system

Abstract

The system according to the embodiment aims to generate efficient walking routes when searching for a new place and to visually provide advance information, history, and characteristics of the place. [Solution] A system according to an embodiment includes a reception unit, a generation unit, a navigation unit, and an AR display unit. The reception unit receives input of a destination or a place of interest. The generation unit generates a walking route based on the information received by the reception unit. The navigation unit performs navigation based on the walking route generated by the generation unit. The AR display unit visualizes advance information, history, and features of the place guided by the navigation unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately generate efficient walking routes when exploring new places, nor do they adequately provide prior information, history, and characteristics of the place visually, leaving room for improvement.

[0005] The system according to the embodiment aims to generate efficient walking routes when searching for a new place and to visually provide advance information, history, and characteristics of the place. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a generation unit, a navigation unit, and an AR display unit. The reception unit receives input of a destination or a place of interest. The generation unit generates a walking route based on the information received by the reception unit. The navigation unit performs navigation based on the walking route generated by the generation unit. The AR display unit visualizes advance information, history, and features of the place guided by the navigation unit. [Effects of the Invention]

[0007] The system according to the embodiment can generate efficient walking routes when exploring new places and visually provide advance information, history, and characteristics of the place. [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 new place search system according to an embodiment of the present invention generates a walking route when searching for a new place and provides navigation based on that route. This system visualizes the user's prior information, history, and characteristics of the place they will visit using AR. Furthermore, the system learns the user's travel history and preferences and makes suggestions that reflect those information for the next search. Furthermore, the generation AI collaborates with local users to provide local knowledge and information. For example, when a user searches for a new place, they input their destination and points of interest. The generation AI then generates an optimal walking route based on the input information. This route is customized taking into account the user's preferences and past travel history. Navigation begins based on the generated route. During navigation, the user's prior information, history, and characteristics of the place they will visit are visualized using AR. For example, when the user stands in front of a historical building, the building's history and characteristics are displayed on the display. This allows the user to obtain information about the place in real time. Furthermore, the generation AI learns the user's travel history and preferences. As a result, suggestions tailored to the user's preferences are made for the next search. For example, if a user has frequently visited historical places in the past, a route centered around that historical place will be suggested again next time. The generative AI also works with local users to provide local knowledge and information. For example, it provides information on hidden attractions and recommended restaurants known to local users. This allows users to enjoy the place from the perspective of locals. This system allows users to efficiently explore new places and gain in-depth knowledge. It also provides a more fulfilling travel experience with customized suggestions tailored to the user's preferences. This allows the new place discovery system to make customized suggestions based on the user's travel history and preferences, allowing users to efficiently explore new places and gain in-depth knowledge.

[0029] A new place search system according to an embodiment includes a reception unit, a generation unit, a navigation unit, and an AR display unit. The reception unit receives input of a destination or a place of interest. For example, the reception unit provides an interface for a user to input places they want to visit or places of interest. The user can input information such as tourist attractions, restaurants, and historical buildings. The generation unit generates a walking route based on the information received by the reception unit. For example, the generation unit calculates an optimal walking route based on the user's input information. The generation unit can also customize the route taking into account the user's preferences and past travel history. The navigation unit performs navigation based on the walking route generated by the generation unit. For example, the navigation unit provides audio guidance and map display to the user, guiding the user along the route in real time. The navigation unit can also track the user's current location and update the user's progress along the route in real time. The AR display unit visualizes advance information, history, features, etc. of the place guided by the navigation unit. For example, the AR display unit displays information about the place the user is visiting using AR technology. When the user stands in front of a historical building, the building's history and features are displayed on the AR display unit. This allows the user to obtain information about the place in real time, thereby enabling the new place discovery system according to the embodiment to enable the user to efficiently explore new places and gain in-depth knowledge.

[0030] The learning unit can learn the user's travel history and preferences. For example, the learning unit collects information such as the user's past visited places, ratings, and reviews to learn the user's travel history and preferences. For example, the learning unit collects information on tourist spots and restaurants the user has visited in the past to understand the user's preferences. The learning unit can also analyze the user's ratings and reviews to learn the user's preferred places. Furthermore, the learning unit can make optimal suggestions for the next exploration based on the user's travel history and preferences. For example, if a user has visited many historical places in the past, the learning unit can suggest a route centered around historical places next time. This allows suggestions to be made that are tailored to the user's preferences. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without AI. For example, the learning unit can input data on the user's travel history and preferences into AI, which can analyze the data and learn the user's preferences. This allows the learning unit to make optimal suggestions based on the user's travel history and preferences.

[0031] The local user collaboration unit can collaborate with local users. The local user collaboration unit can provide information such as hidden attractions and recommended restaurants known to local users. For example, the local user collaboration unit can collect information on tourist spots and restaurants known to local users and provide it to the user. The local user collaboration unit can also provide information on events and guide services hosted by local users. For example, the local user collaboration unit can collect information on events hosted by local users and provide it to the user. This allows the user to enjoy the place from the perspective of local people. Some or all of the above-mentioned processing in the local user collaboration unit can be performed using AI, for example, or without AI. For example, the local user collaboration unit can input information about local users into AI, which can analyze the information and provide it to the user. This allows the local user collaboration unit to provide knowledge and information unique to the local area.

[0032] The generation unit can generate a walking route based on the user's preferences and past travel history. The generation unit generates a walking route, for example, taking the user's preferences and past travel history into consideration. For example, the generation unit proposes an optimal walking route based on places the user has visited in the past and their ratings. The generation unit can also learn the user's preferences and generate a route that includes the user's favorite places. For example, the generation unit proposes a route that centers around historical places the user has visited in the past for the next visit. This generates a walking route that suits the user's preferences. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input data on the user's preferences and past travel history into the generation AI, which can analyze the data and generate an optimal walking route. This allows the generation unit to generate an optimal walking route based on the user's preferences and past travel history.

[0033] The navigation unit can perform navigation based on the generated walking route. The navigation unit, for example, provides audio guidance and a map display to the user based on the generated walking route. For example, the navigation unit provides audio guidance of places the user will visit. The navigation unit can also track the user's current location and update the route progress in real time. For example, the navigation unit provides re-route guidance if the user deviates from the route. In this way, navigation is performed based on the generated walking route. Some or all of the above-described processing in the navigation unit may be performed using, for example, AI, or may be performed without using AI. For example, the navigation unit can input data of the generated walking route into AI, which can analyze the data and perform navigation. In this way, the navigation unit can provide optimal navigation based on the generated walking route.

[0034] The AR display unit can visualize advance information, history, features, etc. of places the user will visit. The AR display unit, for example, displays information about places the user will visit using AR technology. For example, when the user stands in front of a historical building, the AR display unit displays the history and features of the building. The AR display unit can also update information about places the user will visit in real time. For example, the AR display unit displays new information each time the user moves. This allows information about places the user will visit to be visualized in real time. Some or all of the above-described processing in the AR display unit may be performed using, for example, AI, or may be performed without using AI. For example, the AR display unit can input information about places the user will visit into AI, which can analyze the data and display the information. This allows the AR display unit to visualize information about places the user will visit in real time.

[0035] The reception unit can analyze the user's past input history and provide an optimal input interface. The reception unit, for example, collects and analyzes the user's past input history. For example, the reception unit automatically displays destinations and places of interest that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest destinations and places of interest to be used during a specific time period based on the user's past input history. This provides an optimal input interface based on the user's past input history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input data of the user's past input history into AI, which can analyze the data and provide an optimal input interface. This allows the reception unit to provide an optimal input interface based on the user's past input history.

