system

Abstract

To provide a system capable of allowing people with visual impairments to move safely and obtain necessary information.SOLUTION: A system in an embodiment includes a reception unit, an analysis unit, a guide unit, and a danger avoidance unit. The reception unit receives an instruction from a user. The analysis unit analyzes image data from camera-equipped sunglasses based on information received by the reception unit. The guide unit provides voice guidance based on the information analyzed by the analysis unit. The danger avoidance unit detects obstacles and emits an alarm to ensure user safety.SELECTED DRAWING: Figure 1

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there was a problem that it was difficult for visually impaired people to move safely and obtain necessary information.

[0005] The system according to the embodiment aims to enable visually impaired people to move safely and obtain necessary information.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a guidance unit, and a hazard avoidance unit. The reception unit receives instructions from the user. The analysis unit analyzes video data from camera-equipped sunglasses based on the information received by the reception unit. The guidance unit provides voice guidance based on the information analyzed by the analysis unit. The hazard avoidance unit detects obstacles and issues warnings to ensure the user's safety. [Effects of the Invention]

[0007] The system according to this embodiment allows visually impaired individuals to move safely and obtain the necessary information. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) An AI system according to an embodiment of the present invention is a system that eliminates the need for guide dogs by equipping visually impaired individuals with camera-equipped sunglasses and an earphone microphone set. With this system, the user simply tells the AI ​​where they want to go and what their requirements are, and the AI ​​acts as the user's eyes and feet, guiding them to their destination while avoiding danger. Furthermore, when shopping, the AI ​​searches for what the user wants and provides support based on the user's instructions. For example, the user equips the camera-equipped sunglasses and earphone microphone set and tells the AI ​​where they want to go and what their requirements are through the earphone microphone. For example, they might give instructions such as "I want to go to the station" or "I want to buy milk at the supermarket." This information is input into the AI. The AI ​​analyzes the video data from the camera-equipped sunglasses to understand the user's surroundings. For example, it recognizes obstacles on the sidewalk and the status of traffic lights. Next, the AI ​​calculates the optimal route from the user's current location to the destination and guides the user along that route. For example, it provides voice guidance such as "Turn right" or "The light has turned green." Furthermore, when shopping, the AI ​​searches for what the user wants. For example, if a user instructs the AI ​​to "find the milk" in a supermarket, the AI ​​analyzes video data from camera-equipped sunglasses to pinpoint the location of the milk. It then provides voice guidance to the user, such as "The milk is on the shelf on the right." This system enables visually impaired individuals to move around and shop independently without the need for a guide dog. The AI ​​acts as the user's eyes and feet, safely guiding them to their destination while avoiding dangers, thus supporting their daily lives. In this way, the AI ​​system enables visually impaired individuals to move around and shop independently.

[0029] The AI ​​system according to this embodiment comprises a reception unit, an analysis unit, a guidance unit, and a hazard avoidance unit. The reception unit receives user instructions. User instructions include, but are not limited to, voice instructions, touch operations, and gestures. The reception unit receives user voice instructions using, for example, voice recognition technology. The reception unit can also receive user touch operations using a touch panel. Furthermore, the reception unit can also receive user gestures using gesture recognition technology. The analysis unit analyzes video data from sunglasses with a camera. For example, image recognition algorithms and machine learning techniques are used to analyze the video data. The analysis unit detects obstacles on the sidewalk using, for example, object recognition technology. The analysis unit can also recognize the state of traffic lights using color recognition technology. Furthermore, the analysis unit can measure the distance between the user and obstacles using distance measurement technology. The guidance unit provides voice guidance based on the information analyzed by the analysis unit. Voice guidance includes, for example, the type of voice and the timing of the guidance. The guidance unit provides voice guidance to the user using, for example, speech synthesis technology. Furthermore, the guidance unit can calculate the optimal route from the user's current location to their destination and provide voice guidance along that route. In addition, the guidance unit can identify items during shopping based on the user's instructions and guide the user to their location. The hazard avoidance unit detects obstacles and issues warnings to ensure the user's safety. Obstacle detection can be performed using, for example, sensor technology or image recognition technology. The hazard avoidance unit can detect obstacles using, for example, ultrasonic sensors. The hazard avoidance unit can also recognize the status of traffic lights using a camera. Furthermore, the hazard avoidance unit can issue warnings to the user using voice warnings or vibration warnings. As a result, the AI ​​system according to this embodiment enables visually impaired individuals to move around and shop independently. Some or all of the above-described processes in the reception unit may be performed using, for example, AI, or not using AI. For example, the reception unit can input the user's voice instructions into a generating AI and have the generating AI perform analysis of the voice instructions.Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input video data from camera-equipped sunglasses into a generation AI and have the generation AI perform the analysis of the video data. Some or all of the above-described processes in the guidance unit may be performed using AI, for example, or without AI. For example, the guidance unit can input the information analyzed by the analysis unit into a generation AI and have the generation AI perform the generation of voice guidance. Some or all of the above-described processes in the hazard avoidance unit may be performed using AI, for example, or without AI. For example, the hazard avoidance unit can input obstacle detection information into a generation AI and have the generation AI perform the generation of warnings.

[0030] The reception unit receives user instructions. User instructions include, but are not limited to, voice commands, touch operations, and gestures. The reception unit can, for example, receive voice commands using voice recognition technology. Specifically, voice recognition technology includes a process that extracts voice features and converts the voice data into text data. This allows the reception unit to accurately understand the voice commands spoken by the user and process them appropriately. The reception unit can also receive user touch operations using a touch panel. The touch panel uses technologies such as capacitive and resistive touch, allowing users to input instructions by touching the screen. Furthermore, the reception unit can also receive user gestures using gesture recognition technology. Gesture recognition technology uses cameras and sensors to detect the movements of the user's hands and body, analyzes those movements, and recognizes them as instructions. For example, a user can execute a specific command by waving their hand. This allows the reception unit to receive user instructions using a variety of input methods, improving user convenience. Furthermore, the reception unit can also use a combination of these input methods, allowing users to input instructions in the way that is most convenient for them. For example, voice commands and touch operations can be combined to input more complex instructions. This allows the reception desk to respond to diverse user needs and enable flexible operation.

[0031] The analysis unit analyzes video data from the camera-equipped sunglasses. For example, image recognition algorithms and machine learning techniques are used to analyze the video data. Specifically, image recognition algorithms such as convolutional neural networks (CNNs) are used, enabling the detection of specific objects and patterns from the video data. The analysis unit can, for example, detect obstacles on the sidewalk using object recognition technology. Object recognition technology can identify specific objects from video data and determine their location and shape. This reduces the risk of the user colliding with obstacles while walking. The analysis unit can also recognize the status of traffic lights using color recognition technology. Color recognition technology can detect specific colors from video data and track changes in those colors. This allows the user to accurately understand the status of traffic lights and cross the road safely. Furthermore, the analysis unit can measure the distance between the user and obstacles using distance measurement technology. Stereo cameras and laser rangefinders are used as distance measurement technologies, enabling accurate measurement of the distance between the user and obstacles. This allows for warnings to be issued before the user gets too close to an obstacle. By combining these technologies, the analysis unit can gain a detailed understanding of the user's surroundings and provide appropriate information. For example, by combining object recognition technology and distance measurement technology, it can simultaneously determine the location and distance of obstacles and issue more accurate warnings to the user. This allows the analysis unit to ensure the user's safety and provide an environment where they can move with peace of mind.

[0032] The guidance unit provides voice guidance based on information analyzed by the analysis unit. This voice guidance includes, but is not limited to, the type of voice and the timing of the guidance. The guidance unit can, for example, use speech synthesis technology to provide voice guidance to the user. Speech synthesis technology involves a process of converting text data into speech data, thereby providing information to the user in a natural voice. The guidance unit can also calculate the optimal route from the user's current location to their destination and provide voice guidance along that route. Route calculation uses algorithms that identify the shortest and safest routes using GPS data and map data. This allows the user to reach their destination without getting lost. Furthermore, the guidance unit can identify items during shopping based on user instructions and provide directions to their locations. For example, if a user wants to know the location of a specific product, the guidance unit can refer to in-store map data and product databases to locate the product and provide voice directions. This allows the user to shop efficiently. By combining these functions, the guidance unit can provide comprehensive guidance to the user. For example, if a user wants to shop on their way to their destination, the navigation system can calculate the optimal route and guide them to the locations of shops and products they should visit along the way. This allows the user to accomplish multiple objectives in a single trip. Furthermore, the navigation system can learn the user's preferences and past behavioral history to provide more personalized guidance. For instance, it can improve user convenience by prioritizing information on shops and products that the user frequently uses.

[0033] The hazard avoidance unit detects obstacles and issues warnings to ensure user safety. Obstacle detection utilizes technologies such as sensors and image recognition. Specifically, ultrasonic sensors and infrared sensors are used to detect the presence of obstacles. Ultrasonic sensors emit ultrasonic waves and receive the reflected waves to measure the distance to the obstacle. This allows the unit to issue a warning before the user gets too close to the obstacle. The hazard avoidance unit can also recognize the status of traffic lights using a camera. The camera acquires video data and uses image recognition technology to analyze the color and shape of the traffic lights. This allows the user to accurately understand the status of the traffic lights and cross the road safely. Furthermore, the hazard avoidance unit can issue warnings to the user using voice and vibration. Voice warnings use speech synthesis technology to convey specific warning content to the user. For example, it can issue a warning such as, "There is an obstacle ahead. Please be careful." Vibration warnings can convey a warning to the user by vibrating a device they are wearing (e.g., a smartwatch or belt). This ensures that users with visual or hearing impairments can reliably receive the warning. The hazard avoidance unit can ensure user safety by combining these technologies. For example, by combining ultrasonic sensors and cameras, it can simultaneously detect the presence of obstacles and the status of traffic lights, and issue appropriate warnings. This allows users to move with peace of mind. Furthermore, the hazard avoidance unit can learn the user's behavior patterns and environmental changes to provide more effective warnings. For example, if a user frequently travels a specific route at a specific time, it can warn of dangerous areas along that route in advance. In this way, the hazard avoidance unit can maximize user safety and enable them to live their daily lives with peace of mind.

