System

Abstract

An object of a system according to an embodiment is to propose a sightseeing spot, a restaurant, or an accommodation facility on the basis of a user's interest.SOLUTION: A system includes a reception part, a guide part, a collection part, an analysis part, and a proposal part. The reception unit receives an input of a destination. The guidance unit calculates a route on the basis of the information received by the reception unit and performs road guidance. The collection unit collects a past action or a search history of a user. The analysis unit analyzes the data collected by the collection unit and specifies the interest of the user. The proposal unit proposes a sightseeing spot, a restaurant, or an accommodation facility on the basis of the interest specified by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately suggest tourist spots, restaurants, and accommodations based on the user's interests, and there is room for improvement in improving convenience.

[0005] The system according to the embodiment aims to suggest tourist spots, restaurants, and accommodations based on the user's interests. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a guidance unit, a collection unit, an analysis unit, and a suggestion unit. The reception unit accepts input of a destination. The guidance unit calculates a route based on the information accepted by the reception unit and provides route guidance. The collection unit collects the user's past behavior or search history. The analysis unit analyzes the data collected by the collection unit and identifies the user's interests. The suggestion unit suggests tourist spots, restaurants, or accommodations based on the interests identified by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can suggest tourist spots, restaurants, and accommodations based on the user's interests. [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 smartphone car navigation system according to an embodiment of the present invention not only provides route guidance (e.g., route, traffic congestion information, arrival time), but also analyzes the user's past behavior and search history to suggest optimal tourist spots, restaurants, and accommodations. When a user inputs a destination, the smartphone car navigation system calculates the optimal route and begins providing guidance. Information such as route information, traffic congestion information, and arrival time is provided. Next, AI analyzes the user's past behavior and search history to identify the user's interests. For example, based on information about tourist spots visited in the past and restaurants searched for, the system identifies spots that the user might be interested in. Then, based on the identified spots, the system suggests optimal tourist spots, restaurants, and accommodations. For example, if the user is interested in history, the system suggests historical tourist spots. Furthermore, tourist spots provide historical and cultural information using audio guidance, image guidance, and augmented reality (AR). For example, when a user arrives at a tourist spot, the smartphone car navigation system automatically starts audio guidance and provides information about the spot's history and culture. It can also provide visual information using image guidance and AR. This improves the convenience of users who enjoy sightseeing and driving. This allows the smartphone car navigation system to provide information on tourist spots, restaurants, and accommodations tailored to the user's interests, allowing for a more fulfilling travel experience.

[0029] A smartphone car navigation system according to an embodiment includes a reception unit, a guidance unit, a collection unit, an analysis unit, and a suggestion unit. The reception unit receives a user's input of a destination. For example, the user inputs the destination on a smartphone screen. The reception unit can also input the destination using voice input. For example, the user can simply input "from home to the station" by voice, and the starting point and destination are automatically set. The reception unit can also input the destination using gesture input. For example, the user can easily set the starting point and destination by performing a specific gesture on the smartphone screen. The guidance unit calculates an optimal route based on the information received by the reception unit and provides route guidance. For example, the guidance unit calculates the optimal route based on real-time traffic congestion information. The guidance unit can also calculate the optimal route taking into account real-time public transportation operation status. The guidance unit can also calculate a detour route based on real-time road construction information. The collection unit collects the user's past behavior or search history. For example, the collection unit collects the user's behavior history using GPS data from the smartphone. The collection unit can also collect a behavioral history using the user's search history. Furthermore, the collection unit can collect a behavioral history by analyzing the user's social media activities. The analysis unit analyzes the data collected by the collection unit to identify the user's interests. For example, the analysis unit can identify the user's interests based on information about tourist spots visited in the past or restaurants searched for. The analysis unit can also perform analysis that reflects the user's current interests in real time. Furthermore, the analysis unit can also perform analysis taking into account the user's attribute information (e.g., age, gender, occupation, etc.). The suggestion unit suggests optimal tourist spots, restaurants, and accommodations based on the interests identified by the analysis unit. For example, the suggestion unit obtains the latest information about tourist spots, restaurants, and accommodations from online databases or affiliated tourist information sites. The suggestion unit can also collect behavioral history and search history with the user's consent and establish an appropriate privacy policy. Furthermore, the suggestion unit adopts an intuitive and user-friendly design to make it easy for users to operate.As a result, the smartphone car navigation system according to the embodiment improves user convenience by suggesting optimal tourist spots, restaurants, and accommodations based on the user's past behavior and search history.

[0030] The suggestion unit can start audio guidance when the user arrives at a tourist spot and provide information about the history and culture of the spot. The suggestion unit can, for example, automatically start audio guidance when the user arrives at a tourist spot. For example, the suggestion unit can provide audio information about the historical background and cultural significance of the tourist spot. The suggestion unit can also provide audio detailed information about the tourist spot. For example, the suggestion unit can provide audio guidance about the history of the tourist spot's buildings and legends associated with the location. Furthermore, the suggestion unit can also provide audio guidance about the tourist spot's highlights and recommended routes. This improves the user's sightseeing experience by providing information about the history and culture through audio guidance when the user arrives at a tourist spot.

[0031] The suggestion unit can provide information visually by utilizing image guidance or AR. The suggestion unit, for example, provides information about tourist spots using image guidance. For example, the suggestion unit displays photos and illustrations of the tourist spots to provide visual guidance about the features and highlights of the location. The suggestion unit can also provide information about tourist spots using AR (augmented reality). For example, the suggestion unit can display images of tourist spots using a smartphone camera and overlay reproductions and explanations of historical buildings on top of the images. Furthermore, the suggestion unit can display information about the areas surrounding tourist spots and recommended routes using AR. In this way, by utilizing image guidance and AR, information can be provided visually, improving the user's sightseeing experience.

[0032] The collection unit can collect a user's behavioral history by using the smartphone's GPS data or search history. The collection unit collects a user's behavioral history by, for example, using the smartphone's GPS data. For example, the collection unit obtains the places the user has visited and the routes the user has taken from the GPS data. The collection unit can also collect a behavioral history by using the user's search history. For example, the collection unit collects information on tourist spots and restaurants that the user has previously searched for. Furthermore, the collection unit can collect a behavioral history by analyzing the user's social media activity. For example, the collection unit collects a behavioral history based on the places the user has checked in to and the content of their posts on social media. In this way, the user's behavioral history can be accurately collected by using the smartphone's GPS data and search history.

[0033] The analysis unit can identify the user's interests based on information about tourist spots visited in the past and restaurants searched for. The analysis unit can identify the user's interests based on information about tourist spots visited in the past. For example, the analysis unit can analyze data about tourist spots visited in the past by the user to identify spots that the user is likely to be interested in. The analysis unit can also identify the user's interests based on information about restaurants searched for in the past by the user. For example, the analysis unit can analyze data about restaurants searched for in the past by the user to identify restaurants that the user is likely to be interested in. Furthermore, the analysis unit can perform analysis that reflects the user's current interests in real time. For example, the analysis unit can identify the user's interests based on data about tourist spots or restaurants that the user is currently searching for. This makes it possible to identify the user's interests based on past behavioral data and make more appropriate suggestions.

[0034] The suggestion unit can acquire the latest information on tourist spots, restaurants, and accommodations from an online database or an affiliated tourist information site. The suggestion unit, for example, acquires the latest information on tourist spots, restaurants, and accommodations from an online database. For example, the suggestion unit collects the latest information from tourist information sites and travel sites and provides it to the user. The suggestion unit can also acquire the latest information from affiliated tourist information sites. For example, the suggestion unit acquires the latest information on tourist spots, restaurants, and accommodations from affiliated tourist information sites and provides it to the user. Furthermore, the suggestion unit can make optimal suggestions to the user based on the information acquired from the online database or the tourist information site. In this way, by acquiring the latest information, the latest tourist spots, restaurants, and accommodations can be suggested to the user.

[0035] The suggestion unit can collect behavioral history and search history with the user's consent and establish an appropriate privacy policy. The suggestion unit, for example, collects behavioral history and search history with the user's consent. For example, the suggestion unit collects behavioral history and search history only if the user consents. The suggestion unit can also establish an appropriate privacy policy. For example, the suggestion unit can establish a privacy policy that properly manages user data and clearly states that the data will not be provided to third parties. Furthermore, the suggestion unit can anonymize user data so that individuals cannot be identified. This makes it possible to collect behavioral history and search history while protecting the user's privacy.

