System

Abstract

An object of a system according to an embodiment is to enable even a user who is not good at reading a map to intuitively understand how to get to a destination.SOLUTION: A system according to an embodiment includes a moving image generation unit, a viewpoint navigation unit, and a speed adjustment unit. The moving image generation unit generates a moving image to a destination on the basis of a user's input. The viewpoint navigation unit performs navigation of the moving image generated by the moving image generation unit based on the viewpoint of the user. The speed adjustment unit moves the moving image navigated by the viewpoint navigation unit in accordance with the walking speed of the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem that it is difficult for users who have difficulty reading or understanding maps to understand how to get to their destination.

[0005] The system according to the embodiment aims to enable even a user who is not good at reading maps to intuitively understand how to get to a destination. [Means for solving the problem]

[0006] A system according to an embodiment includes a video generation unit, a viewpoint navigation unit, and a speed adjustment unit. The video generation unit generates a video to a destination based on a user's input. The viewpoint navigation unit navigates the video generated by the video generation unit based on the user's viewpoint. The speed adjustment unit moves the video navigated by the viewpoint navigation unit in accordance with the user's walking speed. [Effects of the Invention]

[0007] The system according to the embodiment can enable even a user who is not good at reading maps to intuitively understand how to get to a destination. [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 nonvolatile storage devices that store various programs, various parameters, etc. Examples of nonvolatile 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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) The navigation system according to an embodiment of the present invention is a system that generates a video showing directions from the user's current location to their destination simply by inputting "I want to go from XX to XX." This system starts navigation from the perspective of the user and the smartphone, and the screen moves in sync with the user's walking speed, making it easy to use for both children and the elderly. This makes the navigation system easy to use for all generations, including those who have difficulty reading maps and those who can read maps but have difficulty understanding them.

[0029] A navigation system according to an embodiment includes a video generation unit, a viewpoint navigation unit, and a speed adjustment unit. The video generation unit generates a video to a destination based on a user's input. For example, when a user inputs, "Tell me how to get from my house to the station," the generation AI analyzes the route and generates a video. The generation AI generates a video using a text generation AI (e.g., LLM). The generation AI can also generate video content using a multimodal generation AI. The generation AI can also generate a video based on user instructions. The viewpoint navigation unit navigates the video generated by the video generation unit based on the user's viewpoint. For example, if the user points their smartphone toward north, the video is also displayed with north as the reference point. This allows the user to receive navigation tailored to their viewpoint. The viewpoint navigation unit can display a video based on the user's viewpoint. For example, if the user holds the smartphone horizontally, the video is also displayed horizontally. The speed adjustment unit moves the video navigated by the viewpoint navigation unit in accordance with the user's walking speed. For example, if the user is walking slowly, the video is played slowly. Conversely, if the user is walking fast, the video is played quickly. The speed adjustment unit can play back videos in accordance with the user's walking speed. For example, if the user changes their walking speed, the video playback speed is automatically adjusted. As a result, the navigation system according to the embodiment generates videos to a destination based on user input, and provides viewpoint-based navigation and screen movement in accordance with the walking speed, allowing even those who are not good at reading maps to easily reach their destination. For example, by holding the smartphone horizontally while walking, the user can receive navigation while maintaining a clear view of the road ahead. This can prevent accidents while walking. Furthermore, the navigation system can improve the user's walking experience by playing back videos in accordance with the user's walking speed.

[0030] Furthermore, the navigation system has an image generation unit that learns the user's past travel history and proposes an optimal route based on the travel history. For example, the image generation unit uses a generation AI to analyze the user's past travel history and learn frequently used routes and destinations. For example, the system records the user's daily commute route and proposes the optimal route based on that data. The image generation unit can also propose the optimal route based on the user's travel history. For example, it proposes the most efficient route among the routes the user has used in the past. This allows for more efficient navigation by learning the user's past travel history and proposing the optimal route.

[0031] The navigation system further includes a video generation unit that analyzes real-time traffic information and generates a video of a route that avoids congestion based on the traffic information. The video generation unit, for example, uses a generation AI to collect real-time traffic information and generate a video of a route that avoids congestion. For example, it proposes an optimal route based on traffic congestion and accident information. The video generation unit can also propose a route that avoids congestion based on real-time traffic information. For example, it proposes a route with less traffic or a detour. In this way, by analyzing real-time traffic information and generating a video of a route that avoids congestion, the user can reach their destination efficiently.

