system

Abstract

The system according to this embodiment aims to enable visually impaired individuals to grasp information about their surrounding environment and move around safely. [Solution] The system according to the embodiment comprises a camera unit, an analysis unit, a guide unit, and a GPS unit. The camera unit recognizes the surrounding environment. The analysis unit analyzes the data collected by the camera unit. The guide unit provides voice guidance based on the data analyzed by the analysis unit. The GPS unit determines the user's current location.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is difficult for visually impaired people to grasp the surrounding environmental information during movement, and there are concerns about safety and independence.

[0005] The system according to the embodiment aims to enable visually impaired people to grasp the surrounding environmental information and move safely.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a camera unit, an analysis unit, a guide unit, and a GPS unit. The camera unit recognizes the surrounding environment. The analysis unit analyzes the data collected by the camera unit. The guide unit provides voice guidance based on the data analyzed by the analysis unit. The GPS unit determines the user's current location. [Effects of the Invention]

[0007] The system according to this embodiment can enable visually impaired individuals to grasp information about their surroundings and move around safely. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 2, 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

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

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

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

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

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

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

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

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

[0028] (Example of form 1) The SenseGuard smart glasses according to an embodiment of the present invention are smart glasses that provide visually impaired individuals with the ability to read aloud text and signs, provide directions, and offer hazard information. The SenseGuard smart glasses recognize the surrounding environment and support safe movement by providing real-time voice guidance and warnings. They also feature a stylish and user-friendly design and offer a comfortable fit. First, the SenseGuard smart glasses use a built-in camera to capture images of the surrounding environment. This camera collects data for recognizing text, signs, obstacles, etc. Next, the collected data is analyzed by AI, and the content of text and signs is read aloud. For example, information such as station signs and bus stop signs is provided by voice, allowing visually impaired individuals to obtain the information needed to reach their destination. Furthermore, the SenseGuard smart glasses are equipped with GPS functionality to determine the user's current location. Once the user sets a destination, the AI ​​calculates the optimal route and provides voice guidance. For example, specific instructions such as "Turn right at the next intersection" are provided by voice, allowing visually impaired individuals to reach their destination without getting lost. The SenseGuard smart glasses also provide real-time information on surrounding hazards. For example, if an obstacle is detected ahead or a vehicle is approaching, a voice warning is issued. This allows visually impaired individuals to move safely. SenseGuard smart glasses feature a stylish and user-friendly design, making them comfortable even during prolonged wear. They are lightweight yet highly durable, making them suitable for everyday use. Furthermore, the volume and content of voice guidance and warnings can be customized to the user's preferences. SenseGuard smart glasses are an innovative device designed to enhance the independence and support safe mobility for visually impaired individuals. They overcome the limitations imposed by a lack of visual information, enabling a freer and safer life. SenseGuard smart glasses can provide visually impaired individuals with features such as reading text and signs aloud, providing directions, and offering hazard information.

[0029] The SenseGuard smart glasses according to this embodiment include a camera unit, an analysis unit, a guide unit, and a GPS unit. The camera unit recognizes the surrounding environment. The camera unit, for example, uses a built-in camera to photograph the surrounding environment. The camera unit collects data for recognizing text, signs, obstacles, etc. The camera unit collects information such as station signs and bus stop signs. The analysis unit analyzes the data collected by the camera unit. The analysis unit analyzes the collected data using AI, for example. The analysis unit analyzes the content of text and signs and generates data for reading aloud. The analysis unit analyzes the content of station signs and bus stop signs, for example, and generates data for reading aloud. The guide unit provides voice guidance based on the data analyzed by the analysis unit. The guide unit provides directions to the user using voice guidance, for example. When the user sets a destination, the AI ​​calculates the optimal route and provides directions using voice guidance. The guide unit provides specific instructions via voice, such as, "Turn right at the next intersection." The GPS unit determines the user's current location. The GPS unit uses GPS functionality to determine the user's current location. When the user sets a destination, the GPS unit provides data for the AI ​​to calculate the optimal route and provide voice guidance. As a result, the SenseGuard smart glasses according to this embodiment can provide visually impaired individuals with the ability to read text and signs aloud, provide directions, and provide hazard information.

[0030] The camera unit recognizes the surrounding environment. For example, the camera unit uses a built-in camera to capture images of the surrounding environment. Specifically, the camera unit is equipped with a high-resolution camera and a wide-angle lens to cover a wide field of view. The camera unit collects data to recognize text, signs, obstacles, etc. For example, when collecting information such as station signs or bus stop signs, the camera unit automatically focuses and optimizes image clarity. In addition, the camera unit is equipped with an infrared sensor and night vision function to perform well even in low-light environments. This allows for accurate information collection even at night or in dark places. Furthermore, the camera unit has a tracking function to track moving objects and provides stable images even when the user is moving. As a result, the camera unit can accurately recognize the surrounding environment and collect necessary information for people with visual impairments.

[0031] The analysis unit analyzes the data collected by the camera unit. For example, the analysis unit uses AI to analyze the collected data. Specifically, the analysis unit uses image recognition technology to analyze the content of text and signs and generates data for reading aloud. For example, when analyzing the content of station signs or bus stop information boards and generating data for reading aloud, it uses OCR (optical character recognition) technology to convert the text into digital data. Furthermore, the analysis unit uses natural language processing (NLP) technology to convert the text information into audio data. The analysis unit uses an AI model to extract important information from the collected data and provide useful information to the user. For example, the analysis unit identifies the departure time and destination of the next train from a station sign and generates data to read aloud. The analysis unit also identifies the location and type of obstacles and generates data to alert the user. This allows the analysis unit to quickly and accurately provide visually impaired individuals with important information about their surroundings.

[0032] The guide unit provides voice guidance based on data analyzed by the analysis unit. For example, the guide unit provides directions to the user using voice guidance. Specifically, when the user sets a destination, the AI ​​calculates the optimal route and provides directions via voice guidance. The guide unit provides specific instructions via voice, such as, "Turn right at the next intersection." The guide unit uses speech synthesis technology to convey information to the user in a natural voice. Furthermore, the guide unit calculates the optimal route based on real-time updated map information and provides the user with the latest information. The guide unit can flexibly change the route considering the user's current location and surrounding conditions. For example, if a road is under construction and impassable, or if there are unexpected obstacles, the guide unit immediately calculates a new route and guides the user. In addition, the guide unit can learn the user's walking speed and movement patterns to provide optimal guidance to each individual user. As a result, the guide unit can support visually impaired individuals so that they can move safely and provide directions to their destination.

[0033] The GPS unit determines the user's current location. For example, the GPS unit uses GPS functionality to determine the user's current location. Specifically, the GPS unit is equipped with a high-precision GPS module and acquires the user's location information in real time. When the user sets a destination, the GPS unit's AI calculates the optimal route and provides data for voice guidance. The GPS unit receives signals from satellites and accurately identifies the user's current location. Furthermore, by using auxiliary location information technologies in conjunction with the GPS unit, it can provide accurate location information even in places where GPS signals are weak, such as indoors or between tall buildings. The GPS unit records the user's movement history and can optimize routes based on past data and automatically recognize frequently visited places. As a result, the GPS unit can provide a safe environment for visually impaired individuals by helping them understand their current location and providing the optimal route to their destination. In addition, the GPS unit has a function to quickly identify the user's current location in emergencies and notify rescue teams and family members. As a result, the GPS unit can ensure the user's safety and support a rapid response.

[0034] The camera unit recognizes the surrounding environment. For example, the camera unit uses its built-in camera to photograph the surrounding environment. The camera unit collects data to recognize text, signs, obstacles, etc. For example, the camera unit collects information such as station signs and bus stop signs. The camera unit uses AI to analyze the collected data. For example, the camera unit uses AI to analyze the collected data and generate data for reading aloud the contents of text and signs. This allows the camera unit to provide visually impaired individuals with the ability to read aloud text and signs, provide directions, and provide hazard information. Some or all of the above processing in the camera unit may be performed using AI, for example, or without AI. For example, the camera unit can use its built-in camera to photograph the surrounding environment, input the collected data into AI, and the AI ​​can analyze the data to generate an audio guide.

