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 technology]

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

[0003] [Patent Document 1] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has the problem that it is difficult for visually impaired people to move around safely and obtain the information they need.

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

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a guidance unit, and a danger avoidance unit. The reception unit receives instructions from a user. The analysis unit analyzes video data from the camera sunglasses based on the information received by the reception unit. The guidance unit provides audio guidance based on the information analyzed by the analysis unit. The danger avoidance unit detects obstacles and issues a warning to ensure the safety of the user. Effect of the Invention

[0007] The system according to the embodiment enables visually impaired people to move around safely and obtain the information they need. [Brief description of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

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

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

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

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

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

[0014] In the following embodiments, a communication I / F (Interface) with a code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 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." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "and / or."

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

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

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

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

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

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

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The AI ​​system according to the embodiment of the present invention is a system in which a blind person is equipped with camera-equipped sunglasses and an earphone / microphone set, and a guide dog is not required. In this system, the user only needs to tell the AI ​​where he / she wants to go and what he / she needs, and the AI ​​will act as the user's eyes and feet, and guide the user to the 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 wears camera-equipped sunglasses and an earphone / microphone set, and tells the AI ​​where he / she wants to go and what he / she needs through the earphone / microphone. For example, the user issues instructions such as "I want to go to the station" or "I want to buy milk at the supermarket." This information is input to the AI. The AI ​​analyzes the video data from the camera-equipped sunglasses and grasps the situation around the user. 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 traffic light has turned green." Furthermore, when shopping, the AI ​​searches for what the user wants. For example, if you say "Find milk" at a supermarket, the AI ​​will analyze the video data from the camera-equipped sunglasses and pinpoint the location of the milk. It will then give the user voice guidance such as "Milk is on the shelf on the right." This system will enable the visually impaired to move around and shop independently without the need for a guide dog. The AI ​​will act as the user's eyes and legs, guiding them safely to their destination while avoiding danger, supporting the user's daily life. In this way, the AI ​​system will enable the visually impaired to move around and shop independently.

[0029] The AI ​​system according to the embodiment includes a reception unit, an analysis unit, a guidance unit, and a danger avoidance unit. The reception unit receives a user's instruction. The user's instruction includes, for example, a voice instruction, a touch operation, a gesture, and the like, but is not limited to such examples. The reception unit receives the user's voice instruction, for example, using a voice recognition technology. The reception unit can also receive the user's touch operation using a touch panel. Furthermore, the reception unit can also receive the user's gesture using a gesture recognition technology. The analysis unit analyzes video data from sunglasses with a camera. For example, an image recognition algorithm or a machine learning technology is used to analyze the video data. The analysis unit detects obstacles on the sidewalk, for example, using an object recognition technology. Furthermore, the analysis unit can recognize the state of a traffic light using a color recognition technology. Furthermore, the analysis unit can measure the distance between the user and the obstacle using a distance measurement technology. The guidance unit provides voice guidance based on the information analyzed by the analysis unit. The voice guidance includes, for example, the type of voice and the timing of guidance, but is not limited to such examples. The guidance unit provides voice guidance to the user, for example, using a voice synthesis technology. The guidance unit can also calculate the optimal route from the user's current location to the destination and provide voice guidance along the route. Furthermore, the guidance unit can identify items during shopping based on the user's instructions and provide guidance on their locations. The danger avoidance unit detects obstacles and issues a warning to ensure the safety of the user. For example, sensor technology or image recognition technology is used to detect obstacles. For example, the danger avoidance unit detects obstacles using an ultrasonic sensor. Furthermore, the danger avoidance unit can recognize the status of traffic lights using a camera. Furthermore, the danger avoidance unit can issue a warning to the user using a voice warning or a vibration warning. As a result, the AI ​​system according to the embodiment enables visually impaired people to move around and shop independently. Part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's voice instructions to the generation AI and cause the generation AI to execute analysis of the voice instructions.A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input video data from camera-equipped sunglasses to the generation AI and cause the generation AI to analyze the video data. A part or all of the above-mentioned processing in the guidance unit may be performed, for example, using AI, or may be performed without using AI. For example, the guidance unit can input information analyzed by the analysis unit to the generation AI and cause the generation AI to generate voice guidance. A part or all of the above-mentioned processing in the danger avoidance unit may be performed, for example, using AI, or may be performed without using AI. For example, the danger avoidance unit can input obstacle detection information to the generation AI and cause the generation AI to generate a warning.

[0030] The reception unit receives a user's instruction. The user's instruction includes, for example, a voice instruction, a touch operation, a gesture, and the like, but is not limited to such examples. The reception unit receives the user's voice instruction using, for example, a voice recognition technology. Specifically, the voice recognition technology includes a process of extracting voice characteristics and converting voice data into text data. This makes it possible to accurately understand the voice instruction issued by the user and perform appropriate processing. The reception unit can also receive the user's touch operation using a touch panel. The touch panel uses a technology such as a capacitive type or a resistive film type, and the user can input instructions by touching the screen. Furthermore, the reception unit can also receive the user's gesture using a gesture recognition technology. The gesture recognition technology detects the movement of the user's hands or body using a camera or a sensor, analyzes the movement, and recognizes it as an instruction. For example, a specific command can be executed by the user performing a waving motion. This allows the reception unit to receive the user's instruction using a variety of input means, thereby improving the user's convenience. Furthermore, the reception unit can use a combination of these input means, allowing the user to input instructions in the most user-friendly way. For example, more complicated instructions can be input by combining voice instructions and touch operations, allowing the reception unit to respond to the diverse needs of users and realize flexible operations.

[0031] The analysis unit analyzes the video data from the camera-equipped sunglasses. For example, image recognition algorithms and machine learning technology are used to analyze the video data. Specifically, a convolutional neural network (CNN) or the like is used as the image recognition algorithm, which can detect specific objects and patterns from the video data. For example, the analysis unit detects obstacles on the sidewalk using object recognition technology. The object recognition technology can identify specific objects from the video data and specify their positions and shapes. This can reduce 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. The color recognition technology can detect specific colors from the video data and track changes in the colors. This allows the user to accurately grasp the status of the traffic lights and cross the road safely. Furthermore, the analysis unit can also measure the distance between the user and the obstacle using distance measurement technology. As the distance measurement technology, a stereo camera or a laser distance sensor is used, which can accurately measure the distance between the user and the obstacle. This can issue a warning before the user gets too close to the obstacle. By combining these technologies, the analysis unit can grasp the situation around the user in detail and provide appropriate information. For example, by combining object recognition technology and distance measurement technology, the position and distance of an obstacle can be grasped simultaneously, and a more accurate warning can be issued to the user. This allows the analysis unit to ensure the safety of the user and provide an environment in which the user can move around with peace of mind.

[0032] The guidance unit provides voice guidance based on the information analyzed by the analysis unit. The voice guidance includes, for example, the type of voice and the timing of guidance, but is not limited to such examples. The guidance unit provides voice guidance to the user using, for example, voice synthesis technology. The voice synthesis technology includes a process of converting text data into voice data, which allows the user to be provided with information in a natural voice. The guidance unit can also calculate the optimal route from the user's current location to the destination and provide voice guidance along the route. An algorithm is used to calculate the route, using GPS data and map data to identify the shortest route and the safest route. This allows the user to reach the destination without getting lost. Furthermore, the guidance unit can identify items during shopping based on the user's instructions and provide guidance on their location. For example, when a user wants to know the location of a specific product, the guidance unit can refer to map data and a product database in the store to identify the location of the product and provide voice guidance. This allows the user to shop efficiently. The guidance unit can provide comprehensive guidance to the user by using these functions in combination. For example, if a user wants to do some shopping on the way to a destination, the guidance unit can calculate the optimal route and provide guidance on the locations of stores and products to stop at along the way. This allows the user to achieve multiple goals in one trip. Furthermore, the guidance unit can learn the user's preferences and past behavioral history to provide more personalized guidance. For example, by preferentially providing information on stores and products that the user frequently uses, the user's convenience can be improved.

[0033] The danger avoidance unit detects an obstacle and issues a warning to ensure the safety of the user. For example, sensor technology or image recognition technology is used to detect the obstacle. Specifically, ultrasonic sensors, infrared sensors, etc. are used, which can detect the presence of an obstacle. The ultrasonic sensor measures the distance to the obstacle by emitting ultrasonic waves and receiving the reflected waves. This allows a warning to be issued before the user gets too close to the obstacle. The danger avoidance unit can also recognize the status of a traffic light using a camera. The camera acquires video data and analyzes the color and shape of the traffic light using image recognition technology. This allows the user to accurately understand the status of the traffic light and cross the road safely. Furthermore, the danger avoidance unit can also issue a warning to the user using a voice warning or a vibration warning. The voice warning can convey specific warning content to the user using voice synthesis technology. For example, a warning such as "There is an obstacle ahead. Please be careful" can be issued. The vibration warning can convey a warning to the user by vibrating a device (for example, a smart watch or a belt) worn by the user. This allows even users with visual or hearing impairments to receive the warning reliably. The danger avoidance unit can ensure the safety of the user by combining these technologies. For example, by combining an ultrasonic sensor and a camera, it is possible to simultaneously grasp the presence of obstacles and the status of traffic lights and issue an appropriate warning. This allows the user to move around with peace of mind. Furthermore, the danger avoidance unit can also learn the user's behavioral patterns and changes in the environment to provide more effective warnings. For example, if a user often takes a specific route at a specific time of day, it can warn of dangerous spots on that route in advance. In this way, the danger avoidance unit can ensure the user's safety to the maximum extent possible, allowing them to live their daily lives with peace of mind.