[0032] The navigation system further includes a video generation unit that proposes routes that pass through tourist attractions and famous places, generating videos that can also be used as a tourist guide. The video generation unit, for example, uses a generation AI to collect information on tourist attractions and famous places and propose routes that pass through them. For example, it generates a route that includes historical buildings and art museums and uses it as a tourist guide. The video generation unit can also propose routes that pass through tourist attractions and famous places, generating videos that can also be used as a tourist guide. For example, if a user wants to enjoy sightseeing, it proposes a route that passes through tourist attractions and famous places. This allows the user to enjoy sightseeing by suggesting routes that pass through tourist attractions and famous places and generating videos that can also be used as a tourist guide.

[0033] The navigation system further includes an video generation unit that takes into account the user's health condition and proposes a route with adjusted walking distance or time. For example, the video generation unit uses a generation AI to analyze the user's health data and propose a route with adjusted walking distance and time. For example, if the user gets tired easily, it generates a shorter route. The video generation unit can also consider the user's health condition and propose a route with adjusted walking distance and time. For example, the video generation unit proposes an appropriate route depending on the user's health condition. This makes it possible to support the user's health by considering the user's health condition and proposing a route with adjusted walking distance and time.

[0034] The navigation system further includes a viewpoint navigation unit that displays detailed information about buildings and landmarks based on the user's viewpoint. The viewpoint navigation unit, for example, uses a generation AI to display detailed information about buildings and landmarks based on the user's viewpoint. For example, when a user points their smartphone at a specific building, the unit displays the building's history and features. The viewpoint navigation unit can also display detailed information about buildings and landmarks based on the user's viewpoint. For example, if the user is interested in a landmark, the unit displays detailed information about that landmark. This allows the user to obtain information about their surroundings in real time by displaying detailed information about buildings and landmarks based on the user's viewpoint.

[0035] The navigation system further includes a viewpoint navigation unit that provides audio guidance of the surroundings based on the user's viewpoint. The viewpoint navigation unit, for example, uses a generation AI to provide audio guidance of the surroundings based on the user's viewpoint. For example, when a user points their smartphone in a specific direction, audio guidance of buildings and landmarks in that direction is played. The viewpoint navigation unit can also provide audio guidance of the surroundings based on the user's viewpoint. For example, if the user requests audio guidance, that guidance is provided. In this way, by providing audio guidance of the surroundings based on the user's viewpoint, the user can obtain information not only visually but also aurally.

[0036] The navigation system further includes a viewpoint navigation unit that provides navigation to assist visibility at night or in bad weather based on the user's viewpoint. The viewpoint navigation unit, for example, provides navigation to assist visibility at night or in bad weather based on the user's viewpoint using a generation AI. For example, the viewpoint navigation unit applies a filter to brighten the view in dark places. The viewpoint navigation unit can also provide navigation to assist visibility at night or in bad weather based on the user's viewpoint. For example, if the user wants to ensure visibility, the viewpoint navigation unit provides such assistance. This ensures the user's safety by providing navigation to assist visibility at night or in bad weather based on the user's viewpoint.

[0037] The navigation system further includes a viewpoint navigation unit that warns of obstacles or dangerous areas in real time based on the user's viewpoint. The viewpoint navigation unit, for example, uses a generation AI to warn of obstacles and dangerous areas in real time based on the user's viewpoint. For example, a warning is displayed when the user approaches a step on the sidewalk. The viewpoint navigation unit can also warn of obstacles and dangerous areas in real time based on the user's viewpoint. For example, a warning is issued when the user approaches a dangerous area. This ensures the user's safety by warning of obstacles and dangerous areas in real time based on the user's viewpoint.

[0038] The navigation system further includes a speed adjustment unit that learns the user's walking pattern and provides navigation at an optimal speed based on the walking pattern. The speed adjustment unit, for example, uses a generation AI to analyze the user's walking pattern and provides navigation at an optimal speed. For example, if the user walks faster than their normal walking speed, the speed adjustment unit generates a video that matches that speed. The speed adjustment unit can also provide navigation at an optimal speed based on the user's walking pattern. For example, if the user changes their walking speed, navigation that matches that speed is provided. This allows the system to learn the user's walking pattern and provide navigation at an optimal speed, thereby improving the user's walking experience.