[0035] The analysis unit analyzes the data collected by the camera unit. The analysis unit analyzes the collected data using, for example, AI. The analysis unit analyzes the content of text and signs and generates data for reading aloud. The analysis unit analyzes the content of, for example, station signs and bus stop signs and generates data for reading aloud. The analysis unit analyzes the collected data using AI. The analysis unit analyzes the collected data using, for example, AI, and generates data for reading aloud the content of text and signs. This allows the analysis unit to provide visually impaired individuals with the reading of text and signs, directions, and information about potential hazards. Some or all of the above-described processes in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input data collected by the camera unit into AI, which can then analyze the data and generate an audio guide.

[0036] The guide unit provides voice guidance based on data analyzed by the analysis unit. The guide unit provides directions to the user using voice guidance, for example. When the user sets a destination, the AI ​​calculates the optimal route and provides directions using voice guidance. The guide unit provides specific instructions by voice, for example, "Turn right at the next intersection." The guide unit provides voice guidance based on data analyzed using AI. The guide unit generates voice guidance based on data analyzed by AI and provides it to the user. This allows the guide unit to provide visually impaired individuals with the ability to read text and signs, provide directions, and provide hazard information. Some or all of the above processing in the guide unit may be performed using AI, for example, or without AI. For example, the guide unit can input data analyzed by the analysis unit into AI, and the AI ​​can generate voice guidance based on the data.

[0037] The GPS unit determines the user's current location. The GPS unit determines the user's current location using, for example, GPS functionality. When the user sets a destination, the GPS unit provides data for the AI ​​to calculate the optimal route and provide voice guidance. The GPS unit determines the user's current location using AI. The GPS unit determines the user's current location by, for example, having the AI ​​analyze GPS data. This allows the GPS unit to provide visually impaired individuals with services such as reading text and signs aloud, providing directions, and providing hazard information. Some or all of the above-described processes in the GPS unit may be performed using, for example, AI, or not using AI. For example, the GPS unit can input GPS data into the AI, which can then analyze the data to determine the user's current location.

[0038] The camera unit has a function to automatically adjust the exposure according to the ambient brightness. For example, in bright places, the camera unit automatically lowers the exposure to suppress excessive light. For example, in dark places, the camera unit automatically increases the exposure to improve visibility. For example, in environments with fluctuating brightness, the camera unit adjusts the exposure in real time to provide the optimal image. In this way, the camera unit can provide the optimal exposure according to the ambient brightness. Some or all of the above processing in the camera unit may be performed using AI, for example, or without AI. For example, the camera unit can input ambient brightness data into the AI, and the AI ​​can analyze the data and adjust the exposure.

[0039] The camera unit has the function of highlighting specific objects or characters when taking pictures. For example, the camera unit can highlight signs and notices when taking pictures to provide important information to visually impaired people. For example, the camera unit can highlight obstacles when taking pictures to draw the user's attention. For example, the camera unit can highlight text information when taking pictures and convey it accurately with a text-to-speech function. In this way, the camera unit can provide important information to visually impaired people. Some or all of the above processing in the camera unit may be performed using AI, for example, or without AI. For example, the camera unit can input data on specific objects or characters into AI, and the AI ​​can analyze the data and highlight them.

[0040] The camera unit has a function to automatically adjust its field of view in response to the user's head movements. For example, when the user moves their head, the camera widens its field of view in that direction and acquires information about the surroundings. For example, when the user is looking in a specific direction, the camera focuses in that direction and provides detailed information. For example, when the user moves their head up and down, the camera adjusts its vertical field of view and acquires the necessary information. In this way, the camera unit can provide an optimal field of view in response to the user's head movements. Some or all of the above processing in the camera unit may be performed using AI, for example, or without AI. For example, the camera unit can input the user's head movement data into the AI, which can analyze the data and adjust the field of view.

[0041] The camera unit has the function of simultaneously collecting ambient audio information and transmitting it to the analysis unit. For example, the camera unit collects ambient audio and the analysis unit incorporates it into the audio guide. For example, the camera unit detects specific sounds (such as car horns) and notifies the user as a warning. For example, the camera unit collects ambient conversations, extracts important information, and provides it to the user. This allows the camera unit to incorporate ambient audio information into its analysis. Some or all of the above processing in the camera unit may be performed using AI, for example, or without AI. For example, the camera unit can input ambient audio data into the AI, which can then analyze the data and generate the audio guide.

[0042] The analysis unit has the function of prioritizing the analysis of specific characters or signs. For example, the analysis unit prioritizes the analysis of station signs and bus stop signs and reads them aloud. For example, the analysis unit prioritizes the analysis of important warning signs and notifies the user. For example, the analysis unit prioritizes the analysis of signs and notices related to the destination and provides guidance information. In this way, the analysis unit can prioritize the provision of important information to visually impaired people. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data of specific characters or signs into AI, and the AI ​​can analyze the data and process it preferentially.

[0043] The analysis unit has the function of improving analysis accuracy by integrating multiple data sources. For example, the analysis unit integrates camera video data and GPS data to provide accurate location information. For example, the analysis unit integrates audio data and video data to analyze the surrounding situation in detail. For example, the analysis unit integrates the user's past travel history and real-time data to suggest the optimal route. In this way, the analysis unit can improve analysis accuracy by integrating multiple data sources. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input multiple data sources into AI, and the AI ​​can integrate the data to improve analysis accuracy.

[0044] The analysis unit has a function to improve analysis accuracy by referring to the user's past travel history. For example, the analysis unit proposes the optimal route based on routes the user has used in the past. For example, the analysis unit proposes a route that avoids congestion based on the user's past travel history. For example, the analysis unit analyzes the user's past travel history and proposes the most efficient route. In this way, the analysis unit can improve analysis accuracy by referring to past travel history. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's past travel history data into AI, and the AI ​​can analyze the data and propose the optimal route.

[0045] The analysis unit has the function of analyzing weather information in real time and providing it to the user. For example, the analysis unit proposes the optimal route based on real-time weather information. For example, in rainy weather, the analysis unit will prioritize suggesting routes with roofs or underpasses. For example, in sunny weather, the analysis unit will propose routes with good scenery. In this way, the analysis unit can provide real-time weather information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input real-time weather data into AI, and the AI ​​can analyze the data and propose the optimal route.

[0046] The guide unit has a function to adjust the timing of the audio guide according to the user's movement speed. For example, if the user is walking slowly, the guide unit will delay the timing of the audio guide. For example, if the user is walking fast, the guide unit will speed up the timing of the audio guide. For example, if the user is standing still, the guide unit will pause the audio guide and resume it when the user starts moving again. In this way, the guide unit can provide optimal audio guidance according to the user's movement speed. Some or all of the above processing in the guide unit may be performed using AI, for example, or without AI. For example, the guide unit can input the user's movement speed data into the AI, which can analyze the data and adjust the timing of the audio guide.

[0047] The guide unit has the function of providing audio guidance based on specific landmarks. For example, the guide unit provides information about a specific landmark when the user approaches that landmark. For example, the guide unit instructs the user on the next action when the user passes a landmark. For example, the guide unit provides guidance to find the landmark again if the user loses sight of it. In this way, the guide unit can provide audio guidance based on landmarks. Some or all of the above processing in the guide unit may be performed using AI, for example, or without AI. For example, the guide unit can input landmark data into AI, and the AI ​​can analyze the data to provide audio guidance.

[0048] The guide unit has a function to provide multilingual audio guides according to the user's language settings. For example, the guide unit automatically sets the language of the audio guide based on the language settings of the user's device. For example, the guide unit provides a language switching function when the user uses multiple languages. For example, if the user selects a specific language, the guide unit provides the audio guide in that language. In this way, the guide unit can provide multilingual audio guides. Some or all of the above processing in the guide unit may be performed using AI, for example, or without AI. For example, the guide unit can input the user's language setting data into the AI, and the AI ​​can analyze the data to set the language of the audio guide.

[0049] The guide unit has a function to adjust the level of detail of the audio guide according to the user's preferences. For example, if the user is seeking detailed information, the guide unit will provide a detailed audio guide. For example, if the user is seeking concise information, the guide unit will provide a concise audio guide. For example, if the user is seeking specific information, the guide unit will provide an audio guide tailored to that information. In this way, the guide unit can provide the optimal audio guide according to the user's preferences. Some or all of the above processing in the guide unit may be performed using AI, for example, or without AI. For example, the guide unit can input user preference data into the AI, and the AI ​​can analyze the data to adjust the level of detail of the audio guide.