[0034] The analysis unit can analyze the video data from the camera-equipped sunglasses and grasp the situation around the user. The analysis unit can analyze the video data from the camera-equipped sunglasses, for example, using object recognition technology, and grasp the situation around the user. For example, the analysis unit uses an object recognition algorithm to detect obstacles on the sidewalk. The analysis unit can also use color recognition technology to recognize the status of a traffic light. Furthermore, the analysis unit can measure the distance between the user and the obstacle using distance measurement technology. For example, the analysis unit analyzes the video data from the camera-equipped sunglasses in real time and grasps the situation around the user. As a result, appropriate guidance can be provided by grasping the situation around the user. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the video data from the camera-equipped sunglasses to the generation AI and cause the generation AI to execute the analysis of the video data.

[0035] The guidance unit can calculate an efficient route from the user's current location to the destination and provide voice guidance along the route. The guidance unit uses a route calculation algorithm, for example, to calculate an efficient route from the user's current location to the destination. For example, the guidance unit uses Dijkstra's algorithm or A* algorithm to calculate the shortest distance. The guidance unit can also acquire real-time traffic information to calculate a route taking into account traffic conditions. Furthermore, the guidance unit can identify the user's current location based on GPS data and calculate a route to the destination. For example, the guidance unit calculates an optimal route from the user's current location to the destination and provides voice guidance along the route. As a result, the optimal route is calculated and voice guidance is provided, allowing the user to safely reach the destination. A part or all of the above-mentioned processing in the guidance unit may be performed, for example, using AI or may be performed without using AI. For example, the guidance unit can input information on the user's current location and destination to the generation AI and cause the generation AI to execute route calculation and voice guidance generation.

[0036] The danger avoidance unit can recognize obstacles on the sidewalk and the state of traffic lights with a camera and issue a voice warning to the user. The danger avoidance unit, for example, recognizes obstacles on the sidewalk using a camera. For example, the danger avoidance unit detects obstacles on the sidewalk using object recognition technology. The danger avoidance unit can also use color recognition technology to recognize the state of traffic lights. For example, the danger avoidance unit recognizes the color of traffic lights and issues a voice warning to the user to stop when the light is red. Furthermore, the danger avoidance unit can also use voice synthesis technology to issue a voice warning to the user. For example, the danger avoidance unit issues 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 the state of obstacles and traffic lights and issuing a voice warning. A part or all of the above-mentioned processing in the danger avoidance unit may be performed, for example, using AI or may be performed without using AI. For example, the danger avoidance unit can input video data acquired by a camera to a generation AI and cause the generation AI to recognize obstacles and the state of traffic lights and generate a warning.

[0037] The reception unit can receive user instructions and input them to the AI. The reception unit, for example, uses voice recognition technology to receive user voice instructions. For example, when the user issues a voice instruction such as "I want to go to the station," the reception unit recognizes the voice and inputs it to the AI. The reception unit can also receive the user's touch operation using a touch panel. For example, when the user selects a destination on the touch panel, the information is input to the AI. Furthermore, the reception unit can also receive user gestures using gesture recognition technology. For example, when the user performs a specific gesture, the gesture is recognized and input to the AI. As a result, the system performs an operation according to the user's request by receiving the user's instructions and inputting them to the AI. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's voice instructions to the generation AI and cause the generation AI to execute an analysis of the voice instructions.

[0038] The analysis unit can identify an item during shopping and guide the user to its location. The analysis unit, for example, analyzes video data from sunglasses with a camera and identifies the item. For example, the analysis unit uses image recognition technology to identify milk on a supermarket shelf. The analysis unit can also use barcode recognition technology to read the barcode of the item and identify its location. Furthermore, after identifying the location of the item, the analysis unit can provide voice guidance to guide the user to the location. For example, the analysis unit provides voice guidance to the user such as "milk is on the shelf on the right side." This allows the user to shop efficiently by identifying the item and guiding the user to its location. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI or may be performed without using AI. For example, the analysis unit can input video data from sunglasses with a camera to a generation AI and cause the generation AI to identify the item and generate a location guide.

[0039] The reception unit can analyze the user's past instruction history and select an appropriate reception method. The reception unit can analyze the user's past instruction history using, for example, a database search technology. For example, the reception unit can automatically display instructions that the user frequently issued in the past as candidates. The reception unit can also preferentially suggest input methods (such as voice and text) that the user used in the past. Furthermore, the reception unit can predict and suggest instructions to be used in a specific time period from the user's past instruction history. For example, the reception unit preferentially displays instructions related to a specific time period based on instructions that the user issued in the past during that time period. In this way, the reception unit provides the user with an optimal reception method by analyzing the past instruction history. A part or all of the above-mentioned processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input the user's past instruction history to the generation AI and cause the generation AI to analyze the instruction history and select the reception method.

[0040] The reception unit can perform filtering based on the user's current situation and environment when receiving an instruction. The reception unit, for example, uses location information technology to identify the user's current situation and environment. For example, the reception unit identifies the user's current location based on GPS data and filters the instruction based on the information. The reception unit can also use voice recognition technology to analyze surrounding sounds. For example, when the user is outdoors, the reception unit adjusts the sensitivity of the voice input taking into account the surrounding noise. Furthermore, when the user is indoors, the reception unit can increase the sensitivity of the voice input to match a quiet environment. For example, when the user is moving, the reception unit preferentially receives instructions related to the current location based on GPS information. As a result, appropriate instructions are received by filtering according to the user's situation and environment. Part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's location information and surrounding sound data to the generation AI and cause the generation AI to perform filtering.

[0041] When receiving an instruction, the reception unit can preferentially receive an instruction with high relevance in consideration of the geographical location information of the user. The reception unit, for example, acquires the geographical location information of the user using GPS data. For example, when the user is in a specific location, the reception unit preferentially receives an instruction related to the location. In addition, when the user is moving, the reception unit can also preferentially receive an instruction related to the current location. Furthermore, when the user is in a specific area, the reception unit can also preferentially receive an instruction related to the area. For example, when the user is in a specific tourist spot, the reception unit preferentially receives an instruction related to the tourist spot. In this way, by considering the geographical location information, a highly relevant instruction is preferentially received. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the geographical location information of the user to the generation AI and cause the generation AI to select a highly relevant instruction.

[0042] The reception unit can analyze the user's social media activity and accept related instructions when receiving the instruction. 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 related 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 related instructions. Furthermore, the reception unit can accept related instructions based on the user's social media activity history. For example, when the user posts "I want to buy milk at the supermarket" on social media, the reception unit accepts related instructions based on the information. In this way, the social media activity is analyzed and related instructions are accepted. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media activity data to the generation AI and cause the generation AI to select related instructions.

[0043] When analyzing the video data, the analysis unit can improve the accuracy of the analysis by referring to the user's past behavior history. The analysis unit, for example, refers to the user's past behavior history using a database search technique. For example, the analysis unit analyzes the current route based on the route the user has taken in the past. The analysis unit can also extract a specific pattern from the user's past behavior history to improve the accuracy of the analysis. Furthermore, the analysis unit can analyze the user's past behavior history and perform an analysis suitable for the current situation. For example, the analysis unit predicts the user's behavior at a specific location based on the behavior the user performed at that location in the past, and improves the accuracy of the analysis. As a result, the accuracy of the analysis is improved by referring to the past behavior history. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past behavior history to the generation AI and cause the generation AI to refer to the behavior history and improve the accuracy of the analysis.

[0044] The analysis unit can customize the analysis algorithm based on the user's current situation when analyzing the video data. The analysis unit uses, for example, location information technology and an environmental sensor to identify the user's current situation. For example, when the user is outdoors, the analysis unit uses an analysis algorithm suitable for the surrounding environment. In addition, when the user is indoors, the analysis unit can also use an analysis algorithm suitable for the indoor environment. Furthermore, when the user is moving, the analysis unit can also use an analysis algorithm suitable for the moving situation. For example, when the user is outdoors, the analysis unit uses an analysis algorithm that takes into account the effects of light reflection and shadows. This enables appropriate analysis by customizing the analysis algorithm according to the current situation. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the user's current situation data to the generation AI and cause the generation AI to execute customization of the analysis algorithm.

[0045] The analysis unit can improve the accuracy of the analysis by considering the geographical location information of the user when analyzing the video data. The analysis unit, for example, uses GPS data to acquire the geographical location information of the user. For example, when the user is in a specific location, the analysis unit preferentially analyzes information related to the location. In addition, when the user is moving, the analysis unit can also preferentially analyze information related to the current location. Furthermore, when the user is in a specific area, the analysis unit can also preferentially analyze information related to the area. For example, when the user is in a specific tourist spot, the analysis unit preferentially analyzes information related to the tourist spot. As a result, the accuracy of the analysis is improved by considering the geographical location information. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the geographical location information of the user to the generation AI and cause the generation AI to execute the improvement of the accuracy of the analysis.

[0046] The analysis unit can improve the accuracy of the analysis by analyzing the user's social media activities when analyzing the video data. The analysis unit can, for example, use social media analysis technology to analyze the user's social media activities. For example, the analysis unit analyzes related 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 the related information. Furthermore, the analysis unit can analyze the related information based on the user's social media activity history. For example, if the user posts "I want to buy milk at the supermarket" on social media, the analysis unit analyzes the related information based on that information. This improves the accuracy of the analysis by analyzing the social media activities. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's social media activity data to the generation AI and cause the generation AI to execute the improvement of the accuracy of the analysis.

[0047] The guidance unit can select the optimal guidance method by referring to the user's past movement history when providing voice guidance. The guidance unit, for example, uses a database search technology to refer to the user's past movement history. For example, the guidance unit proposes the optimal guidance method based on the route used by the user in the past. The guidance unit can also propose a route that avoids congestion from the user's past movement history. Furthermore, the guidance unit can analyze the user's past movement history and propose the most efficient route. For example, the guidance unit proposes the optimal route for a specific time period based on the route used by the user in the past during that time period. In this way, the optimal guidance method is provided by referring to the past movement history. Part or all of the above-mentioned processing in the guidance unit may be performed, for example, using AI, or may be performed without using AI. For example, the guidance unit can input the user's past movement history into the generation AI and cause the generation AI to refer to the movement history and select a guidance method.