[0039] The navigation system further includes a speed adjustment unit that suggests rest point or bench locations according to the user's walking speed. The speed adjustment unit, for example, uses a generation AI to analyze the user's walking speed and suggest appropriate rest point or bench locations. For example, if the user is tired, it suggests nearby benches or cafes. The speed adjustment unit can also suggest rest point or bench locations according to the user's walking speed. For example, if the user needs a break, it suggests the location. This makes it possible to reduce the user's fatigue by suggesting rest point or bench locations according to the user's walking speed.

[0040] The navigation system further includes a speed adjustment unit that suggests exercises or stretches according to the user's walking speed. The speed adjustment unit, for example, uses a generation AI to analyze the user's walking speed and suggest appropriate exercises or stretches. For example, if the user is walking fast, it suggests walking exercises. The speed adjustment unit can also suggest exercises or stretches according to the user's walking speed. For example, if the user needs to exercise, it makes such suggestions. This makes it possible to support the user's health by suggesting exercises or stretches according to the user's walking speed.

[0041] The navigation system further includes a speed adjustment unit that adjusts the playback speed of music or podcasts according to the user's walking speed. The speed adjustment unit, for example, uses a generation AI to analyze the user's walking speed and adjusts the playback speed of music or podcasts. For example, if the user is walking fast, the playback speed is increased. The speed adjustment unit can also adjust the playback speed of music or podcasts according to the user's walking speed. For example, if the user is walking slowly, the playback speed is decreased. This allows the user's walking experience to be improved by adjusting the playback speed of music or podcasts according to the user's walking speed.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] In addition, the navigation system has an animation generation unit that learns the user's past movement history and proposes an optimal route based on the movement history. For example, the navigation system can learn the routes and destinations frequently used by the user and propose an optimal route based on that data. For example, the navigation system can record the route the user takes to work every day and propose an optimal route based on that data. The animation generation unit can also propose an optimal route based on the user's movement history. For example, the navigation system can propose the most efficient route among the routes the user has used in the past. This allows the navigation system to learn the user's past movement history and propose an optimal route, enabling more efficient navigation.

[0044] The navigation system further includes a video generation unit that analyzes real-time traffic information and generates a video of a route that avoids congestion based on the traffic information. For example, the system can propose an optimal route based on traffic congestion and accident information. For example, it can propose a route with less traffic or a detour. The video generation unit can also propose a route that avoids congestion based on real-time traffic information. For example, if the user is in a hurry, it can propose the fastest route. In this way, by analyzing real-time traffic information and generating a video of a route that avoids congestion, the user can reach their destination efficiently.

[0045] The navigation system further includes a video generation unit that proposes routes that pass through tourist attractions and famous places, and generates videos that can be used as a tour guide. For example, a route that includes historical buildings and art museums can be generated and used as a tour guide. For example, if a user wants to enjoy sightseeing, the video generation unit proposes a route that passes through tourist attractions and famous places. The video generation unit can also propose routes that pass through tourist attractions and famous places, and generate videos that can be used as a tour guide. This allows the user to enjoy sightseeing by proposing routes that pass through tourist attractions and famous places and generating videos that can be used as a tour guide.

[0046] The navigation system further includes an animation generation unit that takes into account the user's health condition and proposes a route that adjusts the walking distance or time. For example, if the user gets tired easily, a shorter route can be generated. For example, an appropriate route is proposed depending on the user's health condition. The animation generation unit can also propose a route that adjusts the walking distance or time in consideration of the user's health condition. This makes it possible to support the user's health by proposing a route that adjusts the walking distance or time in consideration of the user's health condition.

[0047] The navigation system further includes a viewpoint navigation unit that displays detailed information about buildings and landmarks based on the user's viewpoint. For example, when a user points their smartphone at a specific building, the history and characteristics of that building can be displayed. For example, if the user is interested in a landmark, detailed information about that landmark can be displayed. The viewpoint navigation unit can also display detailed information about buildings and landmarks based on the user's viewpoint. This allows the user to obtain information about their surroundings in real time by displaying detailed information about buildings and landmarks based on the user's viewpoint.