[0036] The suggestion unit can adopt an intuitive and easy-to-use design to enable the user to operate it easily. The suggestion unit, for example, adopts an intuitive and easy-to-use design. For example, the suggestion unit designs the user interface to be simple and easy to understand. The suggestion unit can also minimize operation steps to enable the user to operate it easily. For example, the suggestion unit provides a simple interface when the user inputs a destination, minimizing the input steps. Furthermore, the suggestion unit can provide various input methods, such as voice input and gesture input. As a result, by adopting an intuitive and easy-to-use design, the user can operate it easily.

[0037] When a destination is input, the reception unit can present input candidates by referring to the user's past input history. For example, the reception unit automatically displays destinations that the user has frequently input in the past as candidates. For example, the reception unit preferentially suggests input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest destinations to be used during a specific time period based on the user's past input history. For example, the reception unit analyzes the user's past input history and presents the most relevant input candidates. In this way, appropriate input candidates can be presented to the user by referring to the past input history.

[0038] The reception unit can present optimal input candidates taking into account the user's current location information when the user inputs a destination. For example, when the user opens the app, the reception unit automatically acquires the user's current location and sets it as the starting point. For example, when the user inputs a destination, the reception unit proposes optimal candidate locations taking into account the distance from the user's current location. Furthermore, when the user uses the app while on the move, the reception unit can also update the user's current location in real time and reflect it as the starting point. For example, the reception unit presents optimal input candidates based on the user's current location information. In this way, optimal input candidates can be presented to the user by taking into account the current location information.

[0039] The reception unit can provide multiple input methods, such as voice input or gesture input, when inputting a destination. For example, the reception unit can automatically set the starting point and destination when the user simply voice-inputs "from home to the station." For example, the reception unit can easily set the starting point and destination by the user performing a specific gesture on the smartphone screen. The reception unit can also combine voice input and gesture input to set the starting point and destination more intuitively. For example, the reception unit can allow the user to input the destination using voice input and set the starting point using gesture input. This allows the user to input a destination more intuitively by providing a variety of input methods.

[0040] When a destination is input, the reception unit can suggest related destinations by referring to the user's past travel history. The reception unit can suggest related destinations, for example, based on tourist spots that the user has visited in the past. For example, the reception unit can suggest related destinations based on information about restaurants that the user has searched for in the past. The reception unit can also suggest destinations related to specific seasons or events from the user's past travel history. For example, the reception unit can analyze the user's past travel history and suggest the most relevant destination. This makes it possible to suggest destinations related to the user by referring to the past travel history.

[0041] When a destination is input, the reception unit can analyze the user's social media activity and suggest related destinations. For example, the reception unit can suggest related destinations based on places where the user has checked in on social media. For example, the reception unit can analyze the content of the user's social media posts and suggest related destinations. The reception unit can also suggest related destinations by referring to the activities of the user's friends on social media. For example, the reception unit can analyze the user's social media activity and suggest the most relevant destination. In this way, it is possible to suggest destinations related to the user by analyzing social media activity.

[0042] The reception unit can customize the input method by reflecting user feedback when inputting a destination. The reception unit, for example, suggests the optimal input method based on feedback previously provided by the user. For example, the reception unit adjusts the design of the input interface by reflecting user feedback. The reception unit can also improve the accuracy of voice input and gesture input based on user feedback. For example, the reception unit analyzes user feedback and provides the most appropriate input method. In this way, a more appropriate input method can be provided by reflecting user feedback.

[0043] The guidance unit can recalculate the optimal route by reflecting the congestion status of the route in real time when providing guidance. The guidance unit recalculates the optimal route based on, for example, real-time traffic congestion information. For example, the guidance unit recalculates the optimal route by taking into account the real-time operation status of public transportation. The guidance unit can also recalculate a detour route based on real-time road construction information. For example, the guidance unit provides the optimal route by reflecting the congestion status in real time. In this way, the optimal route can be provided by reflecting the congestion status in real time.

[0044] The guidance unit can analyze the user's driving style and provide the optimal guidance method when providing guidance. For example, if the user prioritizes safe driving, the guidance unit can provide guidance that proceeds at a slow pace. For example, if the user prioritizes speed, the guidance unit can provide guidance that emphasizes the shortest route. The guidance unit can also analyze the user's driving style and suggest the optimal guidance method. For example, the guidance unit can provide the most appropriate guidance method based on the user's driving style. In this way, a more appropriate guidance method can be provided by analyzing the user's driving style.

[0045] When providing guidance, the guidance unit can improve the accuracy of the guidance by referring to the user's past route history. The guidance unit, for example, proposes an optimal guidance method based on routes the user has used in the past. For example, the guidance unit proposes a guidance method that avoids congestion based on the user's past route history. The guidance unit can also analyze the user's past route history and propose the most efficient guidance method. For example, the guidance unit provides the most appropriate guidance method based on the user's past route history. In this way, the accuracy of the guidance is improved by referring to the past route history.

[0046] When providing guidance, the guidance unit can propose an optimal route by taking into account the user's current traffic conditions. The guidance unit, for example, proposes an optimal route based on real-time traffic congestion information. For example, the guidance unit proposes an optimal route by taking into account the real-time operation status of public transportation. The guidance unit can also propose a detour route based on real-time road construction information. For example, the guidance unit provides an optimal route based on the user's current traffic conditions. In this way, the optimal route can be proposed to the user by taking into account the current traffic conditions.

[0047] When providing guidance, the guidance unit can provide the optimal guidance method by referring to the user's vehicle information. The guidance unit, for example, proposes the optimal route based on fuel efficiency information of the user's vehicle. For example, the guidance unit proposes a route that avoids narrow roads based on size information of the user's vehicle. The guidance unit can also propose the optimal route based on maintenance information of the user's vehicle. For example, the guidance unit provides the most appropriate guidance method based on the user's vehicle information. In this way, by referring to the vehicle information, the optimal guidance method can be provided to the user.

[0048] The guidance unit can customize the guidance method by reflecting user feedback when providing guidance. For example, the guidance unit suggests the optimal guidance method based on feedback previously provided by the user. For example, the guidance unit can adjust the design of the guidance interface by reflecting user feedback. The guidance unit can also improve the accuracy of audio guidance and visual guidance based on user feedback. For example, the guidance unit can analyze user feedback and provide the most appropriate guidance method. In this way, the optimal guidance method can be provided to the user by reflecting the feedback.

[0049] When collecting behavioral history, the collection unit can analyze the user's past behavioral patterns and select the optimal collection method. The collection unit selects the optimal collection method, for example, based on places the user has frequently visited in the past. For example, the collection unit collects behavioral history during a specific time period based on the user's past behavioral patterns. The collection unit can also analyze the user's past behavioral patterns and select the most efficient collection method. For example, the collection unit provides the most appropriate collection method based on the user's past behavioral patterns. This makes it possible to select the optimal collection method by analyzing past behavioral patterns.

[0050] When collecting behavioral history, the collection unit can adjust the collection method taking into account the user's current activity status. For example, when the user is on the move, the collection unit collects behavioral history in real time. For example, when the user is taking a break, the collection unit periodically collects behavioral history. Furthermore, when the user is participating in a specific event, the collection unit can also collect behavioral history related to the event. For example, the collection unit provides the most appropriate collection method based on the user's current activity status. This allows a more appropriate behavioral history to be collected by taking into account the current activity status.

[0051] When collecting a behavioral history, the collection unit can select the optimal collection means depending on the user's input method (voice, text, image, etc.). For example, if the user uses voice input, the collection unit collects voice data and saves it as a behavioral history. For example, if the user uses text input, the collection unit collects text data and saves it as a behavioral history. Furthermore, if the user uses image input, the collection unit can also collect image data and save it as a behavioral history. For example, the collection unit provides the most appropriate collection means depending on the user's input method. This allows for more appropriate behavioral history to be collected by selecting the optimal collection means depending on the input method.