[0034] The analysis unit can analyze video data from camera-equipped sunglasses to understand the user's surroundings. For example, the analysis unit can use object recognition technology to analyze video data from camera-equipped sunglasses and understand the user's surroundings. For example, the analysis unit can use an object recognition algorithm to detect obstacles on the sidewalk. The analysis unit can also use color recognition technology to recognize the state of traffic lights. Furthermore, the analysis unit can use distance measurement technology to measure the distance between the user and obstacles. For example, the analysis unit can analyze video data from camera-equipped sunglasses in real time to understand the user's surroundings. This allows for appropriate guidance by understanding the user's surroundings. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input video data from camera-equipped sunglasses into a generating AI and have the generating AI perform the analysis of the video data.

[0035] The guidance unit can calculate an efficient route from the user's current location to their destination and provide voice guidance along that route. For example, the guidance unit uses a route calculation algorithm to calculate an efficient route from the user's current location to their destination. For example, the guidance unit uses Dijkstra's algorithm or the A* algorithm to calculate the shortest distance. The guidance unit can also acquire real-time traffic information to calculate a route considering traffic conditions. Furthermore, the guidance unit can identify the user's current location based on GPS data and calculate a route to their destination. For example, the guidance unit calculates the optimal route from the user's current location to their destination and provides voice guidance along that route. This ensures that the user can safely reach their destination by calculating the optimal route and providing voice guidance. Some or all of the above processes in the guidance unit may be performed using AI, for example, or without AI. For example, the guidance unit can input the user's current location and destination information into a generating AI and have the generating AI perform route calculation and voice guidance generation.

[0036] The hazard avoidance unit can recognize obstacles on the sidewalk and the status of traffic lights using a camera and provide voice warnings to the user. For example, the hazard avoidance unit can recognize obstacles on the sidewalk using a camera. For example, the hazard avoidance unit can detect obstacles on the sidewalk using object recognition technology. The hazard avoidance unit can also use color recognition technology to recognize the status of traffic lights. For example, the hazard avoidance unit can recognize the color of the traffic light and provide a voice warning to the user to stop if the light is red. Furthermore, the hazard avoidance unit can also use speech synthesis technology to provide voice warnings to the user. For example, the hazard avoidance unit can provide a voice warning to the user such as, "There is an obstacle ahead." In this way, the safety of the user is ensured by recognizing obstacles and the status of traffic lights and providing voice warnings. Some or all of the above processing in the hazard avoidance unit may be performed using AI, for example, or without AI. For example, the hazard avoidance unit can input video data acquired by the camera into a generating AI and have the generating AI perform the recognition of obstacles and the status of traffic lights and the generation of warnings.

[0037] The reception unit can receive user instructions and input them into the AI. For example, the reception unit can receive user voice instructions using speech recognition technology. For instance, if a user gives a voice instruction such as "I want to go to the station," the reception unit recognizes the voice and inputs it into the AI. The reception unit can also receive user touch operations using a touch panel. For example, if a user selects a destination on the touch panel, that information is input into the AI. Furthermore, the reception unit can also receive user gestures using gesture recognition technology. For example, if a user performs a specific gesture, the gesture is recognized and input into the AI. In this way, by receiving user instructions and inputting them into the AI, the system performs actions according to the user's requests. Some or all of the above processing in the reception unit may be performed using AI, or not using AI. For example, the reception unit can input the user's voice instructions into a generating AI and have the generating AI perform analysis of the voice instructions.

[0038] The analysis unit can identify items and guide users to their locations while they are shopping. For example, the analysis unit can analyze video data from camera-equipped sunglasses to identify items. For example, the analysis unit can use image recognition technology to identify milk on a supermarket shelf. The analysis unit can also use barcode recognition technology to read the barcode of an item and identify its location. Furthermore, after identifying the location of an item, the analysis unit can provide voice guidance to the user to guide them to that location. For example, the analysis unit can provide voice guidance to the user such as, "The milk is on the shelf on the right." This allows users to shop efficiently by identifying items and guiding them to their locations. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input video data from camera-equipped sunglasses into a generating AI and have the generating AI perform item identification and generate location guidance.

[0039] The reception unit can analyze the user's past instruction history and select an appropriate reception method. For example, the reception unit can analyze the user's past instruction history using database search technology. For example, the reception unit can automatically display instructions that the user has frequently given in the past as candidates. The reception unit can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest instructions to be used during specific time periods based on the user's past instruction history. For example, the reception unit can prioritize displaying instructions related to a specific time period based on instructions the user has given in the past during that time period. In this way, by analyzing the past instruction history, the reception unit can provide the user with the most suitable reception method. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input the user's past instruction history into a generating AI and have the generating AI perform the analysis of the instruction history and the selection of a reception method.

[0040] The reception unit can filter instructions based on the user's current situation and environment when receiving them. For example, the reception unit can use location information technology to identify the user's current situation and environment. For example, the reception unit can identify the user's current location based on GPS data and filter instructions based on that information. The reception unit can also use speech recognition technology to analyze ambient sounds. For example, if the user is outdoors, the reception unit can adjust the sensitivity of voice input considering the surrounding noise. Furthermore, if the user is indoors, the reception unit can increase the sensitivity of voice input to match the quiet environment. For example, if the user is on the move, the reception unit prioritizes receiving instructions related to the user's current location based on GPS information. This allows the reception unit to receive appropriate instructions by filtering according to the user's situation and environment. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's location information and ambient sound data into a generating AI and have the generating AI perform the filtering.

[0041] The reception unit can prioritize receiving instructions that are highly relevant, taking into account the user's geographical location information. For example, the reception unit can obtain the user's geographical location information using GPS data. For example, if the user is in a specific location, the reception unit will prioritize receiving instructions related to that location. The reception unit can also prioritize receiving instructions related to the user's current location if the user is on the move. Furthermore, if the reception unit is in a specific area, the reception unit can prioritize receiving instructions related to that area. For example, if the reception unit is in a specific tourist destination, the reception unit will prioritize receiving instructions related to that tourist destination. In this way, by considering geographical location information, highly relevant instructions are prioritized. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location information into a generating AI and have the generating AI select highly relevant instructions.

[0042] The reception unit can analyze the user's social media activity when receiving instructions and accept relevant instructions. The reception unit can analyze the user's social media activity using, for example, social media analysis technology. For example, the reception unit can accept relevant instructions based on information shared by the user on social media. The reception unit can also analyze the content of the user's social media posts and accept relevant instructions. Furthermore, the reception unit can accept relevant instructions based on the user's social media activity history. For example, if the reception unit posts on social media, "I want to buy milk at the supermarket," it will accept relevant instructions based on that information. In this way, relevant instructions are received by analyzing social media activity. Some or all of the above processing in the reception unit may be performed using, for example, AI, or not using AI. For example, the reception unit can input the user's social media activity data into a generating AI and have the generating AI select relevant instructions.

[0043] The analysis unit can improve the accuracy of its analysis by referring to the user's past behavior history when analyzing video data. For example, the analysis unit can refer to the user's past behavior history using database search technology. For example, the analysis unit can analyze the current route based on routes the user has taken in the past. The analysis unit can also extract specific patterns from the user's past behavior history to improve the accuracy of its analysis. Furthermore, the analysis unit can analyze the user's past behavior history and perform analysis appropriate to the current situation. For example, the analysis unit can predict the user's actions at a specific location based on actions the user has taken there in the past, thereby improving the accuracy of its analysis. In this way, the accuracy of the analysis is improved by referring to past behavior history. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's past behavior history into a generating AI and have the generating AI perform behavior history referencing and analysis accuracy improvement.

[0044] The analysis unit can customize the analysis algorithm based on the user's current situation when analyzing video data. For example, the analysis unit uses location information technology and environmental sensors to identify the user's current situation. For instance, if the user is outdoors, the analysis unit uses an analysis algorithm appropriate for the surrounding environment. Similarly, if the user is indoors, the analysis unit can use an analysis algorithm appropriate for the indoor environment. Furthermore, if the user is moving, the analysis unit can use an analysis algorithm appropriate for the movement situation. For example, if the user is outdoors, the analysis unit uses an analysis algorithm that takes into account the effects of light reflection and shadows. This allows for appropriate analysis by customizing the analysis algorithm according to the current situation. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's current situation data into a generating AI and have the generating AI perform the customization of the analysis algorithm.

[0045] The analysis unit can improve the accuracy of its analysis by considering the user's geographical location information when analyzing video data. For example, the analysis unit can acquire the user's geographical location information using GPS data. For example, if the user is in a specific location, the analysis unit will prioritize analyzing information related to that location. The analysis unit can also prioritize analyzing information related to the user's current location if the user is on the move. Furthermore, if the user is in a specific area, the analysis unit can prioritize analyzing information related to that area. For example, if the user is in a specific tourist destination, the analysis unit will prioritize analyzing information related to that tourist destination. This improves the accuracy of the analysis by considering geographical location information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's geographical location information into a generating AI and have the generating AI perform the analysis accuracy improvement.

