System

Abstract

An object of the system according to the embodiment is to integrate and analyze a wearable camera, position information, and map information and to provide feedback to a user in real time.SOLUTION: A system includes an image acquisition unit, a position information acquisition unit, a map information acquisition unit, a space analysis unit, and a feedback unit. The image acquisition unit acquires image information with a wearable camera. The position information acquisition unit acquires position information by ichimill. The map information acquisition unit acquires map information from Mapbox. The space analysis unit integrates and analyzes information from the image acquisition unit, the position information acquisition unit, and the map information acquisition unit. The feedback unit feeds back the information analyzed by the space analysis unit to the user by using 5G communication.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 does not adequately integrate wearable camera, location information, and map information, analyze them in real time, and provide feedback to users, so there is room for improvement.

[0005] The system according to the embodiment aims to integrate and analyze data from a wearable camera, location information, and map information, and provide feedback to the user in real time. [Means for solving the problem]

[0006] The system according to the embodiment includes an image acquisition unit, a location information acquisition unit, a map information acquisition unit, a spatial analysis unit, and a feedback unit. The image acquisition unit acquires image information using a wearable camera. The location information acquisition unit acquires location information using ichimill. The map information acquisition unit acquires map information using Mapbox. The spatial analysis unit integrates and analyzes information from the image acquisition unit, location information acquisition unit, and map information acquisition unit. The feedback unit feeds back the information analyzed by the spatial analysis unit to the user using 5G communication. [Effects of the Invention]

[0007] The system according to the embodiment can integrate and analyze data from a wearable camera, location information, and map information, and provide feedback to the user in real time. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate 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 spatial analysis system according to an embodiment of the present invention uses image information acquired by a wearable camera, accurate location information from ichimill, and highly accurate map information from Mapbox to analyze space using generative AI, and utilizes 5G communications to provide users with low-latency, real-time information in natural language feedback. This allows the spatial analysis system to provide users with guidance to their destination and timely risk assessment of the surrounding environment.

[0029] The spatial analysis system according to the embodiment includes an image acquisition unit, a location information acquisition unit, a map information acquisition unit, a spatial analysis unit, and a feedback unit. The image acquisition unit acquires image information using a wearable camera. For example, the image acquisition unit acquires surrounding image information in real time using a wearable camera worn by the user. The location information acquisition unit acquires location information using ichimill. For example, the location information acquisition unit accurately determines the user's current location using GPS or other location information technology. The map information acquisition unit acquires map information using Mapbox. For example, the map information acquisition unit acquires detailed map information such as road layout, building locations, and traffic conditions. The spatial analysis unit integrates and analyzes information from the image acquisition unit, location information acquisition unit, and map information acquisition unit. For example, the generation AI uses this information to understand the user's current environment and generate appropriate feedback. The feedback unit feeds the information analyzed by the spatial analysis unit back to the user using 5G communication. For example, while the user is walking, the generation AI provides natural language guidance such as, "There is an obstacle ahead. Please turn right." This enables the spatial analysis system to provide users with guidance to their destination and timely understanding of risks in the surrounding environment.

[0030] The image acquisition unit transmits image information acquired by the wearable camera to the generation AI in real time, and the generation AI analyzes the information to learn and predict the user's behavioral patterns. The image acquisition unit, for example, transmits image information acquired by the wearable camera to the generation AI in real time, and the generation AI analyzes the information to learn the user's behavioral patterns. For example, it predicts the user's frequently taken routes and specific actions. The generation AI, for example, learns the user's behavioral patterns based on past behavioral data and predicts the user's next actions. This allows for more accurate guidance by learning and predicting the user's behavioral patterns.

[0031] The image acquisition unit analyzes image information acquired by the wearable camera, identifies the user's line of sight, and can highlight important information in front of the user's line of sight. The image acquisition unit, for example, analyzes image information acquired by the wearable camera, and the generation AI identifies the user's line of sight. For example, it uses gaze tracking technology to identify the object the user is looking at. The generation AI, for example, highlights important information in front of the user's line of sight. For example, it uses gaze tracking technology to highlight the object the user is looking at. In this way, the user's line of sight is identified and important information is highlighted, making it easier to attract the user's attention.

[0032] The image acquisition unit can add a voice input function to the wearable camera and integrate and analyze voice information and image information. The image acquisition unit, for example, adds a voice input function to the wearable camera and acquires the user's voice instructions. For example, if the user says, "Tell me where the next intersection is," the voice information is analyzed and appropriate guidance is provided. The generation AI, for example, integrates and analyzes voice information and image information. For example, it uses voice recognition technology to analyze the user's voice instructions and provide appropriate feedback. This enables more accurate feedback by integrating and analyzing voice information and image information.