[0052] When collecting behavioral history, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, when the user is at a specific tourist spot, the collection unit prioritizes collecting behavioral history related to that location. For example, when the user is at a specific restaurant, the collection unit prioritizes collecting behavioral history related to that location. Furthermore, when the user is at a specific accommodation facility, the collection unit can also prioritize collecting behavioral history related to that location. For example, the collection unit provides the most relevant data based on the user's geographical location information. This allows highly relevant data to be collected preferentially by taking into account the geographical location information.

[0053] When collecting the behavioral history, the collection unit can analyze the user's social media activities to collect related data. For example, the collection unit collects behavioral history related to places where the user checked in on social media. For example, the collection unit analyzes the content of the user's posts on social media and collects related behavioral history. The collection unit can also collect related behavioral history by referring to the activities of the user's friends on social media. For example, the collection unit analyzes the user's social media activities and provides the most relevant data. This makes it possible to collect related data by analyzing social media activities.

[0054] When collecting behavioral history, the collection unit can customize the collection method by reflecting the user's past feedback. For example, the collection unit suggests the optimal collection method based on feedback provided by the user in the past. For example, the collection unit adjusts the design of the collection interface by reflecting the user's feedback. The collection unit can also improve the accuracy of voice input and text input based on the user's feedback. For example, the collection unit analyzes the user's feedback and provides the most appropriate collection method. In this way, a more appropriate collection method can be provided by reflecting the past feedback.

[0055] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past behavioral data. The analysis unit improves the accuracy of the analysis, for example, based on data on tourist spots that the user has visited in the past. For example, the analysis unit improves the accuracy of the analysis based on data on restaurants that the user has searched for in the past. The analysis unit can also analyze the user's past behavioral data and provide the most relevant information. For example, the analysis unit provides the most appropriate analysis results based on the user's past behavioral data. In this way, the accuracy of the analysis is improved by referring to the past behavioral data.

[0056] The analysis unit can perform analysis by reflecting the user's current interests in real time during analysis. For example, the analysis unit performs analysis based on data on tourist spots in which the user is currently interested. For example, the analysis unit performs analysis based on data on restaurants that the user is currently searching for. The analysis unit can also reflect the user's current interests in real time and provide the most relevant information. For example, the analysis unit provides the most appropriate analysis results based on the user's current interests. This allows the analysis unit to provide more appropriate analysis results by reflecting the user's current interests.

[0057] The analysis unit can perform the analysis while taking into consideration the user's attribute information (age, gender, occupation, etc.). For example, the analysis unit analyzes the most suitable tourist spot based on the user's age information. For example, the analysis unit analyzes the most suitable restaurant based on the user's gender information. The analysis unit can also analyze the most suitable accommodation based on the user's occupation information. For example, the analysis unit provides the most appropriate analysis result based on the user's attribute information. In this way, by taking the attribute information into consideration, it is possible to provide a more appropriate analysis result.

[0058] The analysis unit can perform analysis taking into account the user's geographical location information. For example, if the user is at a specific tourist spot, the analysis unit performs analysis based on data related to that location. For example, if the user is at a specific restaurant, the analysis unit performs analysis based on data related to that location. Furthermore, if the user is at a specific accommodation facility, the analysis unit can also perform analysis based on data related to that location. For example, the analysis unit provides the most appropriate analysis result based on the user's geographical location information. In this way, by taking geographical location information into consideration, more appropriate analysis results can be provided.

[0059] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's social media activity. The analysis unit improves the accuracy of the analysis, for example, based on data on locations where the user checked in on social media. For example, the analysis unit analyzes the content of the user's social media posts and performs the analysis based on related data. The analysis unit can also perform the analysis based on related data by referring to the activities of the user's friends on social media. For example, the analysis unit provides the most appropriate analysis results based on the user's social media activity. In this way, the accuracy of the analysis is improved by referring to social media activity.

[0060] The analysis unit can customize the analysis method by reflecting the user's past feedback during analysis. For example, the analysis unit proposes the optimal analysis method based on feedback provided by the user in the past. For example, the analysis unit adjusts the design of the analysis interface by reflecting the user's feedback. The analysis unit can also improve the accuracy of the analysis based on the user's feedback. For example, the analysis unit analyzes the user's feedback and provides the most appropriate analysis method. In this way, a more appropriate analysis method can be provided by reflecting the past feedback.

[0061] When making a suggestion, the suggestion unit can improve the accuracy of the suggestion by referring to the user's past behavioral data. The suggestion unit improves the accuracy of the suggestion, for example, based on data on tourist spots that the user has visited in the past. For example, the suggestion unit improves the accuracy of the suggestion based on data on restaurants that the user has searched for in the past. The suggestion unit can also analyze the user's past behavioral data and provide the most relevant information. For example, the suggestion unit provides the most appropriate suggestion based on the user's past behavioral data. In this way, the accuracy of the suggestion is improved by referring to the past behavioral data.

[0062] When making a suggestion, the suggestion unit can reflect the user's current interests in real time. For example, the suggestion unit makes a suggestion based on data on tourist spots in which the user is currently interested. For example, the suggestion unit makes a suggestion based on data on restaurants that the user is currently searching for. The suggestion unit can also reflect the user's current interests in real time and provide the most relevant information. For example, the suggestion unit provides the most appropriate suggestion based on the user's current interests. This allows more appropriate suggestions to be provided by reflecting the user's current interests.

[0063] When making a suggestion, the suggestion unit can take into consideration the user's attribute information (age, gender, occupation, etc.). For example, the suggestion unit can suggest the most suitable tourist spot based on the user's age information. For example, the suggestion unit can suggest the most suitable restaurant based on the user's gender information. The suggestion unit can also suggest the most suitable accommodation based on the user's occupation information. For example, the suggestion unit can provide the most appropriate suggestion based on the user's attribute information. In this way, by taking the attribute information into consideration, more appropriate suggestions can be provided.

[0064] When making a suggestion, the suggestion unit can make the most appropriate suggestion by taking into account the user's geographical location information. For example, if the user is at a specific tourist spot, the suggestion unit makes a suggestion related to that location. For example, if the user is at a specific restaurant, the suggestion unit makes a suggestion related to that location. Furthermore, if the user is at a specific accommodation facility, the suggestion unit can also make a suggestion related to that location. For example, the suggestion unit provides the most appropriate suggestion based on the user's geographical location information. In this way, the most appropriate suggestion can be provided to the user by taking into account the geographical location information.

[0065] When making a suggestion, the suggestion unit can make a relevant suggestion by referring to the user's social media activity. For example, the suggestion unit makes a suggestion related to places where the user has checked in on social media. For example, the suggestion unit analyzes the content of the user's social media posts and suggests related tourist spots and restaurants. The suggestion unit can also suggest related places and events by referring to the activity of the user's friends on social media. For example, the suggestion unit provides the most appropriate suggestion based on the user's social media activity. In this way, relevant suggestions can be provided to the user by referring to the social media activity.

[0066] The suggestion unit can customize the suggestion method by reflecting the user's past feedback when making a suggestion. The suggestion unit proposes the optimal suggestion method, for example, based on feedback provided by the user in the past. For example, the suggestion unit adjusts the design of the suggestion interface by reflecting the user's feedback. The suggestion unit can also improve the accuracy of the suggestion based on the user's feedback. For example, the suggestion unit analyzes the user's feedback and provides the most appropriate suggestion method. In this way, a more appropriate suggestion method can be provided by reflecting the past feedback.

[0067] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0068] The suggestion unit can also provide real-time information on nearby tourist spots, restaurants, and accommodations based on the user's current location information. For example, if the user is at a specific tourist spot, the suggestion unit can suggest recommended spots in the area. If the user is looking for a restaurant, the suggestion unit can suggest restaurants close to the user's current location. Furthermore, if the user is looking for accommodation, the suggestion unit can suggest accommodations that are easily accessible from the user's current location. This makes it possible to provide more convenient information by making suggestions that take the user's current location information into consideration.

[0069] The collection unit can also analyze the user's social media activities and collect related behavioral history. For example, the collection unit can collect behavioral history related to the places where the user checked in on social media. The collection unit can also analyze the content of the user's posts on social media and collect related behavioral history. Furthermore, the collection unit can also collect related behavioral history by referring to the activities of the user's friends on social media. In this way, by analyzing social media activities, more relevant behavioral history can be collected.