[0046] The analysis unit can improve the accuracy of its analysis by analyzing the user's social media activity when analyzing video data. For example, the analysis unit can analyze the user's social media activity using social media analysis technology. For example, the analysis unit can analyze relevant information based on information shared by the user on social media. The analysis unit can also analyze the content of the user's social media posts and analyze relevant information. Furthermore, the analysis unit can analyze relevant information based on the user's social media activity history. For example, if the analysis unit posts "I want to buy milk at the supermarket" on social media, it will analyze relevant information based on that information. By analyzing social media activity, the accuracy of the analysis is improved. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's social media activity data into a generating AI and have the generating AI perform the task of improving the accuracy of the analysis.

[0047] The guidance unit can select the optimal guidance method by referring to the user's past travel history when providing voice guidance. For example, the guidance unit can refer to the user's past travel history using database search technology. For example, the guidance unit can suggest the optimal guidance method based on routes the user has used in the past. The guidance unit can also suggest routes that avoid congestion based on the user's past travel history. Furthermore, the guidance unit can analyze the user's past travel history and suggest the most efficient route. For example, the guidance unit can suggest the optimal route for a given time period based on routes the user has used in the past during a specific time period. In this way, the guidance unit provides the optimal guidance method by referring to past travel history. Some or all of the above processing in the guidance unit may be performed using AI, for example, or without AI. For example, the guidance unit can input the user's past travel history into a generating AI and have the generating AI perform the referencing of the travel history and the selection of a guidance method.

[0048] The guidance unit can customize the guidance algorithm based on the user's current situation when providing voice guidance. For example, the guidance unit uses location information technology and environmental sensors to determine the user's current situation. For example, if the user is outdoors, the guidance unit uses a guidance algorithm appropriate to the surrounding environment. The guidance unit can also use a guidance algorithm appropriate to the indoor environment if the user is indoors. Furthermore, if the user is moving, the guidance unit can use a guidance algorithm appropriate to the movement situation. For example, if the user is outdoors, the guidance unit uses a guidance algorithm that takes into account the effects of light reflection and shadows. This allows for appropriate guidance by customizing the guidance algorithm according to the current situation. Some or all of the above processing in the guidance unit may be performed using AI, for example, or without AI. For example, the guidance unit can input the user's current situation data into a generating AI and have the generating AI perform the customization of the guidance algorithm.

[0049] The guidance unit can select the optimal guidance method by considering the user's geographical location information when providing voice guidance. For example, the guidance unit can obtain the user's geographical location information using GPS data. For example, if the user is in a specific location, the guidance unit can prioritize guidance related to that location. The guidance unit can also prioritize guidance related to the user's current location if the user is on the move. Furthermore, if the guidance unit is in a specific area, the guidance unit can prioritize guidance related to that area. For example, if the user is in a specific tourist destination, the guidance unit can prioritize guidance related to that tourist destination. In this way, by considering geographical location information, the guidance unit can provide the optimal guidance method. Some or all of the above processing in the guidance unit may be performed using AI, for example, or without AI. For example, the guidance unit can input the user's geographical location information into a generating AI and have the generating AI select the guidance method.

[0050] The guidance unit can improve the accuracy of its guidance by analyzing the user's social media activity during voice guidance. For example, the guidance unit can analyze the user's social media activity using social media analysis technology. For example, the guidance unit can provide relevant guidance based on information shared by the user on social media. The guidance unit can also analyze the content of the user's social media posts and provide relevant guidance. Furthermore, the guidance unit can provide relevant guidance based on the user's social media activity history. For example, if the guidance unit posts on social media that "I want to buy milk at the supermarket," it will provide relevant guidance based on that information. In this way, the accuracy of the guidance is improved by analyzing social media activity. Some or all of the above processing in the guidance unit may be performed using AI, for example, or without AI. For example, the guidance unit can input the user's social media activity data into a generating AI and have the generating AI perform the task of improving the accuracy of the guidance.

[0051] The hazard avoidance unit can select the optimal avoidance method by referring to the user's past behavior history when avoiding danger. For example, the hazard avoidance unit can refer to the user's past behavior history using database search technology. For example, the hazard avoidance unit can propose the optimal avoidance method based on dangerous situations the user has encountered in the past. The hazard avoidance unit can also extract specific patterns from the user's past behavior history and select the optimal avoidance method. Furthermore, the hazard avoidance unit can analyze the user's past behavior history and propose an avoidance method suitable for the current situation. For example, the hazard avoidance unit can propose an avoidance method for a specific location based on dangerous situations the user has encountered in that location in the past. In this way, by referring to past behavior history, it provides the optimal avoidance method. Some or all of the above processing in the hazard avoidance unit may be performed using AI, for example, or without AI. For example, the hazard avoidance unit can input the user's past behavior history into a generating AI and have the generating AI perform the referencing of the behavior history and the selection of an avoidance method.

[0052] The hazard avoidance unit can customize the avoidance algorithm based on the user's current situation when avoiding a hazard. For example, the hazard avoidance unit uses location information technology and environmental sensors to identify the user's current situation. For example, if the user is outdoors, the hazard avoidance unit uses an avoidance algorithm appropriate to the surrounding environment. Also, if the user is indoors, the hazard avoidance unit can use an avoidance algorithm appropriate to the indoor environment. Furthermore, if the user is moving, the hazard avoidance unit can use an avoidance algorithm appropriate to the movement situation. For example, if the user is outdoors, the hazard avoidance unit uses an avoidance algorithm that takes into account the effects of light reflection and shadows. This allows for appropriate avoidance by customizing the avoidance algorithm according to the current situation. Some or all of the above processing in the hazard avoidance unit may be performed using AI, for example, or without AI. For example, the hazard avoidance unit can input the user's current situation data into a generating AI and have the generating AI perform the customization of the avoidance algorithm.

[0053] The hazard avoidance unit can select the optimal avoidance method when avoiding a hazard, taking into account the user's geographical location information. For example, the hazard avoidance unit can acquire the user's geographical location information using GPS data. For example, if the user is in a specific location, the hazard avoidance unit can prioritize providing hazard avoidance information related to that location. Also, if the user is on the move, the hazard avoidance unit can prioritize providing hazard avoidance information related to the user's current location. Furthermore, if the user is in a specific area, the hazard avoidance unit can prioritize providing hazard avoidance information related to that area. For example, if the user is in a specific tourist destination, the hazard avoidance unit can prioritize providing hazard avoidance information related to that tourist destination. In this way, by taking geographical location information into consideration, the optimal avoidance method can be provided. Some or all of the above processing in the hazard avoidance unit may be performed using AI, for example, or without using AI. For example, the hazard avoidance unit can input the user's geographical location information into a generating AI and have the generating AI select an avoidance method.

[0054] The risk avoidance unit can improve the accuracy of risk avoidance by analyzing the user's social media activity during risk avoidance. For example, the risk avoidance unit analyzes the user's social media activity using social media analysis technology. For example, the risk avoidance unit provides relevant risk avoidance information based on information shared by the user on social media. The risk avoidance unit can also analyze the content of the user's social media posts and provide relevant risk avoidance information. Furthermore, the risk avoidance unit can provide relevant risk avoidance information based on the user's social media activity history. For example, if the risk avoidance unit posts on social media that "I want to buy milk at the supermarket," it will provide relevant risk avoidance information based on that information. This improves the accuracy of avoidance by analyzing social media activity. Some or all of the above processing in the risk avoidance unit may be performed using AI, for example, or without AI. For example, the risk avoidance unit can input the user's social media activity data into a generating AI and have the generating AI perform the task of improving the accuracy of avoidance.

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

[0056] The analysis unit can improve the accuracy of its analysis by taking weather information into account when assessing the user's surroundings. For example, in rainy weather, the analysis unit can detect slippery surfaces and warn the user. On snowy days, it can assess the snow conditions and provide information to ensure walking safety. Furthermore, in strong winds, it can adjust the user's travel route by taking wind effects into account. In this way, by considering weather information, the accuracy of the analysis is improved and the user's safety can be ensured.

[0057] The navigation system can optimize the route from the user's current location to their destination by taking into account the user's walking speed. For example, it can measure the user's walking speed in real time and suggest the optimal route based on that speed. It can also suggest a route that includes rest stops if the user is tired. Furthermore, if the user is in a hurry, it can calculate the route that will get them to their destination in the shortest time. This allows for more appropriate route guidance by considering the user's walking speed.

[0058] The hazard avoidance unit can analyze ambient sound information and detect hazards to ensure user safety. For example, it can detect car horns and emergency vehicle sirens and issue warnings to the user. It can also analyze the voices and footsteps of people in the surrounding area and provide information to help avoid hazards in crowded places. Furthermore, the hazard avoidance unit can detect noise from construction sites and suggest routes to avoid those areas. In this way, user safety can be ensured by analyzing ambient sound information.

[0059] The reception desk can learn the user's past behavior patterns when receiving instructions and automatically suggest predictable instructions. For example, if a user commutes using the same route every morning, the reception desk will automatically suggest that route. It can also suggest instructions based on information if the user has a habit of going to a specific place on a particular day of the week. Furthermore, if a user tends to give specific instructions at a particular time of day, instructions related to that time period can be prioritized. In this way, by learning past behavior patterns, the system can provide the user with the most appropriate instructions.

[0060] The analytics unit can not only identify items during shopping, but also suggest related products by considering the user's preferences and past purchase history. For example, the analytics unit can suggest related products based on items the user has purchased in the past. It can also learn the user's preferences and suggest products that match those preferences. Furthermore, the analytics unit can suggest products according to the season or events. In this way, by considering the user's preferences and past purchase history, a more personalized shopping experience can be provided.