[0048] The navigation system further includes a viewpoint navigation unit that provides audio guidance of the surroundings based on the user's viewpoint. For example, when the user points the smartphone in a specific direction, audio guidance of buildings and landmarks in that direction can be played. For example, if the user requests audio guidance, that guidance is provided. The viewpoint navigation unit can also provide audio guidance of the surroundings based on the user's viewpoint. As a result, by providing audio guidance of the surroundings based on the user's viewpoint, the user can obtain information not only visually but also aurally.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The video generation unit generates a video to the destination based on the user's input. For example, if the user inputs "Tell me how to get from my house to the station," the generation AI analyzes the route and generates a video. The generation AI can generate the content of the video using text generation AI (e.g., LLM) or multimodal generation AI. Step 2: The viewpoint navigation unit navigates the video generated by the video generation unit based on the user's viewpoint. For example, if the user points their smartphone north, the video is also displayed with north as the reference point. This allows the user to receive navigation tailored to their viewpoint. The viewpoint navigation unit can display the video based on the user's viewpoint. Step 3: The speed adjustment unit moves the video navigated by the viewpoint navigation unit in accordance with the user's walking speed. For example, if the user is walking slowly, the video is also played slowly. Conversely, if the user is walking fast, the video is also played quickly. The speed adjustment unit can play the video in accordance with the user's walking speed.

[0051] (Example 2) The navigation system according to an embodiment of the present invention is a system that generates a video showing directions from the user's current location to their destination simply by inputting "I want to go from XX to XX." This system starts navigation from the perspective of the user and the smartphone, and the screen moves in sync with the user's walking speed, making it easy to use for both children and the elderly. This makes the navigation system easy to use for all generations, including those who have difficulty reading maps and those who can read maps but have difficulty understanding them.

[0052] A navigation system according to an embodiment includes a video generation unit, a viewpoint navigation unit, and a speed adjustment unit. The video generation unit generates a video to a destination based on a user's input. For example, when a user inputs, "Tell me how to get from my house to the station," the generation AI analyzes the route and generates a video. The generation AI generates a video using a text generation AI (e.g., LLM). The generation AI can also generate video content using a multimodal generation AI. The generation AI can also generate a video based on user instructions. The viewpoint navigation unit navigates the video generated by the video generation unit based on the user's viewpoint. For example, if the user points their smartphone toward north, the video is also displayed with north as the reference point. This allows the user to receive navigation tailored to their viewpoint. The viewpoint navigation unit can display a video based on the user's viewpoint. For example, if the user holds the smartphone horizontally, the video is also displayed horizontally. The speed adjustment unit moves the video navigated by the viewpoint navigation unit in accordance with the user's walking speed. For example, if the user is walking slowly, the video is played slowly. Conversely, if the user is walking fast, the video is played quickly. The speed adjustment unit can play back videos in accordance with the user's walking speed. For example, if the user changes their walking speed, the video playback speed is automatically adjusted. As a result, the navigation system according to the embodiment generates videos to a destination based on user input, and provides viewpoint-based navigation and screen movement in accordance with the walking speed, allowing even those who are not good at reading maps to easily reach their destination. For example, by holding the smartphone horizontally while walking, the user can receive navigation while maintaining a clear view of the road ahead. This can prevent accidents while walking. Furthermore, the navigation system can improve the user's walking experience by playing back videos in accordance with the user's walking speed.

[0053] Furthermore, the navigation system has an image generation unit that learns the user's past travel history and proposes an optimal route based on the travel history. For example, the image generation unit uses a generation AI to analyze the user's past travel history and learn frequently used routes and destinations. For example, the system records the user's daily commute route and proposes the optimal route based on that data. The image generation unit can also propose the optimal route based on the user's travel history. For example, it proposes the most efficient route among the routes the user has used in the past. This allows for more efficient navigation by learning the user's past travel history and proposing the optimal route.

[0054] The navigation system further includes a video generation unit that analyzes real-time traffic information and generates a video of a route that avoids congestion based on the traffic information. The video generation unit, for example, uses a generation AI to collect real-time traffic information and generate a video of a route that avoids congestion. For example, it proposes an optimal route based on traffic congestion and accident information. The video generation unit can also propose a route that avoids congestion based on real-time traffic information. For example, it proposes a route with less traffic or a detour. In this way, by analyzing real-time traffic information and generating a video of a route that avoids congestion, the user can reach their destination efficiently.