[0033] The image acquisition unit can acquire 360-degree image information by linking multiple wearable cameras. For example, the image acquisition unit links multiple wearable cameras to build a system that acquires 360-degree image information. For example, it links a head-mounted camera with a chest-mounted camera. The generation AI, for example, analyzes the 360-degree image information and provides appropriate feedback to the user. As a result, by acquiring 360-degree image information, the user can see the entire surroundings.

[0034] The location information acquisition unit allows the generation AI to analyze the user's movement history based on ichimill's location information and propose the optimal movement route. The location information acquisition unit, for example, allows the generation AI to analyze the user's movement history based on location information acquired by ichimill. For example, it proposes the optimal route based on past movement data. The generation AI, for example, analyzes the user's movement history and proposes the optimal movement route. For example, it proposes the optimal route based on past movement data. This allows for efficient movement by analyzing the user's movement history and proposing the optimal movement route.

[0035] The location information acquisition unit combines location information and time information from ichimill, allowing the generation AI to predict the user's behavioral patterns and suggest the next action. The location information acquisition unit, for example, combines location information and time information from ichimill, allowing the generation AI to predict the user's behavioral patterns. For example, it learns the tendency to go to specific places at specific times of the day. The generation AI, for example, predicts the user's behavioral patterns and suggests the next action. For example, it learns the tendency to go to specific places at specific times of the day and suggests the next action. This combines location information and time information to predict behavioral patterns and suggest the next action, improving user convenience.

[0036] The location information acquisition unit can integrate ichimill's location information with other sensor information (temperature, humidity, etc.) to suggest the optimal route depending on the environment. For example, the location information acquisition unit integrates ichimill's location information with temperature sensor data, and the generation AI suggests the optimal route depending on the environment. For example, on a hot day, it will suggest a route with plenty of shade. For example, the generation AI will suggest the optimal route depending on the environment based on sensor information such as temperature and humidity. This improves user comfort by integrating location information and sensor information to suggest the optimal route depending on the environment.

[0037] The location information acquisition unit can display a history of places the user has visited in the past based on the location information, providing a function for looking back on memories. The location information acquisition unit, for example, uses ichimill's location information to have the generation AI display the user's past visit history. For example, it displays information about tourist spots and stores visited in the past. The generation AI, for example, displays a history of places the user has visited in the past, providing a function for looking back on memories. For example, it displays information about tourist spots and stores visited in the past. This improves user satisfaction by providing a function for looking back on memories, displaying a history of places the user has visited in the past.

[0038] The map information acquisition unit allows the generation AI to analyze traffic conditions in real time based on map information from Mapbox and propose the optimal travel route. The map information acquisition unit allows the generation AI to analyze traffic conditions in real time based on map information from Mapbox, for example. For example, the generation AI proposes the optimal travel route taking into account congestion information and accident information. For example, the generation AI analyzes traffic conditions in real time and proposes the optimal travel route. For example, the generation AI proposes the optimal travel route based on congestion information and accident information. This enables efficient travel by analyzing traffic conditions in real time and proposing the optimal travel route.

[0039] The map information acquisition unit allows the generation AI to provide information about nearby tourist spots and stores based on the map information. The map information acquisition unit allows the generation AI to analyze information about nearby tourist spots and stores based on, for example, map information from Mapbox, and provide the information to the user. For example, information about nearby restaurants and tourist attractions is displayed. The generation AI provides information about nearby tourist spots and stores based on, for example, map information. For example, information about nearby restaurants and tourist attractions is displayed. This improves user convenience by providing information about nearby tourist spots and stores.

[0040] The map information acquisition unit can display Mapbox map information in 3D, allowing users to intuitively understand it. The map information acquisition unit, for example, builds a system that displays Mapbox map information in 3D, allowing users to intuitively understand it. For example, it displays buildings and terrain as 3D models. The generation AI, for example, provides appropriate feedback to the user based on the 3D displayed map information. As a result, displaying map information in 3D makes it easier for users to intuitively understand it.

[0041] The map information acquisition unit can combine map information with AR (augmented reality) technology and display information superimposed on real scenery. The map information acquisition unit, for example, combines AR technology with map information from Mapbox to build a system that displays information superimposed on real scenery. For example, the map information is displayed through a smartphone camera. The generation AI, for example, uses AR technology to display information superimposed on real scenery. In this way, by superimposing information on real scenery using AR technology, users can more easily understand the information intuitively.