[0048] The guidance unit can customize the guidance algorithm based on the current situation of the user when providing voice guidance. The guidance unit uses, for example, location information technology or an environmental sensor to identify the current situation of the user. For example, when the user is outdoors, the guidance unit uses a guidance algorithm suitable for the surrounding environment. In addition, when the user is indoors, the guidance unit can also use a guidance algorithm suitable for the indoor environment. Furthermore, when the user is moving, the guidance unit can also use a guidance algorithm suitable for the moving situation. For example, when the user is outdoors, the guidance unit uses a guidance algorithm that takes into account the influence of light reflection and shadows. This enables appropriate guidance by customizing the guidance algorithm according to the current situation. Part or all of the above-mentioned processing in the guidance unit may be performed, for example, using AI, or may be performed without using AI. For example, the guidance unit can input the user's current situation data to the generation AI and cause the generation AI to execute customization of the guidance algorithm.

[0049] The guidance unit can select the optimal guidance method in consideration of the geographical location information of the user when providing voice guidance. The guidance unit acquires the geographical location information of the user using, for example, GPS data. For example, when the user is in a specific location, the guidance unit prioritizes guidance related to the location. In addition, when the user is moving, the guidance unit can also prioritize guidance related to the current location. Furthermore, when the user is in a specific area, the guidance unit can also prioritize guidance related to the area. For example, when the user is in a specific tourist spot, the guidance unit prioritizes guidance related to the tourist spot. In this way, the optimal guidance method is provided by considering the geographical location information. A part or all of the above-mentioned processing in the guidance unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the guidance unit can input the geographical location information of the user to the generation AI and cause the generation AI to select the guidance method.

[0050] The guidance unit can analyze the user's social media activity during voice guidance to improve the accuracy of the guidance. The guidance unit, for example, uses social media analysis technology to analyze the user's social media activity. For example, the guidance unit provides related 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 related guidance. Furthermore, the guidance unit can provide related guidance based on the user's social media activity history. For example, if the user posts on social media that "I want to buy milk at the supermarket," the guidance unit provides related guidance based on that information. This improves the accuracy of the guidance by analyzing social media activity. A part or all of the above-mentioned processing in the guidance unit may be performed, for example, using AI, or may be performed without using AI. For example, the guidance unit can input the user's social media activity data into the generation AI and cause the generation AI to execute the improvement of the accuracy of the guidance.

[0051] The danger avoidance unit can select an optimal avoidance method by referring to the user's past behavior history when avoiding danger. The danger avoidance unit, for example, refers to the user's past behavior history using a database search technique. For example, the danger avoidance unit proposes an optimal avoidance method based on a dangerous situation the user has encountered in the past. The danger avoidance unit can also extract a specific pattern from the user's past behavior history and select an optimal avoidance method. Furthermore, the danger avoidance unit can analyze the user's past behavior history and propose an avoidance method suitable for the current situation. For example, the danger avoidance unit proposes an avoidance method at a specific location based on a dangerous situation the user has encountered in the past at that location. In this way, the optimal avoidance method is provided by referring to the past behavior history. A part or all of the above-mentioned processing in the danger avoidance unit may be performed, for example, using AI or may be performed without using AI. For example, the danger avoidance unit can input the user's past behavior history to the generation AI and cause the generation AI to refer to the behavior history and select an avoidance method.

[0052] The danger avoidance unit can customize the avoidance algorithm based on the current situation of the user when avoiding danger. The danger avoidance unit uses, for example, location information technology or an environmental sensor to identify the current situation of the user. For example, when the user is outdoors, the danger avoidance unit uses an avoidance algorithm suitable for the surrounding environment. In addition, when the user is indoors, the danger avoidance unit can also use an avoidance algorithm suitable for the indoor environment. Furthermore, when the user is moving, the danger avoidance unit can also use an avoidance algorithm suitable for the moving situation. For example, when the user is outdoors, the danger avoidance unit uses an avoidance algorithm that takes into account the influence of light reflection and shadows. This enables appropriate avoidance by customizing the avoidance algorithm according to the current situation. Part or all of the above-mentioned processing in the danger avoidance unit may be performed, for example, using AI, or may be performed without using AI. For example, the danger avoidance unit can input the user's current situation data to the generation AI and cause the generation AI to execute customization of the avoidance algorithm.

[0053] The danger avoidance unit can select the optimal avoidance method in consideration of the geographical location information of the user when avoiding danger. The danger avoidance unit acquires the geographical location information of the user, for example, using GPS data. For example, when the user is in a specific location, the danger avoidance unit provides preferentially danger avoidance information related to the location. In addition, when the user is moving, the danger avoidance unit can also provide preferentially danger avoidance information related to the current location. Furthermore, when the user is in a specific area, the danger avoidance unit can also provide preferentially danger avoidance information related to the area. For example, when the user is in a specific tourist spot, the danger avoidance unit provides preferentially danger avoidance information related to the tourist spot. In this way, the optimal avoidance method is provided by considering the geographical location information. A part or all of the above-mentioned processing in the danger avoidance unit may be performed, for example, using AI, or may be performed without using AI. For example, the danger avoidance unit can input the geographical location information of the user to the generation AI and cause the generation AI to select an avoidance method.

[0054] The danger avoidance unit can analyze the user's social media activity during risk avoidance to improve the accuracy of risk avoidance. The danger avoidance unit, for example, uses social media analysis technology to analyze the user's social media activity. For example, the danger avoidance unit provides related risk avoidance information based on information shared by the user on social media. The danger avoidance unit can also analyze the content of the user's social media posts and provide related risk avoidance information. Furthermore, the danger avoidance unit can provide related risk avoidance information based on the user's social media activity history. For example, if the user posts "I want to buy milk at the supermarket" on social media, the danger avoidance unit provides related risk avoidance information based on that information. This improves the accuracy of risk avoidance by analyzing social media activity. A part or all of the above-mentioned processing in the danger avoidance unit may be performed, for example, using AI or may be performed without using AI. For example, the danger avoidance unit can input the user's social media activity data to the generation AI and cause the generation AI to execute improvement of risk avoidance accuracy.

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

[0056] The analysis unit can improve the accuracy of the analysis by taking into account weather information when grasping the situation around the user. For example, the analysis unit can detect slippery road surfaces in rainy weather and warn the user. Also, on snowy days, the analysis unit can grasp the snowfall situation and provide information to ensure the safety of walking. Furthermore, in the case of strong winds, the user's movement route can be adjusted by taking into account the influence of wind. In this way, by taking into account weather information, the accuracy of the analysis can be improved and the safety of the user can be ensured.

[0057] When calculating an efficient route from the user's current location to the destination, the guidance unit can optimize the route by taking into account the user's walking speed. For example, the guidance unit measures the user's walking speed in real time and proposes an optimal route according to the speed. In addition, if the user is tired, the guidance unit can also propose a route including a rest point. Furthermore, if the user is in a hurry, the guidance unit can calculate a route that allows the user to reach the destination in the shortest time. This allows more appropriate route guidance to be provided by taking into account the user's walking speed.

[0058] The danger avoidance unit can analyze surrounding sound information and detect danger in order to ensure the safety of the user. For example, the danger avoidance unit can detect car horns and emergency vehicle sirens and issue a warning to the user. It can also analyze the voices and footsteps of people in the vicinity and provide information for avoiding danger in crowded places. Furthermore, the danger avoidance unit can detect noise from a construction site and suggest a route that avoids that area. In this way, the safety of the user can be ensured by analyzing the surrounding sound information.

[0059] When receiving instructions from a user, the reception unit can learn the user's past behavioral patterns and automatically suggest predicted instructions. For example, if the user commutes to work by the same route every morning, the reception unit automatically suggests that route. Also, if the user has a habit of going to a specific place on a specific day of the week, the reception unit can suggest instructions based on that information. Furthermore, if the user tends to give specific instructions during a specific time period, instructions related to that time period can be preferentially displayed. In this way, by learning past behavioral patterns, it is possible to provide optimal instructions to the user.

[0060] The analysis unit can not only identify items when shopping, but also suggest related products by taking into account the user's preferences and past purchase history. For example, the analysis unit can suggest related products based on products the user has purchased in the past. The analysis unit can also learn the user's preferences and suggest products that match those preferences. Furthermore, the analysis unit can suggest products according to seasons and events. This makes it possible to provide a more personalized shopping experience by taking into account the user's preferences and past purchase history.

[0061] The reception unit can not only analyze the user's past instruction history, but also learn the user's lifestyle and daily routine, and automatically suggest predicted instructions. For example, if the user has the habit of drinking coffee at the same time every morning, the reception unit can suggest preparing coffee at that time. Also, if the user has the habit of performing a specific activity on a specific day of the week, the reception unit can suggest instructions based on that information. Furthermore, if the user tends to perform a specific activity in a specific season, instructions related to that season can be preferentially displayed. In this way, optimal instructions can be provided to the user by learning the lifestyle and daily routine.

[0062] When receiving instructions, the reception unit can not only filter based on the user's current situation and environment, but also determine the priority of instructions by taking into account the user's schedule information. For example, the reception unit obtains the user's calendar information, and when the user has an important event, it can preferentially receive instructions related to the event. Also, when the user is in a meeting, it can also preferentially receive instructions related to the meeting. Furthermore, when the user is on vacation, it can also preferentially receive instructions that will help the user relax. In this way, more appropriate instructions can be received by taking into account the schedule information.

[0063] The process flow of the first embodiment will be briefly described below.