[0061] The reception desk can not only analyze the user's past instruction history but also learn the user's lifestyle and daily routines to automatically suggest predictable instructions. For example, if the reception desk has a habit of drinking coffee at the same time every morning, it will suggest preparing the coffee at that time. It can also suggest instructions based on information if the user has a habit of performing a specific activity on a specific day of the week. Furthermore, if the user tends to perform a specific activity during a particular season, it can prioritize displaying instructions related to that season. In this way, by learning the user's lifestyle and daily routines, it can provide the most suitable instructions.

[0062] The reception desk can not only filter instructions based on the user's current situation and environment, but also prioritize instructions by considering the user's schedule information. For example, the reception desk can retrieve the user's calendar information and prioritize instructions related to important appointments. It can also prioritize instructions related to meetings if the user is in one. Furthermore, if the user is on vacation, it can prioritize instructions that promote relaxation. This allows for more appropriate instructions to be provided by considering the user's schedule.

[0063] The following briefly describes the processing flow for example form 1.

[0064] Step 1: The reception desk receives user instructions. User instructions include voice commands, touch operations, and gestures. The reception desk can receive user voice commands using voice recognition technology, and can also receive user touch operations using a touch panel. Furthermore, it can also receive user gestures using gesture recognition technology. Step 2: The analysis unit analyzes the video data from the camera-equipped sunglasses based on the information received by the reception unit. Image recognition algorithms and machine learning techniques are used to analyze the video data. The analysis unit can detect obstacles on the sidewalk using object recognition technology and recognize the status of traffic lights using color recognition technology. Furthermore, it can measure the distance between the user and obstacles using distance measurement technology. Step 3: The guidance unit provides voice guidance based on the information analyzed by the analysis unit. This voice guidance includes the type of voice and the timing of the guidance. The guidance unit can use speech synthesis technology to provide voice guidance to the user, calculate the optimal route from the user's current location to the destination, and provide voice guidance along that route. Furthermore, it can identify items during shopping based on user instructions and guide the user to their location. Step 4: The hazard avoidance unit detects obstacles and issues warnings to ensure user safety. Sensor technology and image recognition technology are used for obstacle detection. The hazard avoidance unit can also detect obstacles using ultrasonic sensors and recognize the status of traffic lights using cameras. Furthermore, it can issue warnings to the user using voice warnings and vibration warnings.

[0065] (Example of form 2) An AI system according to an embodiment of the present invention is a system that eliminates the need for guide dogs by equipping visually impaired individuals with camera-equipped sunglasses and an earphone microphone set. With this system, the user simply tells the AI ​​where they want to go and what their requirements are, and the AI ​​acts as the user's eyes and feet, guiding them to their destination while avoiding danger. Furthermore, when shopping, the AI ​​searches for what the user wants and provides support based on the user's instructions. For example, the user equips the camera-equipped sunglasses and earphone microphone set and tells the AI ​​where they want to go and what their requirements are through the earphone microphone. For example, they might give instructions such as "I want to go to the station" or "I want to buy milk at the supermarket." This information is input into the AI. The AI ​​analyzes the video data from the camera-equipped sunglasses to understand the user's surroundings. For example, it recognizes obstacles on the sidewalk and the status of traffic lights. Next, the AI ​​calculates the optimal route from the user's current location to the destination and guides the user along that route. For example, it provides voice guidance such as "Turn right" or "The light has turned green." Furthermore, when shopping, the AI ​​searches for what the user wants. For example, if a user instructs the AI ​​to "find the milk" in a supermarket, the AI ​​analyzes video data from camera-equipped sunglasses to pinpoint the location of the milk. It then provides voice guidance to the user, such as "The milk is on the shelf on the right." This system enables visually impaired individuals to move around and shop independently without the need for a guide dog. The AI ​​acts as the user's eyes and feet, safely guiding them to their destination while avoiding dangers, thus supporting their daily lives. In this way, the AI ​​system enables visually impaired individuals to move around and shop independently.

[0066] The AI ​​system according to this embodiment comprises a reception unit, an analysis unit, a guidance unit, and a hazard avoidance unit. The reception unit receives user instructions. User instructions include, but are not limited to, voice instructions, touch operations, and gestures. The reception unit receives user voice instructions using, for example, voice recognition technology. The reception unit can also receive user touch operations using a touch panel. Furthermore, the reception unit can also receive user gestures using gesture recognition technology. The analysis unit analyzes video data from sunglasses with a camera. For example, image recognition algorithms and machine learning techniques are used to analyze the video data. The analysis unit detects obstacles on the sidewalk using, for example, object recognition technology. The analysis unit can also recognize the state of traffic lights using color recognition technology. Furthermore, the analysis unit can measure the distance between the user and obstacles using distance measurement technology. The guidance unit provides voice guidance based on the information analyzed by the analysis unit. Voice guidance includes, for example, the type of voice and the timing of the guidance. The guidance unit provides voice guidance to the user using, for example, speech synthesis technology. Furthermore, the guidance unit can calculate the optimal route from the user's current location to their destination and provide voice guidance along that route. In addition, the guidance unit can identify items during shopping based on the user's instructions and guide the user to their location. The hazard avoidance unit detects obstacles and issues warnings to ensure the user's safety. Obstacle detection can be performed using, for example, sensor technology or image recognition technology. The hazard avoidance unit can detect obstacles using, for example, ultrasonic sensors. The hazard avoidance unit can also recognize the status of traffic lights using a camera. Furthermore, the hazard avoidance unit can issue warnings to the user using voice warnings or vibration warnings. As a result, the AI ​​system according to this embodiment enables visually impaired individuals to move around and shop independently. Some or all of the above-described processes in the reception unit may be performed using, for example, AI, or not using AI. For example, the reception unit can input the user's voice instructions into a generating AI and have the generating AI perform analysis of the voice instructions.Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input video data from camera-equipped sunglasses into a generation AI and have the generation AI perform the analysis of the video data. Some or all of the above-described processes in the guidance unit may be performed using AI, for example, or without AI. For example, the guidance unit can input the information analyzed by the analysis unit into a generation AI and have the generation AI perform the generation of voice guidance. Some or all of the above-described processes in the hazard avoidance unit may be performed using AI, for example, or without AI. For example, the hazard avoidance unit can input obstacle detection information into a generation AI and have the generation AI perform the generation of warnings.

[0067] The reception unit receives user instructions. User instructions include, but are not limited to, voice commands, touch operations, and gestures. The reception unit can, for example, receive voice commands using voice recognition technology. Specifically, voice recognition technology includes a process that extracts voice features and converts the voice data into text data. This allows the reception unit to accurately understand the voice commands spoken by the user and process them appropriately. The reception unit can also receive user touch operations using a touch panel. The touch panel uses technologies such as capacitive and resistive touch, allowing users to input instructions by touching the screen. Furthermore, the reception unit can also receive user gestures using gesture recognition technology. Gesture recognition technology uses cameras and sensors to detect the movements of the user's hands and body, analyzes those movements, and recognizes them as instructions. For example, a user can execute a specific command by waving their hand. This allows the reception unit to receive user instructions using a variety of input methods, improving user convenience. Furthermore, the reception unit can also use a combination of these input methods, allowing users to input instructions in the way that is most convenient for them. For example, voice commands and touch operations can be combined to input more complex instructions. This allows the reception desk to respond to diverse user needs and enable flexible operation.

[0068] The analysis unit analyzes video data from the camera-equipped sunglasses. For example, image recognition algorithms and machine learning techniques are used to analyze the video data. Specifically, image recognition algorithms such as convolutional neural networks (CNNs) are used, enabling the detection of specific objects and patterns from the video data. The analysis unit can, for example, detect obstacles on the sidewalk using object recognition technology. Object recognition technology can identify specific objects from video data and determine their location and shape. This reduces the risk of the user colliding with obstacles while walking. The analysis unit can also recognize the status of traffic lights using color recognition technology. Color recognition technology can detect specific colors from video data and track changes in those colors. This allows the user to accurately understand the status of traffic lights and cross the road safely. Furthermore, the analysis unit can measure the distance between the user and obstacles using distance measurement technology. Stereo cameras and laser rangefinders are used as distance measurement technologies, enabling accurate measurement of the distance between the user and obstacles. This allows for warnings to be issued before the user gets too close to an obstacle. By combining these technologies, the analysis unit can gain a detailed understanding of the user's surroundings and provide appropriate information. For example, by combining object recognition technology and distance measurement technology, it can simultaneously determine the location and distance of obstacles and issue more accurate warnings to the user. This allows the analysis unit to ensure the user's safety and provide an environment where they can move with peace of mind.

[0069] The guidance unit provides voice guidance based on information analyzed by the analysis unit. This voice guidance includes, but is not limited to, the type of voice and the timing of the guidance. The guidance unit can, for example, use speech synthesis technology to provide voice guidance to the user. Speech synthesis technology involves a process of converting text data into speech data, thereby providing information to the user in a natural voice. The guidance unit can also calculate the optimal route from the user's current location to their destination and provide voice guidance along that route. Route calculation uses algorithms that identify the shortest and safest routes using GPS data and map data. This allows the user to reach their destination without getting lost. Furthermore, the guidance unit can identify items during shopping based on user instructions and provide directions to their locations. For example, if a user wants to know the location of a specific product, the guidance unit can refer to in-store map data and product databases to locate the product and provide voice directions. This allows the user to shop efficiently. By combining these functions, the guidance unit can provide comprehensive guidance to the user. For example, if a user wants to shop on their way to their destination, the navigation system can calculate the optimal route and guide them to the locations of shops and products they should visit along the way. This allows the user to accomplish multiple objectives in a single trip. Furthermore, the navigation system can learn the user's preferences and past behavioral history to provide more personalized guidance. For instance, it can improve user convenience by prioritizing information on shops and products that the user frequently uses.