[0042] The spatial analysis unit learns the user's past behavioral data when the generation AI performs spatial analysis, enabling highly accurate analysis. For example, when the generation AI performs spatial analysis, the spatial analysis unit learns the user's past behavioral data. For example, the analysis is performed based on past movement history and visit history. The generation AI performs highly accurate spatial analysis based on, for example, past behavioral data. In this way, by learning past behavioral data, the accuracy of the spatial analysis is improved.

[0043] The spatial analysis unit allows the generation AI to predict the user's behavior based on the spatial analysis results and provide proactive guidance. The spatial analysis unit, for example, allows the generation AI to predict the user's behavior based on the spatial analysis results. For example, it predicts the direction the user will take next and provides appropriate guidance. The generation AI, for example, provides proactive guidance. For example, it predicts the direction the user will take next and provides appropriate guidance. This improves user convenience by predicting behavior and providing proactive guidance.

[0044] The spatial analysis unit can link the spatial analysis results with other devices (smartphones, tablets, etc.) and share information across multiple devices. For example, the spatial analysis unit constructs a system in which the generation AI links the spatial analysis results with other devices and shares information. For example, the analysis results are displayed on a smartphone or tablet. The generation AI shares information across multiple devices, for example. This allows the spatial analysis results to be linked with other devices and information to be shared, improving user convenience.

[0045] The spatial analysis unit can suggest events and activities that the user might be interested in based on the spatial analysis results. For example, the generation AI in the spatial analysis unit suggests events and activities that the user might be interested in based on the spatial analysis results. For example, it displays information about events being held nearby. The generation AI suggests events and activities that the user might be interested in. This improves user satisfaction by suggesting events and activities that the user might be interested in.

[0046] The feedback unit utilizes low-latency communication to enable the generation AI to provide an interactive guide that corresponds to the user's actions. The feedback unit utilizes, for example, 5G communication to enable the generation AI to provide an interactive guide that corresponds to the user's actions. For example, when the user reaches a specific location, information about the location is provided in real time. The generation AI provides, for example, an interactive guide that corresponds to the user's actions. This improves user convenience by providing an interactive guide that corresponds to the user's actions.

[0047] The feedback unit can use 5G communications to share information in real time among multiple users and provide a function for them to act cooperatively. The feedback unit, for example, uses 5G communications to build a system for sharing information in real time among multiple users. For example, location information and direction of travel can be shared when traveling in a group. The generation AI, for example, provides a function for multiple users to share information and act cooperatively. This allows multiple users to share information and act cooperatively, improving user convenience.

[0048] The feedback unit utilizes low-latency communications to enable the generating AI to monitor the user's health condition and immediately notify if any abnormalities are detected. The feedback unit, for example, utilizes 5G communications to build a system in which the generating AI monitors the user's health condition in real time. For example, it monitors heart rate and blood pressure and immediately notifies if any abnormalities are detected. The generating AI, for example, monitors the health condition and immediately notifies if any abnormalities are detected. This improves the safety of the user by monitoring the health condition and immediately notifying if any abnormalities are detected.

[0049] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0050] The spatial analysis system may further include a voice analysis unit. The voice analysis unit can analyze the user's voice instructions and provide appropriate feedback. For example, if the user says, "Tell me where the next intersection is," the voice analysis unit analyzes the user's voice instructions and provides appropriate guidance. The voice analysis unit uses voice recognition technology to analyze the user's voice instructions and provide appropriate feedback. This allows for more intuitive operation by analyzing voice information.

[0051] The spatial analysis system can further include a temperature sensor unit. The temperature sensor unit acquires ambient temperature information, and the generation AI analyzes that information to provide appropriate feedback to the user. For example, on a hot day, a cool route can be suggested. The temperature sensor unit acquires temperature information, and the generation AI analyzes that information to provide appropriate feedback. This makes it possible to provide feedback that takes temperature information into account.

[0052] The spatial analysis system may further include a vibration feedback unit. The vibration feedback unit may provide feedback to the user by vibration. For example, if the user needs to move in a specific direction, the user may feel a vibration in that direction. The vibration feedback unit provides feedback by vibration, allowing the user to intuitively understand the direction. This allows feedback using senses other than sight and hearing.

[0053] The spatial analysis system may further include an optical feedback unit. The optical feedback unit may provide optical feedback to the user. For example, the optical feedback unit may flash a light in the direction to move to indicate the direction to the user. The optical feedback unit provides optical feedback to allow the user to visually understand the direction. This enhances the visual feedback.