[0064] Step 1: The reception unit receives a user's instruction. The user's instruction includes a voice instruction, a touch operation, a gesture, and the like. The reception unit can receive a user's voice instruction using a voice recognition technology, and can also receive a user's touch operation using a touch panel. Furthermore, the reception unit can also receive a user's gesture using a 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 technology 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. In addition, it can also measure the distance between the user and the obstacle using distance measurement technology. Step 3: The guidance unit provides voice guidance based on the information analyzed by the analysis unit. The voice guidance includes the type of voice and the timing of the guidance. The guidance unit can provide voice guidance to the user using voice synthesis technology, calculate the optimal route from the user's current location to the destination, and provide voice guidance along that route. Furthermore, the guidance unit can identify items when shopping based on the user's instructions and provide guidance on their location. Step 4: The danger avoidance unit detects obstacles and issues a warning to ensure the user's safety. Sensor technology and image recognition technology are used to detect obstacles. The danger avoidance unit can also detect obstacles using ultrasonic sensors and recognize the status of traffic lights using cameras. In addition, it can also warn the user using audio and vibration warnings.

[0065] (Example 2) The AI ​​system according to the embodiment of the present invention is a system in which a blind person is equipped with camera-equipped sunglasses and an earphone / microphone set, and a guide dog is not required. In this system, the user only needs to tell the AI ​​where he / she wants to go and what he / she needs, and the AI ​​will act as the user's eyes and feet, and guide the user to the 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 wears camera-equipped sunglasses and an earphone / microphone set, and tells the AI ​​where he / she wants to go and what he / she needs through the earphone / microphone. For example, the user issues instructions such as "I want to go to the station" or "I want to buy milk at the supermarket." This information is input to the AI. The AI ​​analyzes the video data from the camera-equipped sunglasses and grasps the situation around the user. 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 traffic light has turned green." Furthermore, when shopping, the AI ​​searches for what the user wants. For example, if you say "Find milk" at a supermarket, the AI ​​will analyze the video data from the camera-equipped sunglasses and pinpoint the location of the milk. It will then give the user voice guidance such as "Milk is on the shelf on the right." This system will enable the visually impaired to move around and shop independently without the need for a guide dog. The AI ​​will act as the user's eyes and legs, guiding them safely to their destination while avoiding danger, supporting the user's daily life. In this way, the AI ​​system will enable the visually impaired to move around and shop independently.

[0066] The AI ​​system according to the embodiment includes a reception unit, an analysis unit, a guidance unit, and a danger avoidance unit. The reception unit receives a user's instruction. The user's instruction includes, for example, a voice instruction, a touch operation, a gesture, and the like, but is not limited to such examples. The reception unit receives the user's voice instruction, for example, using a voice recognition technology. The reception unit can also receive the user's touch operation using a touch panel. Furthermore, the reception unit can also receive the user's gesture using a gesture recognition technology. The analysis unit analyzes video data from sunglasses with a camera. For example, an image recognition algorithm or a machine learning technology is used to analyze the video data. The analysis unit detects obstacles on the sidewalk, for example, using an object recognition technology. Furthermore, the analysis unit can recognize the state of a traffic light using a color recognition technology. Furthermore, the analysis unit can measure the distance between the user and the obstacle using a distance measurement technology. The guidance unit provides voice guidance based on the information analyzed by the analysis unit. The voice guidance includes, for example, the type of voice and the timing of guidance, but is not limited to such examples. The guidance unit provides voice guidance to the user, for example, using a voice synthesis technology. The guidance unit can also calculate the optimal route from the user's current location to the destination and provide voice guidance along the route. Furthermore, the guidance unit can identify items during shopping based on the user's instructions and provide guidance on their locations. The danger avoidance unit detects obstacles and issues a warning to ensure the safety of the user. For example, sensor technology or image recognition technology is used to detect obstacles. For example, the danger avoidance unit detects obstacles using an ultrasonic sensor. Furthermore, the danger avoidance unit can recognize the status of traffic lights using a camera. Furthermore, the danger avoidance unit can issue a warning to the user using a voice warning or a vibration warning. As a result, the AI ​​system according to the embodiment enables visually impaired people to move around and shop independently. Part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's voice instructions to the generation AI and cause the generation AI to execute analysis of the voice instructions.A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input video data from camera-equipped sunglasses to the generation AI and cause the generation AI to analyze the video data. A part or all of the above-mentioned processing in the guidance unit may be performed, for example, using AI, or may be performed without using AI. For example, the guidance unit can input information analyzed by the analysis unit to the generation AI and cause the generation AI to generate voice guidance. A part or all of the above-mentioned processing in the danger avoidance unit may be performed, for example, using AI, or may be performed without using AI. For example, the danger avoidance unit can input obstacle detection information to the generation AI and cause the generation AI to generate a warning.

[0067] The reception unit receives a user's instruction. The user's instruction includes, for example, a voice instruction, a touch operation, a gesture, and the like, but is not limited to such examples. The reception unit receives the user's voice instruction using, for example, a voice recognition technology. Specifically, the voice recognition technology includes a process of extracting voice characteristics and converting voice data into text data. This makes it possible to accurately understand the voice instruction issued by the user and perform appropriate processing. The reception unit can also receive the user's touch operation using a touch panel. The touch panel uses a technology such as a capacitive type or a resistive film type, and the user can input instructions by touching the screen. Furthermore, the reception unit can also receive the user's gesture using a gesture recognition technology. The gesture recognition technology detects the movement of the user's hands or body using a camera or a sensor, analyzes the movement, and recognizes it as an instruction. For example, a specific command can be executed by the user performing a waving motion. This allows the reception unit to receive the user's instruction using a variety of input means, thereby improving the user's convenience. Furthermore, the reception unit can use a combination of these input means, allowing the user to input instructions in the most user-friendly way. For example, more complicated instructions can be input by combining voice instructions and touch operations, allowing the reception unit to respond to the diverse needs of users and realize flexible operations.

[0068] The analysis unit analyzes the video data from the camera-equipped sunglasses. For example, image recognition algorithms and machine learning technology are used to analyze the video data. Specifically, a convolutional neural network (CNN) or the like is used as the image recognition algorithm, which can detect specific objects and patterns from the video data. For example, the analysis unit detects obstacles on the sidewalk using object recognition technology. The object recognition technology can identify specific objects from the video data and specify their positions and shapes. This can reduce 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. The color recognition technology can detect specific colors from the video data and track changes in the colors. This allows the user to accurately grasp the status of the traffic lights and cross the road safely. Furthermore, the analysis unit can also measure the distance between the user and the obstacle using distance measurement technology. As the distance measurement technology, a stereo camera or a laser distance sensor is used, which can accurately measure the distance between the user and the obstacle. This can issue a warning before the user gets too close to the obstacle. By combining these technologies, the analysis unit can grasp the situation around the user in detail and provide appropriate information. For example, by combining object recognition technology and distance measurement technology, the position and distance of an obstacle can be grasped simultaneously, and a more accurate warning can be issued to the user. This allows the analysis unit to ensure the safety of the user and provide an environment in which the user can move around with peace of mind.

[0069] The guidance unit provides voice guidance based on the information analyzed by the analysis unit. The voice guidance includes, for example, the type of voice and the timing of guidance, but is not limited to such examples. The guidance unit provides voice guidance to the user using, for example, voice synthesis technology. The voice synthesis technology includes a process of converting text data into voice data, which allows the user to be provided with information in a natural voice. The guidance unit can also calculate the optimal route from the user's current location to the destination and provide voice guidance along the route. An algorithm is used to calculate the route, using GPS data and map data to identify the shortest route and the safest route. This allows the user to reach the destination without getting lost. Furthermore, the guidance unit can identify items during shopping based on the user's instructions and provide guidance on their location. For example, when a user wants to know the location of a specific product, the guidance unit can refer to map data and a product database in the store to identify the location of the product and provide voice guidance. This allows the user to shop efficiently. The guidance unit can provide comprehensive guidance to the user by using these functions in combination. For example, if a user wants to do some shopping on the way to a destination, the guidance unit can calculate the optimal route and provide guidance on the locations of stores and products to stop at along the way. This allows the user to achieve multiple goals in one trip. Furthermore, the guidance unit can learn the user's preferences and past behavioral history to provide more personalized guidance. For example, by preferentially providing information on stores and products that the user frequently uses, the user's convenience can be improved.

[0070] The danger avoidance unit detects an obstacle and issues a warning to ensure the safety of the user. For example, sensor technology or image recognition technology is used to detect the obstacle. Specifically, ultrasonic sensors, infrared sensors, etc. are used, which can detect the presence of an obstacle. The ultrasonic sensor measures the distance to the obstacle by emitting ultrasonic waves and receiving the reflected waves. This allows a warning to be issued before the user gets too close to the obstacle. The danger avoidance unit can also recognize the status of a traffic light using a camera. The camera acquires video data and analyzes the color and shape of the traffic light using image recognition technology. This allows the user to accurately understand the status of the traffic light and cross the road safely. Furthermore, the danger avoidance unit can also issue a warning to the user using a voice warning or a vibration warning. The voice warning can convey specific warning content to the user using voice synthesis technology. For example, a warning such as "There is an obstacle ahead. Please be careful" can be issued. The vibration warning can convey a warning to the user by vibrating a device (for example, a smart watch or a belt) worn by the user. This allows even users with visual or hearing impairments to receive the warning reliably. The danger avoidance unit can ensure the safety of the user by combining these technologies. For example, by combining an ultrasonic sensor and a camera, it is possible to simultaneously grasp the presence of obstacles and the status of traffic lights and issue an appropriate warning. This allows the user to move around with peace of mind. Furthermore, the danger avoidance unit can also learn the user's behavioral patterns and changes in the environment to provide more effective warnings. For example, if a user often takes a specific route at a specific time of day, it can warn of dangerous spots on that route in advance. In this way, the danger avoidance unit can ensure the user's safety to the maximum extent possible, allowing them to live their daily lives with peace of mind.