[0050] The GPS unit has a function to suggest the optimal route by referring to the user's travel history. For example, the GPS unit suggests the optimal route based on routes the user has used in the past. For example, the GPS unit suggests a route that avoids congestion based on the user's past travel history. For example, the GPS unit analyzes the user's past travel history and suggests the most efficient route. In this way, the GPS unit can provide the optimal route based on the user's travel history. Some or all of the above processing in the GPS unit may be performed using AI, for example, or without AI. For example, the GPS unit can input the user's travel history data into AI, and the AI ​​can analyze the data and suggest the optimal route.

[0051] The GPS unit has the function of analyzing traffic information in real time and providing it to the user. For example, the GPS unit proposes the optimal route based on real-time traffic congestion information. For example, the GPS unit proposes the optimal route considering the real-time operation status of public transportation. For example, the GPS unit proposes an alternative route based on real-time road construction information. In this way, the GPS unit can provide real-time traffic information. Some or all of the above processing in the GPS unit may be performed using AI, for example, or without AI. For example, the GPS unit can input real-time traffic data into AI, and the AI ​​can analyze the data and propose the optimal route.

[0052] The GPS unit has the function of providing information on nearby facilities based on the user's geographical location. For example, the GPS unit can provide information on restaurants and cafes near the user's current location. For example, the GPS unit can provide information on public facilities (toilets, bus stops, etc.) near the user's current location. For example, the GPS unit can provide information on tourist spots near the user's current location. In this way, the GPS unit can provide information on nearby facilities based on the user's geographical location. Some or all of the above processing in the GPS unit may be performed using AI, for example, or without AI. For example, the GPS unit can input the user's geographical location data into AI, and the AI ​​can analyze the data to provide information on nearby facilities.

[0053] The GPS unit has the function of learning the user's movement patterns and suggesting the optimal route for the next trip. For example, the GPS unit suggests the optimal route based on routes the user has used in the past. For example, the GPS unit suggests a route that avoids congestion based on the user's past movement patterns. For example, the GPS unit analyzes the user's past movement patterns and suggests the most efficient route. In this way, the GPS unit can provide the optimal route based on the user's movement patterns. Some or all of the above processing in the GPS unit may be performed using AI, for example, or without AI. For example, the GPS unit can input the user's movement pattern data into AI, and the AI ​​can analyze the data and suggest the optimal route.

[0054] The stylish design features adjustable functionality to fit the user's face shape. For example, the stylish design scans the user's face shape to provide an optimal fit. The stylish design includes adjustable pads and straps that adjust to the user's face. The stylish design provides a customized design based on the user's face shape. This allows the stylish design to provide an optimal design that fits the user's face shape. Some or all of the above processes in the stylish design may be performed using AI, or not. For example, the stylish design can input the user's face shape data into AI, which can then analyze the data and adjust the fit.

[0055] Stylish design features customizable functionality to match the user's fashion style. For example, stylish design can customize the design to match the user's chosen fashion style. For example, stylish design can provide customization features that allow the user to select specific colors or patterns. For example, stylish design can provide features that allow the user to change the design according to the season or event. In this way, stylish design can provide the optimal design to match the user's fashion style. Some or all of the above processes in stylish design may be performed using AI, for example, or not using AI. For example, stylish design can input the user's fashion style data into AI, which can then analyze the data and customize the design.

[0056] The voice guide customization function includes the ability to provide optimal guide content by referring to the user's past usage history. For example, the voice guide customization function provides optimal guide content based on the guide content the user has used in the past. For example, the voice guide customization function suggests preferred guide content based on the user's past usage history. For example, the voice guide customization function analyzes the user's past usage history and provides the most efficient guide content. In this way, the voice guide customization function can provide optimal guide content based on the user's past usage history. Some or all of the above processing in the voice guide customization function may be performed using AI, for example, or without AI. For example, the voice guide customization function can input the user's past usage history data into AI, and the AI ​​can analyze the data to provide optimal guide content.

[0057] The voice guide customization feature includes the ability to change the gender and voice quality of the voice according to the user's preferences. For example, if the user prefers a male voice, the voice guide customization feature will provide a male voice guide. For example, if the user prefers a female voice, the voice guide customization feature will provide a female voice guide. For example, if the user prefers a specific voice quality, the voice guide customization feature will provide a voice guide tailored to that voice quality. In this way, the voice guide customization feature can provide the optimal voice guide according to the user's preferences. Some or all of the above processing in the voice guide customization feature may be performed using AI, for example, or without AI. For example, the voice guide customization feature can input user preference data into AI, and the AI ​​can analyze the data to change the gender and voice quality of the voice.

[0058] The warning unit has the function of providing the optimal warning method by referring to the user's past risk avoidance history. For example, the warning unit provides the optimal warning method based on risk information that the user has avoided in the past. For example, the warning unit suggests a preferred warning method from the user's past risk avoidance history. For example, the warning unit analyzes the user's past risk avoidance history and provides the most efficient warning method. In this way, the warning unit can provide the optimal warning method based on the user's past risk avoidance history. Some or all of the above processing in the warning unit may be performed using AI, for example, or without AI. For example, the warning unit can input the user's past risk avoidance history data into AI, and the AI ​​can analyze the data to provide the optimal warning method.

[0059] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0060] SenseGuard smart glasses can also be equipped with a function that detects the user's walking speed and adjusts the timing of voice guidance according to that speed. For example, if the user is walking quickly, the voice guidance will provide instructions earlier so that the user can take the next action more quickly. Conversely, if the user is walking slowly, the voice guidance will be delayed so that the user can easily understand the instructions. Furthermore, if the user stops, the voice guidance can be paused and resumed when the user starts walking again. This allows for the provision of optimal voice guidance tailored to the user's walking speed.

[0061] SenseGuard smart glasses can also be equipped with the ability to simultaneously collect ambient audio information and transmit it to the analysis unit. For example, they can collect ambient audio and incorporate it into voice guidance in the analysis unit. They can also detect specific sounds (such as car horns) and notify the user as a warning. Furthermore, they can collect ambient conversations, extract important information, and provide it to the user. This allows ambient audio information to be incorporated into the analysis, providing the user with more information.

[0062] SenseGuard smart glasses can also be equipped with a function that learns the user's travel patterns and suggests the optimal route for the next trip. For example, it can suggest the best route based on routes the user has used in the past. It can also suggest routes that avoid congestion based on the user's past travel patterns. Furthermore, it can analyze the user's past travel patterns and suggest the most efficient route. In this way, it can provide the optimal route based on the user's travel patterns.

[0063] SenseGuard smart glasses can also feature adjustable capabilities to fit the user's face shape. For example, they can scan the user's face shape to provide an optimal fit. They also feature adjustable pads and straps to fit the user's face. Furthermore, they can offer a customized design based on the user's face shape, providing an optimal design that fits the user's face shape.

[0064] SenseGuard smart glasses can also be equipped with a function to provide information about nearby facilities based on the user's geographical location. For example, they can provide information about restaurants and cafes near the user's current location. They can also provide information about public facilities (restrooms, bus stops, etc.) near the user's current location. Furthermore, they can provide information about tourist attractions near the user's current location. In this way, they can provide information about nearby facilities based on the user's geographical location.

[0065] The following briefly describes the processing flow for example form 1.

[0066] Step 1: The camera unit recognizes the surrounding environment. For example, the camera unit uses its built-in camera to photograph the surrounding environment and collect data to recognize text, signs, obstacles, etc. The camera unit collects information such as station signs and bus stop information boards. Step 2: The analysis unit analyzes the data collected by the camera unit. For example, the analysis unit uses AI to analyze the collected data and generate data for reading aloud the content of text and signs. The analysis unit analyzes the content of station signs and bus stop information boards and generates data for reading aloud. Step 3: The guide unit provides voice guidance based on the data analyzed by the analysis unit. For example, the guide unit provides directions to the user using voice guidance. When the user sets a destination, the AI ​​calculates the optimal route and provides directions via voice guidance. The guide unit provides specific instructions via voice, such as "Turn right at the next intersection." Step 4: The GPS unit determines the user's current location. The GPS unit, for example, uses GPS functionality to determine the user's current location. Once the user sets a destination, the AI ​​calculates the optimal route and provides data for voice guidance.