[0036] The reception unit can present input candidates based on the user's current activity status and areas of interest upon reception. The reception unit, for example, collects and analyzes the user's current activity status and areas of interest. For example, the reception unit suggests related destinations and places of interest based on the user's current activity status. The reception unit can also present related input candidates based on the user's areas of interest. Furthermore, the reception unit can combine the user's current activity status and areas of interest to present optimal input candidates. As a result, input candidates based on the user's current activity status and areas of interest are presented. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input data on the user's current activity status and areas of interest into AI, which can analyze the data and present optimal input candidates. As a result, the reception unit can provide optimal input candidates based on the user's current activity status and areas of interest.

[0037] The reception unit can prioritize presenting highly relevant input candidates in consideration of the user's geographical location information when receiving the input. The reception unit, for example, collects and analyzes the user's geographical location information. For example, the reception unit prioritizes presenting nearby destinations and places of interest based on the user's current location. The reception unit can also prioritize displaying related input candidates based on the user's geographical location information. Furthermore, the reception unit can combine the user's current location with past movement history to present optimal input candidates. As a result, input candidates based on the user's geographical location information are prioritized. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input data of the user's geographical location information into AI, which can analyze the data and present optimal input candidates. As a result, the reception unit can provide optimal input candidates based on the user's geographical location information.

[0038] The reception unit can analyze the user's social media activity and present related input candidates at the time of reception. The reception unit, for example, collects and analyzes the user's social media activity. For example, the reception unit analyzes the user's social media activity and suggests related destinations or places of interest. The reception unit can also present related input candidates based on the user's social media check-in history. Furthermore, the reception unit can analyze the content of the user's social media posts and present related input candidates. As a result, input candidates based on the user's social media activity are presented. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input data on the user's social media activity into AI, which can analyze the data and present optimal input candidates. As a result, the reception unit can provide optimal input candidates based on the user's social media activity.

[0039] When generating a walking route, the generation unit can select an optimal route by referring to the user's past travel history. The generation unit, for example, collects and analyzes the user's past travel history. For example, the generation unit can propose an optimal walking route based on routes the user has used in the past. The generation unit can also propose a route that avoids crowded areas based on the user's past travel history. Furthermore, the generation unit can analyze the user's past travel history and propose the most efficient walking route. In this way, an optimal walking route based on the user's past travel history is selected. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the generation unit can input data of the user's past travel history into the generation AI, which can analyze the data and select an optimal walking route. In this way, the generation unit can provide an optimal walking route based on the user's past travel history.

[0040] When generating a walking route, the generation unit can customize the route based on the user's current activity status. The generation unit, for example, collects and analyzes the user's current activity status. For example, the generation unit proposes an optimal walking route based on the user's current activity status. The generation unit can also adjust the difficulty of the route based on the user's current activity status. Furthermore, the generation unit can propose an optimal walking route by combining the user's current activity status with past travel history. This customizes the walking route based on the user's current activity status. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the generation unit can input data on the user's current activity status into the generation AI, which can analyze the data and propose an optimal walking route. This allows the generation unit to provide an optimal walking route based on the user's current activity status.

[0041] When generating a walking route, the generation unit can select an optimal route by taking into account the user's geographical location information. The generation unit, for example, collects and analyzes the user's geographical location information. For example, the generation unit proposes an optimal walking route based on the user's current location. The generation unit can also preferentially display related routes based on the user's geographical location information. Furthermore, the generation unit can propose an optimal walking route by combining the user's current location and past movement history. In this way, an optimal walking route based on the user's geographical location information is selected. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input data of the user's geographical location information into the generation AI, which can analyze the data and select an optimal walking route. In this way, the generation unit can provide an optimal walking route based on the user's geographical location information.

[0042] When generating a walking route, the generation unit can analyze the user's social media activity to customize the route. The generation unit, for example, collects and analyzes the user's social media activity. For example, the generation unit analyzes the user's social media activity and proposes related walking routes. The generation unit can also present related routes based on the user's social media check-in history. Furthermore, the generation unit can analyze the content of the user's social media posts and propose related walking routes. In this way, a walking route based on the user's social media activity is customized. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the generation unit can input data on the user's social media activity into the generation AI, which can analyze the data and propose an optimal walking route. In this way, the generation unit can provide an optimal walking route based on the user's social media activity.

[0043] During navigation, the navigation unit can provide an optimal guidance method by referring to the user's past navigation history. The navigation unit, for example, collects and analyzes the user's past navigation history. For example, the navigation unit proposes an optimal guidance method based on routes the user has used in the past. The navigation unit can also propose a guidance method that avoids congestion based on the user's past navigation history. Furthermore, the navigation unit can analyze the user's past navigation history and propose the most efficient guidance method. This provides an optimal guidance method based on the user's past navigation history. Some or all of the above-described processing in the navigation unit may be performed using, for example, AI, or may be performed without using AI. For example, the navigation unit can input data on the user's past navigation history into AI, which can analyze the data and provide an optimal guidance method. This allows the navigation unit to provide an optimal guidance method based on the user's past navigation history.

[0044] The navigation unit can customize a guidance method based on the user's current activity status during navigation. The navigation unit, for example, collects and analyzes the user's current activity status. For example, the navigation unit proposes an optimal guidance method based on the user's current activity status. The navigation unit can also adjust the level of detail of the guidance based on the user's current activity status. Furthermore, the navigation unit can propose an optimal guidance method by combining the user's current activity status with past navigation history. In this way, the guidance method is customized based on the user's current activity status. Some or all of the above-described processing in the navigation unit may be performed using, for example, AI, or may be performed without using AI. For example, the navigation unit can input data on the user's current activity status into AI, which can analyze the data and propose an optimal guidance method. In this way, the navigation unit can provide an optimal guidance method based on the user's current activity status.

[0045] During navigation, the navigation unit can provide an optimal guidance method by taking into account the user's geographical location information. The navigation unit, for example, collects and analyzes the user's geographical location information. For example, the navigation unit proposes an optimal guidance method based on the user's current location. The navigation unit can also preferentially display related guidance methods based on the user's geographical location information. Furthermore, the navigation unit can propose an optimal guidance method by combining the user's current location with past movement history. This provides an optimal guidance method based on the user's geographical location information. Some or all of the above-described processing in the navigation unit may be performed using, for example, AI, or may be performed without using AI. For example, the navigation unit can input data on the user's geographical location information into AI, which can analyze the data and provide an optimal guidance method. This allows the navigation unit to provide an optimal guidance method based on the user's geographical location information.

[0046] The navigation unit can customize a guidance method by analyzing a user's social media activity during navigation. The navigation unit, for example, collects and analyzes the user's social media activity. For example, the navigation unit analyzes the user's social media activity and suggests a related guidance method. The navigation unit can also present a related guidance method based on the user's social media check-in history. Furthermore, the navigation unit can analyze the content of the user's social media posts and suggest a related guidance method. In this way, the guidance method is customized based on the user's social media activity. Some or all of the above-described processing in the navigation unit may be performed using, for example, AI, or may be performed without using AI. For example, the navigation unit can input data on the user's social media activity into AI, which can analyze the data and suggest an optimal guidance method. In this way, the navigation unit can provide an optimal guidance method based on the user's social media activity.