[0071] The analysis unit can analyze the video data from the camera-equipped sunglasses and grasp the situation around the user. The analysis unit can analyze the video data from the camera-equipped sunglasses, for example, using object recognition technology, and grasp the situation around the user. For example, the analysis unit uses an object recognition algorithm to detect obstacles on the sidewalk. The analysis unit can also use color recognition technology to recognize the status of a traffic light. Furthermore, the analysis unit can measure the distance between the user and the obstacle using distance measurement technology. For example, the analysis unit analyzes the video data from the camera-equipped sunglasses in real time and grasps the situation around the user. As a result, appropriate guidance can be provided by grasping the situation around the user. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the video data from the camera-equipped sunglasses to the generation AI and cause the generation AI to execute the analysis of the video data.

[0072] The guidance unit can calculate an efficient route from the user's current location to the destination and provide voice guidance along the route. The guidance unit uses a route calculation algorithm, for example, to calculate an efficient route from the user's current location to the destination. For example, the guidance unit uses Dijkstra's algorithm or A* algorithm to calculate the shortest distance. The guidance unit can also acquire real-time traffic information to calculate a route taking into account traffic conditions. Furthermore, the guidance unit can identify the user's current location based on GPS data and calculate a route to the destination. For example, the guidance unit calculates an optimal route from the user's current location to the destination and provides voice guidance along the route. As a result, the optimal route is calculated and voice guidance is provided, allowing the user to safely reach the destination. A part or all of the above-mentioned processing in the guidance unit may be performed, for example, using AI or may be performed without using AI. For example, the guidance unit can input information on the user's current location and destination to the generation AI and cause the generation AI to execute route calculation and voice guidance generation.

[0073] The danger avoidance unit can recognize obstacles on the sidewalk and the state of traffic lights with a camera and issue a voice warning to the user. The danger avoidance unit, for example, recognizes obstacles on the sidewalk using a camera. For example, the danger avoidance unit detects obstacles on the sidewalk using object recognition technology. The danger avoidance unit can also use color recognition technology to recognize the state of traffic lights. For example, the danger avoidance unit recognizes the color of traffic lights and issues a voice warning to the user to stop when the light is red. Furthermore, the danger avoidance unit can also use voice synthesis technology to issue a voice warning to the user. For example, the danger avoidance unit issues 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 the state of obstacles and traffic lights and issuing a voice warning. A part or all of the above-mentioned processing in the danger avoidance unit may be performed, for example, using AI or may be performed without using AI. For example, the danger avoidance unit can input video data acquired by a camera to a generation AI and cause the generation AI to recognize obstacles and the state of traffic lights and generate a warning.

[0074] The reception unit can receive user instructions and input them to the AI. The reception unit, for example, uses voice recognition technology to receive user voice instructions. For example, when the user issues a voice instruction such as "I want to go to the station," the reception unit recognizes the voice and inputs it to the AI. The reception unit can also receive the user's touch operation using a touch panel. For example, when the user selects a destination on the touch panel, the information is input to the AI. Furthermore, the reception unit can also receive user gestures using gesture recognition technology. For example, when the user performs a specific gesture, the gesture is recognized and input to the AI. As a result, the system performs an operation according to the user's request by receiving the user's instructions and inputting them to the AI. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's voice instructions to the generation AI and cause the generation AI to execute an analysis of the voice instructions.

[0075] The analysis unit can identify an item during shopping and guide the user to its location. The analysis unit, for example, analyzes video data from sunglasses with a camera and identifies the item. For example, the analysis unit uses image recognition technology to identify milk on a supermarket shelf. The analysis unit can also use barcode recognition technology to read the barcode of the item and identify its location. Furthermore, after identifying the location of the item, the analysis unit can provide voice guidance to guide the user to the location. For example, the analysis unit provides voice guidance to the user such as "milk is on the shelf on the right side." This allows the user to shop efficiently by identifying the item and guiding the user to its location. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI or may be performed without using AI. For example, the analysis unit can input video data from sunglasses with a camera to a generation AI and cause the generation AI to identify the item and generate a location guide.

[0076] The reception unit can estimate the user's emotion and adjust the instruction reception method based on the estimated user's emotion. The reception unit estimates the user's emotion using, for example, facial expression recognition technology. For example, the reception unit analyzes the user's facial expression data captured by a camera and estimates whether the user is nervous or relaxed. The reception unit can also estimate the user's emotion using voice analysis technology. For example, the reception unit analyzes the tone and speed of the user's voice to estimate the user's emotion. Furthermore, the reception unit adjusts the instruction reception method based on the estimated user's emotion. For example, when the user is nervous, a simple and intuitive interface is provided to facilitate the input of instructions. Also, when the user is relaxed, detailed options can be provided and customizable instruction input can be suggested. Furthermore, when the user is in a hurry, voice input can be prioritized and instructions can be quickly accepted. In this way, a more appropriate interface is provided by adjusting the instruction reception method according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit may input the user's facial expression data to the generation AI and cause the generation AI to estimate emotions and adjust the method of receiving instructions.

[0077] The reception unit can analyze the user's past instruction history and select an appropriate reception method. The reception unit can analyze the user's past instruction history using, for example, a database search technology. For example, the reception unit can automatically display instructions that the user frequently issued in the past as candidates. The reception unit can also preferentially suggest input methods (such as voice and text) that the user used in the past. Furthermore, the reception unit can predict and suggest instructions to be used in a specific time period from the user's past instruction history. For example, the reception unit preferentially displays instructions related to a specific time period based on instructions that the user issued in the past during that time period. In this way, the reception unit provides the user with an optimal reception method by analyzing the past instruction history. A part or all of the above-mentioned processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input the user's past instruction history to the generation AI and cause the generation AI to analyze the instruction history and select the reception method.

[0078] The reception unit can perform filtering based on the user's current situation and environment when receiving an instruction. The reception unit, for example, uses location information technology to identify the user's current situation and environment. For example, the reception unit identifies the user's current location based on GPS data and filters the instruction based on the information. The reception unit can also use voice recognition technology to analyze surrounding sounds. For example, when the user is outdoors, the reception unit adjusts the sensitivity of the voice input taking into account the surrounding noise. Furthermore, when the user is indoors, the reception unit can increase the sensitivity of the voice input to match a quiet environment. For example, when the user is moving, the reception unit preferentially receives instructions related to the current location based on GPS information. As a result, appropriate instructions are received by filtering according to the user's situation and environment. Part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's location information and surrounding sound data to the generation AI and cause the generation AI to perform filtering.

[0079] The reception unit can estimate the user's emotion and determine the priority of instructions to be received based on the estimated user's emotion. The reception unit estimates the user's emotion using, for example, facial expression recognition technology. For example, the reception unit analyzes facial expression data of the user captured by a camera and estimates whether the user is nervous or relaxed. The reception unit can also estimate the user's emotion using voice analysis technology. For example, the reception unit analyzes the tone and speed of the user's voice to estimate the user's emotion. Furthermore, the reception unit determines the priority of instructions to be received based on the estimated user's emotion. For example, when the user is nervous, important instructions are preferentially received. Also, when the user is relaxed, detailed instructions can be received. Furthermore, when the user is in a hurry, instructions that require quick processing can be preferentially received. In this way, by determining the priority of instructions according to the user's emotion, important instructions are preferentially received. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (for example, LLM) or a multimodal generation AI. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may input the user's facial expression data to the generation AI and cause the generation AI to estimate the emotion and determine the priority of the instruction.

[0080] When receiving an instruction, the reception unit can preferentially receive an instruction with high relevance in consideration of the geographical location information of the user. The reception unit, for example, acquires the geographical location information of the user using GPS data. For example, when the user is in a specific location, the reception unit preferentially receives an instruction related to the location. In addition, when the user is moving, the reception unit can also preferentially receive an instruction related to the current location. Furthermore, when the user is in a specific area, the reception unit can also preferentially receive an instruction related to the area. For example, when the user is in a specific tourist spot, the reception unit preferentially receives an instruction related to the tourist spot. In this way, by considering the geographical location information, a highly relevant instruction is preferentially received. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the geographical location information of the user to the generation AI and cause the generation AI to select a highly relevant instruction.

[0081] The reception unit can analyze the user's social media activity and accept related instructions when receiving the instruction. 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 related 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 related instructions. Furthermore, the reception unit can accept related instructions based on the user's social media activity history. For example, when the user posts "I want to buy milk at the supermarket" on social media, the reception unit accepts related instructions based on the information. In this way, the social media activity is analyzed and related instructions are accepted. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media activity data to the generation AI and cause the generation AI to select related instructions.

[0082] The analysis unit can estimate the user's emotion and adjust the analysis method of the video data based on the estimated user's emotion. The analysis unit estimates the user's emotion using, for example, facial expression recognition technology. For example, the analysis unit analyzes the user's facial expression data captured by a camera and estimates whether the user is tense or relaxed. The analysis unit can also estimate the user's emotion using voice analysis technology. For example, the analysis unit analyzes the tone and speed of the user's voice to estimate the user's emotion. Furthermore, the analysis unit adjusts the analysis method of the video data based on the estimated user's emotion. For example, when the user is relaxed, a detailed analysis is performed to grasp the surrounding situation in detail. Furthermore, when the user is tense, important information can be analyzed with priority. Furthermore, when the user is in a hurry, analysis can be performed quickly to provide the necessary information. This allows for more appropriate analysis by adjusting the analysis method according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input the user's facial expression data to the generation AI and cause the generation AI to estimate emotions and adjust the analysis method.