[0070] The hazard avoidance unit detects obstacles and issues warnings to ensure user safety. Obstacle detection utilizes technologies such as sensors and image recognition. Specifically, ultrasonic sensors and infrared sensors are used to detect the presence of obstacles. Ultrasonic sensors emit ultrasonic waves and receive the reflected waves to measure the distance to the obstacle. This allows the unit to issue a warning before the user gets too close to the obstacle. The hazard avoidance unit can also recognize the status of traffic lights using a camera. The camera acquires video data and uses image recognition technology to analyze the color and shape of the traffic lights. This allows the user to accurately understand the status of the traffic lights and cross the road safely. Furthermore, the hazard avoidance unit can issue warnings to the user using voice and vibration. Voice warnings use speech synthesis technology to convey specific warning content to the user. For example, it can issue a warning such as, "There is an obstacle ahead. Please be careful." Vibration warnings can convey a warning to the user by vibrating a device they are wearing (e.g., a smartwatch or belt). This ensures that users with visual or hearing impairments can reliably receive the warning. The hazard avoidance unit can ensure user safety by combining these technologies. For example, by combining ultrasonic sensors and cameras, it can simultaneously detect the presence of obstacles and the status of traffic lights, and issue appropriate warnings. This allows users to move with peace of mind. Furthermore, the hazard avoidance unit can learn the user's behavior patterns and environmental changes to provide more effective warnings. For example, if a user frequently travels a specific route at a specific time, it can warn of dangerous areas along that route in advance. In this way, the hazard avoidance unit can maximize user safety and enable them to live their daily lives with peace of mind.

[0071] The analysis unit can analyze video data from camera-equipped sunglasses to understand the user's surroundings. For example, the analysis unit can use object recognition technology to analyze video data from camera-equipped sunglasses and understand the user's surroundings. For example, the analysis unit can use an object recognition algorithm to detect obstacles on the sidewalk. The analysis unit can also use color recognition technology to recognize the state of traffic lights. Furthermore, the analysis unit can use distance measurement technology to measure the distance between the user and obstacles. For example, the analysis unit can analyze video data from camera-equipped sunglasses in real time to understand the user's surroundings. This allows for appropriate guidance by understanding the user's surroundings. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input video data from camera-equipped sunglasses into a generating AI and have the generating AI perform the analysis of the video data.

[0072] The guidance unit can calculate an efficient route from the user's current location to their destination and provide voice guidance along that route. For example, the guidance unit uses a route calculation algorithm to calculate an efficient route from the user's current location to their destination. For example, the guidance unit uses Dijkstra's algorithm or the A* algorithm to calculate the shortest distance. The guidance unit can also acquire real-time traffic information to calculate a route considering traffic conditions. Furthermore, the guidance unit can identify the user's current location based on GPS data and calculate a route to their destination. For example, the guidance unit calculates the optimal route from the user's current location to their destination and provides voice guidance along that route. This ensures that the user can safely reach their destination by calculating the optimal route and providing voice guidance. Some or all of the above processes in the guidance unit may be performed using AI, for example, or without AI. For example, the guidance unit can input the user's current location and destination information into a generating AI and have the generating AI perform route calculation and voice guidance generation.

[0073] The hazard avoidance unit can recognize obstacles on the sidewalk and the status of traffic lights using a camera and provide voice warnings to the user. For example, the hazard avoidance unit can recognize obstacles on the sidewalk using a camera. For example, the hazard avoidance unit can detect obstacles on the sidewalk using object recognition technology. The hazard avoidance unit can also use color recognition technology to recognize the status of traffic lights. For example, the hazard avoidance unit can recognize the color of the traffic light and provide a voice warning to the user to stop if the light is red. Furthermore, the hazard avoidance unit can also use speech synthesis technology to provide voice warnings to the user. For example, the hazard avoidance unit can provide a voice warning to the user such as, "There is an obstacle ahead." In this way, the safety of the user is ensured by recognizing obstacles and the status of traffic lights and providing voice warnings. Some or all of the above processing in the hazard avoidance unit may be performed using AI, for example, or without AI. For example, the hazard avoidance unit can input video data acquired by the camera into a generating AI and have the generating AI perform the recognition of obstacles and the status of traffic lights and the generation of warnings.

[0074] The reception unit can receive user instructions and input them into the AI. For example, the reception unit can receive user voice instructions using speech recognition technology. For instance, if a user gives a voice instruction such as "I want to go to the station," the reception unit recognizes the voice and inputs it into the AI. The reception unit can also receive user touch operations using a touch panel. For example, if a user selects a destination on the touch panel, that information is input into the AI. Furthermore, the reception unit can also receive user gestures using gesture recognition technology. For example, if a user performs a specific gesture, the gesture is recognized and input into the AI. In this way, by receiving user instructions and inputting them into the AI, the system performs actions according to the user's requests. Some or all of the above processing in the reception unit may be performed using AI, or not using AI. For example, the reception unit can input the user's voice instructions into a generating AI and have the generating AI perform analysis of the voice instructions.

[0075] The analysis unit can identify items and guide users to their locations while they are shopping. For example, the analysis unit can analyze video data from camera-equipped sunglasses to identify items. For example, the analysis unit can use image recognition technology to identify milk on a supermarket shelf. The analysis unit can also use barcode recognition technology to read the barcode of an item and identify its location. Furthermore, after identifying the location of an item, the analysis unit can provide voice guidance to the user to guide them to that location. For example, the analysis unit can provide voice guidance to the user such as, "The milk is on the shelf on the right." This allows users to shop efficiently by identifying items and guiding them to their locations. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input video data from camera-equipped sunglasses into a generating AI and have the generating AI perform item identification and generate location guidance.

[0076] The reception desk can estimate the user's emotions and adjust how instructions are received based on the estimated emotions. For example, the reception desk can estimate the user's emotions using facial recognition technology. For instance, it can analyze facial data captured by a camera to estimate whether the user is tense or relaxed. Alternatively, the reception desk can estimate the user's emotions using voice analysis technology. For example, it can analyze the tone and speed of the user's voice to estimate their emotions. Furthermore, the reception desk adjusts how instructions are received based on the estimated emotions. For example, if the user is tense, it can provide a simple and intuitive interface to facilitate instruction input. If the user is relaxed, it can offer detailed options and suggest customizable instruction input. Furthermore, if the user is in a hurry, it can prioritize voice input to quickly receive instructions. This allows for a more appropriate interface by adjusting how instructions are received according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. The generating AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the reception area may be performed using AI, or not using AI. For example, the reception area may input user facial expression data into the generating AI and have the generating AI perform emotion estimation and adjustment of the method of receiving instructions.

[0077] The reception unit can analyze the user's past instruction history and select an appropriate reception method. For example, the reception unit can analyze the user's past instruction history using database search technology. For example, the reception unit can automatically display instructions that the user has frequently given in the past as candidates. The reception unit can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest instructions to be used during specific time periods based on the user's past instruction history. For example, the reception unit can prioritize displaying instructions related to a specific time period based on instructions the user has given in the past during that time period. In this way, by analyzing the past instruction history, the reception unit can provide the user with the most suitable reception method. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input the user's past instruction history into a generating AI and have the generating AI perform the analysis of the instruction history and the selection of a reception method.

[0078] The reception unit can filter instructions based on the user's current situation and environment when receiving them. For example, the reception unit can use location information technology to identify the user's current situation and environment. For example, the reception unit can identify the user's current location based on GPS data and filter instructions based on that information. The reception unit can also use speech recognition technology to analyze ambient sounds. For example, if the user is outdoors, the reception unit can adjust the sensitivity of voice input considering the surrounding noise. Furthermore, if the user is indoors, the reception unit can increase the sensitivity of voice input to match the quiet environment. For example, if the user is on the move, the reception unit prioritizes receiving instructions related to the user's current location based on GPS information. This allows the reception unit to receive appropriate instructions by filtering according to the user's situation and environment. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's location information and ambient sound data into a generating AI and have the generating AI perform the filtering.

[0079] The reception desk can estimate the user's emotions and determine the priority of instructions to accept based on the estimated emotions. For example, the reception desk can estimate the user's emotions using facial recognition technology. For instance, it can analyze facial data captured by a camera to estimate whether the user is tense or relaxed. Alternatively, the reception desk can estimate the user's emotions using voice analysis technology. For example, it can analyze the tone and speed of the user's voice to estimate their emotions. Furthermore, the reception desk determines the priority of instructions to accept based on the estimated emotions. For example, if the user is tense, important instructions will be given priority. If the user is relaxed, detailed instructions may also be accepted. Additionally, if the user is in a hurry, instructions requiring quick processing may be given priority. This ensures that important instructions are prioritized by determining the priority of instructions according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes at the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input user facial expression data into a generating AI and have the generating AI perform emotion estimation and determine the priority of instructions.

[0080] The reception unit can prioritize receiving instructions that are highly relevant, taking into account the user's geographical location information. For example, the reception unit can obtain the user's geographical location information using GPS data. For example, if the user is in a specific location, the reception unit will prioritize receiving instructions related to that location. The reception unit can also prioritize receiving instructions related to the user's current location if the user is on the move. Furthermore, if the reception unit is in a specific area, the reception unit can prioritize receiving instructions related to that area. For example, if the reception unit is in a specific tourist destination, the reception unit will prioritize receiving instructions related to that tourist destination. In this way, by considering geographical location information, highly relevant instructions are prioritized. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location information into a generating AI and have the generating AI select highly relevant instructions.