[0047] The AR display unit can provide an optimal display method by referring to the user's past display history when displaying AR. The AR display unit, for example, collects and analyzes the user's past display history. For example, the AR display unit can suggest related AR displays based on information previously displayed by the user. The AR display unit can also prioritize displaying information of interest from the user's past display history. Furthermore, the AR display unit can analyze the user's past display history and suggest the most efficient AR display method. This provides an optimal display method based on the user's past display history. Some or all of the above-described processing in the AR display unit may be performed using, for example, AI, or may be performed without AI. For example, the AR display unit can input data of the user's past display history into AI, which can analyze the data and provide an optimal display method. This allows the AR display unit to provide an optimal display method based on the user's past display history.

[0048] The AR display unit can customize the display method based on the user's current activity status when displaying AR. The AR display unit, for example, collects and analyzes the user's current activity status. For example, the AR display unit proposes an optimal AR display method based on the user's current activity status. The AR display unit can also adjust the level of detail of the display based on the user's current activity status. Furthermore, the AR display unit can combine the user's current activity status with past display history to propose an optimal AR display method. This customizes the display method based on the user's current activity status. Some or all of the above-described processing in the AR display unit may be performed using, for example, AI, or may be performed without AI. For example, the AR display unit can input data on the user's current activity status into AI, which can analyze the data and propose an optimal display method. This allows the AR display unit to provide an optimal display method based on the user's current activity status.

[0049] The AR display unit can provide an optimal display method during AR display, taking into account the user's geographical location information. The AR display unit, for example, collects and analyzes the user's geographical location information. For example, the AR display unit proposes an optimal AR display method based on the user's current location. The AR display unit can also preferentially display related AR display methods based on the user's geographical location information. Furthermore, the AR display unit can propose an optimal AR display method by combining the user's current location and past movement history. This provides an optimal display method based on the user's geographical location information. Some or all of the above-described processing in the AR display unit may be performed using, for example, AI, or may be performed without AI. For example, the AR display unit can input data of the user's geographical location information into AI, which can analyze the data and provide an optimal display method. This allows the AR display unit to provide an optimal display method based on the user's geographical location information.

[0050] The AR display unit can analyze the user's social media activity and customize the display method when displaying AR. The AR display unit, for example, collects and analyzes the user's social media activity. For example, the AR display unit analyzes the user's social media activity and suggests a relevant AR display method. The AR display unit can also present a relevant AR display method based on the user's social media check-in history. Furthermore, the AR display unit can analyze the content of the user's social media posts and suggest a relevant AR display method. In this way, the display method is customized based on the user's social media activity. Some or all of the above-described processing in the AR display unit may be performed using, for example, AI, or may be performed without using AI. For example, the AR display unit can input data on the user's social media activity into AI, which can analyze the data and suggest an optimal display method. In this way, the AR display unit can provide an optimal display method based on the user's social media activity.

[0051] During learning, the learning unit can optimize the learning algorithm by referring to past learning data. The learning unit, for example, collects and analyzes past learning data. For example, the learning unit selects an optimal learning algorithm based on the past learning data. The learning unit can also propose an efficient learning method from the past learning data. Furthermore, the learning unit can analyze the past learning data and optimize the learning algorithm. This provides an optimal learning algorithm based on the past learning data. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input data of the past learning data into AI, which can analyze the data and provide an optimal learning algorithm. This allows the learning unit to provide an optimal learning algorithm based on the past learning data.

[0052] During learning, the learning unit can weight the learning data based on the time when the travel history was submitted. The learning unit, for example, collects and analyzes the time when the travel history was submitted. For example, the learning unit weights the learning data by placing emphasis on recent travel history. The learning unit can also weight the learning data by referring to past travel history. Furthermore, the learning unit can adjust the weighting of the learning data based on the time when the travel history was submitted. In this way, the learning data is weighted based on the time when the travel history was submitted. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input data on the time when the travel history was submitted into AI, which can analyze the data and weight the learning data. In this way, the learning unit can provide optimal weighting of the learning data based on the time when the travel history was submitted.

[0053] When linking with a local user, the linking unit with the local user can provide an optimal linking method by referring to past linking history. The linking unit with the local user, for example, collects and analyzes past linking history. For example, the linking unit with the local user proposes an optimal linking method based on the past linking history. The linking unit with the local user can also propose an efficient linking method from the past linking history. Furthermore, the linking unit with the local user can analyze the past linking history and provide an optimal linking method. This provides an optimal linking method based on the past linking history. Some or all of the above-described processing in the linking unit with the local user may be performed using, for example, AI, or may be performed without using AI. For example, the linking unit with the local user can input data of the past linking history into AI, which can analyze the data and provide an optimal linking method. This allows the linking unit with the local user to provide an optimal linking method based on the past linking history.

[0054] The local user collaboration unit can provide an optimal collaboration method by taking into account the user's geographical location information when collaborating with a local user. The local user collaboration unit, for example, collects and analyzes the user's geographical location information. For example, the local user collaboration unit proposes an optimal collaboration method based on the user's current location. The local user collaboration unit can also preferentially display related collaboration methods based on the user's geographical location information. Furthermore, the local user collaboration unit can propose an optimal collaboration method by combining the user's current location and past movement history. This provides an optimal collaboration method based on the user's geographical location information. Some or all of the above-described processing in the local user collaboration unit may be performed using, for example, AI, or may be performed without AI. For example, the local user collaboration unit can input the user's geographical location information data into AI, which can analyze the data and provide an optimal collaboration method. This allows the local user collaboration unit to provide an optimal collaboration method based on the user's geographical location information.

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

[0056] The reception unit can also monitor the user's current health condition and adjust the input method based on the health condition. For example, if the user is tired, a simple interface can be provided to minimize input steps. Alternatively, if the user is in good health, detailed input options can be provided and a customizable input method can be suggested. Furthermore, if the user is in a hurry, voice input can be prioritized to allow the user to quickly enter destinations and points of interest. In this way, an input method according to the user's health condition can be provided.

[0057] The learning unit can also analyze the user's social media activity to learn about the user's interests. For example, it can analyze the places the user frequently checks in on social media and the content of posts to understand the user's preferences. It can also collect information on the accounts the user follows and the groups the user participates in to learn about the user's interests. Furthermore, it can make optimal suggestions for the next search based on the user's social media activity history. This allows for suggestions based on the user's social media activity.

[0058] The local user collaboration unit can also adjust the collaboration method with local users taking into account the user's current activity status. For example, if the user is sightseeing, collaboration with local users who are familiar with tourist spots can be prioritized. Also, if the user is looking for a meal, collaboration with local users who are familiar with restaurants can be prioritized. Furthermore, if the user is participating in an event, collaboration with local users who are familiar with event information can be prioritized. This provides a collaboration method with local users that suits the user's current activity status.

[0059] The generation unit can also generate a walking route taking into account the user's current weather information. For example, if it is raining, a route centered around indoor tourist spots can be suggested. On the other hand, if it is sunny, a route centered around outdoor tourist spots can be suggested. Furthermore, if it is windy, a route that avoids the wind can be suggested. In this way, an optimal walking route is generated based on the user's current weather information.

