system

Abstract

The system according to this embodiment aims to provide real-time data utilizing visual recognition and to provide integrated functions such as navigation, online shopping, and voice conversation. [Solution] The system according to the embodiment comprises a field of view recognition unit, a data provision unit, a navigation unit, a shopping unit, and a voice conversation unit. The field of view recognition unit recognizes the field of view. The data provision unit provides data in real time based on the field of view recognized by the field of view recognition unit. The navigation unit performs navigation based on the data provided by the data provision unit. The shopping unit performs online shopping based on the data provided by the data provision unit. The voice conversation unit engages in voice conversation based on the data provided by the data provision unit.

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, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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, functions such as real-time data provision, navigation, online shopping, and voice conversation utilizing visual recognition have not been sufficiently provided integrally, and there is room for improvement.

[0005] The system according to the embodiment aims to provide data in real time by utilizing visual recognition and integrally provide functions such as navigation, online shopping, and voice conversation.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a field of view recognition unit, a data provision unit, a navigation unit, a shopping unit, and a voice conversation unit. The field of view recognition unit recognizes the field of view. The data provision unit provides data in real time based on the field of view recognized by the field of view recognition unit. The navigation unit performs navigation based on the data provided by the data provision unit. The shopping unit performs online shopping based on the data provided by the data provision unit. The voice conversation unit engages in voice conversation based on the data provided by the data provision unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide data in real time by utilizing visual recognition and can provide integrated functions such as navigation, online shopping, and voice conversation. [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 signed communication I / F (Interface) is an interface including a communication processor, an antenna, etc. The communication I / F manages communication between multiple 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 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The smart glasses system according to an embodiment of the present invention incorporates a generating AI into the smart glasses, realizing an AI agent that recognizes the user's field of vision and provides various data in real time. This smart glasses system allows for voice conversation with the AI ​​agent installed in the smart glasses and provides life assistance and services using a vast amount of up-to-date data. Examples include navigation, displaying the lowest prices for online shopping and in physical stores, purchasing, and displaying images after trying on clothes. For example, if a user inputs "I want to go from XX to XX," the generating AI creates a video showing the route from the current location to the destination and displays it on the smart glasses. Furthermore, the system can display the lowest price for a product the user is viewing through the smart glasses and handle the purchase process on their behalf. In addition, it provides a wide range of functions, including tourist guides and route guidance, suggestions for nearby recommended spots, symptom diagnosis, navigation to top surgeons, real-time translation, assistance for the visually impaired, cooking recipe suggestions, and alerts from the surrounding environment. As a result, the smart glasses system can support the user's life in many ways and improve convenience.

[0029] The smart glasses system according to this embodiment comprises a field of view recognition unit, a data provision unit, a navigation unit, a shopping unit, and a voice conversation unit. The field of view recognition unit recognizes the user's field of view. The field of view recognition unit captures the user's field of view using a camera, for example, and analyzes the field of view using a recognition algorithm. The field of view recognition unit can also improve the accuracy of field of view recognition using a generation AI. For example, the field of view recognition unit can input video data acquired by the camera into the generation AI and have the generation AI perform field of view recognition. The data provision unit provides data in real time based on the field of view recognized by the field of view recognition unit. The data provision unit provides, for example, information about objects the user is looking at. The data provision unit can also provide optimal data based on the user's field of view using a generation AI. For example, the data provision unit inputs field of view data acquired from the field of view recognition unit into the generation AI, and the generation AI provides the data. The navigation unit performs navigation based on the data provided by the data provision unit. The navigation unit calculates and displays, for example, a route from the user's current location to the destination. The navigation unit can also optimize the route in real time using a generation AI. For example, the navigation unit inputs data obtained from the data provision unit into a generating AI, which then calculates a route. The shopping unit performs online shopping based on the data provided by the data provision unit. The shopping unit, for example, displays the lowest price for the product the user is viewing and handles the purchase process. The shopping unit can also use the generating AI to improve the user's shopping experience. For example, the shopping unit inputs data obtained from the data provision unit into a generating AI, which displays the lowest price and handles the purchase process. The voice conversation unit engages in voice conversation based on the data provided by the data provision unit. The voice conversation unit, for example, interacts with the user via voice and provides necessary information. The voice conversation unit can also use the generating AI to achieve natural conversation. For example, the voice conversation unit inputs data obtained from the data provision unit into a generating AI, which then engages in voice conversation. As a result, the smart glasses system according to this embodiment enables vision recognition, data provision, navigation, online shopping, and voice conversation.

[0030] The field of view recognition unit recognizes the user's field of view. For example, the field of view recognition unit captures the user's field of view using a camera and analyzes the field of view using a recognition algorithm. The field of view recognition unit can also improve the accuracy of field of view recognition using generative AI. Specifically, the field of view recognition unit can input video data acquired by the camera into the generative AI and have the generative AI perform field of view recognition. The generative AI analyzes the video data using a deep learning model and recognizes objects and the environment the user is looking at with high accuracy. For example, if the user is walking in a city, the field of view recognition unit identifies buildings, signs, vehicles, pedestrians, etc. from the video captured by the camera and understands their respective positions and movements. Based on this information, the generative AI optimizes the information displayed in the user's field of view. Furthermore, by combining this with user eye-tracking technology, the field of view recognition unit can identify specific objects or areas that the user is focusing on and prioritize the analysis of that information. As a result, the field of view recognition unit can analyze the user's field of view in real time and quickly provide the necessary information. In addition, the field of view recognition unit can dynamically adjust the recognition algorithm in response to changes in the environment and maintain high recognition accuracy at all times. For example, the camera settings and recognition algorithms are automatically optimized in response to changes in day / night cycles and weather conditions. This enables the field of view recognition unit to achieve high-precision field of view recognition in any environment and provide the user with optimal information.