[0081] The reception unit can analyze the user's social media activity when receiving instructions and accept relevant instructions. The reception unit can analyze the user's social media activity using, for example, social media analysis technology. For example, the reception unit can accept relevant instructions based on information shared by the user on social media. The reception unit can also analyze the content of the user's social media posts and accept relevant instructions. Furthermore, the reception unit can accept relevant instructions based on the user's social media activity history. For example, if the reception unit posts on social media, "I want to buy milk at the supermarket," it will accept relevant instructions based on that information. In this way, relevant instructions are received by analyzing social media activity. Some or all of the above processing in the reception unit may be performed using, for example, AI, or not using AI. For example, the reception unit can input the user's social media activity data into a generating AI and have the generating AI select relevant instructions.

[0082] The analysis unit can estimate the user's emotions and adjust the video data analysis method based on the estimated user emotions. For example, the analysis unit can estimate the user's emotions using facial recognition technology. For example, the analysis unit can analyze the user's facial expression data captured by the camera to estimate whether the user is tense or relaxed. The analysis unit can also estimate the user's emotions using voice analysis technology. For example, the analysis unit can analyze the tone and speed of the user's voice to estimate the user's emotions. Furthermore, the analysis unit adjusts the video data analysis method based on the estimated user emotions. For example, if the user is relaxed, it can perform a detailed analysis to grasp the surrounding situation in detail. If the user is tense, it can prioritize the analysis of important information. Furthermore, if the user is in a hurry, it can perform a rapid analysis to provide the necessary information. In this way, adjusting the analysis method according to the user's emotions enables more appropriate analysis. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user facial expression data into a generating AI and have the generating AI perform emotion estimation and adjustment of the analysis method.

[0083] The analysis unit can improve the accuracy of its analysis by referring to the user's past behavior history when analyzing video data. For example, the analysis unit can refer to the user's past behavior history using database search technology. For example, the analysis unit can analyze the current route based on routes the user has taken in the past. The analysis unit can also extract specific patterns from the user's past behavior history to improve the accuracy of its analysis. Furthermore, the analysis unit can analyze the user's past behavior history and perform analysis appropriate to the current situation. For example, the analysis unit can predict the user's actions at a specific location based on actions the user has taken there in the past, thereby improving the accuracy of its analysis. In this way, the accuracy of the analysis is improved by referring to past behavior history. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's past behavior history into a generating AI and have the generating AI perform behavior history referencing and analysis accuracy improvement.

[0084] The analysis unit can customize the analysis algorithm based on the user's current situation when analyzing video data. For example, the analysis unit uses location information technology and environmental sensors to identify the user's current situation. For instance, if the user is outdoors, the analysis unit uses an analysis algorithm appropriate for the surrounding environment. Similarly, if the user is indoors, the analysis unit can use an analysis algorithm appropriate for the indoor environment. Furthermore, if the user is moving, the analysis unit can use an analysis algorithm appropriate for the movement situation. For example, if the user is outdoors, the analysis unit uses an analysis algorithm that takes into account the effects of light reflection and shadows. This allows for appropriate analysis by customizing the analysis algorithm according to the current situation. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's current situation data into a generating AI and have the generating AI perform the customization of the analysis algorithm.

[0085] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, the analysis unit can estimate the user's emotions using facial recognition technology. For example, the analysis unit can analyze the user's facial expression data captured by a camera to estimate whether the user is tense or relaxed. The analysis unit can also estimate the user's emotions using voice analysis technology. For example, the analysis unit can analyze the tone and speed of the user's voice to estimate the user's emotions. Furthermore, the analysis unit adjusts the display method of the analysis results based on the estimated emotions. For example, if the user is tense, a simple and highly visible display method is provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that focuses on the essentials can be provided. By adjusting the display method according to the user's emotions, highly visible displays become possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user facial expression data into a generating AI and have the generating AI perform emotion estimation and adjustment of the display method.

[0086] The analysis unit can improve the accuracy of its analysis by considering the user's geographical location information when analyzing video data. For example, the analysis unit can acquire the user's geographical location information using GPS data. For example, if the user is in a specific location, the analysis unit will prioritize analyzing information related to that location. The analysis unit can also prioritize analyzing information related to the user's current location if the user is on the move. Furthermore, if the user is in a specific area, the analysis unit can prioritize analyzing information related to that area. For example, if the user is in a specific tourist destination, the analysis unit will prioritize analyzing information related to that tourist destination. This improves the accuracy of the analysis by considering geographical location information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's geographical location information into a generating AI and have the generating AI perform the analysis accuracy improvement.

[0087] The analysis unit can improve the accuracy of its analysis by analyzing the user's social media activity when analyzing video data. For example, the analysis unit can analyze the user's social media activity using social media analysis technology. For example, the analysis unit can analyze relevant information based on information shared by the user on social media. The analysis unit can also analyze the content of the user's social media posts and analyze relevant information. Furthermore, the analysis unit can analyze relevant information based on the user's social media activity history. For example, if the analysis unit posts "I want to buy milk at the supermarket" on social media, it will analyze relevant information based on that information. By analyzing social media activity, the accuracy of the analysis is improved. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's social media activity data into a generating AI and have the generating AI perform the task of improving the accuracy of the analysis.

[0088] The guidance unit can estimate the user's emotions and adjust the way the voice guidance is delivered based on the estimated emotions. For example, the guidance unit can estimate the user's emotions using facial recognition technology. For example, the guidance unit can analyze the user's facial expression data captured by a camera to estimate whether the user is tense or relaxed. The guidance unit can also estimate the user's emotions using voice analysis technology. For example, the guidance unit can analyze the tone and speed of the user's voice to estimate the user's emotions. Furthermore, the guidance unit adjusts the way the voice guidance is delivered based on the estimated emotions. For example, if the user is tense, the guidance can be delivered in a calm voice. If the user is relaxed, the guidance can be delivered in a cheerful voice. Furthermore, if the user is in a hurry, the voice guidance can be delivered quickly and concisely. In this way, by adjusting the way the voice guidance is delivered according to the user's emotions, more appropriate guidance becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the guidance unit may be performed using AI, for example, or without AI. For example, the guidance unit can input user facial expression data into a generating AI and have the generating AI perform emotion estimation and adjustment of the expression method of voice guidance.

[0089] The guidance unit can select the optimal guidance method by referring to the user's past travel history when providing voice guidance. For example, the guidance unit can refer to the user's past travel history using database search technology. For example, the guidance unit can suggest the optimal guidance method based on routes the user has used in the past. The guidance unit can also suggest routes that avoid congestion based on the user's past travel history. Furthermore, the guidance unit can analyze the user's past travel history and suggest the most efficient route. For example, the guidance unit can suggest the optimal route for a given time period based on routes the user has used in the past during a specific time period. In this way, the guidance unit provides the optimal guidance method by referring to past travel history. Some or all of the above processing in the guidance unit may be performed using AI, for example, or without AI. For example, the guidance unit can input the user's past travel history into a generating AI and have the generating AI perform the referencing of the travel history and the selection of a guidance method.

[0090] The guidance unit can customize the guidance algorithm based on the user's current situation when providing voice guidance. For example, the guidance unit uses location information technology and environmental sensors to determine the user's current situation. For example, if the user is outdoors, the guidance unit uses a guidance algorithm appropriate to the surrounding environment. The guidance unit can also use a guidance algorithm appropriate to the indoor environment if the user is indoors. Furthermore, if the user is moving, the guidance unit can use a guidance algorithm appropriate to the movement situation. For example, if the user is outdoors, the guidance unit uses a guidance algorithm that takes into account the effects of light reflection and shadows. This allows for appropriate guidance by customizing the guidance algorithm according to the current situation. Some or all of the above processing in the guidance unit may be performed using AI, for example, or without AI. For example, the guidance unit can input the user's current situation data into a generating AI and have the generating AI perform the customization of the guidance algorithm.

[0091] The guidance unit can estimate the user's emotions and determine the priority of voice guidance based on the estimated emotions. For example, the guidance unit can estimate the user's emotions using facial recognition technology. For instance, it can analyze facial data captured by a camera to estimate whether the user is tense or relaxed. Alternatively, the guidance unit can estimate the user's emotions using voice analysis technology. For example, it can analyze the tone and speed of the user's voice to estimate their emotions. Furthermore, the guidance unit determines the priority of voice guidance based on the estimated emotions. For example, if the user is tense, important guidance will be prioritized. If the user is relaxed, detailed guidance may be provided. Furthermore, if the user is in a hurry, guidance requiring quick processing may be prioritized. This ensures that important guidance is prioritized by determining the priority of guidance according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the guidance unit may be performed using AI, for example, or without AI. For example, the guidance unit can input user facial expression data into a generating AI and have the generating AI perform emotion estimation and determine the priority of voice guidance.

[0092] The guidance unit can select the optimal guidance method by considering the user's geographical location information when providing voice guidance. For example, the guidance unit can obtain the user's geographical location information using GPS data. For example, if the user is in a specific location, the guidance unit can prioritize guidance related to that location. The guidance unit can also prioritize guidance related to the user's current location if the user is on the move. Furthermore, if the guidance unit is in a specific area, the guidance unit can prioritize guidance related to that area. For example, if the user is in a specific tourist destination, the guidance unit can prioritize guidance related to that tourist destination. In this way, by considering geographical location information, the guidance unit can provide the optimal guidance method. Some or all of the above processing in the guidance unit may be performed using AI, for example, or without AI. For example, the guidance unit can input the user's geographical location information into a generating AI and have the generating AI select the guidance method.