[0060] The navigation unit can also refer to the user's past navigation history to provide a guidance method that the user prefers. For example, if the user has previously preferred voice guidance, the navigation unit can provide voice guidance preferentially. Also, if the user has previously preferred map display, the navigation unit can provide map display preferentially. Furthermore, the navigation unit can suggest an optimal guidance method based on the guidance methods the user has previously used. This allows the navigation unit to provide an optimal guidance method based on the user's past navigation history.

[0061] The processing flow of the first embodiment will be briefly explained below.

[0062] Step 1: The reception unit accepts input of a destination or place of interest. For example, the reception unit provides an interface for the user to input places they want to visit or places of interest. The user can input information about tourist spots, restaurants, historical buildings, etc. Step 2: The generation unit generates a walking route based on the information received by the reception unit. For example, the generation unit calculates the optimal walking route based on the information input by the user. The generation unit can also customize the route taking into account the user's preferences and past travel history. Step 3: The navigation unit performs navigation based on the walking route generated by the generation unit. For example, the navigation unit provides the user with voice guidance and map display, and guides the user along the route in real time. The navigation unit can also track the user's current location and update the user's progress along the route in real time. Step 4: The AR display unit visualizes advance information, history, and features of the location guided by the navigation unit. For example, the AR display unit uses AR technology to display information about the location the user is visiting. When the user stands in front of a historical building, the history and features of the building are displayed on the AR display unit. This allows the user to obtain information about the location in real time.

[0063] (Example 2) A new place search system according to an embodiment of the present invention generates a walking route when searching for a new place and provides navigation based on that route. This system visualizes the user's prior information, history, and characteristics of the place they will visit using AR. Furthermore, the system learns the user's travel history and preferences and makes suggestions that reflect those information for the next search. Furthermore, the generation AI collaborates with local users to provide local knowledge and information. For example, when a user searches for a new place, they input their destination and points of interest. The generation AI then generates an optimal walking route based on the input information. This route is customized taking into account the user's preferences and past travel history. Navigation begins based on the generated route. During navigation, the user's prior information, history, and characteristics of the place they will visit are visualized using AR. For example, when the user stands in front of a historical building, the building's history and characteristics are displayed on the display. This allows the user to obtain information about the place in real time. Furthermore, the generation AI learns the user's travel history and preferences. As a result, suggestions tailored to the user's preferences are made for the next search. For example, if a user has frequently visited historical places in the past, a route centered around that historical place will be suggested again next time. The generative AI also works with local users to provide local knowledge and information. For example, it provides information on hidden attractions and recommended restaurants known to local users. This allows users to enjoy the place from the perspective of locals. This system allows users to efficiently explore new places and gain in-depth knowledge. It also provides a more fulfilling travel experience with customized suggestions tailored to the user's preferences. This allows the new place discovery system to make customized suggestions based on the user's travel history and preferences, allowing users to efficiently explore new places and gain in-depth knowledge.

[0064] A new place search system according to an embodiment includes a reception unit, a generation unit, a navigation unit, and an AR display unit. The reception unit receives input of a destination or a place of interest. For example, the reception unit provides an interface for a user to input places they want to visit or places of interest. The user can input information such as tourist attractions, restaurants, and historical buildings. The generation unit generates a walking route based on the information received by the reception unit. For example, the generation unit calculates an optimal walking route based on the user's input information. The generation unit can also customize the route taking into account the user's preferences and past travel history. The navigation unit performs navigation based on the walking route generated by the generation unit. For example, the navigation unit provides audio guidance and map display to the user, guiding the user along the route in real time. The navigation unit can also track the user's current location and update the user's progress along the route in real time. The AR display unit visualizes advance information, history, features, etc. of the place guided by the navigation unit. For example, the AR display unit displays information about the place the user is visiting using AR technology. When the user stands in front of a historical building, the building's history and features are displayed on the AR display unit. This allows the user to obtain information about the place in real time, thereby enabling the new place discovery system according to the embodiment to enable the user to efficiently explore new places and gain in-depth knowledge.

[0065] The learning unit can learn the user's travel history and preferences. For example, the learning unit collects information such as the user's past visited places, ratings, and reviews to learn the user's travel history and preferences. For example, the learning unit collects information on tourist spots and restaurants the user has visited in the past to understand the user's preferences. The learning unit can also analyze the user's ratings and reviews to learn the user's preferred places. Furthermore, the learning unit can make optimal suggestions for the next exploration based on the user's travel history and preferences. For example, if a user has visited many historical places in the past, the learning unit can suggest a route centered around historical places next time. This allows suggestions to be made that are tailored to the user's preferences. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without AI. For example, the learning unit can input data on the user's travel history and preferences into AI, which can analyze the data and learn the user's preferences. This allows the learning unit to make optimal suggestions based on the user's travel history and preferences.

[0066] The local user collaboration unit can collaborate with local users. The local user collaboration unit can provide information such as hidden attractions and recommended restaurants known to local users. For example, the local user collaboration unit can collect information on tourist spots and restaurants known to local users and provide it to the user. The local user collaboration unit can also provide information on events and guide services hosted by local users. For example, the local user collaboration unit can collect information on events hosted by local users and provide it to the user. This allows the user to enjoy the place from the perspective of local people. Some or all of the above-mentioned processing in the local user collaboration unit can be performed using AI, for example, or without AI. For example, the local user collaboration unit can input information about local users into AI, which can analyze the information and provide it to the user. This allows the local user collaboration unit to provide knowledge and information unique to the local area.

[0067] The generation unit can generate a walking route based on the user's preferences and past travel history. The generation unit generates a walking route, for example, taking the user's preferences and past travel history into consideration. For example, the generation unit proposes an optimal walking route based on places the user has visited in the past and their ratings. The generation unit can also learn the user's preferences and generate a route that includes the user's favorite places. For example, the generation unit proposes a route that centers around historical places the user has visited in the past for the next visit. This generates a walking route that suits the user's preferences. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input data on the user's preferences and past travel history into the generation AI, which can analyze the data and generate an optimal walking route. This allows the generation unit to generate an optimal walking route based on the user's preferences and past travel history.

[0068] The navigation unit can perform navigation based on the generated walking route. The navigation unit, for example, provides audio guidance and a map display to the user based on the generated walking route. For example, the navigation unit provides audio guidance of places the user will visit. The navigation unit can also track the user's current location and update the route progress in real time. For example, the navigation unit provides re-route guidance if the user deviates from the route. In this way, navigation is performed based on the generated walking route. Some or all of the above-described processing in the navigation unit may be performed using, for example, AI, or may be performed without using AI. For example, the navigation unit can input data of the generated walking route into AI, which can analyze the data and perform navigation. In this way, the navigation unit can provide optimal navigation based on the generated walking route.

[0069] The AR display unit can visualize advance information, history, features, etc. of places the user will visit. The AR display unit, for example, displays information about places the user will visit using AR technology. For example, when the user stands in front of a historical building, the AR display unit displays the history and features of the building. The AR display unit can also update information about places the user will visit in real time. For example, the AR display unit displays new information each time the user moves. This allows information about places the user will visit to be visualized in real time. Some or all of the above-described processing in the AR display unit may be performed using, for example, AI, or may be performed without using AI. For example, the AR display unit can input information about places the user will visit into AI, which can analyze the data and display the information. This allows the AR display unit to visualize information about places the user will visit in real time.