[0070] The analysis unit can also identify a user's interests based on the user's past behavioral data. For example, it can analyze data on tourist spots the user has visited in the past to identify spots that the user might be interested in. It can also identify interests based on data on restaurants the user has searched for in the past. It can also perform analysis that reflects the user's current interests in real time. This allows for more appropriate suggestions to be made by identifying the user's interests based on past behavioral data.

[0071] The suggestion unit can also make suggestions by taking into consideration the user's attribute information (age, gender, occupation, etc.). For example, the most suitable tourist spot can be suggested based on the user's age information. The most suitable restaurant can also be suggested based on the user's gender information. Furthermore, the most suitable accommodation can also be suggested based on the user's occupation information. In this way, by taking attribute information into consideration, more appropriate suggestions can be provided.

[0072] The guidance unit can also analyze the user's driving style and provide the optimal guidance method. For example, if the user prioritizes safe driving, guidance that proceeds at a slow pace can be provided. Alternatively, if the user prioritizes speed, guidance that emphasizes the shortest route can be provided. Furthermore, the guidance unit can analyze the user's driving style and suggest the optimal guidance method. In this way, by analyzing the user's driving style, a more appropriate guidance method can be provided.

[0073] The processing flow of the first embodiment will be briefly explained below.

[0074] Step 1: The reception unit accepts the user's input of a destination. For example, the user inputs the destination on the screen of a smartphone. The reception unit can also input the destination using voice input. For example, the user can simply input "from home to the station" by voice, and the starting point and destination are automatically set. The reception unit can also input the destination using gesture input. For example, the user can easily set the starting point and destination by making a specific gesture on the screen of a smartphone. Step 2: The guidance unit calculates the optimal route based on the information received by the reception unit and provides route guidance. For example, the guidance unit calculates the optimal route based on real-time traffic congestion information. The guidance unit can also calculate the optimal route taking into account the real-time operation status of public transportation. Furthermore, the guidance unit can also calculate a detour route based on real-time road construction information. Step 3: The collection unit collects the user's past behavior or search history. For example, the collection unit collects the user's behavior history using GPS data from the smartphone. The collection unit can also collect the behavior history using the user's search history. Furthermore, the collection unit can collect the behavior history by analyzing the user's social media activity. Step 4: The analysis unit analyzes the data collected by the collection unit and identifies the user's interests. For example, the analysis unit identifies the user's interests based on information about tourist spots visited in the past or restaurants searched for. The analysis unit can also perform analysis that reflects the user's current interests in real time. Furthermore, the analysis unit can also perform analysis taking into account the user's attribute information (age, gender, occupation, etc.). Step 5: The suggestion unit suggests optimal tourist spots, restaurants, and accommodations based on the interests identified by the analysis unit. For example, the suggestion unit obtains the latest information on tourist spots, restaurants, and accommodations from online databases and affiliated tourist information sites. The suggestion unit may also collect user behavior and search history with the user's consent and establish an appropriate privacy policy. Furthermore, the suggestion unit employs an intuitive and user-friendly design to make it easy for users to operate.

[0075] (Example 2) A smartphone car navigation system according to an embodiment of the present invention not only provides route guidance (e.g., route, traffic congestion information, arrival time), but also analyzes the user's past behavior and search history to suggest optimal tourist spots, restaurants, and accommodations. When a user inputs a destination, the smartphone car navigation system calculates the optimal route and begins providing guidance. Information such as route information, traffic congestion information, and arrival time is provided. Next, AI analyzes the user's past behavior and search history to identify the user's interests. For example, based on information about tourist spots visited in the past and restaurants searched for, the system identifies spots that the user might be interested in. Then, based on the identified spots, the system suggests optimal tourist spots, restaurants, and accommodations. For example, if the user is interested in history, the system suggests historical tourist spots. Furthermore, tourist spots provide historical and cultural information using audio guidance, image guidance, and augmented reality (AR). For example, when a user arrives at a tourist spot, the smartphone car navigation system automatically starts audio guidance and provides information about the spot's history and culture. It can also provide visual information using image guidance and AR. This improves the convenience of users who enjoy sightseeing and driving. This allows the smartphone car navigation system to provide information on tourist spots, restaurants, and accommodations tailored to the user's interests, allowing for a more fulfilling travel experience.

[0076] A smartphone car navigation system according to an embodiment includes a reception unit, a guidance unit, a collection unit, an analysis unit, and a suggestion unit. The reception unit receives a user's input of a destination. For example, the user inputs the destination on a smartphone screen. The reception unit can also input the destination using voice input. For example, the user can simply input "from home to the station" by voice, and the starting point and destination are automatically set. The reception unit can also input the destination using gesture input. For example, the user can easily set the starting point and destination by performing a specific gesture on the smartphone screen. The guidance unit calculates an optimal route based on the information received by the reception unit and provides route guidance. For example, the guidance unit calculates the optimal route based on real-time traffic congestion information. The guidance unit can also calculate the optimal route taking into account real-time public transportation operation status. The guidance unit can also calculate a detour route based on real-time road construction information. The collection unit collects the user's past behavior or search history. For example, the collection unit collects the user's behavior history using GPS data from the smartphone. The collection unit can also collect a behavioral history using the user's search history. Furthermore, the collection unit can collect a behavioral history by analyzing the user's social media activities. The analysis unit analyzes the data collected by the collection unit to identify the user's interests. For example, the analysis unit can identify the user's interests based on information about tourist spots visited in the past or restaurants searched for. The analysis unit can also perform analysis that reflects the user's current interests in real time. Furthermore, the analysis unit can also perform analysis taking into account the user's attribute information (e.g., age, gender, occupation, etc.). The suggestion unit suggests optimal tourist spots, restaurants, and accommodations based on the interests identified by the analysis unit. For example, the suggestion unit obtains the latest information about tourist spots, restaurants, and accommodations from online databases or affiliated tourist information sites. The suggestion unit can also collect behavioral history and search history with the user's consent and establish an appropriate privacy policy. Furthermore, the suggestion unit adopts an intuitive and user-friendly design to make it easy for users to operate.As a result, the smartphone car navigation system according to the embodiment improves user convenience by suggesting optimal tourist spots, restaurants, and accommodations based on the user's past behavior and search history.

[0077] The suggestion unit can start audio guidance when the user arrives at a tourist spot and provide information about the history and culture of the spot. The suggestion unit can, for example, automatically start audio guidance when the user arrives at a tourist spot. For example, the suggestion unit can provide audio information about the historical background and cultural significance of the tourist spot. The suggestion unit can also provide audio detailed information about the tourist spot. For example, the suggestion unit can provide audio guidance about the history of the tourist spot's buildings and legends associated with the location. Furthermore, the suggestion unit can also provide audio guidance about the tourist spot's highlights and recommended routes. This improves the user's sightseeing experience by providing information about the history and culture through audio guidance when the user arrives at a tourist spot.

[0078] The suggestion unit can provide information visually by utilizing image guidance or AR. The suggestion unit, for example, provides information about tourist spots using image guidance. For example, the suggestion unit displays photos and illustrations of the tourist spots to provide visual guidance about the features and highlights of the location. The suggestion unit can also provide information about tourist spots using AR (augmented reality). For example, the suggestion unit can display images of tourist spots using a smartphone camera and overlay reproductions and explanations of historical buildings on top of the images. Furthermore, the suggestion unit can display information about the areas surrounding tourist spots and recommended routes using AR. In this way, by utilizing image guidance and AR, information can be provided visually, improving the user's sightseeing experience.

[0079] The collection unit can collect a user's behavioral history by using the smartphone's GPS data or search history. The collection unit collects a user's behavioral history by, for example, using the smartphone's GPS data. For example, the collection unit obtains the places the user has visited and the routes the user has taken from the GPS data. The collection unit can also collect a behavioral history by using the user's search history. For example, the collection unit collects information on tourist spots and restaurants that the user has previously searched for. Furthermore, the collection unit can collect a behavioral history by analyzing the user's social media activity. For example, the collection unit collects a behavioral history based on the places the user has checked in to and the content of their posts on social media. In this way, the user's behavioral history can be accurately collected by using the smartphone's GPS data and search history.