[0055] The navigation system further includes a video generation unit that uses an emotion estimation function to generate videos containing scenery or music that can relax the user. For example, the video generation unit uses a generation AI to analyze the user's emotional state and generate videos containing scenery or music that can relax the user. For example, if the user is feeling stressed, the video generation unit suggests videos containing natural scenery or calming music. The video generation unit can also use the emotion estimation function to generate videos containing scenery or music that can relax the user. For example, if the user feels like relaxing, the video generation unit generates videos containing scenery or music that has a relaxing effect. In this way, the emotion estimation function can be used to generate videos containing scenery or music that can relax the user, thereby reducing the user's stress.

[0056] The navigation system further includes a video generation unit that proposes routes that pass through tourist attractions and famous places, generating videos that can also be used as a tourist guide. The video generation unit, for example, uses a generation AI to collect information on tourist attractions and famous places and propose routes that pass through them. For example, it generates a route that includes historical buildings and art museums and uses it as a tourist guide. The video generation unit can also propose routes that pass through tourist attractions and famous places, generating videos that can also be used as a tourist guide. For example, if a user wants to enjoy sightseeing, it proposes a route that passes through tourist attractions and famous places. This allows the user to enjoy sightseeing by suggesting routes that pass through tourist attractions and famous places and generating videos that can also be used as a tourist guide.

[0057] The navigation system further includes an video generation unit that takes into account the user's health condition and proposes a route with adjusted walking distance or time. For example, the video generation unit uses a generation AI to analyze the user's health data and propose a route with adjusted walking distance and time. For example, if the user gets tired easily, it generates a shorter route. The video generation unit can also consider the user's health condition and propose a route with adjusted walking distance and time. For example, the video generation unit proposes an appropriate route depending on the user's health condition. This makes it possible to support the user's health by considering the user's health condition and proposing a route with adjusted walking distance and time.

[0058] The navigation system further includes a video generation unit that uses an emotion estimation function to suggest a route that includes events or activities that the user can enjoy. For example, the video generation unit uses a generation AI to analyze the user's emotional state and suggest a route that includes events and activities that the user can enjoy. For example, if the user is bored, the video generation unit generates a route that passes through entertainment facilities. The video generation unit can also use the emotion estimation function to suggest a route that includes events and activities that the user can enjoy. For example, if the user feels like having fun, the video generation unit suggests a route that includes events and activities that the user can enjoy. This makes it possible to improve user satisfaction by using the emotion estimation function to suggest a route that includes events and activities that the user can enjoy.

[0059] The navigation system further includes a viewpoint navigation unit that displays detailed information about buildings and landmarks based on the user's viewpoint. The viewpoint navigation unit, for example, uses a generation AI to display detailed information about buildings and landmarks based on the user's viewpoint. For example, when a user points their smartphone at a specific building, the unit displays the building's history and features. The viewpoint navigation unit can also display detailed information about buildings and landmarks based on the user's viewpoint. For example, if the user is interested in a landmark, the unit displays detailed information about that landmark. This allows the user to obtain information about their surroundings in real time by displaying detailed information about buildings and landmarks based on the user's viewpoint.

[0060] The navigation system further includes a viewpoint navigation unit that provides audio guidance of the surroundings based on the user's viewpoint. The viewpoint navigation unit, for example, uses a generation AI to provide audio guidance of the surroundings based on the user's viewpoint. For example, when a user points their smartphone in a specific direction, audio guidance of buildings and landmarks in that direction is played. The viewpoint navigation unit can also provide audio guidance of the surroundings based on the user's viewpoint. For example, if the user requests audio guidance, that guidance is provided. In this way, by providing audio guidance of the surroundings based on the user's viewpoint, the user can obtain information not only visually but also aurally.

[0061] The navigation system further includes a viewpoint navigation unit that uses an emotion estimation function to provide navigation from a viewpoint that gives the user peace of mind. The viewpoint navigation unit, for example, uses a generation AI to analyze the user's emotional state and provide navigation from a viewpoint that gives the user peace of mind. For example, if the user is feeling anxious, the viewpoint navigation unit can also use the emotion estimation function to provide navigation from a viewpoint that gives the user peace of mind. For example, if the user wants to feel at ease, the viewpoint navigation unit can provide navigation from a viewpoint that gives the user peace of mind. In this way, the emotion estimation function can be used to provide navigation from a viewpoint that gives the user peace of mind, thereby reducing the user's anxiety.