[0070] The reception unit can estimate the user's emotions and adjust the input method for destinations and places of interest based on the estimated user emotions. The reception unit, for example, analyzes the user's facial expressions and voice to estimate emotions. For example, if the user is stressed, the reception unit provides a simple interface and minimizes input steps. Furthermore, if the user is relaxed, the reception unit can provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the reception unit prioritizes voice input, allowing the user to quickly input destinations and places of interest. This provides an input method that suits the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's facial expressions and voice data into a generation AI, which can then analyze the data to estimate emotions. This allows the reception unit to provide the optimum input method based on the user's feelings.

[0071] The reception unit can analyze the user's past input history and provide an optimal input interface. The reception unit, for example, collects and analyzes the user's past input history. For example, the reception unit automatically displays destinations and places of interest that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest destinations and places of interest to be used during a specific time period based on the user's past input history. This provides an optimal input interface based on the user's past input history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input data of the user's past input history into AI, which can analyze the data and provide an optimal input interface. This allows the reception unit to provide an optimal input interface based on the user's past input history.

[0072] The reception unit can present input candidates based on the user's current activity status and areas of interest upon reception. The reception unit, for example, collects and analyzes the user's current activity status and areas of interest. For example, the reception unit suggests related destinations and places of interest based on the user's current activity status. The reception unit can also present related input candidates based on the user's areas of interest. Furthermore, the reception unit can combine the user's current activity status and areas of interest to present optimal input candidates. As a result, input candidates based on the user's current activity status and areas of interest are presented. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input data on the user's current activity status and areas of interest into AI, which can analyze the data and present optimal input candidates. As a result, the reception unit can provide optimal input candidates based on the user's current activity status and areas of interest.

[0073] The reception unit can estimate the user's emotions and prioritize input contents based on the estimated user emotions. The reception unit, for example, analyzes the user's facial expressions and voice to estimate emotions. For example, if the user is stressed, the reception unit can prioritize displaying important input contents. Furthermore, if the user is relaxed, the reception unit can prioritize displaying detailed input contents. Furthermore, if the user is in a hurry, the reception unit can prioritize displaying the minimum necessary input contents. This allows the priority of input contents to be determined according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's facial expressions and voice data into the generation AI, which can analyze the data to estimate emotions. This allows the reception unit to provide optimal input content priorities based on the user's emotions.

[0074] The reception unit can prioritize presenting highly relevant input candidates in consideration of the user's geographical location information when receiving the input. The reception unit, for example, collects and analyzes the user's geographical location information. For example, the reception unit prioritizes presenting nearby destinations and places of interest based on the user's current location. The reception unit can also prioritize displaying related input candidates based on the user's geographical location information. Furthermore, the reception unit can combine the user's current location with past movement history to present optimal input candidates. As a result, input candidates based on the user's geographical location information are prioritized. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input data of the user's geographical location information into AI, which can analyze the data and present optimal input candidates. As a result, the reception unit can provide optimal input candidates based on the user's geographical location information.

[0075] The reception unit can analyze the user's social media activity and present related input candidates at the time of reception. The reception unit, for example, collects and analyzes the user's social media activity. For example, the reception unit analyzes the user's social media activity and suggests related destinations or places of interest. The reception unit can also present related input candidates based on the user's social media check-in history. Furthermore, the reception unit can analyze the content of the user's social media posts and present related input candidates. As a result, input candidates based on the user's social media activity are presented. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input data on the user's social media activity into AI, which can analyze the data and present optimal input candidates. As a result, the reception unit can provide optimal input candidates based on the user's social media activity.

[0076] The generation unit can estimate the user's emotions and adjust the method for generating a walking route based on the estimated user emotions. The generation unit, for example, analyzes the user's facial expressions and voice to estimate the emotions. For example, if the user is relaxed, the generation unit can generate a walking route that proceeds at a leisurely pace. If the user is in a hurry, the generation unit can also generate a walking route that emphasizes the shortest route. Furthermore, if the user is excited, the generation unit can generate a walking route that adds visually stimulating effects. This provides a method for generating a walking route that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the generation unit can input the user's facial expressions and voice data into the generation AI, which can then analyze the data to estimate the emotions. This allows the generation unit to provide a method for generating an optimal walking route based on the user's emotions.

[0077] When generating a walking route, the generation unit can select an optimal route by referring to the user's past travel history. The generation unit, for example, collects and analyzes the user's past travel history. For example, the generation unit can propose an optimal walking route based on routes the user has used in the past. The generation unit can also propose a route that avoids crowded areas based on the user's past travel history. Furthermore, the generation unit can analyze the user's past travel history and propose the most efficient walking route. In this way, an optimal walking route based on the user's past travel history is selected. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the generation unit can input data of the user's past travel history into the generation AI, which can analyze the data and select an optimal walking route. In this way, the generation unit can provide an optimal walking route based on the user's past travel history.

[0078] When generating a walking route, the generation unit can customize the route based on the user's current activity status. The generation unit, for example, collects and analyzes the user's current activity status. For example, the generation unit proposes an optimal walking route based on the user's current activity status. The generation unit can also adjust the difficulty of the route based on the user's current activity status. Furthermore, the generation unit can propose an optimal walking route by combining the user's current activity status with past travel history. This customizes the walking route based on the user's current activity status. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the generation unit can input data on the user's current activity status into the generation AI, which can analyze the data and propose an optimal walking route. This allows the generation unit to provide an optimal walking route based on the user's current activity status.

[0079] The generation unit can estimate the user's emotions and determine the priority of routes to be generated based on the estimated user emotions. The generation unit, for example, analyzes the user's facial expressions and voice to estimate emotions. For example, if the user is feeling stressed, the generation unit can prioritize suggesting relaxing routes. Furthermore, if the user is relaxed, the generation unit can also prioritize suggesting routes that include many interesting places. Furthermore, if the user is in a hurry, the generation unit can also prioritize suggesting the shortest route. In this way, the priority of routes is determined according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the generation unit can input the user's facial expressions and voice data into the generation AI, which can then analyze the data to estimate emotions. This allows the generation unit to provide a priority order of optimal routes based on the user's emotions.

[0080] When generating a walking route, the generation unit can select an optimal route by taking into account the user's geographical location information. The generation unit, for example, collects and analyzes the user's geographical location information. For example, the generation unit proposes an optimal walking route based on the user's current location. The generation unit can also preferentially display related routes based on the user's geographical location information. Furthermore, the generation unit can propose an optimal walking route by combining the user's current location and past movement history. In this way, an optimal walking route based on the user's geographical location information is selected. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input data of the user's geographical location information into the generation AI, which can analyze the data and select an optimal walking route. In this way, the generation unit can provide an optimal walking route based on the user's geographical location information.

[0081] When generating a walking route, the generation unit can analyze the user's social media activity to customize the route. The generation unit, for example, collects and analyzes the user's social media activity. For example, the generation unit analyzes the user's social media activity and proposes related walking routes. The generation unit can also present related routes based on the user's social media check-in history. Furthermore, the generation unit can analyze the content of the user's social media posts and propose related walking routes. In this way, a walking route based on the user's social media activity is customized. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the generation unit can input data on the user's social media activity into the generation AI, which can analyze the data and propose an optimal walking route. In this way, the generation unit can provide an optimal walking route based on the user's social media activity.