[0083] When analyzing the video data, the analysis unit can improve the accuracy of the analysis by referring to the user's past behavior history. The analysis unit, for example, refers to the user's past behavior history using a database search technique. For example, the analysis unit analyzes the current route based on the route the user has taken in the past. The analysis unit can also extract a specific pattern from the user's past behavior history to improve the accuracy of the analysis. Furthermore, the analysis unit can analyze the user's past behavior history and perform an analysis suitable for the current situation. For example, the analysis unit predicts the user's behavior at a specific location based on the behavior the user performed at that location in the past, and improves the accuracy of the analysis. As a result, the accuracy of the analysis is improved by referring to the past behavior history. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past behavior history to the generation AI and cause the generation AI to refer to the behavior history and improve the accuracy of the analysis.

[0084] The analysis unit can customize the analysis algorithm based on the user's current situation when analyzing the video data. The analysis unit uses, for example, location information technology and an environmental sensor to identify the user's current situation. For example, when the user is outdoors, the analysis unit uses an analysis algorithm suitable for the surrounding environment. In addition, when the user is indoors, the analysis unit can also use an analysis algorithm suitable for the indoor environment. Furthermore, when the user is moving, the analysis unit can also use an analysis algorithm suitable for the moving situation. For example, when the user is outdoors, the analysis unit uses an analysis algorithm that takes into account the effects of light reflection and shadows. This enables appropriate analysis by customizing the analysis algorithm according to the current situation. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the user's current situation data to the generation AI and cause the generation AI to execute customization of the analysis algorithm.

[0085] The analysis unit can estimate the user's emotion and adjust the display method of the analysis result based on the estimated user's emotion. The analysis unit estimates the user's emotion using, for example, facial expression recognition technology. For example, the analysis unit analyzes the user's facial expression data captured by a camera and estimates whether the user is tense or relaxed. The analysis unit can also estimate the user's emotion using voice analysis technology. For example, the analysis unit analyzes the tone and speed of the user's voice to estimate the user's emotion. Furthermore, the analysis unit adjusts the display method of the analysis result based on the estimated user's emotion. For example, when the user is tense, a simple and highly visible display method is provided. Also, when the user is relaxed, a display method including detailed information can be provided. Furthermore, when the user is in a hurry, a display method that focuses on the main points can be provided. As a result, by adjusting the display method according to the user's emotion, a highly visible display is possible. The emotion estimation is realized using, for example, an emotion engine or a generation AI using an emotion estimation function. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input the user's facial expression data to the generation AI and cause the generation AI to estimate the emotion and adjust the display method.

[0086] The analysis unit can improve the accuracy of the analysis by considering the geographical location information of the user when analyzing the video data. The analysis unit, for example, uses GPS data to acquire the geographical location information of the user. For example, when the user is in a specific location, the analysis unit preferentially analyzes information related to the location. In addition, when the user is moving, the analysis unit can also preferentially analyze information related to the current location. Furthermore, when the user is in a specific area, the analysis unit can also preferentially analyze information related to the area. For example, when the user is in a specific tourist spot, the analysis unit preferentially analyzes information related to the tourist spot. As a result, the accuracy of the analysis is improved by considering the geographical location information. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the geographical location information of the user to the generation AI and cause the generation AI to execute the improvement of the accuracy of the analysis.

[0087] The analysis unit can improve the accuracy of the analysis by analyzing the user's social media activities when analyzing the video data. The analysis unit can, for example, use social media analysis technology to analyze the user's social media activities. For example, the analysis unit analyzes related 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 the related information. Furthermore, the analysis unit can analyze the related information based on the user's social media activity history. For example, if the user posts "I want to buy milk at the supermarket" on social media, the analysis unit analyzes the related information based on that information. This improves the accuracy of the analysis by analyzing the social media activities. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's social media activity data to the generation AI and cause the generation AI to execute the improvement of the accuracy of the analysis.

[0088] The guidance unit can estimate the user's emotion and adjust the expression method of the voice guidance based on the estimated user's emotion. The guidance unit estimates the user's emotion using, for example, facial expression recognition technology. For example, the guidance unit analyzes facial expression data of the user captured by a camera and estimates whether the user is tense or relaxed. The guidance unit can also estimate the user's emotion using voice analysis technology. For example, the guidance unit analyzes the tone and speed of the user's voice to estimate the user's emotion. Furthermore, the guidance unit adjusts the expression method of the voice guidance based on the estimated user's emotion. For example, if the user is tense, the guidance is provided in a calm voice. Also, if the user is relaxed, the guidance can be provided in a bright voice. Furthermore, if the user is in a hurry, quick and concise voice guidance can be provided. This allows more appropriate guidance to be provided by adjusting the expression method of the voice guidance according to the user's emotion. The emotion estimation is realized using, for example, an emotion engine or a generation AI using an emotion estimation function. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the guidance unit may be performed using, for example, AI, or may be performed without using AI. For example, the guidance unit may input the user's facial expression data to the generation AI and cause the generation AI to estimate the emotion and adjust the expression method of the voice guidance.

[0089] The guidance unit can select the optimal guidance method by referring to the user's past movement history when providing voice guidance. The guidance unit, for example, uses a database search technology to refer to the user's past movement history. For example, the guidance unit proposes the optimal guidance method based on the route used by the user in the past. The guidance unit can also propose a route that avoids congestion from the user's past movement history. Furthermore, the guidance unit can analyze the user's past movement history and propose the most efficient route. For example, the guidance unit proposes the optimal route for a specific time period based on the route used by the user in the past during that time period. In this way, the optimal guidance method is provided by referring to the past movement history. Part or all of the above-mentioned processing in the guidance unit may be performed, for example, using AI, or may be performed without using AI. For example, the guidance unit can input the user's past movement history into the generation AI and cause the generation AI to refer to the movement history and select a guidance method.

[0090] The guidance unit can customize the guidance algorithm based on the current situation of the user when providing voice guidance. The guidance unit uses, for example, location information technology or an environmental sensor to identify the current situation of the user. For example, when the user is outdoors, the guidance unit uses a guidance algorithm suitable for the surrounding environment. In addition, when the user is indoors, the guidance unit can also use a guidance algorithm suitable for the indoor environment. Furthermore, when the user is moving, the guidance unit can also use a guidance algorithm suitable for the moving situation. For example, when the user is outdoors, the guidance unit uses a guidance algorithm that takes into account the influence of light reflection and shadows. This enables appropriate guidance by customizing the guidance algorithm according to the current situation. Part or all of the above-mentioned processing in the guidance unit may be performed, for example, using AI, or may be performed without using AI. For example, the guidance unit can input the user's current situation data to the generation AI and cause the generation AI to execute customization of the guidance algorithm.

[0091] The guidance unit can estimate the user's emotion and determine the priority of the voice guidance based on the estimated user's emotion. The guidance unit estimates the user's emotion using, for example, facial expression recognition technology. For example, the guidance unit analyzes facial expression data of the user captured by a camera and estimates whether the user is tense or relaxed. The guidance unit can also estimate the user's emotion using voice analysis technology. For example, the guidance unit analyzes the tone and speed of the user's voice and estimates the user's emotion. Furthermore, the guidance unit determines the priority of the voice guidance based on the estimated user's emotion. For example, if the user is tense, important guidance is given priority. Also, detailed guidance can be given if the user is relaxed. Furthermore, if the user is in a hurry, guidance that requires quick processing can be given priority. In this way, important guidance is given priority by determining the priority of guidance according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (for example, LLM) or a multimodal generation AI. A part or all of the above-mentioned processing in the guidance unit may be performed using, for example, AI, or may be performed without using AI. For example, the guidance unit may input the user's facial expression data to the generation AI and cause the generation AI to estimate the emotion and determine the priority of the voice guidance.

[0092] The guidance unit can select the optimal guidance method in consideration of the geographical location information of the user when providing voice guidance. The guidance unit acquires the geographical location information of the user using, for example, GPS data. For example, when the user is in a specific location, the guidance unit prioritizes guidance related to the location. In addition, when the user is moving, the guidance unit can also prioritize guidance related to the current location. Furthermore, when the user is in a specific area, the guidance unit can also prioritize guidance related to the area. For example, when the user is in a specific tourist spot, the guidance unit prioritizes guidance related to the tourist spot. In this way, the optimal guidance method is provided by considering the geographical location information. A part or all of the above-mentioned processing in the guidance unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the guidance unit can input the geographical location information of the user to the generation AI and cause the generation AI to select the guidance method.

[0093] The guidance unit can analyze the user's social media activity during voice guidance to improve the accuracy of the guidance. The guidance unit, for example, uses social media analysis technology to analyze the user's social media activity. For example, the guidance unit provides related 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 related guidance. Furthermore, the guidance unit can provide related guidance based on the user's social media activity history. For example, if the user posts on social media that "I want to buy milk at the supermarket," the guidance unit provides related guidance based on that information. This improves the accuracy of the guidance by analyzing social media activity. A part or all of the above-mentioned processing in the guidance unit may be performed, for example, using AI, or may be performed without using AI. For example, the guidance unit can input the user's social media activity data into the generation AI and cause the generation AI to execute the improvement of the accuracy of the guidance.

[0094] The danger avoidance unit can estimate the user's emotion and adjust the danger avoidance method based on the estimated user's emotion. The danger avoidance unit estimates the user's emotion using, for example, facial expression recognition technology. For example, the danger avoidance unit analyzes the user's facial expression data captured by a camera and estimates whether the user is tense or relaxed. The danger avoidance unit can also estimate the user's emotion using voice analysis technology. For example, the danger avoidance unit analyzes the tone and speed of the user's voice and estimates the user's emotion. Furthermore, the danger avoidance unit adjusts the danger avoidance method based on the estimated user's emotion. For example, when the user is tense, detailed danger avoidance information is provided. Furthermore, when the user is relaxed, concise danger avoidance information can also be provided. Furthermore, when the user is in a hurry, danger avoidance information can also be provided quickly. This enables more appropriate danger avoidance by adjusting the danger avoidance method according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the danger avoidance unit may be performed using, for example, AI, or may be performed without using AI. For example, the danger avoidance unit may input the user's facial expression data to the generation AI, and cause the generation AI to estimate the emotion and adjust the danger avoidance method.