[0062] The navigation system further includes a viewpoint navigation unit that provides navigation to assist visibility at night or in bad weather based on the user's viewpoint. The viewpoint navigation unit, for example, provides navigation to assist visibility at night or in bad weather based on the user's viewpoint using a generation AI. For example, the viewpoint navigation unit applies a filter to brighten the view in dark places. The viewpoint navigation unit can also provide navigation to assist visibility at night or in bad weather based on the user's viewpoint. For example, if the user wants to ensure visibility, the viewpoint navigation unit provides such assistance. This ensures the user's safety by providing navigation to assist visibility at night or in bad weather based on the user's viewpoint.

[0063] The navigation system further includes a viewpoint navigation unit that warns of obstacles or dangerous areas in real time based on the user's viewpoint. The viewpoint navigation unit, for example, uses a generation AI to warn of obstacles and dangerous areas in real time based on the user's viewpoint. For example, a warning is displayed when the user approaches a step on the sidewalk. The viewpoint navigation unit can also warn of obstacles and dangerous areas in real time based on the user's viewpoint. For example, a warning is issued when the user approaches a dangerous area. This ensures the user's safety by warning of obstacles and dangerous areas in real time based on the user's viewpoint.

[0064] The navigation system further includes a viewpoint navigation unit that uses an emotion estimation function to provide tourist information from a viewpoint that interests the user. The viewpoint navigation unit, for example, uses a generation AI to analyze the user's emotional state and provide tourist information from a viewpoint that interests the user. For example, if the user is excited, the viewpoint navigation unit suggests a route that passes through places with a lot of activity. The viewpoint navigation unit can also use the emotion estimation function to provide tourist information from a viewpoint that interests the user. For example, if the user is interested, tourist information from that viewpoint is provided. In this way, by using the emotion estimation function to provide tourist information from a viewpoint that interests the user, user satisfaction can be improved.

[0065] The navigation system further includes a speed adjustment unit that learns the user's walking pattern and provides navigation at an optimal speed based on the walking pattern. The speed adjustment unit, for example, uses a generation AI to analyze the user's walking pattern and provides navigation at an optimal speed. For example, if the user walks faster than their normal walking speed, the speed adjustment unit generates a video that matches that speed. The speed adjustment unit can also provide navigation at an optimal speed based on the user's walking pattern. For example, if the user changes their walking speed, navigation that matches that speed is provided. This allows the system to learn the user's walking pattern and provide navigation at an optimal speed, thereby improving the user's walking experience.

[0066] The navigation system further includes a speed adjustment unit that suggests rest point or bench locations according to the user's walking speed. The speed adjustment unit, for example, uses a generation AI to analyze the user's walking speed and suggest appropriate rest point or bench locations. For example, if the user is tired, it suggests nearby benches or cafes. The speed adjustment unit can also suggest rest point or bench locations according to the user's walking speed. For example, if the user needs a break, it suggests the location. This makes it possible to reduce the user's fatigue by suggesting rest point or bench locations according to the user's walking speed.

[0067] The navigation system further includes a speed adjustment unit that uses an emotion estimation function to provide navigation at a speed that allows the user to relax. The speed adjustment unit, for example, uses a generation AI to analyze the user's emotional state and provides navigation at a speed that allows the user to relax. For example, if the user is feeling stressed, the speed adjustment unit generates a video at a slow speed. The speed adjustment unit can also use the emotion estimation function to provide navigation at a speed that allows the user to relax. For example, if the user feels like relaxing, the speed adjustment unit provides navigation at that speed. In this way, by using the emotion estimation function to provide navigation at a speed that allows the user to relax, the user's stress can be reduced.

[0068] The navigation system further includes a speed adjustment unit that suggests exercises or stretches according to the user's walking speed. The speed adjustment unit, for example, uses a generation AI to analyze the user's walking speed and suggest appropriate exercises or stretches. For example, if the user is walking fast, it suggests walking exercises. The speed adjustment unit can also suggest exercises or stretches according to the user's walking speed. For example, if the user needs to exercise, it makes such suggestions. This makes it possible to support the user's health by suggesting exercises or stretches according to the user's walking speed.

[0069] The navigation system further includes a speed adjustment unit that adjusts the playback speed of music or podcasts according to the user's walking speed. The speed adjustment unit, for example, uses a generation AI to analyze the user's walking speed and adjusts the playback speed of music or podcasts. For example, if the user is walking fast, the playback speed is increased. The speed adjustment unit can also adjust the playback speed of music or podcasts according to the user's walking speed. For example, if the user is walking slowly, the playback speed is decreased. This allows the user's walking experience to be improved by adjusting the playback speed of music or podcasts according to the user's walking speed.

