referring to the user's past movement history. Some or all of the above-described processing in the route providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the route providing unit can input the user's past movement history into AI and have the AI ​​analyze the movement history.

[0090] When providing a route, the route providing unit can customize the route based on the user's current means of transportation. For example, if the user is traveling on foot, the route providing unit can provide a route within a walking distance. Furthermore, if the user is traveling by car, the route providing unit can also provide a route within a car's distance. Furthermore, if the user is using public transportation, the route providing unit can also provide an optimal transfer route. In this way, the route providing unit can provide a more appropriate route by customizing the route based on the user's current means of transportation. Some or all of the above-described processing in the route providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the route providing unit can input data on the user's current means of transportation into AI and have the AI ​​analyze the means of transportation.

[0091] The route providing unit can estimate the user's emotions and prioritize routes based on the estimated user emotions. For example, if the user is relaxed, the route providing unit can prioritize scenic routes. Furthermore, if the user is in a hurry, the route providing unit can prioritize the shortest route. Furthermore, if the user is excited, the route providing unit can prioritize entertaining routes. This allows the route providing unit to prioritize routes based on the user's emotions, thereby providing a more appropriate route. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the route providing unit can be performed using, for example, an AI, or without an AI. For example, the route providing unit can input the user's emotion data into the generation AI and have the generation AI perform emotion analysis.

[0092] When providing a route, the route providing unit can provide an optimal route by taking into account the user's geographical location information. For example, the route providing unit can provide a route that includes tourist spots near the user's current location. The route providing unit can also provide a route that includes restaurants near the user's current location. Furthermore, the route providing unit can also provide a route that includes an event venue near the user's current location. In this way, the route providing unit can provide an optimal route by taking into account the user's geographical location information. Some or all of the above-described processing in the route providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the route providing unit can input the user's geographical location information into AI and have the AI ​​analyze the location information.

[0093] When providing a route, the route providing unit can analyze the user's social media activity and suggest a related route. For example, the route providing unit can suggest a route that includes tourist spots related to posts that the user has "liked" on social media. The route providing unit can also suggest a route that includes restaurants related to accounts the user follows on social media. Furthermore, the route providing unit can suggest a route that includes event venues related to posts the user has shared on social media. In this way, the route providing unit can suggest a related route by analyzing the user's social media activity. Some or all of the above-described processing in the route providing unit may be performed using, or without, AI. For example, the route providing unit can input the user's social media activity data into AI and have the AI ​​perform social media analysis.

[0094] The history collection unit can estimate the user's emotions and adjust the behavioral history collection method based on the estimated user emotions. For example, when the user is relaxed, the history collection unit collects detailed behavioral history. Furthermore, when the user is in a hurry, the history collection unit can collect only important behavioral history. Furthermore, when the user is excited, the history collection unit can prioritize collecting entertainment-related behavioral history. This allows the history collection unit to adjust the behavioral history collection method according to the user's emotions, thereby collecting more appropriate history. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the history collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the history collection unit may input the user's emotion data into the generation AI and have the generation AI perform emotion analysis.

[0095] When collecting history, the history collection unit can improve the accuracy of collection by referring to the user's past behavioral history. The history collection unit can improve the accuracy of collection, for example, based on data on tourist spots visited by the user in the past. The history collection unit can also improve the accuracy of collection based on data on restaurants used by the user in the past. Furthermore, the history collection unit can improve the accuracy of collection based on data on events attended by the user in the past. In this way, the history collection unit improves the accuracy of collection by referring to the user's past behavioral history. Some or all of the above-described processing in the history collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the history collection unit can input the user's past behavioral history into AI and have the AI ​​analyze the behavioral history.

[0096] The history collection unit can estimate the user's emotions and determine the priority of the history to be collected based on the estimated user emotions. For example, when the user is relaxed, the history collection unit prioritizes collecting detailed behavioral history. Furthermore, when the user is in a hurry, the history collection unit can prioritize collecting only important behavioral history. Furthermore, when the user is excited, the history collection unit can prioritize collecting entertainment-related behavioral history. This allows the history collection unit to prioritize the history to be collected according to the user's emotions, thereby collecting more appropriate history. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the history collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the history collection unit may input the user's emotion data into the generation AI and have the generation AI perform emotion analysis.