[0082] The navigation unit can estimate the user's emotions and adjust the navigation guidance method based on the estimated user emotions. The navigation unit, for example, analyzes the user's facial expressions and voice to estimate the emotions. For example, if the user is nervous, the navigation unit can provide a simple, highly visible guidance method. If the user is relaxed, the navigation unit can also provide a guidance method that includes detailed information. Furthermore, if the user is in a hurry, the navigation unit can also provide a guidance method that focuses on the main points. This allows the navigation guidance method to be provided according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the navigation unit may be performed using, for example, AI, or may be performed without using AI. For example, the navigation unit can input the user's facial expressions and voice data into the generation AI, which can analyze the data and estimate the emotion. This allows the navigation unit to provide an optimal navigation guidance method based on the user's emotions.

[0083] During navigation, the navigation unit can provide an optimal guidance method by referring to the user's past navigation history. The navigation unit, for example, collects and analyzes the user's past navigation history. For example, the navigation unit proposes an optimal guidance method based on routes the user has used in the past. The navigation unit can also propose a guidance method that avoids congestion based on the user's past navigation history. Furthermore, the navigation unit can analyze the user's past navigation history and propose the most efficient guidance method. This provides an optimal guidance method based on the user's past navigation history. Some or all of the above-described processing in the navigation unit may be performed using, for example, AI, or may be performed without using AI. For example, the navigation unit can input data on the user's past navigation history into AI, which can analyze the data and provide an optimal guidance method. This allows the navigation unit to provide an optimal guidance method based on the user's past navigation history.

[0084] The navigation unit can customize a guidance method based on the user's current activity status during navigation. The navigation unit, for example, collects and analyzes the user's current activity status. For example, the navigation unit proposes an optimal guidance method based on the user's current activity status. The navigation unit can also adjust the level of detail of the guidance based on the user's current activity status. Furthermore, the navigation unit can propose an optimal guidance method by combining the user's current activity status with past navigation history. In this way, the guidance method is customized based on the user's current activity status. Some or all of the above-described processing in the navigation unit may be performed using, for example, AI, or may be performed without using AI. For example, the navigation unit can input data on the user's current activity status into AI, which can analyze the data and propose an optimal guidance method. In this way, the navigation unit can provide an optimal guidance method based on the user's current activity status.

[0085] The navigation unit can estimate the user's emotions and determine navigation priorities based on the estimated user emotions. The navigation unit, for example, analyzes the user's facial expressions and voice to estimate emotions. For example, if the user is feeling stressed, the navigation unit can prioritize providing guidance that helps the user relax. Furthermore, if the user is relaxed, the navigation unit can prioritize providing guidance that includes a lot of interesting information. Furthermore, if the user is in a hurry, the navigation unit can prioritize providing guidance for the shortest route. This determines navigation priorities according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the navigation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the navigation unit can input the user's facial expressions and voice data into the generation AI, which can analyze the data to estimate emotions. This allows the navigation unit to provide optimal navigation priorities based on the user's emotions.

[0086] During navigation, the navigation unit can provide an optimal guidance method by taking into account the user's geographical location information. The navigation unit, for example, collects and analyzes the user's geographical location information. For example, the navigation unit proposes an optimal guidance method based on the user's current location. The navigation unit can also preferentially display related guidance methods based on the user's geographical location information. Furthermore, the navigation unit can propose an optimal guidance method by combining the user's current location with past movement history. This provides an optimal guidance method based on the user's geographical location information. Some or all of the above-described processing in the navigation unit may be performed using, for example, AI, or may be performed without using AI. For example, the navigation unit can input data on the user's geographical location information into AI, which can analyze the data and provide an optimal guidance method. This allows the navigation unit to provide an optimal guidance method based on the user's geographical location information.

[0087] The navigation unit can customize a guidance method by analyzing a user's social media activity during navigation. The navigation unit, for example, collects and analyzes the user's social media activity. For example, the navigation unit analyzes the user's social media activity and suggests a related guidance method. The navigation unit can also present a related guidance method based on the user's social media check-in history. Furthermore, the navigation unit can analyze the content of the user's social media posts and suggest a related guidance method. In this way, the guidance method is customized based on the user's social media activity. Some or all of the above-described processing in the navigation unit may be performed using, for example, AI, or may be performed without using AI. For example, the navigation unit can input data on the user's social media activity into AI, which can analyze the data and suggest an optimal guidance method. In this way, the navigation unit can provide an optimal guidance method based on the user's social media activity.

[0088] The AR display unit can estimate the user's emotions and adjust the AR display method based on the estimated user emotions. The AR display unit, for example, analyzes the user's facial expressions and voice to estimate emotions. For example, if the user is nervous, the AR display unit can provide a simple, highly visible AR display. Furthermore, if the user is relaxed, the AR display unit can provide an AR display that includes detailed information. Furthermore, if the user is in a hurry, the AR display unit can provide an AR display that focuses on the main points. This provides an AR display method that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the AR display unit may be performed using, for example, AI, or without AI. For example, the AR display unit can input the user's facial expressions and voice data into the generation AI, which can analyze the data to estimate emotions. This allows the AR display unit to provide an optimal AR display method based on the user's emotions.

[0089] The AR display unit can provide an optimal display method by referring to the user's past display history when displaying AR. The AR display unit, for example, collects and analyzes the user's past display history. For example, the AR display unit can suggest related AR displays based on information previously displayed by the user. The AR display unit can also prioritize displaying information of interest from the user's past display history. Furthermore, the AR display unit can analyze the user's past display history and suggest the most efficient AR display method. This provides an optimal display method based on the user's past display history. Some or all of the above-described processing in the AR display unit may be performed using, for example, AI, or may be performed without AI. For example, the AR display unit can input data of the user's past display history into AI, which can analyze the data and provide an optimal display method. This allows the AR display unit to provide an optimal display method based on the user's past display history.

[0090] The AR display unit can customize the display method based on the user's current activity status when displaying AR. The AR display unit, for example, collects and analyzes the user's current activity status. For example, the AR display unit proposes an optimal AR display method based on the user's current activity status. The AR display unit can also adjust the level of detail of the display based on the user's current activity status. Furthermore, the AR display unit can combine the user's current activity status with past display history to propose an optimal AR display method. This customizes the display method based on the user's current activity status. Some or all of the above-described processing in the AR display unit may be performed using, for example, AI, or may be performed without AI. For example, the AR display unit can input data on the user's current activity status into AI, which can analyze the data and propose an optimal display method. This allows the AR display unit to provide an optimal display method based on the user's current activity status.

[0091] The AR display unit can estimate the user's emotions and determine the priority of AR display based on the estimated user's emotions. The AR display unit, for example, analyzes the user's facial expressions and voice to estimate emotions. For example, if the user is feeling stressed, the AR display unit can prioritize displaying relaxing information. Furthermore, if the user is relaxed, the AR display unit can also prioritize providing AR display containing a lot of interesting information. Furthermore, if the user is in a hurry, the AR display unit can also prioritize providing AR display that focuses on the main points. This determines the priority of AR display according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the AR display unit may be performed using, for example, AI, or without AI. For example, the AR display unit can input the user's facial expressions and voice data into the generation AI, which can analyze the data to estimate emotions. This allows the AR display unit to provide optimal AR display priorities based on the user's emotions.

[0092] The AR display unit can provide an optimal display method during AR display, taking into account the user's geographical location information. The AR display unit, for example, collects and analyzes the user's geographical location information. For example, the AR display unit proposes an optimal AR display method based on the user's current location. The AR display unit can also preferentially display related AR display methods based on the user's geographical location information. Furthermore, the AR display unit can propose an optimal AR display method by combining the user's current location and past movement history. This provides an optimal display method based on the user's geographical location information. Some or all of the above-described processing in the AR display unit may be performed using, for example, AI, or may be performed without AI. For example, the AR display unit can input data of the user's geographical location information into AI, which can analyze the data and provide an optimal display method. This allows the AR display unit to provide an optimal display method based on the user's geographical location information.