[0070] The navigation system further includes a speed adjustment unit that uses an emotion estimation function to provide tourist information at a speed that the user can enjoy. The speed adjustment unit, for example, uses a generation AI to analyze the user's emotional state and provides tourist information at an enjoyable speed. For example, if the user is excited, the speed adjustment unit provides tourist information at a fast speed. The speed adjustment unit can also use the emotion estimation function to provide tourist information at a speed that the user can enjoy. For example, if the user feels like having fun, the speed adjustment unit provides tourist information at that speed. In this way, by using the emotion estimation function to provide tourist information at a speed that the user can enjoy, user satisfaction can be improved.

[0071] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0072] In addition, the navigation system has an animation generation unit that learns the user's past movement history and proposes an optimal route based on the movement history. For example, the navigation system can learn the routes and destinations frequently used by the user and propose an optimal route based on that data. For example, the navigation system can record the route the user takes to work every day and propose an optimal route based on that data. The animation generation unit can also propose an optimal route based on the user's movement history. For example, the navigation system can propose the most efficient route among the routes the user has used in the past. This allows the navigation system to learn the user's past movement history and propose an optimal route, enabling more efficient navigation.

[0073] The navigation system further includes a video generation unit that analyzes real-time traffic information and generates a video of a route that avoids congestion based on the traffic information. For example, the system can propose an optimal route based on traffic congestion and accident information. For example, it can propose a route with less traffic or a detour. The video generation unit can also propose a route that avoids congestion based on real-time traffic information. For example, if the user is in a hurry, it can propose the fastest route. In this way, by analyzing real-time traffic information and generating a video of a route that avoids congestion, the user can reach their destination efficiently.

[0074] The navigation system further includes a video generation unit that uses the emotion estimation function to generate a video including scenery or music that helps the user relax. For example, if the user is feeling stressed, a video including natural scenery or calming music can be suggested. For example, if the user feels like relaxing, a video including scenery or music that has a relaxing effect can be generated. The video generation unit can also use the emotion estimation function to generate a video including scenery or music that helps the user relax. In this way, by using the emotion estimation function to generate a video including scenery or music that helps the user relax, the user's stress can be reduced.

[0075] The navigation system further includes a video generation unit that proposes routes that pass through tourist attractions and famous places, and generates videos that can be used as a tour guide. For example, a route that includes historical buildings and art museums can be generated and used as a tour guide. For example, if a user wants to enjoy sightseeing, the video generation unit proposes a route that passes through tourist attractions and famous places. The video generation unit can also propose routes that pass through tourist attractions and famous places, and generate videos that can be used as a tour guide. This allows the user to enjoy sightseeing by proposing routes that pass through tourist attractions and famous places and generating videos that can be used as a tour guide.

[0076] The navigation system further includes an animation generation unit that takes into account the user's health condition and proposes a route that adjusts the walking distance or time. For example, if the user gets tired easily, a shorter route can be generated. For example, an appropriate route is proposed depending on the user's health condition. The animation generation unit can also propose a route that adjusts the walking distance or time in consideration of the user's health condition. This makes it possible to support the user's health by proposing a route that adjusts the walking distance or time in consideration of the user's health condition.

[0077] The navigation system further includes a video generation unit that uses the emotion estimation function to suggest a route that includes events or activities that the user can enjoy. For example, if the user is bored, a route that passes through entertainment facilities can be generated. For example, if the user feels like having fun, a route that includes events or activities that the user can enjoy can be suggested. The video generation unit can also use the emotion estimation function to suggest a route that includes events or activities that the user can enjoy. This makes it possible to improve user satisfaction by using the emotion estimation function to suggest a route that includes events or activities that the user can enjoy.

[0078] The navigation system further includes a viewpoint navigation unit that displays detailed information about buildings and landmarks based on the user's viewpoint. For example, when a user points their smartphone at a specific building, the history and characteristics of that building can be displayed. For example, if the user is interested in a landmark, detailed information about that landmark can be displayed. The viewpoint navigation unit can also display detailed information about buildings and landmarks based on the user's viewpoint. This allows the user to obtain information about their surroundings in real time by displaying detailed information about buildings and landmarks based on the user's viewpoint.