[0067] (Example of form 2) The SenseGuard smart glasses according to an embodiment of the present invention are smart glasses that provide visually impaired individuals with the ability to read aloud text and signs, provide directions, and offer hazard information. The SenseGuard smart glasses recognize the surrounding environment and support safe movement by providing real-time voice guidance and warnings. They also feature a stylish and user-friendly design and offer a comfortable fit. First, the SenseGuard smart glasses use a built-in camera to capture images of the surrounding environment. This camera collects data for recognizing text, signs, obstacles, etc. Next, the collected data is analyzed by AI, and the content of text and signs is read aloud. For example, information such as station signs and bus stop signs is provided by voice, allowing visually impaired individuals to obtain the information needed to reach their destination. Furthermore, the SenseGuard smart glasses are equipped with GPS functionality to determine the user's current location. Once the user sets a destination, the AI ​​calculates the optimal route and provides voice guidance. For example, specific instructions such as "Turn right at the next intersection" are provided by voice, allowing visually impaired individuals to reach their destination without getting lost. The SenseGuard smart glasses also provide real-time information on surrounding hazards. For example, if an obstacle is detected ahead or a vehicle is approaching, a voice warning is issued. This allows visually impaired individuals to move safely. SenseGuard smart glasses feature a stylish and user-friendly design, making them comfortable even during prolonged wear. They are lightweight yet highly durable, making them suitable for everyday use. Furthermore, the volume and content of voice guidance and warnings can be customized to the user's preferences. SenseGuard smart glasses are an innovative device designed to enhance the independence and support safe mobility for visually impaired individuals. They overcome the limitations imposed by a lack of visual information, enabling a freer and safer life. SenseGuard smart glasses can provide visually impaired individuals with features such as reading text and signs aloud, providing directions, and offering hazard information.

[0068] The SenseGuard smart glasses according to this embodiment include a camera unit, an analysis unit, a guide unit, and a GPS unit. The camera unit recognizes the surrounding environment. The camera unit, for example, uses a built-in camera to photograph the surrounding environment. The camera unit collects data for recognizing text, signs, obstacles, etc. The camera unit collects information such as station signs and bus stop signs. The analysis unit analyzes the data collected by the camera unit. The analysis unit analyzes the collected data using AI, for example. The analysis unit analyzes the content of text and signs and generates data for reading aloud. The analysis unit analyzes the content of station signs and bus stop signs, for example, and generates data for reading aloud. The guide unit provides voice guidance based on the data analyzed by the analysis unit. The guide unit provides directions to the user using voice guidance, for example. When the user sets a destination, the AI ​​calculates the optimal route and provides directions using voice guidance. The guide unit provides specific instructions via voice, such as, "Turn right at the next intersection." The GPS unit determines the user's current location. The GPS unit uses GPS functionality to determine the user's current location. When the user sets a destination, the GPS unit provides data for the AI ​​to calculate the optimal route and provide voice guidance. As a result, the SenseGuard smart glasses according to this embodiment can provide visually impaired individuals with the ability to read text and signs aloud, provide directions, and provide hazard information.

[0069] The camera unit recognizes the surrounding environment. For example, the camera unit uses a built-in camera to capture images of the surrounding environment. Specifically, the camera unit is equipped with a high-resolution camera and a wide-angle lens to cover a wide field of view. The camera unit collects data to recognize text, signs, obstacles, etc. For example, when collecting information such as station signs or bus stop signs, the camera unit automatically focuses and optimizes image clarity. In addition, the camera unit is equipped with an infrared sensor and night vision function to perform well even in low-light environments. This allows for accurate information collection even at night or in dark places. Furthermore, the camera unit has a tracking function to track moving objects and provides stable images even when the user is moving. As a result, the camera unit can accurately recognize the surrounding environment and collect necessary information for people with visual impairments.

[0070] The analysis unit analyzes the data collected by the camera unit. For example, the analysis unit uses AI to analyze the collected data. Specifically, the analysis unit uses image recognition technology to analyze the content of text and signs and generates data for reading aloud. For example, when analyzing the content of station signs or bus stop information boards and generating data for reading aloud, it uses OCR (optical character recognition) technology to convert the text into digital data. Furthermore, the analysis unit uses natural language processing (NLP) technology to convert the text information into audio data. The analysis unit uses an AI model to extract important information from the collected data and provide useful information to the user. For example, the analysis unit identifies the departure time and destination of the next train from a station sign and generates data to read aloud. The analysis unit also identifies the location and type of obstacles and generates data to alert the user. This allows the analysis unit to quickly and accurately provide visually impaired individuals with important information about their surroundings.

[0071] The guide unit provides voice guidance based on data analyzed by the analysis unit. For example, the guide unit provides directions to the user using voice guidance. Specifically, when the user sets a destination, the AI ​​calculates the optimal route and provides directions via voice guidance. The guide unit provides specific instructions via voice, such as, "Turn right at the next intersection." The guide unit uses speech synthesis technology to convey information to the user in a natural voice. Furthermore, the guide unit calculates the optimal route based on real-time updated map information and provides the user with the latest information. The guide unit can flexibly change the route considering the user's current location and surrounding conditions. For example, if a road is under construction and impassable, or if there are unexpected obstacles, the guide unit immediately calculates a new route and guides the user. In addition, the guide unit can learn the user's walking speed and movement patterns to provide optimal guidance to each individual user. As a result, the guide unit can support visually impaired individuals so that they can move safely and provide directions to their destination.

[0072] The GPS unit determines the user's current location. For example, the GPS unit uses GPS functionality to determine the user's current location. Specifically, the GPS unit is equipped with a high-precision GPS module and acquires the user's location information in real time. When the user sets a destination, the GPS unit's AI calculates the optimal route and provides data for voice guidance. The GPS unit receives signals from satellites and accurately identifies the user's current location. Furthermore, by using auxiliary location information technologies in conjunction with the GPS unit, it can provide accurate location information even in places where GPS signals are weak, such as indoors or between tall buildings. The GPS unit records the user's movement history and can optimize routes based on past data and automatically recognize frequently visited places. As a result, the GPS unit can provide a safe environment for visually impaired individuals by helping them understand their current location and providing the optimal route to their destination. In addition, the GPS unit has a function to quickly identify the user's current location in emergencies and notify rescue teams and family members. As a result, the GPS unit can ensure the user's safety and support a rapid response.

[0073] The camera unit recognizes the surrounding environment. For example, the camera unit uses its built-in camera to photograph the surrounding environment. The camera unit collects data to recognize text, signs, obstacles, etc. For example, the camera unit collects information such as station signs and bus stop signs. The camera unit uses AI to analyze the collected data. For example, the camera unit uses AI to analyze the collected data and generate data for reading aloud the contents of text and signs. This allows the camera unit to provide visually impaired individuals with the ability to read aloud text and signs, provide directions, and provide hazard information. Some or all of the above processing in the camera unit may be performed using AI, for example, or without AI. For example, the camera unit can use its built-in camera to photograph the surrounding environment, input the collected data into AI, and the AI ​​can analyze the data to generate an audio guide.

[0074] The analysis unit analyzes the data collected by the camera unit. The analysis unit analyzes the collected data using, for example, AI. The analysis unit analyzes the content of text and signs and generates data for reading aloud. The analysis unit analyzes the content of, for example, station signs and bus stop signs and generates data for reading aloud. The analysis unit analyzes the collected data using AI. The analysis unit analyzes the collected data using, for example, AI, and generates data for reading aloud the content of text and signs. This allows the analysis unit to provide visually impaired individuals with the reading of text and signs, directions, and information about potential hazards. Some or all of the above-described processes in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input data collected by the camera unit into AI, which can then analyze the data and generate an audio guide.

[0075] The guide unit provides voice guidance based on data analyzed by the analysis unit. The guide unit provides directions to the user using voice guidance, for example. When the user sets a destination, the AI ​​calculates the optimal route and provides directions using voice guidance. The guide unit provides specific instructions by voice, for example, "Turn right at the next intersection." The guide unit provides voice guidance based on data analyzed using AI. The guide unit generates voice guidance based on data analyzed by AI and provides it to the user. This allows the guide unit to provide visually impaired individuals with the ability to read text and signs, provide directions, and provide hazard information. Some or all of the above processing in the guide unit may be performed using AI, for example, or without AI. For example, the guide unit can input data analyzed by the analysis unit into AI, and the AI ​​can generate voice guidance based on the data.