[0093] The AR display unit can analyze the user's social media activity and customize the display method when displaying AR. The AR display unit, for example, collects and analyzes the user's social media activity. For example, the AR display unit analyzes the user's social media activity and suggests a relevant AR display method. The AR display unit can also present a relevant AR display method based on the user's social media check-in history. Furthermore, the AR display unit can analyze the content of the user's social media posts and suggest a relevant AR display method. In this way, the display method is customized based on the user's social media activity. Some or all of the above-described processing in the AR display unit may be performed using, for example, AI, or may be performed without using AI. For example, the AR display unit can input data on the user's social media activity into AI, which can analyze the data and suggest an optimal display method. In this way, the AR display unit can provide an optimal display method based on the user's social media activity.

[0094] The learning unit can estimate the user's emotions and select training data based on the estimated user emotions. The learning unit, for example, analyzes the user's facial expressions and voice to estimate emotions. For example, if the user is relaxed, the learning unit selects detailed training data. Furthermore, if the user is in a hurry, the learning unit can select training data that focuses on the main points. Furthermore, if the user is excited, the learning unit can select visually stimulating training data. Thus, the training data is selected according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the learning unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the learning unit can input the user's facial expressions and voice data into the generation AI, which can analyze the data and estimate emotions. This allows the learning unit to provide optimal training data based on the user's emotions.

[0095] During learning, the learning unit can optimize the learning algorithm by referring to past learning data. The learning unit, for example, collects and analyzes past learning data. For example, the learning unit selects an optimal learning algorithm based on the past learning data. The learning unit can also propose an efficient learning method from the past learning data. Furthermore, the learning unit can analyze the past learning data and optimize the learning algorithm. This provides an optimal learning algorithm based on the past learning data. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input data of the past learning data into AI, which can analyze the data and provide an optimal learning algorithm. This allows the learning unit to provide an optimal learning algorithm based on the past learning data.

[0096] The learning unit can estimate the user's emotions and adjust the frequency of learning based on the estimated user emotions. The learning unit, for example, analyzes the user's facial expressions and voice to estimate emotions. For example, the learning unit can reduce the frequency of learning when the user is stressed. The learning unit can also increase the frequency of learning when the user is relaxed. Furthermore, the learning unit can adjust the frequency of learning when the user is in a hurry. This adjusts the frequency of learning according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the learning unit can input the user's facial expressions and voice data into the generation AI, which can analyze the data to estimate emotions. This allows the learning unit to provide an optimal learning frequency based on the user's emotions.

[0097] During learning, the learning unit can weight the learning data based on the time when the travel history was submitted. The learning unit, for example, collects and analyzes the time when the travel history was submitted. For example, the learning unit weights the learning data by placing emphasis on recent travel history. The learning unit can also weight the learning data by referring to past travel history. Furthermore, the learning unit can adjust the weighting of the learning data based on the time when the travel history was submitted. In this way, the learning data is weighted based on the time when the travel history was submitted. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input data on the time when the travel history was submitted into AI, which can analyze the data and weight the learning data. In this way, the learning unit can provide optimal weighting of the learning data based on the time when the travel history was submitted.

[0098] The local user collaboration unit can estimate the user's emotions and adjust a collaboration method with local users based on the estimated user emotions. The local user collaboration unit can, for example, analyze the user's facial expressions and voice to estimate emotions. For example, if the user is relaxed, the local user collaboration unit can prioritize collaboration with local users who provide detailed information. Furthermore, if the user is in a hurry, the local user collaboration unit can prioritize collaboration with local users who provide concise information. Furthermore, if the user is excited, the local user collaboration unit can prioritize collaboration with local users who provide visually stimulating information. This provides a collaboration method with local users that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the local user collaboration unit can be performed, for example, using AI or without AI. For example, the local user collaboration unit can input the user's facial expressions and voice data into the generation AI, which can then analyze the data and infer emotions. This allows the local user collaboration unit to provide the optimal collaboration method based on the user's emotions.

[0099] When linking with a local user, the linking unit with the local user can provide an optimal linking method by referring to past linking history. The linking unit with the local user, for example, collects and analyzes past linking history. For example, the linking unit with the local user proposes an optimal linking method based on the past linking history. The linking unit with the local user can also propose an efficient linking method from the past linking history. Furthermore, the linking unit with the local user can analyze the past linking history and provide an optimal linking method. This provides an optimal linking method based on the past linking history. Some or all of the above-described processing in the linking unit with the local user may be performed using, for example, AI, or may be performed without using AI. For example, the linking unit with the local user can input data of the past linking history into AI, which can analyze the data and provide an optimal linking method. This allows the linking unit with the local user to provide an optimal linking method based on the past linking history.

[0100] The local user collaboration unit can estimate the user's emotions and determine collaboration priorities based on the estimated user emotions. The local user collaboration unit, for example, analyzes the user's facial expressions and voice to estimate emotions. For example, if the user is feeling stressed, the local user collaboration unit prioritizes collaboration with local users who provide relaxing information. Furthermore, if the user is relaxed, the local user collaboration unit can also prioritize collaboration with local users who provide interesting information. Furthermore, if the user is in a hurry, the local user collaboration unit can also prioritize collaboration with local users who provide concise information. This determines collaboration priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the local user collaboration unit can be performed, for example, using AI, or without AI. For example, the local user collaboration unit can input the user's facial expressions and voice data into the generation AI, which can then analyze the data and infer emotions. This allows the local user collaboration unit to provide optimal collaboration priorities based on the user's emotions.

[0101] The local user collaboration unit can provide an optimal collaboration method by taking into account the user's geographical location information when collaborating with a local user. The local user collaboration unit, for example, collects and analyzes the user's geographical location information. For example, the local user collaboration unit proposes an optimal collaboration method based on the user's current location. The local user collaboration unit can also preferentially display related collaboration methods based on the user's geographical location information. Furthermore, the local user collaboration unit can propose an optimal collaboration method by combining the user's current location and past movement history. This provides an optimal collaboration method based on the user's geographical location information. Some or all of the above-described processing in the local user collaboration unit may be performed using, for example, AI, or may be performed without AI. For example, the local user collaboration unit can input the user's geographical location information data into AI, which can analyze the data and provide an optimal collaboration method. This allows the local user collaboration unit to provide an optimal collaboration method based on the user's geographical location information. === Hard Collateral 1-1 === Each of the above-described elements, including the reception unit, generation unit, navigation unit, and AR display 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 provides an interface for the user to input places they want to visit or places of interest. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and calculates an optimal walking route based on the user's input information. The navigation unit is implemented, for example, by the control unit 46A of the smart device 14 and provides audio guidance and map display, guiding the route in real time. The AR display unit is implemented, for example, by the output device 40 of the smart device 14 and displays information about places to visit using AR technology. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned reception unit, generation unit, navigation unit, and AR display unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214 and provides an interface for the user to input places they want to visit or places of interest. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and calculates an optimal walking route based on the user's input information. The navigation unit is realized, for example, by the control unit 46A of the smart glasses 214 and provides audio guidance and map display, guiding the route in real time. The AR display unit is realized, for example, by the speaker 240 of the smart glasses 214 and displays information about places to visit using AR technology. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, generation unit, navigation unit, and AR display unit is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset terminal 314 and provides an interface for the user to input places they want to visit or places of interest. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and calculates an optimal walking route based on the information input by the user. The navigation unit is realized, for example, by the control unit 46A of the headset terminal 314 and provides audio guidance and map display, guiding the route in real time. The AR display unit is realized, for example, by the display 343 of the headset terminal 314 and displays information about places to visit using AR technology. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, generation unit, navigation unit, and AR display unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414 and provides an interface for the user to input places they want to visit or places of interest. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and calculates an optimal walking route based on the information input by the user. The navigation unit is realized, for example, by the control unit 46A of the robot 414 and provides audio guidance and map display, guiding the route in real time. The AR display unit is realized, for example, by the speaker 240 of the robot 414 and displays information about places to visit using AR technology.