[0097] When collecting history, the history collection unit can prioritize collecting highly relevant history by taking into account the user's geographical location information. For example, the history collection unit prioritizes collecting history of tourist spots near the user's current location. The history collection unit can also prioritize collecting history of restaurants near the user's current location. Furthermore, the history collection unit can also prioritize collecting history of event venues near the user's current location. In this way, the history collection unit can prioritize collecting highly relevant history by taking into account the user's geographical location information. Some or all of the above-described processing in the history collection unit may be performed using AI, for example, or may be performed without using AI. For example, the history collection unit can input the user's geographical location information into AI and have the AI ​​analyze the location information.

[0098] The detail providing unit can estimate the user's emotions and adjust the display method of the detailed information based on the estimated user's emotions. For example, when the user is nervous, the detail providing unit can provide a simple, highly visible display method. Furthermore, when the user is relaxed, the detail providing unit can provide a display method including detailed information. Furthermore, when the user is in a hurry, the detail providing unit can provide a display method that focuses on the main points. This allows the detail providing unit to adjust the display method of the detailed information according to the user's emotions, enabling more appropriate display. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the detail providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the detail providing unit can input the user's emotion data into the generation AI and have the generation AI perform emotion analysis.

[0099] When providing detailed information, the detail providing unit can provide optimal information by referring to the user's past behavioral history. For example, the detail providing unit can provide optimal information based on data on tourist spots visited by the user in the past. The detail providing unit can also provide optimal information based on data on restaurants used by the user in the past. Furthermore, the detail providing unit can provide optimal information based on data on events attended by the user in the past. In this way, the detail providing unit can provide optimal information by referring to the user's past behavioral history. Some or all of the above-described processing in the detail providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the detail providing unit can input the user's past behavioral history into AI and have the AI ​​analyze the behavioral history.

[0100] The detail providing unit can estimate the user's emotions and prioritize detailed information based on the estimated user's emotions. For example, when the user is relaxed, the detail providing unit can prioritize providing detailed information. Furthermore, when the user is in a hurry, the detail providing unit can prioritize providing only important information. Furthermore, when the user is excited, the detail providing unit can prioritize providing entertainment-related information. This allows the detail providing unit to prioritize detailed information according to the user's emotions, thereby providing more appropriate information. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the detail providing unit can be performed using, for example, an AI, or without an AI. For example, the detail providing unit can input the user's emotion data into the generation AI and have the generation AI perform emotion analysis.

[0101] When providing detailed information, the detail providing unit can provide optimal information by taking into account the user's geographical location information. For example, the detail providing unit can prioritize providing information about tourist spots near the user's current location. The detail providing unit can also prioritize providing information about restaurants near the user's current location. Furthermore, the detail providing unit can prioritize providing information about event venues near the user's current location. In this way, the detail providing unit can provide optimal information by taking into account the user's geographical location information. Some or all of the above-described processing in the detail providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the detail providing unit can input the user's geographical location information into AI and have the AI ​​analyze the location information.

[0102] The feedback collection unit can estimate the user's emotions and adjust the feedback collection method based on the estimated user's emotions. For example, the feedback collection unit can collect detailed feedback when the user is relaxed. Furthermore, the feedback collection unit can also collect brief feedback when the user is in a hurry. Furthermore, the feedback collection unit can prioritize collecting entertainment-related feedback when the user is excited. This allows the feedback collection unit to collect more appropriate feedback by adjusting the feedback collection method according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the feedback collection unit can be performed using, for example, an AI, or without an AI. For example, the feedback collection unit can input the user's emotion data into the generation AI and have the generation AI perform emotion analysis.

[0103] When collecting feedback, the feedback collection unit can improve the accuracy of the collection by referring to the user's past feedback history. The feedback collection unit improves the accuracy of the collection, for example, based on data of feedback provided by the user in the past. The feedback collection unit can also improve the accuracy of the collection based on data of tourist spots that the user has previously rated. The feedback collection unit can also improve the accuracy of the collection based on data of restaurants that the user has previously rated. In this way, the feedback collection unit improves the accuracy of the collection by referring to the user's past feedback history. Some or all of the above-mentioned processing in the feedback collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback collection unit can input the user's past feedback history into AI and have the AI ​​analyze the feedback history.