[0076] The GPS unit determines the user's current location. The GPS unit determines the user's current location using, for example, GPS functionality. When the user sets a destination, the GPS unit provides data for the AI ​​to calculate the optimal route and provide voice guidance. The GPS unit determines the user's current location using AI. The GPS unit determines the user's current location by, for example, having the AI ​​analyze GPS data. This allows the GPS unit to provide visually impaired individuals with services such as reading text and signs aloud, providing directions, and providing hazard information. Some or all of the above-described processes in the GPS unit may be performed using, for example, AI, or not using AI. For example, the GPS unit can input GPS data into the AI, which can then analyze the data to determine the user's current location.

[0077] The camera unit estimates the user's emotions and adjusts the camera's shooting angle based on the estimated emotions. For example, if the user is tense, the camera automatically widens its field of view to acquire more information about the surroundings. For example, if the user is relaxed, the camera focuses on a specific object to acquire detailed information. For example, if the user is in a hurry, the camera operates quickly to quickly acquire the necessary information. This allows the camera unit to provide the optimal shooting angle according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the camera unit may be performed using AI, for example, or not using AI. For example, the camera unit can input user emotion data into AI, and the AI ​​can analyze the data and adjust the camera's shooting angle.

[0078] The camera unit has a function to automatically adjust the exposure according to the ambient brightness. For example, in bright places, the camera unit automatically lowers the exposure to suppress excessive light. For example, in dark places, the camera unit automatically increases the exposure to improve visibility. For example, in environments with fluctuating brightness, the camera unit adjusts the exposure in real time to provide the optimal image. In this way, the camera unit can provide the optimal exposure according to the ambient brightness. Some or all of the above processing in the camera unit may be performed using AI, for example, or without AI. For example, the camera unit can input ambient brightness data into the AI, and the AI ​​can analyze the data and adjust the exposure.

[0079] The camera unit has the function of highlighting specific objects or characters when taking pictures. For example, the camera unit can highlight signs and notices when taking pictures to provide important information to visually impaired people. For example, the camera unit can highlight obstacles when taking pictures to draw the user's attention. For example, the camera unit can highlight text information when taking pictures and convey it accurately with a text-to-speech function. In this way, the camera unit can provide important information to visually impaired people. Some or all of the above processing in the camera unit may be performed using AI, for example, or without AI. For example, the camera unit can input data on specific objects or characters into AI, and the AI ​​can analyze the data and highlight them.

[0080] The camera unit estimates the user's emotions and adjusts the camera's shooting frequency based on the estimated emotions. For example, if the user is nervous, the camera will shoot frequently to provide detailed information about the surroundings. If the user is relaxed, the camera will reduce the shooting frequency to provide only the necessary information. If the user is in a hurry, the camera will shoot rapidly in a continuous stream to provide information in real time. This allows the camera unit to provide an optimal shooting frequency according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the camera unit may be performed using AI or not using AI. For example, the camera unit can input user emotion data into an AI, which can analyze the data and adjust the shooting frequency.

[0081] The camera unit has a function to automatically adjust its field of view in response to the user's head movements. For example, when the user moves their head, the camera widens its field of view in that direction and acquires information about the surroundings. For example, when the user is looking in a specific direction, the camera focuses in that direction and provides detailed information. For example, when the user moves their head up and down, the camera adjusts its vertical field of view and acquires the necessary information. In this way, the camera unit can provide an optimal field of view in response to the user's head movements. Some or all of the above processing in the camera unit may be performed using AI, for example, or without AI. For example, the camera unit can input the user's head movement data into the AI, which can analyze the data and adjust the field of view.

[0082] The camera unit has the function of simultaneously collecting ambient audio information and transmitting it to the analysis unit. For example, the camera unit collects ambient audio and the analysis unit incorporates it into the audio guide. For example, the camera unit detects specific sounds (such as car horns) and notifies the user as a warning. For example, the camera unit collects ambient conversations, extracts important information, and provides it to the user. This allows the camera unit to incorporate ambient audio information into its analysis. Some or all of the above processing in the camera unit may be performed using AI, for example, or without AI. For example, the camera unit can input ambient audio data into the AI, which can then analyze the data and generate the audio guide.

[0083] The analysis unit estimates the user's emotions and adjusts the priority of the analysis results based on the estimated emotions. For example, if the user is tense, the analysis unit prioritizes analyzing and quickly provides important information. For example, if the user is relaxed, the analysis unit analyzes and provides detailed information. For example, if the user is in a hurry, the analysis unit prioritizes analyzing and providing only the essential information. This allows the analysis unit to provide optimal analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into an AI, which can analyze the data and adjust the priority of the analysis results.

[0084] The analysis unit has the function of prioritizing the analysis of specific characters or signs. For example, the analysis unit prioritizes the analysis of station signs and bus stop signs and reads them aloud. For example, the analysis unit prioritizes the analysis of important warning signs and notifies the user. For example, the analysis unit prioritizes the analysis of signs and notices related to the destination and provides guidance information. In this way, the analysis unit can prioritize the provision of important information to visually impaired people. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data of specific characters or signs into AI, and the AI ​​can analyze the data and process it preferentially.

[0085] The analysis unit has the function of improving analysis accuracy by integrating multiple data sources. For example, the analysis unit integrates camera video data and GPS data to provide accurate location information. For example, the analysis unit integrates audio data and video data to analyze the surrounding situation in detail. For example, the analysis unit integrates the user's past travel history and real-time data to suggest the optimal route. In this way, the analysis unit can improve analysis accuracy by integrating multiple data sources. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input multiple data sources into AI, and the AI ​​can integrate the data to improve analysis accuracy.

[0086] The analysis unit estimates the user's emotions and adjusts the display method of the analysis results based on the estimated user emotions. For example, if the user is tense, the analysis unit provides a simple and highly visible display method. For example, if the user is relaxed, the analysis unit provides a display method that includes detailed information. For example, if the user is in a hurry, the analysis unit provides a display method that gets straight to the point. In this way, the analysis unit can provide the optimal display method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into an AI, and the AI ​​can analyze the data and adjust the display method.

[0087] The analysis unit has a function to improve analysis accuracy by referring to the user's past travel history. For example, the analysis unit proposes the optimal route based on routes the user has used in the past. For example, the analysis unit proposes a route that avoids congestion based on the user's past travel history. For example, the analysis unit analyzes the user's past travel history and proposes the most efficient route. In this way, the analysis unit can improve analysis accuracy by referring to past travel history. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's past travel history data into AI, and the AI ​​can analyze the data and propose the optimal route.

[0088] The analysis unit has the function of analyzing weather information in real time and providing it to the user. For example, the analysis unit proposes the optimal route based on real-time weather information. For example, in rainy weather, the analysis unit will prioritize suggesting routes with roofs or underpasses. For example, in sunny weather, the analysis unit will propose routes with good scenery. In this way, the analysis unit can provide real-time weather information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input real-time weather data into AI, and the AI ​​can analyze the data and propose the optimal route.

[0089] The guide unit estimates the user's emotions and adjusts the tone of the voice guide based on the estimated emotions. For example, if the user is tense, the guide unit provides a calm voice guide. For example, if the user is relaxed, the guide unit provides a bright voice guide. For example, if the user is in a hurry, the guide unit provides a quick and concise voice guide. This allows the guide unit to provide the optimal voice guide according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the guide unit may be performed using AI or not using AI. For example, the guide unit can input user emotion data into an AI, which can analyze the data and adjust the tone of the voice guide.

[0090] The guide unit has a function to adjust the timing of the audio guide according to the user's movement speed. For example, if the user is walking slowly, the guide unit will delay the timing of the audio guide. For example, if the user is walking fast, the guide unit will speed up the timing of the audio guide. For example, if the user is standing still, the guide unit will pause the audio guide and resume it when the user starts moving again. In this way, the guide unit can provide optimal audio guidance according to the user's movement speed. Some or all of the above processing in the guide unit may be performed using AI, for example, or without AI. For example, the guide unit can input the user's movement speed data into the AI, which can analyze the data and adjust the timing of the audio guide.