[0102] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0103] The reception unit can also monitor the user's current health condition and adjust the input method based on the health condition. For example, if the user is tired, a simple interface can be provided to minimize input steps. Alternatively, if the user is in good health, detailed input options can be provided and a customizable input method can be suggested. Furthermore, if the user is in a hurry, voice input can be prioritized to allow the user to quickly enter destinations and points of interest. In this way, an input method according to the user's health condition can be provided.

[0104] The learning unit can also analyze the user's social media activity to learn about the user's interests. For example, it can analyze the places the user frequently checks in on social media and the content of posts to understand the user's preferences. It can also collect information on the accounts the user follows and the groups the user participates in to learn about the user's interests. Furthermore, it can make optimal suggestions for the next search based on the user's social media activity history. This allows for suggestions based on the user's social media activity.

[0105] The local user collaboration unit can also adjust the collaboration method with local users taking into account the user's current activity status. For example, if the user is sightseeing, collaboration with local users who are familiar with tourist spots can be prioritized. Also, if the user is looking for a meal, collaboration with local users who are familiar with restaurants can be prioritized. Furthermore, if the user is participating in an event, collaboration with local users who are familiar with event information can be prioritized. This provides a collaboration method with local users that suits the user's current activity status.

[0106] The generation unit can also generate a walking route taking into account the user's current weather information. For example, if it is raining, a route centered around indoor tourist spots can be suggested. On the other hand, if it is sunny, a route centered around outdoor tourist spots can be suggested. Furthermore, if it is windy, a route that avoids the wind can be suggested. In this way, an optimal walking route is generated based on the user's current weather information.

[0107] The navigation unit can also refer to the user's past navigation history to provide a guidance method that the user prefers. For example, if the user has previously preferred voice guidance, the navigation unit can provide voice guidance preferentially. Also, if the user has previously preferred map display, the navigation unit can provide map display preferentially. Furthermore, the navigation unit can suggest an optimal guidance method based on the guidance methods the user has previously used. This allows the navigation unit to provide an optimal guidance method based on the user's past navigation history.

[0108] The AR display unit can also estimate the user's emotions and adjust the content of the AR display based on the estimated user's emotions. For example, if the user is excited, visually stimulating information can be displayed preferentially. If the user is relaxed, an AR display including detailed information can be provided. Furthermore, if the user is in a hurry, an AR display that focuses on the main points can be provided. In this way, the content of the AR display can be provided according to the user's emotions.

[0109] The reception unit can also estimate the user's emotions and determine the priority of input contents based on the estimated user's emotions. For example, if the user is feeling stressed, important input contents can be displayed with priority. Also, if the user is relaxed, detailed input contents can be displayed with priority. Furthermore, if the user is in a hurry, the minimum necessary input contents can be displayed with priority. In this way, the priority of input contents is determined according to the user's emotions.

[0110] The generation unit can also estimate the user's emotions and adjust the method for generating a walking route based on the estimated user's emotions. For example, if the user is relaxed, a walking route that proceeds at a leisurely pace can be generated. If the user is in a hurry, a walking route that emphasizes the shortest route can be generated. Furthermore, if the user is excited, a walking route that adds visually stimulating effects can be generated. This provides a method for generating a walking route that corresponds to the user's emotions.

[0111] The navigation unit can also estimate the user's emotions and adjust the navigation guidance method based on the estimated user's emotions. For example, if the user is nervous, a simple and highly visible guidance method can be provided. If the user is relaxed, a guidance method including detailed information can be provided. Furthermore, if the user is in a hurry, a guidance method that focuses on the main points can be provided. In this way, a navigation guidance method according to the user's emotions can be provided.

[0112] The local user collaboration unit can also estimate the user's emotions and adjust the collaboration method with local users based on the estimated user's emotions. For example, if the user is relaxed, collaboration with local users who provide detailed information can be prioritized. If the user is in a hurry, collaboration with local users who provide concise information can be prioritized. Furthermore, if the user is excited, collaboration with local users who provide visually stimulating information can be prioritized. This provides a collaboration method with local users that corresponds to the user's emotions.

[0113] The processing flow of the second embodiment will be briefly explained below.

[0114] Step 1: The reception unit accepts input of a destination or place of interest. For example, the reception unit provides an interface for the user to input places they want to visit or places of interest. The user can input information about tourist spots, restaurants, historical buildings, etc. Step 2: The generation unit generates a walking route based on the information received by the reception unit. For example, the generation unit calculates the optimal walking route based on the information input by the user. The generation unit can also customize the route taking into account the user's preferences and past travel history. Step 3: The navigation unit performs navigation based on the walking route generated by the generation unit. For example, the navigation unit provides the user with voice guidance and map display, and guides the user along the route in real time. The navigation unit can also track the user's current location and update the user's progress along the route in real time. Step 4: The AR display unit visualizes advance information, history, and features of the location guided by the navigation unit. For example, the AR display unit uses AR technology to display information about the location the user is visiting. When the user stands in front of a historical building, the history and features of the building are displayed on the AR display unit. This allows the user to obtain information about the location in real time.

[0115] 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.

[0116] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0117] 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.

[0118] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0119] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0120] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0133] 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.

[0134] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0135] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0136] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0149] 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.

[0150] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0151] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0166] 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.

[0167] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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).

[0172] 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 indicated, and when they approach the ideal, a state of pleasure is indicated. 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 indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0173] 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."

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] [Explanation of symbols]

[0187] 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 system comprising: a reception unit that receives input of a destination or a place of interest; a generation unit that generates a walking route based on the information received by the reception unit; a navigation unit that performs navigation based on the walking route generated by the generation unit; and an AR display unit that visualizes advance information, history, and characteristics of the place guided by the navigation unit.

2. Equipped with a learning module that learns the user's travel history and preferences 2. The system of claim 1.

3. Establishing a department to collaborate with local users 2. The system of claim 1.

4. The system according to claim 1 , wherein the generating unit generates a walking route based on the user's preferences and past travel history.

5. The navigation unit Navigate based on the generated walking route 2. The system of claim 1.

6. The AR display unit Visualize advance information, history, and features of the places the user will visit 2. The system of claim 1.

7. The reception unit Inferring user emotions and adjusting the way destinations and points of interest are entered based on the user's emotions 2. The system of claim 1.

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

9. The reception unit At the time of reception, suggestions are provided based on the user's current activity and interests.

2. The system of claim 1.

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

2. The system of claim 1.

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