[0054] The spatial analysis system can further include a humidity sensor unit. The humidity sensor unit acquires ambient humidity information, and the generation AI analyzes that information to provide appropriate feedback to the user. For example, if the humidity is high, a comfortable route is suggested. The humidity sensor unit acquires humidity information, and the generation AI analyzes that information to provide appropriate feedback. This makes it possible to provide feedback that takes humidity information into consideration.

[0055] The processing flow of the first embodiment will be briefly explained below.

[0056] Step 1: The image acquisition unit acquires image information using a wearable camera. For example, the image acquisition unit acquires surrounding image information in real time using a wearable camera worn by a user. Step 2: The location information acquisition unit acquires location information from ichimill. For example, it uses GPS or other location information technology to accurately determine where the user is currently located. Step 3: The map information acquisition unit acquires map information from Mapbox, such as detailed map information about road layouts, building locations, and traffic conditions. Step 4: The spatial analysis unit integrates and analyzes the information from the image acquisition unit, location information acquisition unit, and map information acquisition unit. For example, the generation AI uses this information to understand the user's current environment and generate appropriate feedback. Step 5: The feedback unit uses 5G communications to provide feedback to the user based on the information analyzed by the spatial analysis unit. For example, while the user is walking, the generation AI will provide guidance in natural language, such as "There is an obstacle ahead. Please turn right."

[0057] (Example 2) The spatial analysis system according to an embodiment of the present invention uses image information acquired by a wearable camera, accurate location information from ichimill, and highly accurate map information from Mapbox to analyze space using generative AI, and utilizes 5G communications to provide users with low-latency, real-time information in natural language feedback. This allows the spatial analysis system to provide users with guidance to their destination and timely risk assessment of the surrounding environment.

[0058] The spatial analysis system according to the embodiment includes an image acquisition unit, a location information acquisition unit, a map information acquisition unit, a spatial analysis unit, and a feedback unit. The image acquisition unit acquires image information using a wearable camera. For example, the image acquisition unit acquires surrounding image information in real time using a wearable camera worn by the user. The location information acquisition unit acquires location information using ichimill. For example, the location information acquisition unit accurately determines the user's current location using GPS or other location information technology. The map information acquisition unit acquires map information using Mapbox. For example, the map information acquisition unit acquires detailed map information such as road layout, building locations, and traffic conditions. The spatial analysis unit integrates and analyzes information from the image acquisition unit, location information acquisition unit, and map information acquisition unit. For example, the generation AI uses this information to understand the user's current environment and generate appropriate feedback. The feedback unit feeds the information analyzed by the spatial analysis unit back to the user using 5G communication. For example, while the user is walking, the generation AI provides natural language guidance such as, "There is an obstacle ahead. Please turn right." This enables the spatial analysis system to provide users with guidance to their destination and timely understanding of risks in the surrounding environment.

[0059] The image acquisition unit transmits image information acquired by the wearable camera to the generation AI in real time, and the generation AI analyzes the information to learn and predict the user's behavioral patterns. The image acquisition unit, for example, transmits image information acquired by the wearable camera to the generation AI in real time, and the generation AI analyzes the information to learn the user's behavioral patterns. For example, it predicts the user's frequently taken routes and specific actions. The generation AI, for example, learns the user's behavioral patterns based on past behavioral data and predicts the user's next actions. This allows for more accurate guidance by learning and predicting the user's behavioral patterns.

[0060] The image acquisition unit analyzes image information acquired by the wearable camera, identifies the user's line of sight, and can highlight important information in front of the user's line of sight. The image acquisition unit, for example, analyzes image information acquired by the wearable camera, and the generation AI identifies the user's line of sight. For example, it uses gaze tracking technology to identify the object the user is looking at. The generation AI, for example, highlights important information in front of the user's line of sight. For example, it uses gaze tracking technology to highlight the object the user is looking at. In this way, the user's line of sight is identified and important information is highlighted, making it easier to attract the user's attention.

[0061] The image acquisition unit can analyze image information acquired by the wearable camera, estimate the user's emotions from their facial expressions and movements, and provide feedback to reduce stress and anxiety. The image acquisition unit, for example, analyzes image information acquired by the wearable camera, and the generation AI estimates the user's emotions from their facial expressions and movements. For example, facial recognition technology is used to detect stress and anxiety from the user's facial expressions. The generation AI, for example, estimates the user's emotions and provides feedback to reduce stress and anxiety. For example, it provides advice on how to relax. In this way, the user's psychological burden is reduced by estimating their emotions and providing feedback to reduce stress and anxiety.