[0091] The guide unit has the function of providing audio guidance based on specific landmarks. For example, the guide unit provides information about a specific landmark when the user approaches that landmark. For example, the guide unit instructs the user on the next action when the user passes a landmark. For example, the guide unit provides guidance to find the landmark again if the user loses sight of it. In this way, the guide unit can provide audio guidance based on landmarks. Some or all of the above processing in the guide unit may be performed using AI, for example, or without AI. For example, the guide unit can input landmark data into AI, and the AI ​​can analyze the data to provide audio guidance.

[0092] The guide unit estimates the user's emotions and adjusts the content of the audio guide based on the estimated emotions. For example, if the user is nervous, the guide unit provides concise and to-the-point content. For example, if the user is relaxed, the guide unit provides content that includes detailed information. For example, if the user is in a hurry, the guide unit provides quick and concise content. This allows the guide unit to provide optimal audio guide content that matches the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the guide unit may be performed using AI, for example, or without AI. For example, the guide unit can input user emotion data into AI, and the AI ​​can analyze the data and adjust the content of the audio guide.

[0093] The guide unit has a function to provide multilingual audio guides according to the user's language settings. For example, the guide unit automatically sets the language of the audio guide based on the language settings of the user's device. For example, the guide unit provides a language switching function when the user uses multiple languages. For example, if the user selects a specific language, the guide unit provides the audio guide in that language. In this way, the guide unit can provide multilingual audio guides. Some or all of the above processing in the guide unit may be performed using AI, for example, or without AI. For example, the guide unit can input the user's language setting data into the AI, and the AI ​​can analyze the data to set the language of the audio guide.

[0094] The guide unit has a function to adjust the level of detail of the audio guide according to the user's preferences. For example, if the user is seeking detailed information, the guide unit will provide a detailed audio guide. For example, if the user is seeking concise information, the guide unit will provide a concise audio guide. For example, if the user is seeking specific information, the guide unit will provide an audio guide tailored to that information. In this way, the guide unit can provide the optimal audio guide according to the user's preferences. Some or all of the above processing in the guide unit may be performed using AI, for example, or without AI. For example, the guide unit can input user preference data into the AI, and the AI ​​can analyze the data to adjust the level of detail of the audio guide.

[0095] The GPS unit estimates the user's emotions and adjusts the GPS update frequency based on the estimated emotions. For example, if the user is stressed, the GPS unit increases the GPS update frequency to provide accurate location information. For example, if the user is relaxed, the GPS unit decreases the GPS update frequency to conserve battery power. For example, if the user is in a hurry, the GPS unit increases the GPS update frequency to provide rapid location information. In this way, the GPS unit can provide an optimal GPS update frequency according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the GPS unit may be performed using AI or not using AI. For example, the GPS unit can input user emotion data into an AI, which can analyze the data and adjust the GPS update frequency.

[0096] The GPS unit has a function to suggest the optimal route by referring to the user's travel history. For example, the GPS unit suggests the optimal route based on routes the user has used in the past. For example, the GPS unit suggests a route that avoids congestion based on the user's past travel history. For example, the GPS unit analyzes the user's past travel history and suggests the most efficient route. In this way, the GPS unit can provide the optimal route based on the user's travel history. Some or all of the above processing in the GPS unit may be performed using AI, for example, or without AI. For example, the GPS unit can input the user's travel history data into AI, and the AI ​​can analyze the data and suggest the optimal route.

[0097] The GPS unit has the function of analyzing traffic information in real time and providing it to the user. For example, the GPS unit proposes the optimal route based on real-time traffic congestion information. For example, the GPS unit proposes the optimal route considering the real-time operation status of public transportation. For example, the GPS unit proposes an alternative route based on real-time road construction information. In this way, the GPS unit can provide real-time traffic information. Some or all of the above processing in the GPS unit may be performed using AI, for example, or without AI. For example, the GPS unit can input real-time traffic data into AI, and the AI ​​can analyze the data and propose the optimal route.

[0098] The GPS unit estimates the user's emotions and adjusts the GPS accuracy based on the estimated emotions. For example, if the user is stressed, the GPS unit increases GPS accuracy to provide precise location information. For example, if the user is relaxed, the GPS unit decreases GPS accuracy to conserve battery power. For example, if the user is in a hurry, the GPS unit increases GPS accuracy to provide rapid location information. In this way, the GPS unit can provide optimal GPS accuracy according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the GPS unit may be performed using AI or not using AI. For example, the GPS unit can input user emotion data into AI, and the AI ​​can analyze the data to adjust the GPS accuracy.

[0099] The GPS unit has the function of providing information on nearby facilities based on the user's geographical location. For example, the GPS unit can provide information on restaurants and cafes near the user's current location. For example, the GPS unit can provide information on public facilities (toilets, bus stops, etc.) near the user's current location. For example, the GPS unit can provide information on tourist spots near the user's current location. In this way, the GPS unit can provide information on nearby facilities based on the user's geographical location. Some or all of the above processing in the GPS unit may be performed using AI, for example, or without AI. For example, the GPS unit can input the user's geographical location data into AI, and the AI ​​can analyze the data to provide information on nearby facilities.

[0100] The GPS unit has the function of learning the user's movement patterns and suggesting the optimal route for the next trip. For example, the GPS unit suggests the optimal route based on routes the user has used in the past. For example, the GPS unit suggests a route that avoids congestion based on the user's past movement patterns. For example, the GPS unit analyzes the user's past movement patterns and suggests the most efficient route. In this way, the GPS unit can provide the optimal route based on the user's movement patterns. Some or all of the above processing in the GPS unit may be performed using AI, for example, or without AI. For example, the GPS unit can input the user's movement pattern data into AI, and the AI ​​can analyze the data and suggest the optimal route.

[0101] Stylish design estimates the user's emotions and adjusts the design's color scheme based on those emotions. For example, if the user is tense, stylish design provides a calm color scheme. If the user is relaxed, stylish design provides a bright color scheme. If the user is in a hurry, stylish design provides a highly visible color scheme. This allows stylish design to provide the optimal color scheme for the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in stylish design may be performed using AI or not. For example, stylish design can input user emotion data into AI, which can then analyze the data and adjust the design's color scheme.

[0102] The stylish design features adjustable functionality to fit the user's face shape. For example, the stylish design scans the user's face shape to provide an optimal fit. The stylish design includes adjustable pads and straps that adjust to the user's face. The stylish design provides a customized design based on the user's face shape. This allows the stylish design to provide an optimal design that fits the user's face shape. Some or all of the above processes in the stylish design may be performed using AI, or not. For example, the stylish design can input the user's face shape data into AI, which can then analyze the data and adjust the fit.

[0103] Stylish Design estimates the user's emotions and selects design materials based on those emotions. For example, if the user is tense, Stylish Design will select soft and comfortable materials. If the user is relaxed, Stylish Design will select lightweight and breathable materials. If the user is in a hurry, Stylish Design will select durable materials. This allows Stylish Design to provide optimal design materials tailored to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in Stylish Design may be performed using AI or not. For example, Stylish Design can input user emotion data into an AI, which can then analyze the data and select materials.

[0104] Stylish design features customizable functionality to match the user's fashion style. For example, stylish design can customize the design to match the user's chosen fashion style. For example, stylish design can provide customization features that allow the user to select specific colors or patterns. For example, stylish design can provide features that allow the user to change the design according to the season or event. In this way, stylish design can provide the optimal design to match the user's fashion style. Some or all of the above processes in stylish design may be performed using AI, for example, or not using AI. For example, stylish design can input the user's fashion style data into AI, which can then analyze the data and customize the design.

[0105] The voice guide customization feature estimates the user's emotions and adjusts the volume of the voice guide based on the estimated emotions. For example, if the user is nervous, the voice guide customization feature lowers the volume to provide a calm voice guide. For example, if the user is relaxed, the voice guide customization feature increases the volume to provide a clear voice guide. For example, if the user is in a hurry, the voice guide customization feature appropriately adjusts the volume to provide a fast voice guide. In this way, the voice guide customization feature can provide the optimal voice guide volume according to the user's emotions. Emotion estimation is achieved using an emotion estimation feature, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the voice guide customization feature may be performed using AI or not using AI. For example, the voice guide customization feature can input user emotion data into an AI, which can analyze the data and adjust the volume.