[0080] The analysis unit can identify the user's interests based on information about tourist spots visited in the past and restaurants searched for. The analysis unit can identify the user's interests based on information about tourist spots visited in the past. For example, the analysis unit can analyze data about tourist spots visited in the past by the user to identify spots that the user is likely to be interested in. The analysis unit can also identify the user's interests based on information about restaurants searched for in the past by the user. For example, the analysis unit can analyze data about restaurants searched for in the past by the user to identify restaurants that the user is likely to be interested in. Furthermore, the analysis unit can perform analysis that reflects the user's current interests in real time. For example, the analysis unit can identify the user's interests based on data about tourist spots or restaurants that the user is currently searching for. This makes it possible to identify the user's interests based on past behavioral data and make more appropriate suggestions.

[0081] The suggestion unit can acquire the latest information on tourist spots, restaurants, and accommodations from an online database or an affiliated tourist information site. The suggestion unit, for example, acquires the latest information on tourist spots, restaurants, and accommodations from an online database. For example, the suggestion unit collects the latest information from tourist information sites and travel sites and provides it to the user. The suggestion unit can also acquire the latest information from affiliated tourist information sites. For example, the suggestion unit acquires the latest information on tourist spots, restaurants, and accommodations from affiliated tourist information sites and provides it to the user. Furthermore, the suggestion unit can make optimal suggestions to the user based on the information acquired from the online database or the tourist information site. In this way, by acquiring the latest information, the latest tourist spots, restaurants, and accommodations can be suggested to the user.

[0082] The suggestion unit can collect behavioral history and search history with the user's consent and establish an appropriate privacy policy. The suggestion unit, for example, collects behavioral history and search history with the user's consent. For example, the suggestion unit collects behavioral history and search history only if the user consents. The suggestion unit can also establish an appropriate privacy policy. For example, the suggestion unit can establish a privacy policy that properly manages user data and clearly states that the data will not be provided to third parties. Furthermore, the suggestion unit can anonymize user data so that individuals cannot be identified. This makes it possible to collect behavioral history and search history while protecting the user's privacy.

[0083] The suggestion unit can adopt an intuitive and easy-to-use design to enable the user to operate it easily. The suggestion unit, for example, adopts an intuitive and easy-to-use design. For example, the suggestion unit designs the user interface to be simple and easy to understand. The suggestion unit can also minimize operation steps to enable the user to operate it easily. For example, the suggestion unit provides a simple interface when the user inputs a destination, minimizing the input steps. Furthermore, the suggestion unit can provide various input methods, such as voice input and gesture input. As a result, by adopting an intuitive and easy-to-use design, the user can operate it easily.

[0084] The reception unit can estimate the user's emotion and adjust the destination input method based on the estimated user's emotion. For example, if the user is feeling stressed, the reception unit provides a simple interface and minimizes input steps. For example, if the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input to enable the user to quickly input the destination. For example, the reception unit can estimate the user's emotion and provide an optimal input method based on the estimated emotion. In this way, a more appropriate input method can be provided by adjusting the input method according to the user's emotion.

[0085] When a destination is input, the reception unit can present input candidates by referring to the user's past input history. For example, the reception unit automatically displays destinations that the user has frequently input in the past as candidates. For example, the reception unit preferentially suggests input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest destinations to be used during a specific time period based on the user's past input history. For example, the reception unit analyzes the user's past input history and presents the most relevant input candidates. In this way, appropriate input candidates can be presented to the user by referring to the past input history.

[0086] The reception unit can present optimal input candidates taking into account the user's current location information when the user inputs a destination. For example, when the user opens the app, the reception unit automatically acquires the user's current location and sets it as the starting point. For example, when the user inputs a destination, the reception unit proposes optimal candidate locations taking into account the distance from the user's current location. Furthermore, when the user uses the app while on the move, the reception unit can also update the user's current location in real time and reflect it as the starting point. For example, the reception unit presents optimal input candidates based on the user's current location information. In this way, optimal input candidates can be presented to the user by taking into account the current location information.

[0087] The reception unit can provide multiple input methods, such as voice input or gesture input, when inputting a destination. For example, the reception unit can automatically set the starting point and destination when the user simply voice-inputs "from home to the station." For example, the reception unit can easily set the starting point and destination by the user performing a specific gesture on the smartphone screen. The reception unit can also combine voice input and gesture input to set the starting point and destination more intuitively. For example, the reception unit can allow the user to input the destination using voice input and set the starting point using gesture input. This allows the user to input a destination more intuitively by providing a variety of input methods.

[0088] The reception unit can estimate the user's emotion and determine the priority of input candidates based on the estimated user's emotion. For example, when the user is feeling stressed, the reception unit can prioritize displaying the simplest and most intuitive input candidates. For example, when the user is relaxed, the reception unit can prioritize displaying detailed input candidates. Furthermore, when the user is in a hurry, the reception unit can also prioritize displaying input candidates that have been frequently used in the past. For example, the reception unit can estimate the user's emotion and determine the priority of input candidates based on the estimated emotion. In this way, by prioritizing input candidates according to the user's emotion, more appropriate input candidates can be provided.

[0089] When a destination is input, the reception unit can suggest related destinations by referring to the user's past travel history. The reception unit can suggest related destinations, for example, based on tourist spots that the user has visited in the past. For example, the reception unit can suggest related destinations based on information about restaurants that the user has searched for in the past. The reception unit can also suggest destinations related to specific seasons or events from the user's past travel history. For example, the reception unit can analyze the user's past travel history and suggest the most relevant destination. This makes it possible to suggest destinations related to the user by referring to the past travel history.

[0090] When a destination is input, the reception unit can analyze the user's social media activity and suggest related destinations. For example, the reception unit can suggest related destinations based on places where the user has checked in on social media. For example, the reception unit can analyze the content of the user's social media posts and suggest related destinations. The reception unit can also suggest related destinations by referring to the activities of the user's friends on social media. For example, the reception unit can analyze the user's social media activity and suggest the most relevant destination. In this way, it is possible to suggest destinations related to the user by analyzing social media activity.

[0091] The reception unit can customize the input method by reflecting user feedback when inputting a destination. The reception unit, for example, suggests the optimal input method based on feedback previously provided by the user. For example, the reception unit adjusts the design of the input interface by reflecting user feedback. The reception unit can also improve the accuracy of voice input and gesture input based on user feedback. For example, the reception unit analyzes user feedback and provides the most appropriate input method. In this way, a more appropriate input method can be provided by reflecting user feedback.

[0092] The guidance unit can estimate the user's emotions and adjust the way the guidance is presented based on the estimated user's emotions. For example, if the user is relaxed, the guidance unit provides guidance that proceeds at a leisurely pace. For example, if the user is in a hurry, the guidance unit provides guidance that emphasizes the shortest route. Furthermore, if the user is excited, the guidance unit can also provide guidance that adds visually stimulating effects. For example, the guidance unit can estimate the user's emotions and provide an optimal guidance method based on the estimated emotions. In this way, more appropriate guidance can be provided by adjusting the way the guidance is presented according to the user's emotions.

[0093] The guidance unit can recalculate the optimal route by reflecting the congestion status of the route in real time when providing guidance. The guidance unit recalculates the optimal route based on, for example, real-time traffic congestion information. For example, the guidance unit recalculates the optimal route by taking into account the real-time operation status of public transportation. The guidance unit can also recalculate a detour route based on real-time road construction information. For example, the guidance unit provides the optimal route by reflecting the congestion status in real time. In this way, the optimal route can be provided by reflecting the congestion status in real time.

[0094] The guidance unit can analyze the user's driving style and provide the optimal guidance method when providing guidance. For example, if the user prioritizes safe driving, the guidance unit can provide guidance that proceeds at a slow pace. For example, if the user prioritizes speed, the guidance unit can provide guidance that emphasizes the shortest route. The guidance unit can also analyze the user's driving style and suggest the optimal guidance method. For example, the guidance unit can provide the most appropriate guidance method based on the user's driving style. In this way, a more appropriate guidance method can be provided by analyzing the user's driving style.