[0095] The danger avoidance unit can select an optimal avoidance method by referring to the user's past behavior history when avoiding danger. The danger avoidance unit, for example, refers to the user's past behavior history using a database search technique. For example, the danger avoidance unit proposes an optimal avoidance method based on a dangerous situation the user has encountered in the past. The danger avoidance unit can also extract a specific pattern from the user's past behavior history and select an optimal avoidance method. Furthermore, the danger avoidance unit can analyze the user's past behavior history and propose an avoidance method suitable for the current situation. For example, the danger avoidance unit proposes an avoidance method at a specific location based on a dangerous situation the user has encountered in the past at that location. In this way, the optimal avoidance method is provided by referring to the past behavior history. A part or all of the above-mentioned processing in the danger avoidance unit may be performed, for example, using AI or may be performed without using AI. For example, the danger avoidance unit can input the user's past behavior history to the generation AI and cause the generation AI to refer to the behavior history and select an avoidance method.

[0096] The danger avoidance unit can customize the avoidance algorithm based on the current situation of the user when avoiding danger. The danger avoidance unit uses, for example, location information technology or an environmental sensor to identify the current situation of the user. For example, when the user is outdoors, the danger avoidance unit uses an avoidance algorithm suitable for the surrounding environment. In addition, when the user is indoors, the danger avoidance unit can also use an avoidance algorithm suitable for the indoor environment. Furthermore, when the user is moving, the danger avoidance unit can also use an avoidance algorithm suitable for the moving situation. For example, when the user is outdoors, the danger avoidance unit uses an avoidance algorithm that takes into account the influence of light reflection and shadows. This enables appropriate avoidance by customizing the avoidance algorithm according to the current situation. Part or all of the above-mentioned processing in the danger avoidance unit may be performed, for example, using AI, or may be performed without using AI. For example, the danger avoidance unit can input the user's current situation data to the generation AI and cause the generation AI to execute customization of the avoidance algorithm.

[0097] The danger avoidance unit can estimate the user's emotion and determine the priority of danger avoidance based on the estimated user's emotion. The danger avoidance unit estimates the user's emotion using, for example, facial expression recognition technology. For example, the danger avoidance unit analyzes facial expression data of the user captured by a camera and estimates whether the user is tense or relaxed. The danger avoidance unit can also estimate the user's emotion using voice analysis technology. For example, the danger avoidance unit analyzes the tone and speed of the user's voice and estimates the user's emotion. Furthermore, the danger avoidance unit determines the priority of danger avoidance based on the estimated user's emotion. For example, if the user is tense, important danger avoidance information can be provided preferentially. Also, if the user is relaxed, detailed danger avoidance information can be provided. Furthermore, if the user is in a hurry, danger avoidance information that requires rapid processing can be provided preferentially. In this way, by determining the priority of danger avoidance according to the user's emotion, important danger avoidance information can be provided preferentially. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the danger avoidance unit may be performed, for example, using AI, or may be performed without using AI. For example, the danger avoidance unit may input the user's facial expression data to the generation AI and cause the generation AI to estimate emotions and determine the priority of danger avoidance.

[0098] The danger avoidance unit can select the optimal avoidance method in consideration of the geographical location information of the user when avoiding danger. The danger avoidance unit acquires the geographical location information of the user, for example, using GPS data. For example, when the user is in a specific location, the danger avoidance unit provides preferentially danger avoidance information related to the location. In addition, when the user is moving, the danger avoidance unit can also provide preferentially danger avoidance information related to the current location. Furthermore, when the user is in a specific area, the danger avoidance unit can also provide preferentially danger avoidance information related to the area. For example, when the user is in a specific tourist spot, the danger avoidance unit provides preferentially danger avoidance information related to the tourist spot. In this way, the optimal avoidance method is provided by considering the geographical location information. A part or all of the above-mentioned processing in the danger avoidance unit may be performed, for example, using AI, or may be performed without using AI. For example, the danger avoidance unit can input the geographical location information of the user to the generation AI and cause the generation AI to select an avoidance method.

[0099] The danger avoidance unit can analyze the user's social media activity during risk avoidance to improve the accuracy of risk avoidance. The danger avoidance unit, for example, uses social media analysis technology to analyze the user's social media activity. For example, the danger avoidance unit provides related risk avoidance information based on information shared by the user on social media. The danger avoidance unit can also analyze the content of the user's social media posts and provide related risk avoidance information. Furthermore, the danger avoidance unit can provide related risk avoidance information based on the user's social media activity history. For example, if the user posts "I want to buy milk at the supermarket" on social media, the danger avoidance unit provides related risk avoidance information based on that information. This improves the accuracy of risk avoidance by analyzing social media activity. A part or all of the above-mentioned processing in the danger avoidance unit may be performed, for example, using AI or may be performed without using AI. For example, the danger avoidance unit can input the user's social media activity data to the generation AI and cause the generation AI to execute improvement of risk avoidance accuracy.

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

[0101] When accepting an instruction from the user, the acceptance unit can monitor the user's health condition and adjust the method of accepting the instruction according to the condition. For example, the acceptance unit can measure the user's heart rate and blood pressure, and provide an easier and more intuitive interface when the user is feeling stressed. Also, when the user is relaxed, the acceptance unit can provide detailed options and suggest customizable instruction input. Furthermore, when the user is in a hurry, voice input can be prioritized and instructions can be accepted quickly. In this way, a more appropriate interface can be provided by adjusting the method of accepting instructions according to the user's health condition.

[0102] The analysis unit can improve the accuracy of the analysis by taking into account weather information when grasping the situation around the user. For example, the analysis unit can detect slippery road surfaces in rainy weather and warn the user. Also, on snowy days, the analysis unit can grasp the snowfall situation and provide information to ensure the safety of walking. Furthermore, in the case of strong winds, the user's movement route can be adjusted by taking into account the influence of wind. In this way, by taking into account weather information, the accuracy of the analysis can be improved and the safety of the user can be ensured.

[0103] When calculating an efficient route from the user's current location to the destination, the guidance unit can optimize the route by taking into account the user's walking speed. For example, the guidance unit measures the user's walking speed in real time and proposes an optimal route according to the speed. In addition, if the user is tired, the guidance unit can also propose a route including a rest point. Furthermore, if the user is in a hurry, the guidance unit can calculate a route that allows the user to reach the destination in the shortest time. This allows more appropriate route guidance to be provided by taking into account the user's walking speed.

[0104] The danger avoidance unit can analyze surrounding sound information and detect danger in order to ensure the safety of the user. For example, the danger avoidance unit can detect car horns and emergency vehicle sirens and issue a warning to the user. It can also analyze the voices and footsteps of people in the vicinity and provide information for avoiding danger in crowded places. Furthermore, the danger avoidance unit can detect noise from a construction site and suggest a route that avoids that area. In this way, the safety of the user can be ensured by analyzing the surrounding sound information.

[0105] When receiving instructions from a user, the reception unit can learn the user's past behavioral patterns and automatically suggest predicted instructions. For example, if the user commutes to work by the same route every morning, the reception unit automatically suggests that route. Also, if the user has a habit of going to a specific place on a specific day of the week, the reception unit can suggest instructions based on that information. Furthermore, if the user tends to give specific instructions during a specific time period, instructions related to that time period can be preferentially displayed. In this way, by learning past behavioral patterns, it is possible to provide optimal instructions to the user.

[0106] The analysis unit can not only identify items when shopping, but also suggest related products by taking into account the user's preferences and past purchase history. For example, the analysis unit can suggest related products based on products the user has purchased in the past. The analysis unit can also learn the user's preferences and suggest products that match those preferences. Furthermore, the analysis unit can suggest products according to seasons and events. This makes it possible to provide a more personalized shopping experience by taking into account the user's preferences and past purchase history.

[0107] The reception unit can estimate the user's emotions and suggest entertainment content suitable for the user based on the estimated user's emotions. For example, when the user is relaxed, the reception unit can suggest relaxing music or movies. When the user is stressed, the reception unit can also suggest content that helps relieve stress. Furthermore, when the user is having fun, the reception unit can also suggest content that the user will find even more enjoyable. In this way, entertainment content can be suggested according to the user's emotions, providing a more satisfying experience.

[0108] The reception unit can not only analyze the user's past instruction history, but also learn the user's lifestyle and daily routine, and automatically suggest predicted instructions. For example, if the user has the habit of drinking coffee at the same time every morning, the reception unit can suggest preparing coffee at that time. Also, if the user has the habit of performing a specific activity on a specific day of the week, the reception unit can suggest instructions based on that information. Furthermore, if the user tends to perform a specific activity in a specific season, instructions related to that season can be preferentially displayed. In this way, optimal instructions can be provided to the user by learning the lifestyle and daily routine.

[0109] When receiving instructions, the reception unit can not only filter based on the user's current situation and environment, but also determine the priority of instructions by taking into account the user's schedule information. For example, the reception unit obtains the user's calendar information, and when the user has an important event, it can preferentially receive instructions related to the event. Also, when the user is in a meeting, it can also preferentially receive instructions related to the meeting. Furthermore, when the user is on vacation, it can also preferentially receive instructions that will help the user relax. In this way, more appropriate instructions can be received by taking into account the schedule information.

[0110] The reception unit can estimate the user's emotions and provide appropriate feedback to the user based on the estimated user's emotions. For example, when the user is nervous, the reception unit can provide encouraging words or suggest ways to relax. When the user is relaxed, the reception unit can provide feedback encouraging the user to further relax. Furthermore, when the user is enjoying themselves, the reception unit can provide feedback to share the enjoyment. In this way, by providing appropriate feedback according to the user's emotions, the satisfaction of the user can be improved.

[0111] The process flow of the second embodiment will be briefly described below.