[0106] The voice guide customization function includes the ability to provide optimal guide content by referring to the user's past usage history. For example, the voice guide customization function provides optimal guide content based on the guide content the user has used in the past. For example, the voice guide customization function suggests preferred guide content based on the user's past usage history. For example, the voice guide customization function analyzes the user's past usage history and provides the most efficient guide content. In this way, the voice guide customization function can provide optimal guide content based on the user's past usage history. Some or all of the above processing in the voice guide customization function may be performed using AI, for example, or without AI. For example, the voice guide customization function can input the user's past usage history data into AI, and the AI ​​can analyze the data to provide optimal guide content.

[0107] The audio guide customization feature estimates the user's emotions and selects the language of the audio guide based on the estimated emotions. For example, if the user is nervous, the audio guide customization feature provides an audio guide in the user's native language. For example, if the user is relaxed, the audio guide customization feature provides an audio guide in a language of their choice. For example, if the user is in a hurry, the audio guide customization feature provides an audio guide in a language that can be quickly understood. In this way, the audio guide customization feature can provide the optimal language of the audio guide according to the user's emotions. Emotion estimation is achieved using an emotion estimation feature, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the audio guide customization feature may be performed using AI or not using AI. For example, the audio guide customization feature can input user emotion data into an AI, which can then analyze the data and select a language.

[0108] The voice guide customization feature includes the ability to change the gender and voice quality of the voice according to the user's preferences. For example, if the user prefers a male voice, the voice guide customization feature will provide a male voice guide. For example, if the user prefers a female voice, the voice guide customization feature will provide a female voice guide. For example, if the user prefers a specific voice quality, the voice guide customization feature will provide a voice guide tailored to that voice quality. In this way, the voice guide customization feature can provide the optimal voice guide according to the user's preferences. Some or all of the above processing in the voice guide customization feature may be performed using AI, for example, or without AI. For example, the voice guide customization feature can input user preference data into AI, and the AI ​​can analyze the data to change the gender and voice quality of the voice.

[0109] The warning unit estimates the user's emotions and adjusts the intensity of the warning based on the estimated emotions. For example, if the user is tense, the warning unit lowers the intensity of the warning to provide a calm warning. For example, if the user is relaxed, the warning unit increases the intensity of the warning to provide a clear warning. For example, if the user is in a hurry, the warning unit appropriately adjusts the intensity of the warning to provide a rapid warning. In this way, the warning unit can provide the optimal warning intensity according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the warning unit may be performed using AI, for example, or without AI. For example, the warning unit can input user emotion data into an AI, which can analyze the data and adjust the intensity of the warning.

[0110] The warning unit has the function of providing the optimal warning method by referring to the user's past risk avoidance history. For example, the warning unit provides the optimal warning method based on risk information that the user has avoided in the past. For example, the warning unit suggests a preferred warning method from the user's past risk avoidance history. For example, the warning unit analyzes the user's past risk avoidance history and provides the most efficient warning method. In this way, the warning unit can provide the optimal warning method based on the user's past risk avoidance history. Some or all of the above processing in the warning unit may be performed using AI, for example, or without AI. For example, the warning unit can input the user's past risk avoidance history data into AI, and the AI ​​can analyze the data to provide the optimal warning method.

[0111] The warning unit estimates the user's emotions and adjusts the timing of the warning based on the estimated emotions. For example, if the user is tense, the warning unit will advance the timing of the warning to provide a quick warning. For example, if the user is relaxed, the warning unit will delay the timing of the warning to provide a calm warning. For example, if the user is in a hurry, the warning unit will appropriately adjust the timing of the warning to provide a quick warning. In this way, the warning unit can provide the optimal timing of the warning according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the warning unit may be performed using AI, for example, or not using AI. For example, the warning unit can input user emotion data into an AI, which can analyze the data and adjust the timing of the warning.

[0112] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0113] SenseGuard smart glasses can also be equipped with a function that detects the user's walking speed and adjusts the timing of voice guidance according to that speed. For example, if the user is walking quickly, the voice guidance will provide instructions earlier so that the user can take the next action more quickly. Conversely, if the user is walking slowly, the voice guidance will be delayed so that the user can easily understand the instructions. Furthermore, if the user stops, the voice guidance can be paused and resumed when the user starts walking again. This allows for the provision of optimal voice guidance tailored to the user's walking speed.

[0114] SenseGuard smart glasses can also be equipped with the ability to estimate the user's emotions and adjust the tone of the voice guidance based on those emotions. For example, if the user is tense, the voice guidance will be provided in a calm tone to reduce the user's anxiety. If the user is relaxed, the voice guidance will be provided in a bright tone to further improve the user's mood. Furthermore, if the user is in a hurry, the voice guidance will be provided in a quick and concise tone to enable the user to act efficiently. This allows for the provision of optimal voice guidance tailored to the user's emotions.

[0115] SenseGuard smart glasses can also be equipped with the ability to simultaneously collect ambient audio information and transmit it to the analysis unit. For example, they can collect ambient audio and incorporate it into voice guidance in the analysis unit. They can also detect specific sounds (such as car horns) and notify the user as a warning. Furthermore, they can collect ambient conversations, extract important information, and provide it to the user. This allows ambient audio information to be incorporated into the analysis, providing the user with more information.

[0116] SenseGuard smart glasses can also be equipped with a function that estimates the user's emotions and adjusts the camera's shooting angle based on those emotions. For example, if the user is tense, the camera will automatically widen its field of view to capture more information about the surroundings. If the user is relaxed, the camera will focus on a specific object to capture more detailed information. Furthermore, if the user is in a hurry, the camera can operate quickly to quickly acquire the necessary information. This allows for the provision of the optimal shooting angle according to the user's emotions.

[0117] SenseGuard smart glasses can also be equipped with a function that learns the user's travel patterns and suggests the optimal route for the next trip. For example, it can suggest the best route based on routes the user has used in the past. It can also suggest routes that avoid congestion based on the user's past travel patterns. Furthermore, it can analyze the user's past travel patterns and suggest the most efficient route. In this way, it can provide the optimal route based on the user's travel patterns.

[0118] SenseGuard smart glasses can also be equipped with a function to estimate the user's emotions and adjust the priority of analysis results based on those emotions. For example, if the user is stressed, important information will be prioritized and provided quickly. If the user is relaxed, detailed information will be analyzed and provided. Furthermore, if the user is in a hurry, only the essential information will be prioritized and provided. This allows for the provision of optimal analysis results tailored to the user's emotions.

[0119] SenseGuard smart glasses can also feature adjustable capabilities to fit the user's face shape. For example, they can scan the user's face shape to provide an optimal fit. They also feature adjustable pads and straps to fit the user's face. Furthermore, they can offer a customized design based on the user's face shape, providing an optimal design that fits the user's face shape.

[0120] SenseGuard smart glasses can also be equipped with the ability to estimate the user's emotions and adjust the content of the voice guidance based on those emotions. For example, if the user is nervous, it will provide concise and to-the-point content. If the user is relaxed, it will provide content that includes detailed information. Furthermore, if the user is in a hurry, it can provide quick and concise content. This allows for the provision of optimal voice guidance content tailored to the user's emotions.

[0121] SenseGuard smart glasses can also be equipped with a function to provide information about nearby facilities based on the user's geographical location. For example, they can provide information about restaurants and cafes near the user's current location. They can also provide information about public facilities (restrooms, bus stops, etc.) near the user's current location. Furthermore, they can provide information about tourist attractions near the user's current location. In this way, they can provide information about nearby facilities based on the user's geographical location.

[0122] SenseGuard smart glasses can also be equipped with the ability to estimate the user's emotions and adjust the intensity of warnings based on those emotions. For example, if the user is stressed, the warning intensity can be lowered to provide a calmer warning. Conversely, if the user is relaxed, the warning intensity can be increased to provide a clearer warning. Furthermore, if the user is in a hurry, the warning intensity can be appropriately adjusted to provide a quick warning. This allows for the provision of the optimal warning intensity according to the user's emotions.

[0123] The following briefly describes the processing flow for example form 2.