[0095] When providing guidance, the guidance unit can improve the accuracy of the guidance by referring to the user's past route history. The guidance unit, for example, proposes an optimal guidance method based on routes the user has used in the past. For example, the guidance unit proposes a guidance method that avoids congestion based on the user's past route history. The guidance unit can also analyze the user's past route history and propose the most efficient guidance method. For example, the guidance unit provides the most appropriate guidance method based on the user's past route history. In this way, the accuracy of the guidance is improved by referring to the past route history.

[0096] The guidance unit can estimate the user's emotions and adjust the level of detail of the guidance based on the estimated user's emotions. For example, if the user is nervous, the guidance unit provides simple, highly visible guidance. For example, if the user is relaxed, the guidance unit provides guidance including detailed information. Furthermore, if the user is in a hurry, the guidance unit can also provide guidance that focuses on the main points. For example, the guidance unit can estimate the user's emotions and provide an optimal guidance method based on the estimated emotions. In this way, more appropriate guidance can be provided by adjusting the level of detail of the guidance according to the user's emotions.

[0097] When providing guidance, the guidance unit can propose an optimal route by taking into account the user's current traffic conditions. The guidance unit, for example, proposes an optimal route based on real-time traffic congestion information. For example, the guidance unit proposes an optimal route by taking into account the real-time operation status of public transportation. The guidance unit can also propose a detour route based on real-time road construction information. For example, the guidance unit provides an optimal route based on the user's current traffic conditions. In this way, the optimal route can be proposed to the user by taking into account the current traffic conditions.

[0098] When providing guidance, the guidance unit can provide the optimal guidance method by referring to the user's vehicle information. The guidance unit, for example, proposes the optimal route based on fuel efficiency information of the user's vehicle. For example, the guidance unit proposes a route that avoids narrow roads based on size information of the user's vehicle. The guidance unit can also propose the optimal route based on maintenance information of the user's vehicle. For example, the guidance unit provides the most appropriate guidance method based on the user's vehicle information. In this way, by referring to the vehicle information, the optimal guidance method can be provided to the user.

[0099] The guidance unit can customize the guidance method by reflecting user feedback when providing guidance. For example, the guidance unit suggests the optimal guidance method based on feedback previously provided by the user. For example, the guidance unit can adjust the design of the guidance interface by reflecting user feedback. The guidance unit can also improve the accuracy of audio guidance and visual guidance based on user feedback. For example, the guidance unit can analyze user feedback and provide the most appropriate guidance method. In this way, the optimal guidance method can be provided to the user by reflecting the feedback.

[0100] The collection unit can estimate the user's emotions and adjust the timing of collecting the behavioral history based on the estimated user emotions. For example, when the user is relaxed, the collection unit periodically collects the behavioral history. For example, when the user is feeling stressed, the collection unit reduces the frequency of collecting the behavioral history. Furthermore, when the user is excited, the collection unit can also collect the behavioral history in real time. For example, the collection unit estimates the user's emotions and provides the optimal collection timing based on the estimated emotions. In this way, by adjusting the collection timing according to the user's emotions, a more appropriate behavioral history can be collected.

[0101] When collecting behavioral history, the collection unit can analyze the user's past behavioral patterns and select the optimal collection method. The collection unit selects the optimal collection method, for example, based on places the user has frequently visited in the past. For example, the collection unit collects behavioral history during a specific time period based on the user's past behavioral patterns. The collection unit can also analyze the user's past behavioral patterns and select the most efficient collection method. For example, the collection unit provides the most appropriate collection method based on the user's past behavioral patterns. This makes it possible to select the optimal collection method by analyzing past behavioral patterns.

[0102] When collecting behavioral history, the collection unit can adjust the collection method taking into account the user's current activity status. For example, when the user is on the move, the collection unit collects behavioral history in real time. For example, when the user is taking a break, the collection unit periodically collects behavioral history. Furthermore, when the user is participating in a specific event, the collection unit can also collect behavioral history related to the event. For example, the collection unit provides the most appropriate collection method based on the user's current activity status. This allows a more appropriate behavioral history to be collected by taking into account the current activity status.

[0103] When collecting a behavioral history, the collection unit can select the optimal collection means depending on the user's input method (voice, text, image, etc.). For example, if the user uses voice input, the collection unit collects voice data and saves it as a behavioral history. For example, if the user uses text input, the collection unit collects text data and saves it as a behavioral history. Furthermore, if the user uses image input, the collection unit can also collect image data and save it as a behavioral history. For example, the collection unit provides the most appropriate collection means depending on the user's input method. This allows for more appropriate behavioral history to be collected by selecting the optimal collection means depending on the input method.

[0104] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user's emotions. For example, when the user is relaxed, the collection unit prioritizes collecting detailed behavioral history. For example, when the user is feeling stressed, the collection unit prioritizes collecting only important behavioral history. Furthermore, when the user is excited, the collection unit can also prioritize collecting behavioral history in real time. For example, the collection unit estimates the user's emotions and provides optimal data priority based on the estimated emotions. In this way, by determining the priority of data according to the user's emotions, more important data can be collected preferentially.

[0105] When collecting behavioral history, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, when the user is at a specific tourist spot, the collection unit prioritizes collecting behavioral history related to that location. For example, when the user is at a specific restaurant, the collection unit prioritizes collecting behavioral history related to that location. Furthermore, when the user is at a specific accommodation facility, the collection unit can also prioritize collecting behavioral history related to that location. For example, the collection unit provides the most relevant data based on the user's geographical location information. This allows highly relevant data to be collected preferentially by taking into account the geographical location information.

[0106] When collecting the behavioral history, the collection unit can analyze the user's social media activities to collect related data. For example, the collection unit collects behavioral history related to places where the user checked in on social media. For example, the collection unit analyzes the content of the user's posts on social media and collects related behavioral history. The collection unit can also collect related behavioral history by referring to the activities of the user's friends on social media. For example, the collection unit analyzes the user's social media activities and provides the most relevant data. This makes it possible to collect related data by analyzing social media activities.

[0107] When collecting behavioral history, the collection unit can customize the collection method by reflecting the user's past feedback. For example, the collection unit suggests the optimal collection method based on feedback provided by the user in the past. For example, the collection unit adjusts the design of the collection interface by reflecting the user's feedback. The collection unit can also improve the accuracy of voice input and text input based on the user's feedback. For example, the collection unit analyzes the user's feedback and provides the most appropriate collection method. In this way, a more appropriate collection method can be provided by reflecting the past feedback.

[0108] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. For example, if the user is relaxed, the analysis unit provides detailed analysis results. For example, if the user is feeling stressed, the analysis unit provides concise and to-the-point analysis results. Furthermore, if the user is excited, the analysis unit can also provide analysis results with visually stimulating effects. For example, the analysis unit can estimate the user's emotions and provide optimal analysis results based on the estimated emotions. In this way, by adjusting the way the analysis is presented based on the user's emotions, more appropriate analysis results can be provided.

[0109] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past behavioral data. The analysis unit improves the accuracy of the analysis, for example, based on data on tourist spots that the user has visited in the past. For example, the analysis unit improves the accuracy of the analysis based on data on restaurants that the user has searched for in the past. The analysis unit can also analyze the user's past behavioral data and provide the most relevant information. For example, the analysis unit provides the most appropriate analysis results based on the user's past behavioral data. In this way, the accuracy of the analysis is improved by referring to the past behavioral data.

[0110] The analysis unit can perform analysis by reflecting the user's current interests in real time during analysis. For example, the analysis unit performs analysis based on data on tourist spots in which the user is currently interested. For example, the analysis unit performs analysis based on data on restaurants that the user is currently searching for. The analysis unit can also reflect the user's current interests in real time and provide the most relevant information. For example, the analysis unit provides the most appropriate analysis results based on the user's current interests. This allows the analysis unit to provide more appropriate analysis results by reflecting the user's current interests.

[0111] The analysis unit can perform the analysis while taking into consideration the user's attribute information (age, gender, occupation, etc.). For example, the analysis unit analyzes the most suitable tourist spot based on the user's age information. For example, the analysis unit analyzes the most suitable restaurant based on the user's gender information. The analysis unit can also analyze the most suitable accommodation based on the user's occupation information. For example, the analysis unit provides the most appropriate analysis result based on the user's attribute information. In this way, by taking the attribute information into consideration, it is possible to provide a more appropriate analysis result.