[0112] Step 1: The reception unit receives a user's instruction. The user's instruction includes a voice instruction, a touch operation, a gesture, and the like. The reception unit can receive a user's voice instruction using a voice recognition technology, and can also receive a user's touch operation using a touch panel. Furthermore, the reception unit can also receive a user's gesture using a 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 technology 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. In addition, it can also measure the distance between the user and the obstacle using distance measurement technology. Step 3: The guidance unit provides voice guidance based on the information analyzed by the analysis unit. The voice guidance includes the type of voice and the timing of the guidance. The guidance unit can provide voice guidance to the user using voice synthesis technology, calculate the optimal route from the user's current location to the destination, and provide voice guidance along that route. Furthermore, the guidance unit can identify items when shopping based on the user's instructions and provide guidance on their location. Step 4: The danger avoidance unit detects obstacles and issues a warning to ensure the user's safety. Sensor technology and image recognition technology are used to detect obstacles. The danger avoidance unit can also detect obstacles using ultrasonic sensors and recognize the status of traffic lights using cameras. In addition, it can also warn the user using audio 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0114] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0115] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0116] Each of the multiple elements including the above-mentioned reception unit, analysis unit, guidance unit, and danger avoidance unit is realized, for example, in at least one of the smart device 14 and the data processing device 12. For example, the reception unit receives a user's instruction using the microphone 38B or the touch panel 38A of the smart device 14. The analysis unit analyzes the video data from the camera 42 of the smart device 14 by the specific processing unit 290 of the data processing device 12. The guidance unit provides voice guidance based on the information analyzed by the specific processing unit 290 of the data processing device 12. The danger avoidance unit detects an obstacle using the camera 42 or ultrasonic sensor of the smart device 14, and generates a warning by the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

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

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

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

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

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

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0123] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0124] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0125] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0126] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0127] In the smart glasses 214, the 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 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0128] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

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

[0130] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed 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 required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0132] Each of the multiple elements including the above-mentioned reception unit, analysis unit, guidance unit, and danger avoidance unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit receives a user's instruction using the microphone 238 of the smart glasses 214. The analysis unit analyzes video data from the camera 42 of the smart glasses 214 by the specific processing unit 290 of the data processing device 12. The guidance unit provides voice guidance based on the information analyzed by the specific processing unit 290 of the data processing device 12. The danger avoidance unit detects an obstacle using the camera 42 or ultrasonic sensor of the smart glasses 214, and generates a warning by the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

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

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

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

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

[0137] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0139] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0140] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0141] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0142] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0143] In the headset type terminal 314, the 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 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0144] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

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

[0146] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0148] Each of the multiple elements including the above-mentioned reception unit, analysis unit, guidance unit, and danger avoidance unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit receives a user's instruction using the microphone 238 of the headset type terminal 314. The analysis unit analyzes the video data from the camera 42 of the headset type terminal 314 by the specific processing unit 290 of the data processing device 12. The guidance unit provides voice guidance based on the information analyzed by the specific processing unit 290 of the data processing device 12. The danger avoidance unit detects an obstacle using the camera 42 or ultrasonic sensor of the headset type terminal 314, and generates a warning by the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

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

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

[0151] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

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

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

[0154] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0155] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0156] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0157] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0158] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0159] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0160] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0161] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[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 control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0163] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed 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 required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0165] Each of the multiple elements including the above-mentioned reception unit, analysis unit, guidance unit, and danger avoidance unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit receives a user's instruction using the microphone 238 of the robot 414. The analysis unit analyzes the video data from the camera 42 of the robot 414 by the specific processing unit 290 of the data processing device 12. The guidance unit provides voice guidance based on the information analyzed by the specific processing unit 290 of the data processing device 12. The danger avoidance unit detects an obstacle using the camera 42 or ultrasonic sensor of the robot 414, and generates a warning by the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0166] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0167] FIG. 9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0168] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0169] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0170] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0171] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this 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 positive emotion around "desire" on the response side. In other words, this 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 the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0173] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers, including computer 22.

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

[0175] In addition, 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 upon request from the data processing device 12.

[0176] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0177] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0178] The hardware resource that executes the specific process may be one of these various processors, or may be a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0179] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0180] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0181] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0182] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0183] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0184] (Appendix 1) A reception unit that receives an instruction from a user; an analysis unit that analyzes video data from the camera-equipped sunglasses based on the information received by the reception unit; a guidance unit that provides voice guidance based on the information analyzed by the analysis unit; A danger avoidance unit that detects obstacles and issues a warning to ensure the safety of a user. A system characterized by: (Appendix 2) The analysis unit is Analyzes video data from camera-equipped sunglasses to understand the user's surroundings 2. The system of claim 1, (Appendix 3) The guide portion is Calculates an efficient route from the user's current location to the destination and provides voice guidance along that route 2. The system of claim 1, (Appendix 4) The danger avoidance unit includes: The camera recognizes obstacles on the sidewalk and the status of traffic lights, and issues audio warnings to the user. 2. The system of claim 1, (Appendix 5) The reception unit is Accepts user instructions and inputs them into the AI 2. The system of claim 1, (Appendix 6) The analysis unit is Identifying and locating items when shopping 2. The system of claim 1, (Appendix 7) The reception unit is To estimate a user's emotion and adjust a method of receiving an instruction based on the estimated user's emotion 2. The system of claim 1, (Appendix 8) The reception unit is Analyze the user's past instruction history and select the appropriate reception method 2. The system of claim 1, (Appendix 9) The reception unit is When receiving instructions, filtering is performed based on the user's current situation and environment. 2. The system of claim 1, (Appendix 10) The reception unit is A user's emotion is estimated, and a priority order of instructions to be accepted is determined based on the estimated user's emotion. 2. The system of claim 1, (Appendix 11) The reception unit is When accepting instructions, the system takes into account the user's geographic location information and prioritizes accepting instructions that are more relevant to the user. 2. The system of claim 1, (Appendix 12) The reception unit is Upon receiving an instruction, analyze the user's social media activity and receive relevant instructions. 2. The system of claim 1, (Appendix 13) The analysis unit is To estimate a user's emotion and adjust a method of analyzing video data based on the estimated user's emotion. 2. The system of claim 1, (Appendix 14) The analysis unit is When analyzing video data, the accuracy of the analysis can be improved by referring to the user's past behavior history. 2. The system of claim 1, (Appendix 15) The analysis unit is When analyzing video data, customize the analysis algorithm based on the user's current situation 2. The system of claim 1, (Appendix 16) The analysis unit is Infer user emotions and adjust the way analysis results are displayed based on the inferred user emotions 2. The system of claim 1, (Appendix 17) The analysis unit is When analyzing video data, the accuracy of the analysis is improved by taking into account the user's geographic location information. 2. The system of claim 1, (Appendix 18) The analysis unit is When analyzing video data, analyze users' social media activity to improve the accuracy of the analysis. 2. The system of claim 1, (Appendix 19) The guide portion is Estimating a user's emotions and adjusting the way voice guidance is presented based on the estimated user's emotions 2. The system of claim 1, (Appendix 20) The guide portion is When providing voice guidance, the system selects the optimal guidance method by referring to the user's past movement history. 2. The system of claim 1, (Appendix 21) The guide portion is During voice guidance, customize the guidance algorithm based on the user's current situation 2. The system of claim 1, (Appendix 22) The guide portion is Estimate the user's emotions and determine the priority of voice guidance based on the estimated user's emotions. 2. The system of claim 1, (Appendix 23) The guide portion is When providing voice guidance, the system selects the optimal guidance method taking into account the user's geographical location information. 2. The system of claim 1, (Appendix 24) The guide portion is Analyze users' social media activity during voice guidance to improve guidance accuracy 2. The system of claim 1, (Appendix 25) The danger avoidance unit includes: To estimate a user's emotion and adjust a method of avoiding danger based on the estimated user's emotion. 2. The system of claim 1, (Appendix 26) The danger avoidance unit includes: When avoiding danger, the system selects the optimal avoidance method by referring to the user's past behavior history. 2. The system of claim 1, (Appendix 27) The danger avoidance unit includes: Customize avoidance algorithms based on the user's current situation when avoiding danger 2. The system of claim 1, (Appendix 28) The danger avoidance unit includes: Estimating a user's emotions and determining priority of risk avoidance based on the estimated user's emotions 2. The system of claim 1, (Appendix 29) The danger avoidance unit includes: When avoiding danger, the system selects the optimal avoidance method by taking into account the user's geographical location information. 2. The system of claim 1, (Appendix 30) The danger avoidance unit includes: Analyzing users' social media activity to improve risk avoidance 2. The system of claim 1, [Explanation of symbols]

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

Claims

1. A reception unit that receives an instruction from a user; an analysis unit that analyzes video data from the camera-equipped sunglasses based on the information received by the reception unit; a guidance unit that provides voice guidance based on the information analyzed by the analysis unit; A danger avoidance unit that detects an obstacle and issues a warning to ensure the safety of the user. A system characterized by:

2. The analysis unit is Analyze video data from camera-equipped sunglasses to understand the user's surroundings 2. The system of claim 1 .

3. The guide unit is Calculate an efficient route from the user's current location to the destination and provide voice guidance along that route 2. The system of claim 1 .

4. The danger avoidance unit includes: Recognizes obstacles on the sidewalk and the status of traffic lights, and issues audio warnings to the user 2. The system of claim 1 .

5. The guide portion is When providing voice guidance, the optimal guidance method is selected by referring to the user's past movement history.

2. The system of claim 1 .

6. The analysis unit is Identifying and locating items when shopping 2. The system of claim 1 .

7. The reception unit is Estimating the user's emotion and adjusting a method of accepting an instruction based on the estimated user's emotion 2. The system of claim 1 .

8. The reception unit is Analyze the user's past instruction history and select an appropriate reception method.

2. The system of claim 1 .

Images