[0062] The image acquisition unit can add a voice input function to the wearable camera and integrate and analyze voice information and image information. The image acquisition unit, for example, adds a voice input function to the wearable camera and acquires the user's voice instructions. For example, if the user says, "Tell me where the next intersection is," the voice information is analyzed and appropriate guidance is provided. The generation AI, for example, integrates and analyzes voice information and image information. For example, it uses voice recognition technology to analyze the user's voice instructions and provide appropriate feedback. This enables more accurate feedback by integrating and analyzing voice information and image information.

[0063] The image acquisition unit can acquire 360-degree image information by linking multiple wearable cameras. For example, the image acquisition unit links multiple wearable cameras to build a system that acquires 360-degree image information. For example, it links a head-mounted camera with a chest-mounted camera. The generation AI, for example, analyzes the 360-degree image information and provides appropriate feedback to the user. As a result, by acquiring 360-degree image information, the user can see the entire surroundings.

[0064] The image acquisition unit can use the emotion estimation function to identify objects of interest to the user and provide information about them preferentially. The image acquisition unit, for example, analyzes image information acquired by a wearable camera, and the generation AI identifies objects of interest to the user. For example, it uses gaze tracking technology to identify objects that the user is paying attention to. The generation AI, for example, identifies objects of interest to the user and provides information about them preferentially. For example, it uses gaze tracking technology to provide information about objects that the user is paying attention to preferentially. In this way, by identifying objects of interest to the user and providing information about them preferentially, it becomes easier to attract the user's attention.

[0065] The location information acquisition unit allows the generation AI to analyze the user's movement history based on ichimill's location information and propose the optimal movement route. The location information acquisition unit, for example, allows the generation AI to analyze the user's movement history based on location information acquired by ichimill. For example, it proposes the optimal route based on past movement data. The generation AI, for example, analyzes the user's movement history and proposes the optimal movement route. For example, it proposes the optimal route based on past movement data. This allows for efficient movement by analyzing the user's movement history and proposing the optimal movement route.

[0066] The location information acquisition unit combines location information and time information from ichimill, allowing the generation AI to predict the user's behavioral patterns and suggest the next action. The location information acquisition unit, for example, combines location information and time information from ichimill, allowing the generation AI to predict the user's behavioral patterns. For example, it learns the tendency to go to specific places at specific times of the day. The generation AI, for example, predicts the user's behavioral patterns and suggests the next action. For example, it learns the tendency to go to specific places at specific times of the day and suggests the next action. This combines location information and time information to predict behavioral patterns and suggest the next action, improving user convenience.

[0067] The location information acquisition unit can use the emotion estimation function to associate the user's location information with their emotional state and suggest a less stressful route. The location information acquisition unit, for example, combines ichimill's location information with the emotion estimation function, and the generation AI analyzes the user's emotional state. For example, it learns the tendency to feel stressed in certain places. The generation AI, for example, considers the user's emotional state to suggest a less stressful route. For example, it considers the level of congestion and the comfort of the environment to suggest a route. This reduces the user's psychological burden by suggesting a less stressful route that takes into account the user's emotional state.

[0068] The location information acquisition unit can integrate ichimill's location information with other sensor information (temperature, humidity, etc.) to suggest the optimal route depending on the environment. For example, the location information acquisition unit integrates ichimill's location information with temperature sensor data, and the generation AI suggests the optimal route depending on the environment. For example, on a hot day, it will suggest a route with plenty of shade. For example, the generation AI will suggest the optimal route depending on the environment based on sensor information such as temperature and humidity. This improves user comfort by integrating location information and sensor information to suggest the optimal route depending on the environment.

[0069] The location information acquisition unit can display a history of places the user has visited in the past based on the location information, providing a function for looking back on memories. The location information acquisition unit, for example, uses ichimill's location information to have the generation AI display the user's past visit history. For example, it displays information about tourist spots and stores visited in the past. The generation AI, for example, displays a history of places the user has visited in the past, providing a function for looking back on memories. For example, it displays information about tourist spots and stores visited in the past. This improves user satisfaction by providing a function for looking back on memories, displaying a history of places the user has visited in the past.