[0104] The feedback collection unit can estimate the user's emotions and determine the priority of the feedback to be collected based on the estimated user's emotions. For example, when the user is relaxed, the feedback collection unit can prioritize collecting detailed feedback. Furthermore, when the user is in a hurry, the feedback collection unit can prioritize collecting only important feedback. Furthermore, when the user is excited, the feedback collection unit can prioritize collecting entertainment-related feedback. This allows the feedback collection unit to prioritize the feedback to be collected according to the user's emotions, thereby collecting more appropriate feedback. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the feedback collection unit can be performed using, for example, an AI, or without an AI. For example, the feedback collection unit can input the user's emotion data into the generation AI and have the generation AI perform emotion analysis.

[0105] When collecting feedback, the feedback collection unit can prioritize collecting highly relevant feedback by taking into account the user's geographical location information. For example, the feedback collection unit prioritizes collecting feedback about tourist spots near the user's current location. The feedback collection unit can also prioritize collecting feedback about restaurants near the user's current location. Furthermore, the feedback collection unit can also prioritize collecting feedback about event venues near the user's current location. In this way, the feedback collection unit can prioritize collecting highly relevant feedback by taking into account the user's geographical location information. Some or all of the above-described processing in the feedback collection unit may be performed using AI, for example, or may be performed without using AI. For example, the feedback collection unit can input the user's geographical location information into AI and have the AI ​​analyze the location information. === Hard Collateral 1-1 === Each of the multiple elements, including the reception unit, analysis unit, suggestion unit, route provision unit, history collection unit, detail provision unit, and feedback 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 inputs the user's hobbies and preferences. The analysis unit is realized by the identification processing unit 290 of the data processing device 12 and analyzes the input information. The suggestion unit is realized by the identification processing unit 290 of the data processing device 12 and suggests tourist spots and shops based on the analysis results. The route provision unit is realized by the identification processing unit 290 of the data processing device 12 and provides an optimal route to the suggested spots and shops. The history collection unit is realized by the identification processing unit 290 of the data processing device 12 and collects the user's past behavior history. The detail provision unit is realized by the output device 40 of the smart device 14 and provides detailed information about the suggested spots and shops. The feedback collection unit is realized by the reception device 38 of the smart device 14 and collects user feedback. === Hard Collateral 1-2 === Each of the multiple elements, including the reception unit, analysis unit, suggestion unit, route provision unit, history collection unit, detail provision unit, and feedback 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 inputs the user's hobbies and preferences. The analysis unit is realized by the identification processing unit 290 of the data processing device 12 and analyzes the input information. The suggestion unit is realized by the identification processing unit 290 of the data processing device 12 and suggests tourist spots and shops based on the analysis results. The route provision unit is realized by the identification processing unit 290 of the data processing device 12 and provides an optimal route to the suggested spots and shops. The history collection unit is realized by the identification processing unit 290 of the data processing device 12 and collects the user's past behavior history. The detail provision unit is realized by the speaker 240 of the smart glasses 214 and provides detailed information about the suggested spots and shops. The feedback collector is realized by the microphone 238 of the smart glasses 214 to collect user feedback. === Hard Collateral 1-3 === Each of the multiple elements, including the reception unit, analysis unit, suggestion unit, route provision unit, history collection unit, detail provision unit, and feedback collection unit, is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset terminal 314 and inputs the user's hobbies and preferences. The analysis unit is realized by the identification processing unit 290 of the data processing device 12 and analyzes the input information. The suggestion unit is realized by the identification processing unit 290 of the data processing device 12 and suggests tourist spots and shops based on the analysis results. The route provision unit is realized by the identification processing unit 290 of the data processing device 12 and provides an optimal route to the suggested spots and shops. The history collection unit is realized by the identification processing unit 290 of the data processing device 12 and collects the user's past behavior history. The detail provision unit is realized by the display 343 of the headset terminal 314 and provides detailed information about the suggested spots and shops. The feedback collection unit is realized by the microphone 238 of the headset type terminal 314 and collects the user's feedback. === Hard Collateral 1-4 === Each of the multiple elements, including the reception unit, analysis unit, suggestion unit, route provision unit, history collection unit, detail provision unit, and feedback 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 inputs the user's hobbies and preferences. The analysis unit is realized by the identification processing unit 290 of the data processing device 12 and analyzes the input information. The suggestion unit is realized by the identification processing unit 290 of the data processing device 12 and suggests tourist spots and shops based on the analysis results. The route provision unit is realized by the identification processing unit 290 of the data processing device 12 and provides an optimal route to the suggested spots and shops. The history collection unit is realized by the identification processing unit 290 of the data processing device 12 and collects the user's past behavior history. The detail provision unit is realized by the speaker 240 of the robot 414 and provides detailed information about the suggested spots and shops. The feedback collector is implemented by the microphone 238 of the robot 414 to collect user feedback.