[0124] Step 1: The camera unit recognizes the surrounding environment. For example, the camera unit uses its built-in camera to photograph the surrounding environment and collect data to recognize text, signs, obstacles, etc. The camera unit collects information such as station signs and bus stop information boards. Step 2: The analysis unit analyzes the data collected by the camera unit. For example, the analysis unit uses AI to analyze the collected data and generate data for reading aloud the content of text and signs. The analysis unit analyzes the content of station signs and bus stop information boards and generates data for reading aloud. Step 3: The guide unit provides voice guidance based on the data analyzed by the analysis unit. For example, the guide unit provides directions to the user using voice guidance. When the user sets a destination, the AI ​​calculates the optimal route and provides directions via voice guidance. The guide unit provides specific instructions via voice, such as "Turn right at the next intersection." Step 4: The GPS unit determines the user's current location. The GPS unit, for example, uses GPS functionality to determine the user's current location. Once the user sets a destination, the AI ​​calculates the optimal route and provides data for voice guidance.

[0125] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0126] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0127] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0128] Each of the multiple elements described above, including the camera unit, analysis unit, guide unit, and GPS unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the camera unit is implemented by the camera 42 of the smart device 14 and captures images of the surrounding environment. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the collected data using AI. The guide unit is implemented by the control unit 46A of the smart device 14 and provides voice guidance. The GPS unit is implemented by the GPS function of the smart device 14 and determines the user's current location. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0129] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0130] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0131] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0132] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0133] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0135] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0136] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0137] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0138] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0139] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0140] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0141] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0142] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0143] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0144] Each of the multiple elements described above, including the camera unit, analysis unit, guide unit, and GPS unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the camera unit is implemented by the camera 42 of the smart glasses 214 and captures images of the surrounding environment. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the collected data using AI. The guide unit is implemented by the control unit 46A of the smart glasses 214 and provides voice guidance. The GPS unit is implemented by the GPS function of the smart glasses 214 and determines the user's current location. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0145] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0146] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0147] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0148] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0149] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0151] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0152] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0153] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0154] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0155] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0156] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0157] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0158] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0159] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0160] Each of the multiple elements described above, including the camera unit, analysis unit, guide unit, and GPS unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the camera unit is implemented by the camera 42 of the headset terminal 314 and captures images of the surrounding environment. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the collected data using AI. The guide unit is implemented by the control unit 46A of the headset terminal 314 and provides voice guidance. The GPS unit is implemented by the GPS function of the headset terminal 314 and determines the user's current location. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0161] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0162] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0163] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0164] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0165] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0166] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0167] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0168] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0169] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0170] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0171] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0172] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0173] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0174] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0175] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0176] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0177] Each of the multiple elements described above, including the camera unit, analysis unit, guide unit, and GPS unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the camera unit is implemented by the camera 42 of the robot 414 and captures images of the surrounding environment. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the collected data using AI. The guide unit is implemented by the control unit 46A of the robot 414 and provides voice guidance. The GPS unit is implemented by the GPS function of the robot 414 and determines the user's current location. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0178] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0179] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0180] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0181] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0182] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0183] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0184] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0185] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0186] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0187] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0188] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0189] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0190] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0191] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0192] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0193] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0194] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0195] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0196] (Note 1) A camera unit that recognizes the surrounding environment, An analysis unit that analyzes the data collected by the camera unit, A guide unit provides audio guidance based on the data analyzed by the aforementioned analysis unit, It includes a GPS unit that determines the user's current location. A system characterized by the following features. (Note 2) Features a stylish design The system described in Appendix 1, characterized by the features described herein. (Note 3) Features a customizable audio guide. The system described in Appendix 1, characterized by the features described herein. (Note 4) It is equipped with a warning unit that provides real-time information about surrounding dangers. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned camera unit is It estimates the user's emotions and adjusts the camera's shooting angle based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned camera unit is It features a function that automatically adjusts the exposure according to the ambient light. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned camera unit is It has a function to highlight specific objects or characters when taking photos. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned camera unit is It estimates the user's emotions and adjusts the camera's shooting frequency based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned camera unit is It features a function that automatically adjusts the field of view in response to the user's head movements. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned camera unit is It has a function to simultaneously collect ambient audio information and transmit it to the analysis unit. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, It estimates the user's emotions and adjusts the priority of the analysis results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, It has a function that prioritizes the analysis of specific characters or signs. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, It has the capability to integrate multiple data sources to improve analysis accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, It includes a feature that improves analysis accuracy by referring to the user's past movement history. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It has the functionality to analyze weather information in real time and provide it to users. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned guide portion is It estimates the user's emotions and adjusts the tone of the voice guide based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned guide portion is It features a function that adjusts the timing of the voice guidance according to the user's movement speed. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned guide portion is It features a function that provides audio guides based on specific landmarks. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned guide portion is The system estimates the user's emotions and adjusts the content of the voice guide based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned guide portion is It features a function that provides multilingual audio guidance according to the user's language settings. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned guide portion is It features a function to adjust the level of detail of the audio guide according to the user's preference. The system described in Appendix 1, characterized by the features described herein. (Note 23) The GPS unit is, It estimates the user's emotions and adjusts the GPS update frequency based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The GPS unit is, It features a function that suggests the optimal route by referring to the user's travel history. The system described in Appendix 1, characterized by the features described herein. (Note 25) The GPS unit is, It has the functionality to analyze traffic information in real time and provide it to users. The system described in Appendix 1, characterized by the features described herein. (Note 26) The GPS unit is, It estimates the user's emotions and adjusts the GPS accuracy based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The GPS unit is, It has a function that provides information about nearby facilities based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 28) The GPS unit is, It features a function that learns the user's travel patterns and suggests the optimal route for the next trip. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned stylish design It estimates the user's emotions and adjusts the design's color scheme based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 30) The aforementioned stylish design It features an adjustable function to fit the shape of the user's face. The system described in Appendix 2, characterized by the features described herein. (Note 31) The aforementioned stylish design The system estimates the user's emotions and selects design elements based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 32) The aforementioned stylish design Features that can be customized to match the user's fashion style. The system described in Appendix 2, characterized by the features described herein. (Note 33) The customization function for the aforementioned audio guide is It estimates the user's emotions and adjusts the volume of the voice guide based on those emotions. The system described in Appendix 3, characterized by the features described herein. (Note 34) The customization function for the aforementioned audio guide is It features a function that provides optimal guidance content by referring to the user's past usage history. The system described in Appendix 3, characterized by the features described herein. (Note 35) The customization function for the aforementioned audio guide is The system estimates the user's emotions and selects the language of the voice guide based on those emotions. The system described in Appendix 3, characterized by the features described herein. (Note 36) The customization function for the aforementioned audio guide is It features a function that allows users to change the gender and voice quality of the voice according to their preferences. The system described in Appendix 3, characterized by the features described herein. (Note 37) The aforementioned warning unit is It estimates the user's emotions and adjusts the intensity of the warning based on those emotions. The system described in Appendix 4, characterized by the features described herein. (Note 38) The aforementioned warning unit is It features a function that provides the most suitable warning method by referring to the user's past risk avoidance history. The system described in Appendix 4, characterized by the features described herein. (Note 39) The aforementioned warning unit is It estimates the user's emotions and adjusts the timing of warnings based on those emotions. The system described in Appendix 4, characterized by the features described herein. (Note 40) The aforementioned warning unit is It features a function that provides information on nearby hazards based on the user's geographical location. The system described in Appendix 4, characterized by the features described herein. [Explanation of symbols]

[0197] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A camera unit that recognizes the surrounding environment, An analysis unit that analyzes the data collected by the camera unit, A guide unit provides audio guidance based on the data analyzed by the aforementioned analysis unit, It includes a GPS unit that determines the user's current location. A system characterized by the following features.

2. Features a stylish design The system according to feature 1.

3. Features a customizable audio guide. The system according to feature 1.

4. It is equipped with a warning unit that provides real-time information about surrounding dangers. The system according to feature 1.

5. The aforementioned camera unit is It estimates the user's emotions and adjusts the camera's shooting angle based on those emotions. The system according to feature 1.

6. The aforementioned camera unit is It features a function that automatically adjusts the exposure according to the ambient light. The system according to feature 1.

7. The aforementioned camera unit is It has a function to highlight specific objects or characters when taking photos. The system according to feature 1.

8. The aforementioned camera unit is It estimates the user's emotions and adjusts the camera's shooting frequency based on the estimated emotions. The system according to feature 1.

Images