[0070] The location information acquisition unit can use the emotion estimation function to identify places where the user can relax and suggest a route to those places. The location information acquisition unit, for example, combines ichimill's location information with the emotion estimation function, allowing the generation AI to identify places where the user can relax. For example, it identifies relaxing places such as parks and cafes. The generation AI, for example, identifies places where the user can relax and suggests a route to those places. For example, it suggests a route to relaxing places such as parks and cafes. In this way, by identifying places where the user can relax and suggesting a route to those places, the psychological burden on the user is reduced.

[0071] The map information acquisition unit allows the generation AI to analyze traffic conditions in real time based on map information from Mapbox and propose the optimal travel route. The map information acquisition unit allows the generation AI to analyze traffic conditions in real time based on map information from Mapbox, for example. For example, the generation AI proposes the optimal travel route taking into account congestion information and accident information. For example, the generation AI analyzes traffic conditions in real time and proposes the optimal travel route. For example, the generation AI proposes the optimal travel route based on congestion information and accident information. This enables efficient travel by analyzing traffic conditions in real time and proposing the optimal travel route.

[0072] The map information acquisition unit allows the generation AI to provide information about nearby tourist spots and stores based on the map information. The map information acquisition unit allows the generation AI to analyze information about nearby tourist spots and stores based on, for example, map information from Mapbox, and provide the information to the user. For example, information about nearby restaurants and tourist attractions is displayed. The generation AI provides information about nearby tourist spots and stores based on, for example, map information. For example, information about nearby restaurants and tourist attractions is displayed. This improves user convenience by providing information about nearby tourist spots and stores.

[0073] The map information acquisition unit can use the emotion estimation function to customize map information according to the user's interests and preferences. In the map information acquisition unit, for example, the generation AI customizes information according to the user's interests and preferences based on map information from Mapbox. For example, it prioritizes displaying stores and spots in genres that interest the user. The generation AI customizes map information according to the user's interests and preferences using the emotion estimation function, for example. For example, it prioritizes displaying stores and spots in genres that interest the user. In this way, by customizing map information according to the user's interests and preferences, user satisfaction is improved.

[0074] The map information acquisition unit can display Mapbox map information in 3D, allowing users to intuitively understand it. The map information acquisition unit, for example, builds a system that displays Mapbox map information in 3D, allowing users to intuitively understand it. For example, it displays buildings and terrain as 3D models. The generation AI, for example, provides appropriate feedback to the user based on the 3D displayed map information. As a result, displaying map information in 3D makes it easier for users to intuitively understand it.

[0075] The map information acquisition unit can combine map information with AR (augmented reality) technology and display information superimposed on real scenery. The map information acquisition unit, for example, combines AR technology with map information from Mapbox to build a system that displays information superimposed on real scenery. For example, the map information is displayed through a smartphone camera. The generation AI, for example, uses AR technology to display information superimposed on real scenery. In this way, by superimposing information on real scenery using AR technology, users can more easily understand the information intuitively.

[0076] The map information acquisition unit can use the emotion estimation function to identify places of interest to the user and provide detailed information about those places. In the map information acquisition unit, for example, the generation AI uses the emotion estimation function to identify places of interest to the user based on map information from Mapbox. For example, it uses gaze tracking technology to identify places that the user is paying attention to. The generation AI, for example, identifies places of interest to the user and provides detailed information about those places. For example, it uses gaze tracking technology to provide detailed information about places that the user is paying attention to. In this way, by identifying places of interest to the user and providing detailed information about those places, user satisfaction is improved.

[0077] The spatial analysis unit learns the user's past behavioral data when the generation AI performs spatial analysis, enabling highly accurate analysis. For example, when the generation AI performs spatial analysis, the spatial analysis unit learns the user's past behavioral data. For example, the analysis is performed based on past movement history and visit history. The generation AI performs highly accurate spatial analysis based on, for example, past behavioral data. In this way, by learning past behavioral data, the accuracy of the spatial analysis is improved.

[0078] The spatial analysis unit allows the generation AI to predict the user's behavior based on the spatial analysis results and provide proactive guidance. The spatial analysis unit, for example, allows the generation AI to predict the user's behavior based on the spatial analysis results. For example, it predicts the direction the user will take next and provides appropriate guidance. The generation AI, for example, provides proactive guidance. For example, it predicts the direction the user will take next and provides appropriate guidance. This improves user convenience by predicting behavior and providing proactive guidance.

[0079] The spatial analysis unit uses the emotion estimation function to perform spatial analysis taking into account the user's emotional state and can suggest a less stressful route. In the spatial analysis unit, for example, the generation AI uses the emotion estimation function to analyze the user's emotional state. For example, if the user is feeling stressed, the generation AI suggests a route taking into account the user's emotional state. For example, the generation AI performs spatial analysis taking into account the user's emotional state and suggests a less stressful route. In this way, spatial analysis taking into account the user's emotional state reduces the user's psychological burden.