[0112] 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 provides a simple, highly visible display method. For example, if the user is relaxed, the analysis unit provides a display method including detailed information. Furthermore, if the user is in a hurry, the analysis unit can also provide a display method that focuses on the main points. For example, the analysis unit can estimate the user's emotions and provide an optimal display method based on the estimated emotions. In this way, by adjusting the display method according to the user's emotions, more appropriate analysis results can be provided.

[0113] The analysis unit can perform analysis taking into account the user's geographical location information. For example, if the user is at a specific tourist spot, the analysis unit performs analysis based on data related to that location. For example, if the user is at a specific restaurant, the analysis unit performs analysis based on data related to that location. Furthermore, if the user is at a specific accommodation facility, the analysis unit can also perform analysis based on data related to that location. For example, the analysis unit provides the most appropriate analysis result based on the user's geographical location information. In this way, by taking geographical location information into consideration, more appropriate analysis results can be provided.

[0114] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's social media activity. The analysis unit improves the accuracy of the analysis, for example, based on data on locations where the user checked in on social media. For example, the analysis unit analyzes the content of the user's social media posts and performs the analysis based on related data. The analysis unit can also perform the analysis based on related data by referring to the activities of the user's friends on social media. For example, the analysis unit provides the most appropriate analysis results based on the user's social media activity. In this way, the accuracy of the analysis is improved by referring to social media activity.

[0115] The analysis unit can customize the analysis method by reflecting the user's past feedback during analysis. For example, the analysis unit proposes the optimal analysis method based on feedback provided by the user in the past. For example, the analysis unit adjusts the design of the analysis interface by reflecting the user's feedback. The analysis unit can also improve the accuracy of the analysis based on the user's feedback. For example, the analysis unit analyzes the user's feedback and provides the most appropriate analysis method. In this way, a more appropriate analysis method can be provided by reflecting the past feedback.

[0116] The suggestion unit can estimate the user's emotion and adjust the way in which suggestions are expressed based on the estimated user's emotion. For example, when the user is relaxed, the suggestion unit provides detailed suggestions. For example, when the user is stressed, the suggestion unit provides concise and to-the-point suggestions. Furthermore, when the user is excited, the suggestion unit can also provide suggestions with visually stimulating effects. For example, the suggestion unit can estimate the user's emotion and provide an optimal suggestion method based on the estimated emotion. In this way, more appropriate suggestions can be provided by adjusting the way in which suggestions are expressed according to the user's emotion.

[0117] When making a suggestion, the suggestion unit can improve the accuracy of the suggestion by referring to the user's past behavioral data. The suggestion unit improves the accuracy of the suggestion, for example, based on data on tourist spots that the user has visited in the past. For example, the suggestion unit improves the accuracy of the suggestion based on data on restaurants that the user has searched for in the past. The suggestion unit can also analyze the user's past behavioral data and provide the most relevant information. For example, the suggestion unit provides the most appropriate suggestion based on the user's past behavioral data. In this way, the accuracy of the suggestion is improved by referring to the past behavioral data.

[0118] When making a suggestion, the suggestion unit can reflect the user's current interests in real time. For example, the suggestion unit makes a suggestion based on data on tourist spots in which the user is currently interested. For example, the suggestion unit makes a suggestion based on data on restaurants that the user is currently searching for. The suggestion unit can also reflect the user's current interests in real time and provide the most relevant information. For example, the suggestion unit provides the most appropriate suggestion based on the user's current interests. This allows more appropriate suggestions to be provided by reflecting the user's current interests.

[0119] When making a suggestion, the suggestion unit can take into consideration the user's attribute information (age, gender, occupation, etc.). For example, the suggestion unit can suggest the most suitable tourist spot based on the user's age information. For example, the suggestion unit can suggest the most suitable restaurant based on the user's gender information. The suggestion unit can also suggest the most suitable accommodation based on the user's occupation information. For example, the suggestion unit can provide the most appropriate suggestion based on the user's attribute information. In this way, by taking the attribute information into consideration, more appropriate suggestions can be provided.

[0120] The suggestion unit can estimate the user's emotion and determine the priority of suggestions based on the estimated user's emotion. For example, when the user is relaxed, the suggestion unit can provide detailed suggestions with priority. For example, when the user is stressed, the suggestion unit can provide only important suggestions with priority. Furthermore, when the user is excited, the suggestion unit can also provide visually stimulating suggestions with priority. For example, the suggestion unit can estimate the user's emotion and provide optimal suggestion priorities based on the estimated emotion. In this way, by determining the priority of suggestions according to the user's emotion, more important suggestions can be provided with priority.

[0121] When making a suggestion, the suggestion unit can make the most appropriate suggestion by taking into account the user's geographical location information. For example, if the user is at a specific tourist spot, the suggestion unit makes a suggestion related to that location. For example, if the user is at a specific restaurant, the suggestion unit makes a suggestion related to that location. Furthermore, if the user is at a specific accommodation facility, the suggestion unit can also make a suggestion related to that location. For example, the suggestion unit provides the most appropriate suggestion based on the user's geographical location information. In this way, the most appropriate suggestion can be provided to the user by taking into account the geographical location information.

[0122] When making a suggestion, the suggestion unit can make a relevant suggestion by referring to the user's social media activity. For example, the suggestion unit makes a suggestion related to places where the user has checked in on social media. For example, the suggestion unit analyzes the content of the user's social media posts and suggests related tourist spots and restaurants. The suggestion unit can also suggest related places and events by referring to the activity of the user's friends on social media. For example, the suggestion unit provides the most appropriate suggestion based on the user's social media activity. In this way, relevant suggestions can be provided to the user by referring to the social media activity.

[0123] The suggestion unit can customize the suggestion method by reflecting the user's past feedback when making a suggestion. The suggestion unit proposes the optimal suggestion method, for example, based on feedback provided by the user in the past. For example, the suggestion unit adjusts the design of the suggestion interface by reflecting the user's feedback. The suggestion unit can also improve the accuracy of the suggestion based on the user's feedback. For example, the suggestion unit analyzes the user's feedback and provides the most appropriate suggestion method. In this way, a more appropriate suggestion method can be provided by reflecting the past feedback. === Hard Collateral 1-1 === Each of the above-described elements, including the reception unit, guidance unit, collection unit, analysis unit, and suggestion unit, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14 and accepts a user's input of a destination. For example, the guidance unit is implemented by the identification processing unit 290 of the data processing device 12 and calculates an optimal route and provides directions. For example, the collection unit collects the user's behavioral history using the camera 42 or GPS data of the smart device 14. For example, the analysis unit is implemented by the identification processing unit 290 of the data processing device 12 and analyzes the collected data to identify the user's interests. For example, the suggestion unit is implemented by the identification processing unit 290 of the data processing device 12 and suggests optimal tourist spots, restaurants, and accommodations based on the identified interests. === Hard Collateral 1-2 === Each of the multiple elements, including the reception unit, guidance unit, collection unit, analysis unit, and suggestion unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214 and accepts a user's input of a destination. For example, the guidance unit is realized by the identification processing unit 290 of the data processing device 12 and calculates an optimal route and provides directions. For example, the collection unit collects the user's behavior history using the camera 42 of the smart glasses 214 and GPS data. For example, the analysis unit is realized by the identification processing unit 290 of the data processing device 12 and analyzes the collected data to identify the user's interests. For example, the suggestion unit is realized by the identification processing unit 290 of the data processing device 12 and suggests optimal tourist spots, restaurants, and accommodations based on the identified interests. === Hard Collateral 1-3 === Each of the multiple elements, including the reception unit, guidance unit, collection unit, analysis unit, and suggestion unit, described above, 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 accepts a destination input by the user. For example, the guidance unit is realized by the identification processing unit 290 of the data processing device 12 and calculates an optimal route and provides directions. For example, the collection unit collects the user's behavioral history using the camera 42 or GPS data of the headset terminal 314. For example, the analysis unit is realized by the identification processing unit 290 of the data processing device 12 and analyzes the collected data to identify the user's interests. For example, the suggestion unit is realized by the identification processing unit 290 of the data processing device 12 and suggests optimal tourist spots, restaurants, and accommodations based on the identified interests. === Hard Collateral 1-4 === Each of the multiple elements, including the reception unit, guidance unit, collection unit, analysis unit, and suggestion unit, described above, 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 accepts a destination input by the user. For example, the guidance unit is realized by the identification processing unit 290 of the data processing device 12 and calculates an optimal route and provides directions. For example, the collection unit collects the user's behavior history using the camera 42 or GPS data of the robot 414. For example, the analysis unit is realized by the identification processing unit 290 of the data processing device 12 and analyzes the collected data to identify the user's interests. For example, the suggestion unit is realized by the identification processing unit 290 of the data processing device 12 and suggests optimal tourist spots, restaurants, and accommodations based on the identified interests.