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

[0107] The sightseeing recommendation system may include a health monitoring unit that monitors the user's health condition. The health monitoring unit collects data such as the user's heart rate, number of steps, and sleep time, and provides the data to the analysis unit. The analysis unit evaluates the user's health condition based on this data and can cause the recommendation unit to suggest tourist spots and shops according to the user's health condition. For example, if the user is tired, the system can suggest tourist spots and spas where the user can relax. Also, if the user is active, the system can suggest hiking trails and sports facilities. This makes it possible to provide the user with an optimal sightseeing experience according to their health condition.

[0108] The tourism suggestion system can include a multilingual support unit that provides suggested content in multiple languages ​​based on the user's language setting. The multilingual support unit acquires the user's language setting from the reception unit and instructs the suggestion unit and detail providing unit to provide information in multiple languages. For example, if the user selects English, information about suggested tourist spots and shops will be displayed in English. Also, if the user selects Chinese, information can be provided in Chinese. Furthermore, the multilingual support unit allows the user to select multiple languages ​​and switch languages ​​as needed. This makes it possible to provide appropriate information to users who speak different languages.

[0109] The sightseeing recommendation system can include a budget management unit that adjusts the content of the recommendations taking into account the user's travel budget. The budget management unit obtains the travel budget set by the user from the reception unit and provides it to the analysis unit. The analysis unit suggests tourist spots and shops based on the budget. For example, if the user is planning a trip on a low budget, the analysis unit can suggest free or low-cost tourist spots and shops. Also, if the user is planning a trip on a high budget, the analysis unit can suggest high-end restaurants and luxury hotels. This makes it possible to provide the user with the optimal sightseeing experience according to their budget.

[0110] The sightseeing suggestion system can include a schedule management unit that manages the user's travel itinerary. The schedule management unit obtains the travel itinerary entered by the user from the reception unit and provides it to the analysis unit. The analysis unit suggests tourist spots and shops based on the itinerary. For example, if the user is planning a short trip, it can suggest a route that allows for efficient sightseeing. Also, if the user is planning a long trip, it can suggest a route that allows for sightseeing at a leisurely pace. This makes it possible to provide the user with an optimal sightseeing experience based on their travel itinerary.

[0111] The sightseeing recommendation system can estimate the user's emotions and personalize the recommendations based on the estimated user emotions. For example, if the user is excited, it can suggest activities and events. If the user is relaxed, it can suggest quiet places and relaxation spots. Furthermore, if the user is stressed, it can suggest spots and activities that will help relieve stress. This makes it possible to provide the optimal sightseeing experience according to the user's emotions.

[0112] The tourism suggestion system can analyze a user's past travel history and make new suggestions based on past travel destinations. For example, it can suggest similar tourist spots based on data on tourist spots that the user has visited in the past. It can also suggest restaurants of the same genre based on data on restaurants the user has used in the past. It can also suggest related events based on data on events the user has attended in the past. This allows it to make optimal suggestions based on the user's past travel history.

[0113] The sightseeing recommendation system can include a weather information unit that adjusts the recommendations based on the user's current weather information. The weather information unit acquires weather information for the user's current location and provides it to the analysis unit. The analysis unit makes recommendations for tourist spots and shops based on the weather information. For example, if it is raining, indoor tourist spots and shops can be suggested. If it is sunny, outdoor tourist spots and activities can be suggested. Furthermore, if the temperature is high, cool places and waterside spots can be suggested. This makes it possible to provide the user with an optimal sightseeing experience based on the current weather information.