[0080] The spatial analysis unit can link the spatial analysis results with other devices (smartphones, tablets, etc.) and share information across multiple devices. For example, the spatial analysis unit constructs a system in which the generation AI links the spatial analysis results with other devices and shares information. For example, the analysis results are displayed on a smartphone or tablet. The generation AI shares information across multiple devices, for example. This allows the spatial analysis results to be linked with other devices and information to be shared, improving user convenience.

[0081] The spatial analysis unit can suggest events and activities that the user might be interested in based on the spatial analysis results. For example, the generation AI in the spatial analysis unit suggests events and activities that the user might be interested in based on the spatial analysis results. For example, it displays information about events being held nearby. The generation AI suggests events and activities that the user might be interested in. This improves user satisfaction by suggesting events and activities that the user might be interested in.

[0082] The space analysis unit can use the emotion estimation function to identify a space where the user can relax and suggest how to access that space. In the space analysis unit, for example, the generation AI uses the emotion estimation function to identify a space where the user can relax. For example, it identifies a place where the user can relax, such as a park or a cafe. The generation AI then suggests how to access the space where the user can relax. In this way, by identifying a space where the user can relax and suggesting how to access that space, the psychological burden on the user is reduced.

[0083] The feedback unit utilizes low-latency communication to enable the generation AI to provide an interactive guide that corresponds to the user's actions. The feedback unit utilizes, for example, 5G communication to enable the generation AI to provide an interactive guide that corresponds to the user's actions. For example, when the user reaches a specific location, information about the location is provided in real time. The generation AI provides, for example, an interactive guide that corresponds to the user's actions. This improves user convenience by providing an interactive guide that corresponds to the user's actions.

[0084] The feedback unit can use the emotion estimation function to provide feedback in real time according to the user's emotional state. For example, the feedback unit utilizes 5G communication, and the generation AI uses the emotion estimation function to analyze the user's emotional state in real time and provide feedback. For example, if the user is feeling stressed, the generation AI provides advice on how to relax. For example, the generation AI provides feedback in real time according to the emotional state. In this way, by providing feedback in real time according to the emotional state, the psychological burden on the user is reduced.

[0085] The feedback unit can use 5G communications to share information in real time among multiple users and provide a function for them to act cooperatively. The feedback unit, for example, uses 5G communications to build a system for sharing information in real time among multiple users. For example, location information and direction of travel can be shared when traveling in a group. The generation AI, for example, provides a function for multiple users to share information and act cooperatively. This allows multiple users to share information and act cooperatively, improving user convenience.

[0086] The feedback unit utilizes low-latency communications to enable the generating AI to monitor the user's health condition and immediately notify if any abnormalities are detected. The feedback unit, for example, utilizes 5G communications to build a system in which the generating AI monitors the user's health condition in real time. For example, it monitors heart rate and blood pressure and immediately notifies if any abnormalities are detected. The generating AI, for example, monitors the health condition and immediately notifies if any abnormalities are detected. This improves the safety of the user by monitoring the health condition and immediately notifying if any abnormalities are detected.

[0087] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0088] The spatial analysis system may further include a voice analysis unit. The voice analysis unit can analyze the user's voice instructions and provide appropriate feedback. For example, if the user says, "Tell me where the next intersection is," the voice analysis unit analyzes the user's voice instructions and provides appropriate guidance. The voice analysis unit uses voice recognition technology to analyze the user's voice instructions and provide appropriate feedback. This allows for more intuitive operation by analyzing voice information.

[0089] The spatial analysis system can further include a temperature sensor unit. The temperature sensor unit acquires ambient temperature information, and the generation AI analyzes that information to provide appropriate feedback to the user. For example, on a hot day, a cool route can be suggested. The temperature sensor unit acquires temperature information, and the generation AI analyzes that information to provide appropriate feedback. This makes it possible to provide feedback that takes temperature information into account.

[0090] The spatial analysis system may further include a vibration feedback unit. The vibration feedback unit may provide feedback to the user by vibration. For example, if the user needs to move in a specific direction, the user may feel a vibration in that direction. The vibration feedback unit provides feedback by vibration, allowing the user to intuitively understand the direction. This allows feedback using senses other than sight and hearing.