[0079] The navigation system further includes a viewpoint navigation unit that provides audio guidance of the surroundings based on the user's viewpoint. For example, when the user points the smartphone in a specific direction, audio guidance of buildings and landmarks in that direction can be played. For example, if the user requests audio guidance, that guidance is provided. The viewpoint navigation unit can also provide audio guidance of the surroundings based on the user's viewpoint. As a result, by providing audio guidance of the surroundings based on the user's viewpoint, the user can obtain information not only visually but also aurally.

[0080] The navigation system further includes a viewpoint navigation unit that uses an emotion estimation function to provide navigation from a viewpoint that makes the user feel at ease. For example, if the user feels anxious, a route that takes a bright and wide road can be suggested. For example, if the user wants to feel at ease, navigation from a viewpoint that makes the user feel at ease is provided. Furthermore, the viewpoint navigation unit can use the emotion estimation function to provide navigation from a viewpoint that makes the user feel at ease. In this way, by using the emotion estimation function to provide navigation from a viewpoint that makes the user feel at ease, the user's anxiety can be reduced.

[0081] Furthermore, the navigation system uses the emotion estimation function so that the viewpoint navigation unit provides tourist information from a viewpoint that interests the user. For example, if the user is excited, a route that passes through places with a lot of activity can be suggested. For example, if the user is interested, tourist information from that viewpoint can be provided. Furthermore, the viewpoint navigation unit can use the emotion estimation function to provide tourist information from a viewpoint that interests the user. In this way, by using the emotion estimation function to provide tourist information from a viewpoint that interests the user, it is possible to improve user satisfaction.

[0082] The processing flow of the second embodiment will be briefly explained below.

[0083] Step 1: The video generation unit generates a video to the destination based on the user's input. For example, if the user inputs "Tell me how to get from my house to the station," the generation AI analyzes the route and generates a video. The generation AI can generate the content of the video using text generation AI (e.g., LLM) or multimodal generation AI. Step 2: The viewpoint navigation unit navigates the video generated by the video generation unit based on the user's viewpoint. For example, if the user points their smartphone north, the video is also displayed with north as the reference point. This allows the user to receive navigation tailored to their viewpoint. The viewpoint navigation unit can display the video based on the user's viewpoint. Step 3: The speed adjustment unit moves the video navigated by the viewpoint navigation unit in accordance with the user's walking speed. For example, if the user is walking slowly, the video is also played slowly. Conversely, if the user is walking fast, the video is also played quickly. The speed adjustment unit can play the video in accordance with the user's walking speed.

[0084] 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.

[0085] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0086] 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.

[0087] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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).

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0097] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0098] 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.

[0099] 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.

[0100] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0101] 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.

[0102] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0103] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0110] 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.

[0111] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0112] 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 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0113] 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.

[0114] 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.

[0115] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0116] 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.

[0117] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0118] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0128] In the robot 414, the processor 46 performs the identification process. 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. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0129] 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.

[0130] 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.

[0131] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0138] 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."

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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. [Explanation of symbols]

[0151] 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 video generation unit that generates a video to a destination based on a user's input; a viewpoint navigation unit that navigates the moving image generated by the moving image generation unit based on a user's viewpoint; a speed adjustment unit that moves the video navigated by the viewpoint navigation unit in accordance with the walking speed of the user. A system characterized by:

2. The video generation unit Analyze real-time traffic information and generate a video route to avoid congestion based on the traffic information.

2. The system of claim 1.

3. The video generation unit Proposes routes that pass through tourist spots and famous places, and generates videos that can also be used as tourist guides 2. The system of claim 1.

4. The viewpoint navigation unit Display detailed information about buildings and landmarks based on the user's point of view 2. The system of claim 1.

5. The speed adjustment unit Learns the user's walking pattern and provides navigation at the optimal speed based on the walking pattern 2. The system of claim 1.

6. The video generation unit Using emotion estimation function, we generate videos containing scenery or music that users can relax.

2. The system of claim 1.

7. The viewpoint navigation unit Using emotion estimation function, we provide navigation from a perspective that users can feel comfortable with.

2. The system of claim 1.

8. The speed adjustment unit Using emotion estimation, we provide navigation at a speed that allows users to relax.

2. The system of claim 1.

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