[0114] The sightseeing suggestion system can estimate the user's emotions and adjust the timing of suggestions based on the estimated user emotions. For example, if the user is relaxed, suggestions can be made more frequently. If the user is in a hurry, the frequency of suggestions can be reduced and only important suggestions can be made. Furthermore, if the user is excited, entertainment-related suggestions can be made in a timely manner. This makes it possible to provide optimal suggestion timing according to the user's emotions.

[0115] The tourism suggestion system analyzes a user's social media activity and can suggest tourist spots and shops visited by the user's friends and followers. For example, it can suggest the same spots based on data on tourist spots visited by the user's friends. It can also suggest the same restaurants based on data on restaurants rated by the user's followers. It can also suggest related events based on data on events attended by the user's friends. This allows it to make optimal suggestions based on the user's social media activity.

[0116] The sightseeing recommendation system can estimate the user's emotions and adjust the content of the recommendations based on the estimated user emotions. For example, if the user is relaxed, it can suggest relaxation spots and cafes. If the user is excited, it can also suggest activities and events. Furthermore, if the user is stressed, it can suggest spots and activities that will help relieve stress. This makes it possible to provide optimal recommendations according to the user's emotions.

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

[0118] Step 1: The reception unit inputs the user's hobbies and preferences. The user's hobbies and preferences include, but are not limited to, sports, music, and food. For example, the reception unit allows the user to input information such as "I like historical places" or "I want to enjoy local cuisine." Step 2: The analysis unit analyzes the information input by the reception unit. The analysis is performed by, for example, but not limited to, methods such as data mining and statistical analysis. For example, the analysis unit searches a database of tourist spots and shops based on the user's hobbies and preferences, and makes optimal suggestions. Step 3: The suggestion unit suggests tourist spots and shops based on the results of the analysis by the analysis unit. The suggestions are made based on, for example, user ratings and past data, but are not limited to, these examples. For example, the suggestion unit suggests local historical tourist spots to a user who likes historical places, and suggests well-reviewed restaurants to a user who wants to enjoy local cuisine. Step 4: The route providing unit provides the route to the spots and shops proposed by the suggestion unit. The route provision is performed, for example, by a method such as the shortest route or a tourist route, but is not limited to these examples. For example, if the user is traveling on foot, the route providing unit suggests spots and shops within a walking distance, and if the user is traveling by car, suggests spots and shops within a driving distance.

[0119] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0120] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0121] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

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

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

[0125] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0126] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0127] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0129] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0130] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0131] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0132] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0133] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0134] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0135] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0137] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

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

[0141] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0142] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0143] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0145] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0146] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0147] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0148] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0149] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0150] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0151] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0152] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0153] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

[0156] 7, 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.

[0157] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0158] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0159] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0160] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0161] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0162] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0163] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0164] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0165] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0166] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0167] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0168] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0169] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0170] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0172] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0173] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0174] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0175] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0176] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0177] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0178] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0179] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0180] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0181] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0182] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0183] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0184] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0185] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0186] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0187] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0188] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0189] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0190] [Explanation of symbols]

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

Claims

1. a reception unit for inputting the user's hobbies and preferences; an analysis unit that analyzes the information input by the reception unit; a suggestion unit that suggests tourist spots and shops based on the results of the analysis by the analysis unit; a route providing unit that provides a route to the spots and shops suggested by the suggestion unit; Equipped with A system characterized by:

2. Equipped with a history collection unit that collects the user's past behavior history 2. The system of claim 1.

3. Equipped with a details section that provides detailed information about suggested spots and shops 2. The system of claim 1.

4. A feedback collection unit is provided to collect user feedback.

2. The system of claim 1.

5. The reception unit Analyzes user emotions and adjusts the input method for hobbies and preferences based on the analyzed user emotions 2. The system of claim 1.

6. The reception unit Analyzes the user's input history and provides the optimal input interface 2. The system of claim 1.

7. The reception unit When entering hobbies and preferences, customize the input fields based on the user's current interests 2. The system of claim 1.

8. The reception unit Estimate the user's emotions and prioritize input items based on the estimated user emotions.

2. The system of claim 1.

9. The reception unit When entering hobbies and preferences, the app takes into account the user's geographic location and prioritizes the most relevant input items.

2. The system of claim 1.

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