[0091] The spatial analysis system may further include an optical feedback unit. The optical feedback unit may provide optical feedback to the user. For example, the optical feedback unit may flash a light in the direction to move to indicate the direction to the user. The optical feedback unit provides optical feedback to allow the user to visually understand the direction. This enhances the visual feedback.

[0092] The spatial analysis system can further include a humidity sensor unit. The humidity sensor unit acquires ambient humidity information, and the generation AI analyzes that information to provide appropriate feedback to the user. For example, if the humidity is high, a comfortable route is suggested. The humidity sensor unit acquires humidity information, and the generation AI analyzes that information to provide appropriate feedback. This makes it possible to provide feedback that takes humidity information into consideration.

[0093] The spatial analysis system can further use its emotion estimation function to analyze the user's emotional state and suggest a less stressful route. For example, if the user is feeling stressed, the system will suggest a route taking that emotional state into account. The generation AI will perform spatial analysis taking the user's emotional state into account and suggest a less stressful route. This reduces the user's psychological burden by performing spatial analysis that takes the user's emotional state into account.

[0094] The spatial analysis system can further use its emotion estimation function to identify spaces where users can relax and suggest ways to access those spaces. For example, it can identify relaxing places such as parks and cafes. The generative AI then suggests ways to access those spaces. This reduces the psychological burden on users by identifying relaxing spaces and suggesting ways to access those spaces.

[0095] The spatial analysis system can also use its emotion estimation function to provide real-time feedback according to the user's emotional state. For example, if the user is feeling stressed, it can provide advice on how to relax. The generation AI provides real-time feedback according to the user's emotional state. This reduces the user's psychological burden by providing real-time feedback according to the user's emotional state.

[0096] The spatial analysis system can further use emotion estimation functions to identify objects of interest to the user and provide that information preferentially. For example, eye-tracking technology can be used to identify objects that the user is paying attention to. The generation AI can then identify objects of interest to the user and provide that information preferentially. This makes it easier to attract the user's attention by identifying objects of interest to the user and providing that information preferentially.

[0097] The spatial analysis system can further use its emotion estimation function to analyze the user's emotional state and suggest a less stressful route. For example, if the user is feeling stressed, the system will suggest a route taking that emotional state into account. The generation AI will perform spatial analysis taking the user's emotional state into account and suggest a less stressful route. This reduces the user's psychological burden by performing spatial analysis that takes the user's emotional state into account.

[0098] The processing flow of the second embodiment will be briefly explained below.

[0099] Step 1: The image acquisition unit acquires image information using a wearable camera. For example, the image acquisition unit acquires surrounding image information in real time using a wearable camera worn by a user. Step 2: The location information acquisition unit acquires location information from ichimill. For example, it uses GPS or other location information technology to accurately determine where the user is currently located. Step 3: The map information acquisition unit acquires map information from Mapbox, such as detailed map information about road layouts, building locations, and traffic conditions. Step 4: The spatial analysis unit integrates and analyzes the information from the image acquisition unit, location information acquisition unit, and map information acquisition unit. For example, the generation AI uses this information to understand the user's current environment and generate appropriate feedback. Step 5: The feedback unit uses 5G communications to provide feedback to the user based on the information analyzed by the spatial analysis unit. For example, while the user is walking, the generation AI will provide guidance in natural language, such as "There is an obstacle ahead. Please turn right."

[0100] 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.

[0101] 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.

[0102] 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.

[0103] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0104] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0119] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[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 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.

[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 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.

[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 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.

[0133] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0134] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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."

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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]

[0167] 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. an image acquisition unit that acquires image information using a wearable camera; A location information acquisition unit that acquires location information using ichimill; A map information acquisition unit that acquires map information from Mapbox; a spatial analysis unit that integrates and analyzes information from the image acquisition unit, the location information acquisition unit, and the map information acquisition unit; A feedback unit that feeds back the information analyzed by the spatial analysis unit to a user using 5G communication. A system characterized by:

2. The image acquisition unit The 360-degree image information is acquired by linking multiple wearable cameras.

2. The system of claim 1.

3. The location information acquisition unit Based on the location information from Ichimill, the generation AI analyzes the user's movement history and proposes the optimal movement route.

2. The system of claim 1.

4. The map information acquisition unit Based on the map information from Mapbox, the AI ​​analyzes traffic conditions in real time and suggests the optimal travel route.

2. The system of claim 1.

5. The spatial analysis unit Conduct spatial analysis that takes into account the user's emotional state and suggest a route with minimal stress.

2. The system of claim 1.

6. The feedback unit Providing the feedback in real time according to the emotional state of the user 2. The system of claim 1.

Images