[0093] The guidance unit can improve the accuracy of its guidance by analyzing the user's social media activity during voice guidance. For example, the guidance unit can analyze the user's social media activity using social media analysis technology. For example, the guidance unit can provide relevant guidance based on information shared by the user on social media. The guidance unit can also analyze the content of the user's social media posts and provide relevant guidance. Furthermore, the guidance unit can provide relevant guidance based on the user's social media activity history. For example, if the guidance unit posts on social media that "I want to buy milk at the supermarket," it will provide relevant guidance based on that information. In this way, the accuracy of the guidance is improved by analyzing social media activity. Some or all of the above processing in the guidance unit may be performed using AI, for example, or without AI. For example, the guidance unit can input the user's social media activity data into a generating AI and have the generating AI perform the task of improving the accuracy of the guidance.

[0094] The hazard avoidance unit can estimate the user's emotions and adjust the hazard avoidance method based on the estimated emotions. For example, the hazard avoidance unit can estimate the user's emotions using facial recognition technology. For instance, it can analyze facial data captured by a camera to estimate whether the user is tense or relaxed. The hazard avoidance unit can also estimate the user's emotions using voice analysis technology. For example, it can analyze the tone and speed of the user's voice to estimate their emotions. Furthermore, the hazard avoidance unit adjusts the hazard avoidance method based on the estimated emotions. For example, if the user is tense, it can provide detailed hazard avoidance information. If the user is relaxed, it can provide concise hazard avoidance information. Furthermore, if the user is in a hurry, it can provide hazard avoidance information quickly. This allows for more appropriate hazard avoidance by adjusting the hazard avoidance method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the hazard avoidance unit may be performed using AI, for example, or without AI. For example, the hazard avoidance unit can input user facial expression data into a generating AI and have the generating AI perform emotion estimation and adjustment of hazard avoidance methods.

[0095] The hazard avoidance unit can select the optimal avoidance method by referring to the user's past behavior history when avoiding danger. For example, the hazard avoidance unit can refer to the user's past behavior history using database search technology. For example, the hazard avoidance unit can propose the optimal avoidance method based on dangerous situations the user has encountered in the past. The hazard avoidance unit can also extract specific patterns from the user's past behavior history and select the optimal avoidance method. Furthermore, the hazard avoidance unit can analyze the user's past behavior history and propose an avoidance method suitable for the current situation. For example, the hazard avoidance unit can propose an avoidance method for a specific location based on dangerous situations the user has encountered in that location in the past. In this way, by referring to past behavior history, it provides the optimal avoidance method. Some or all of the above processing in the hazard avoidance unit may be performed using AI, for example, or without AI. For example, the hazard avoidance unit can input the user's past behavior history into a generating AI and have the generating AI perform the referencing of the behavior history and the selection of an avoidance method.

[0096] The hazard avoidance unit can customize the avoidance algorithm based on the user's current situation when avoiding a hazard. For example, the hazard avoidance unit uses location information technology and environmental sensors to identify the user's current situation. For example, if the user is outdoors, the hazard avoidance unit uses an avoidance algorithm appropriate to the surrounding environment. Also, if the user is indoors, the hazard avoidance unit can use an avoidance algorithm appropriate to the indoor environment. Furthermore, if the user is moving, the hazard avoidance unit can use an avoidance algorithm appropriate to the movement situation. For example, if the user is outdoors, the hazard avoidance unit uses an avoidance algorithm that takes into account the effects of light reflection and shadows. This allows for appropriate avoidance by customizing the avoidance algorithm according to the current situation. Some or all of the above processing in the hazard avoidance unit may be performed using AI, for example, or without AI. For example, the hazard avoidance unit can input the user's current situation data into a generating AI and have the generating AI perform the customization of the avoidance algorithm.

[0097] The hazard avoidance unit can estimate the user's emotions and determine the priority of hazard avoidance based on the estimated emotions. For example, the hazard avoidance unit can estimate the user's emotions using facial recognition technology. For instance, it can analyze facial data captured by a camera to estimate whether the user is tense or relaxed. The hazard avoidance unit can also estimate the user's emotions using voice analysis technology. For example, it can analyze the tone and speed of the user's voice to estimate their emotions. Furthermore, the hazard avoidance unit determines the priority of hazard avoidance based on the estimated emotions. For example, if the user is tense, it prioritizes providing important hazard avoidance information. If the user is relaxed, it can also provide detailed hazard avoidance information. Furthermore, if the user is in a hurry, it can prioritize providing hazard avoidance information that requires quick processing. This allows for the prioritization of important hazard avoidance information based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generating AI may be, but is not limited to, text generating AI (e.g., LLM) or multimodal generating AI. Some or all of the processing described above in the hazard avoidance unit may be performed using AI, or not using AI. For example, the hazard avoidance unit may input user facial expression data into the generating AI and have the generating AI perform emotion estimation and determine the priority of hazard avoidance.

[0098] The hazard avoidance unit can select the optimal avoidance method when avoiding a hazard, taking into account the user's geographical location information. For example, the hazard avoidance unit can acquire the user's geographical location information using GPS data. For example, if the user is in a specific location, the hazard avoidance unit can prioritize providing hazard avoidance information related to that location. Also, if the user is on the move, the hazard avoidance unit can prioritize providing hazard avoidance information related to the user's current location. Furthermore, if the user is in a specific area, the hazard avoidance unit can prioritize providing hazard avoidance information related to that area. For example, if the user is in a specific tourist destination, the hazard avoidance unit can prioritize providing hazard avoidance information related to that tourist destination. In this way, by taking geographical location information into consideration, the optimal avoidance method can be provided. Some or all of the above processing in the hazard avoidance unit may be performed using AI, for example, or without using AI. For example, the hazard avoidance unit can input the user's geographical location information into a generating AI and have the generating AI select an avoidance method.

[0099] The risk avoidance unit can improve the accuracy of risk avoidance by analyzing the user's social media activity during risk avoidance. For example, the risk avoidance unit analyzes the user's social media activity using social media analysis technology. For example, the risk avoidance unit provides relevant risk avoidance information based on information shared by the user on social media. The risk avoidance unit can also analyze the content of the user's social media posts and provide relevant risk avoidance information. Furthermore, the risk avoidance unit can provide relevant risk avoidance information based on the user's social media activity history. For example, if the risk avoidance unit posts on social media that "I want to buy milk at the supermarket," it will provide relevant risk avoidance information based on that information. This improves the accuracy of avoidance by analyzing social media activity. Some or all of the above processing in the risk avoidance unit may be performed using AI, for example, or without AI. For example, the risk avoidance unit can input the user's social media activity data into a generating AI and have the generating AI perform the task of improving the accuracy of avoidance.

[0100] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0101] The reception desk can monitor the user's health condition when receiving instructions and adjust the instruction processing method accordingly. For example, the reception desk can measure the user's heart rate and blood pressure, and if the user is stressed, it can provide a simpler, more intuitive interface. If the user is relaxed, it can offer detailed options and suggest customizable instruction input. Furthermore, if the user is in a hurry, it can prioritize voice input and quickly receive instructions. In this way, by adjusting the instruction processing method according to the user's health condition, a more appropriate interface can be provided.

[0102] The analysis unit can improve the accuracy of its analysis by taking weather information into account when assessing the user's surroundings. For example, in rainy weather, the analysis unit can detect slippery surfaces and warn the user. On snowy days, it can assess the snow conditions and provide information to ensure walking safety. Furthermore, in strong winds, it can adjust the user's travel route by taking wind effects into account. In this way, by considering weather information, the accuracy of the analysis is improved and the user's safety can be ensured.

[0103] The navigation system can optimize the route from the user's current location to their destination by taking into account the user's walking speed. For example, it can measure the user's walking speed in real time and suggest the optimal route based on that speed. It can also suggest a route that includes rest stops if the user is tired. Furthermore, if the user is in a hurry, it can calculate the route that will get them to their destination in the shortest time. This allows for more appropriate route guidance by considering the user's walking speed.

[0104] The hazard avoidance unit can analyze ambient sound information and detect hazards to ensure user safety. For example, it can detect car horns and emergency vehicle sirens and issue warnings to the user. It can also analyze the voices and footsteps of people in the surrounding area and provide information to help avoid hazards in crowded places. Furthermore, the hazard avoidance unit can detect noise from construction sites and suggest routes to avoid those areas. In this way, user safety can be ensured by analyzing ambient sound information.

[0105] The reception desk can learn the user's past behavior patterns when receiving instructions and automatically suggest predictable instructions. For example, if a user commutes using the same route every morning, the reception desk will automatically suggest that route. It can also suggest instructions based on information if the user has a habit of going to a specific place on a particular day of the week. Furthermore, if a user tends to give specific instructions at a particular time of day, instructions related to that time period can be prioritized. In this way, by learning past behavior patterns, the system can provide the user with the most appropriate instructions.

[0106] The analytics unit can not only identify items during shopping, but also suggest related products by considering the user's preferences and past purchase history. For example, the analytics unit can suggest related products based on items the user has purchased in the past. It can also learn the user's preferences and suggest products that match those preferences. Furthermore, the analytics unit can suggest products according to the season or events. In this way, by considering the user's preferences and past purchase history, a more personalized shopping experience can be provided.

[0107] The reception desk can estimate the user's emotions and, based on those estimates, suggest entertainment content suitable for the user. For example, if the user is relaxed, the reception desk can suggest relaxing music or movies. If the user is stressed, it can also suggest content that helps relieve stress. Furthermore, if the user is enjoying themselves, it can suggest content that will make them even happier. In this way, by suggesting entertainment content according to the user's emotions, a more satisfying experience can be provided.