[0124] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0125] The suggestion unit can also estimate the user's emotions and adjust the content of suggestions based on the estimated user emotions. For example, if the user is feeling stressed, the suggestion unit can suggest relaxing tourist spots and restaurants. If the user is excited, the suggestion unit can suggest active activities and events. Furthermore, if the user is tired, the suggestion unit can suggest quiet accommodations and relaxation spots. This makes it possible to provide a more personalized travel experience by making suggestions based on the user's emotions.

[0126] The collection unit can also estimate the user's emotions and adjust the behavioral history collection method based on the estimated user emotions. For example, if the user is relaxed, a detailed behavioral history can be collected. Also, if the user is feeling stressed, only important behavioral history can be collected. Furthermore, if the user is excited, a behavioral history can be collected in real time. This makes it possible to collect behavioral history according to the user's emotions, and provides more accurate data.

[0127] The analysis unit can also 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, a simple, highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that focuses on the main points can be provided. In this way, by providing a display method that corresponds to the user's emotions, more appropriate analysis results can be provided.

[0128] The guidance unit can also estimate the user's emotions and adjust the way the guidance is presented based on the estimated user's emotions. For example, if the user is relaxed, guidance that proceeds at a leisurely pace can be provided. If the user is in a hurry, guidance that emphasizes the shortest route can be provided. Furthermore, if the user is excited, guidance that adds visually stimulating effects can be provided. In this way, more appropriate guidance can be provided by providing a guidance method that corresponds to the user's emotions.

[0129] The suggestion unit can also estimate the user's emotions and determine the priority of suggestions based on the estimated user's emotions. For example, if the user is relaxed, detailed suggestions can be provided preferentially. Also, if the user is stressed, only important suggestions can be provided preferentially. Furthermore, if the user is excited, visually stimulating suggestions can be provided preferentially. In this way, by determining the priority of suggestions according to the user's emotions, more important suggestions can be provided preferentially.

[0130] The suggestion unit can also provide real-time information on nearby tourist spots, restaurants, and accommodations based on the user's current location information. For example, if the user is at a specific tourist spot, the suggestion unit can suggest recommended spots in the area. If the user is looking for a restaurant, the suggestion unit can suggest restaurants close to the user's current location. Furthermore, if the user is looking for accommodation, the suggestion unit can suggest accommodations that are easily accessible from the user's current location. This makes it possible to provide more convenient information by making suggestions that take the user's current location information into consideration.

[0131] The collection unit can also analyze the user's social media activities and collect related behavioral history. For example, the collection unit can collect behavioral history related to the places where the user checked in on social media. The collection unit can also analyze the content of the user's posts on social media and collect related behavioral history. Furthermore, the collection unit can also collect related behavioral history by referring to the activities of the user's friends on social media. In this way, by analyzing social media activities, more relevant behavioral history can be collected.

[0132] The analysis unit can also identify a user's interests based on the user's past behavioral data. For example, it can analyze data on tourist spots the user has visited in the past to identify spots that the user might be interested in. It can also identify interests based on data on restaurants the user has searched for in the past. It can also perform analysis that reflects the user's current interests in real time. This allows for more appropriate suggestions to be made by identifying the user's interests based on past behavioral data.

[0133] The suggestion unit can also make suggestions by taking into consideration the user's attribute information (age, gender, occupation, etc.). For example, the most suitable tourist spot can be suggested based on the user's age information. The most suitable restaurant can also be suggested based on the user's gender information. Furthermore, the most suitable accommodation can also be suggested based on the user's occupation information. In this way, by taking attribute information into consideration, more appropriate suggestions can be provided.

[0134] The guidance unit can also analyze the user's driving style and provide the optimal guidance method. For example, if the user prioritizes safe driving, guidance that proceeds at a slow pace can be provided. Alternatively, if the user prioritizes speed, guidance that emphasizes the shortest route can be provided. Furthermore, the guidance unit can analyze the user's driving style and suggest the optimal guidance method. In this way, by analyzing the user's driving style, a more appropriate guidance method can be provided.

[0135] The processing flow of the second embodiment will be briefly explained below.

[0136] Step 1: The reception unit accepts the user's input of a destination. For example, the user inputs the destination on the screen of a smartphone. The reception unit can also input the destination using voice input. For example, the user can simply input "from home to the station" by voice, and the starting point and destination are automatically set. The reception unit can also input the destination using gesture input. For example, the user can easily set the starting point and destination by making a specific gesture on the screen of a smartphone. Step 2: The guidance unit calculates the optimal route based on the information received by the reception unit and provides route guidance. For example, the guidance unit calculates the optimal route based on real-time traffic congestion information. The guidance unit can also calculate the optimal route taking into account the real-time operation status of public transportation. Furthermore, the guidance unit can also calculate a detour route based on real-time road construction information. Step 3: The collection unit collects the user's past behavior or search history. For example, the collection unit collects the user's behavior history using GPS data from the smartphone. The collection unit can also collect the behavior history using the user's search history. Furthermore, the collection unit can collect the behavior history by analyzing the user's social media activity. Step 4: The analysis unit analyzes the data collected by the collection unit and identifies the user's interests. For example, the analysis unit identifies the user's interests based on information about tourist spots visited in the past or restaurants searched for. The analysis unit can also perform analysis that reflects the user's current interests in real time. Furthermore, the analysis unit can also perform analysis taking into account the user's attribute information (age, gender, occupation, etc.). Step 5: The suggestion unit suggests optimal tourist spots, restaurants, and accommodations based on the interests identified by the analysis unit. For example, the suggestion unit obtains the latest information on tourist spots, restaurants, and accommodations from online databases and affiliated tourist information sites. The suggestion unit may also collect user behavior and search history with the user's consent and establish an appropriate privacy policy. Furthermore, the suggestion unit employs an intuitive and user-friendly design to make it easy for users to operate.

[0137] 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.

[0138] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0139] 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.

[0140] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0141] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0150] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0151] In the 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.

[0152] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0153] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0155] The data processing system 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.

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

[0157] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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).

[0163] 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.

[0164] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0171] 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.

[0172] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0173] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0174] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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).

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0188] 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.

[0189] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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).

[0194] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0195] 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."

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] [Explanation of symbols]

[0209] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives an input of a destination; a guidance unit that calculates a route based on the information received by the reception unit and provides route guidance; a collection unit that collects a user's past behavior or search history; an analysis unit that analyzes the data collected by the collection unit and identifies user interests; A suggestion unit that suggests tourist spots, restaurants, and accommodations based on the interests identified by the analysis unit. A system characterized by:

2. The proposal unit When you arrive at a tourist spot, the audio guide will start and provide information about the history and culture of the spot.

2. The system of claim 1.

3. The proposal unit Providing visual information using image guidance or AR 2. The system of claim 1.

4. The collecting unit Collect user behavior history using smartphone GPS data or search history 2. The system of claim 1.

5. The analysis unit Identify user interests based on information about tourist spots visited in the past and restaurants searched for 2. The system of claim 1.

6. The proposal unit Obtain the latest information on tourist spots, restaurants, and accommodations from online databases and affiliated tourist information sites 2. The system of claim 1.

7. The proposal unit Collect user behavioral and search history with user consent and establish an appropriate privacy policy 2. The system of claim 1.

8. The proposal unit Adopt an intuitive and user-friendly design to make it easy for users to operate 2. The system of claim 1.

9. The reception unit Estimate the user's emotions and adjust the destination input method based on the estimated user emotions.

2. The system of claim 1.

10. The reception unit When entering a destination, input candidates are presented based on the user's past input history.

2. The system of claim 1.

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