[0108] The reception desk can not only analyze the user's past instruction history but also learn the user's lifestyle and daily routines to automatically suggest predictable instructions. For example, if the reception desk has a habit of drinking coffee at the same time every morning, it will suggest preparing the coffee at that time. It can also suggest instructions based on information if the user has a habit of performing a specific activity on a specific day of the week. Furthermore, if the user tends to perform a specific activity during a particular season, it can prioritize displaying instructions related to that season. In this way, by learning the user's lifestyle and daily routines, it can provide the most suitable instructions.

[0109] The reception desk can not only filter instructions based on the user's current situation and environment, but also prioritize instructions by considering the user's schedule information. For example, the reception desk can retrieve the user's calendar information and prioritize instructions related to important appointments. It can also prioritize instructions related to meetings if the user is in one. Furthermore, if the user is on vacation, it can prioritize instructions that promote relaxation. This allows for more appropriate instructions to be provided by considering the user's schedule.

[0110] The reception desk can estimate the user's emotions and provide appropriate feedback based on those estimates. For example, if the user is feeling tense, the reception desk can offer words of encouragement or suggest ways to relax. If the user is relaxed, it can provide feedback to further encourage relaxation. Furthermore, if the user is enjoying themselves, it can provide feedback to share that enjoyment. By providing appropriate feedback according to the user's emotions, user satisfaction can be improved.

[0111] The following briefly describes the processing flow for example form 2.

[0112] Step 1: The reception desk receives user instructions. User instructions include voice commands, touch operations, and gestures. The reception desk can receive user voice commands using voice recognition technology, and can also receive user touch operations using a touch panel. Furthermore, it can also receive user gestures using gesture recognition technology. Step 2: The analysis unit analyzes the video data from the camera-equipped sunglasses based on the information received by the reception unit. Image recognition algorithms and machine learning techniques are used to analyze the video data. The analysis unit can detect obstacles on the sidewalk using object recognition technology and recognize the status of traffic lights using color recognition technology. Furthermore, it can measure the distance between the user and obstacles using distance measurement technology. Step 3: The guidance unit provides voice guidance based on the information analyzed by the analysis unit. This voice guidance includes the type of voice and the timing of the guidance. The guidance unit can use speech synthesis technology to provide voice guidance to the user, calculate the optimal route from the user's current location to the destination, and provide voice guidance along that route. Furthermore, it can identify items during shopping based on user instructions and guide the user to their location. Step 4: The hazard avoidance unit detects obstacles and issues warnings to ensure user safety. Sensor technology and image recognition technology are used for obstacle detection. The hazard avoidance unit can also detect obstacles using ultrasonic sensors and recognize the status of traffic lights using cameras. Furthermore, it can issue warnings to the user using voice warnings and vibration warnings.

[0113] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0114] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0115] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0116] Each of the multiple elements described above, including the reception unit, analysis unit, guidance unit, and hazard avoidance unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the reception unit receives user instructions using the microphone 38B and touch panel 38A of the smart device 14. The analysis unit analyzes video data from the camera 42 of the smart device 14 using the specific processing unit 290 of the data processing unit 12. The guidance unit provides voice guidance based on the information analyzed by the specific processing unit 290 of the data processing unit 12. The hazard avoidance unit detects obstacles using the camera 42 and ultrasonic sensors of the smart device 14 and generates a warning using the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0117] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0118] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0119] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0120] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0121] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0123] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0124] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0125] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0126] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0127] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0128] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0129] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0130] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0131] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0132] Each of the multiple elements described above, including the reception unit, analysis unit, guidance unit, and hazard avoidance unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit receives user instructions using the microphone 238 of the smart glasses 214. The analysis unit analyzes video data from the camera 42 of the smart glasses 214 using the identification processing unit 290 of the data processing unit 12. The guidance unit provides voice guidance based on the information analyzed by the identification processing unit 290 of the data processing unit 12. The hazard avoidance unit detects obstacles using the camera 42 of the smart glasses 214 or an ultrasonic sensor and generates a warning using the identification processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0133] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0134] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0135] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0136] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0137] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the 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 image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0139] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0140] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0141] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0142] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0143] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0144] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0145] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0146] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0147] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0148] Each of the multiple elements described above, including the reception unit, analysis unit, guidance unit, and hazard avoidance unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit receives user instructions using the microphone 238 of the headset terminal 314. The analysis unit analyzes video data from the camera 42 of the headset terminal 314 using the specific processing unit 290 of the data processing unit 12. The guidance unit provides voice guidance based on the information analyzed by the specific processing unit 290 of the data processing unit 12. The hazard avoidance unit detects obstacles using the camera 42 of the headset terminal 314 or an ultrasonic sensor and generates a warning using the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0149] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0150] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0151] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0152] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0153] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0154] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0155] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0156] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0157] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0158] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0159] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0160] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0161] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0162] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0163] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0164] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0165] Each of the multiple elements described above, including the reception unit, analysis unit, guidance unit, and hazard avoidance unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the reception unit receives user instructions using the microphone 238 of the robot 414. The analysis unit analyzes video data from the camera 42 of the robot 414 using the specific processing unit 290 of the data processing unit 12. The guidance unit provides voice guidance based on the information analyzed by the specific processing unit 290 of the data processing unit 12. The hazard avoidance unit detects obstacles using the camera 42 of the robot 414 and ultrasonic sensors, and generates a warning using the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0166] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0167] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0168] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0169] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0170] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0171] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0172] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0173] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0174] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0175] 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.

[0176] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0177] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0178] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0179] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0180] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0181] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0182] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0183] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0184] (Note 1) A reception desk that takes user instructions, An analysis unit analyzes video data from sunglasses with a camera based on the information received by the reception unit, An information guidance unit provides voice guidance based on the information analyzed by the aforementioned analysis unit, It includes a hazard avoidance unit that detects obstacles and issues warnings to ensure user safety. A system characterized by the following features. (Note 2) The aforementioned analysis unit, By analyzing video data from sunglasses equipped with a camera, the system helps the user understand their surroundings. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned guide section is The system calculates the most efficient route from the user's current location to their destination and provides voice guidance along that route. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned hazard avoidance unit is The system uses cameras to recognize obstacles on sidewalks and the status of traffic lights, and provides voice warnings to the user. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is It receives user instructions and inputs them into the AI. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit, Identify items while shopping and guide customers to their location. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It estimates the user's emotions and adjusts how instructions are received based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is Analyze the user's past instruction history and select the appropriate method of receiving instructions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When receiving instructions, filtering is performed based on the user's current status and environment. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates the user's emotions and determines the priority of instructions to accept based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When receiving instructions, the system prioritizes accepting instructions that are highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When receiving instructions, the system analyzes the user's social media activity and accepts relevant instructions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the video data analysis method based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, When analyzing video data, we improve the accuracy of the analysis by referring to the user's past behavior history. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, When analyzing video data, the analysis algorithm is customized based on the user's current situation. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, When analyzing video data, the accuracy of the analysis is improved by taking into account the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, When analyzing video data, we analyze users' social media activity to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned guide section is The system estimates the user's emotions and adjusts the way voice guidance is expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned guide section is When providing voice guidance, the system selects the most suitable guidance method by referring to the user's past travel history. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned guide section is When providing voice guidance, the guidance algorithm is customized based on the user's current situation. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned guide section is The system estimates the user's emotions and determines the priority of voice guidance based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned guide section is When providing voice guidance, the system selects the most appropriate guidance method by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned guide section is During voice guidance, we analyze the user's social media activity to improve the accuracy of the guidance. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned hazard avoidance unit is It estimates the user's emotions and adjusts risk avoidance methods based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned hazard avoidance unit is When avoiding danger, the system selects the optimal avoidance method by referring to the user's past behavior history. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned hazard avoidance unit is When avoiding danger, the avoidance algorithm is customized based on the user's current situation. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned hazard avoidance unit is It estimates the user's emotions and determines the priority of risk avoidance based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned hazard avoidance unit is When avoiding danger, the system selects the optimal avoidance method by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned hazard avoidance unit is When avoiding risks, we analyze users' social media activity to improve the accuracy of avoidance. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0185] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk that takes user instructions, An analysis unit analyzes video data from sunglasses with a camera based on the information received by the reception unit, An information guidance unit provides voice guidance based on the information analyzed by the aforementioned analysis unit, The device includes a hazard avoidance unit that detects obstacles and issues warnings in order to ensure the safety of the user, The aforementioned reception unit is Using a generative AI that estimates emotions when facial expression data is input, the system estimates the user's emotions by inputting the user's facial expression data captured by the camera into the generative AI. If it estimates that the user is tense, the system adjusts the method of receiving instructions to provide a simple and intuitive interface to facilitate the input of instructions. If it estimates that the user is relaxed, the system adjusts the method of receiving instructions to provide detailed options to suggest customizable instructions. If it estimates that the user is in a hurry, the system adjusts the method of receiving instructions to prioritize voice input to quickly receive instructions. A system characterized by the following features.

2. The aforementioned analysis unit, The system analyzes video data from sunglasses equipped with a camera to understand the surrounding environment of the user. The system according to feature 1.

3. The aforementioned guide section is The system calculates the most efficient route from the user's current location to their destination and provides voice guidance along that route. The system according to feature 1.

4. The aforementioned hazard avoidance unit is The system recognizes obstacles on the sidewalk and the status of traffic lights, and provides voice warnings to the user. The system according to feature 1.

5. The aforementioned guide section is During voice guidance, the system selects the optimal guidance method by referring to the user's past travel history. The system according to feature 1.

6. The aforementioned analysis unit, Identify items while shopping and guide customers to their location. The system according to feature 1.

7. The aforementioned reception unit is The system analyzes the user's past instruction history and selects an appropriate method for receiving instructions. The system according to feature 1.

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