[0031] The data provision unit provides data in real time based on the field of view recognized by the field of view recognition unit. For example, the data provision unit provides information about objects that the user is looking at. Specifically, the data provision unit inputs the field of view data acquired from the field of view recognition unit into a generating AI, which then provides the data. The generating AI generates detailed information about the objects and places the user is looking at and overlays it onto the user's field of view. For example, if a user is looking at an exhibit in a museum, the data provision unit can display historical background and related information about that exhibit in real time. Also, if a user is looking at a restaurant menu, the data provision unit can provide detailed descriptions of each dish, calorie information, allergy information, etc. Furthermore, the data provision unit can select and provide the most relevant information based on the user's interests and preferences. For example, if a user is interested in a particular brand or product, the data provision unit will prioritize displaying the latest information and promotional information about that brand or product. This allows the data provision unit to provide personalized information tailored to the user's needs and improve the user experience. The data provision unit can also collect user feedback and continuously improve the accuracy and relevance of the information it provides. This allows the data provision department to always provide users with the latest and most relevant information, thereby increasing user satisfaction.

[0032] The navigation unit performs navigation based on data provided by the data provision unit. For example, the navigation unit calculates and displays a route from the user's current location to their destination. Specifically, the navigation unit inputs data obtained from the data provision unit into a generating AI, which then calculates the route. The generating AI analyzes traffic conditions and road congestion in real time to provide the optimal route. For example, if the user is driving a car, the navigation unit considers traffic congestion and accident information to calculate and present the most efficient route. For pedestrians, the navigation unit provides a safe and comfortable walking route, helping the user reach their destination without getting lost. Furthermore, the navigation unit can respond to unexpected situations that may arise during the user's journey. For example, if road construction or an event causes a road closure, the navigation unit immediately calculates a new route and notifies the user. This allows the navigation unit to always provide optimal navigation based on the latest information, smoothly supporting the user's journey. The navigation unit can also learn the user's past travel history and preferences to provide more personalized navigation. This allows the navigation unit to improve the user's travel experience and achieve comfortable and efficient travel.

[0033] The Shopping Department performs online shopping based on data provided by the Data Provision Department. For example, the Shopping Department displays the lowest price for the product the user is viewing and handles the purchase process on their behalf. Specifically, the Shopping Department inputs data obtained from the Data Provision Department into a Generating AI, which displays the lowest price and handles the purchase process. The Generating AI searches multiple online shopping sites in real time to identify the lowest price for the product the user is viewing. For example, if a user is viewing home appliances, the Shopping Department displays the lowest price for that product, making it easy for the user to purchase. The Shopping Department can also learn the user's purchase history and preferences and recommend the most suitable products to the user. This allows the Shopping Department to improve the user's shopping experience. Furthermore, the Shopping Department simplifies the purchase process, allowing users to purchase products smoothly. For example, it enhances user convenience by offering features such as one-click purchase completion and multiple payment methods. The Shopping Department can also track the delivery status of products in real time and notify the user. This allows the Shopping Department to provide users with a fast and reliable shopping experience, increasing their satisfaction.

[0034] The voice conversation unit engages in voice conversations based on data provided by the data provision unit. For example, the voice conversation unit interacts with the user via voice and provides necessary information. Specifically, the voice conversation unit inputs data obtained from the data provision unit into a generating AI, which then engages in voice conversation. The generating AI uses natural language processing technology to analyze the user's statements and generate appropriate responses. For instance, if a user asks for information about a specific location, the voice conversation unit provides detailed information about that location via voice. Similarly, if a user wants to know more about a product, the voice conversation unit can explain its features, price, reviews, and other information via voice. Furthermore, the voice conversation unit can learn the user's speech history and preferences to provide more personalized responses. This enables the voice conversation unit to achieve natural conversations with users and increase user satisfaction. The voice conversation unit also supports multiple languages ​​and can accommodate users who speak different languages. This allows the voice conversation unit to provide high-quality voice conversations to a global user base. Additionally, the voice conversation unit can collect user feedback and continuously improve the accuracy and naturalness of its responses. This allows the voice conversation unit to always provide the latest and most optimal voice dialogue, improving the user experience.

[0035] The data provider can display the lowest price for the product the user is viewing. For example, the data provider collects price information for the product the user is viewing and displays the lowest price. The data provider can also use a generating AI to analyze price information and display the lowest price. For example, the data provider inputs price information into the generating AI, and the generating AI displays the lowest price. This enables efficient shopping by displaying the lowest price for the product the user is viewing.

[0036] The data provision department can handle the purchase process for products viewed by users. For example, the data provision department can automatically complete the purchase process for products viewed by users. The data provision department can also handle the purchase process using a generating AI. For example, the data provision department inputs the purchase information into the generating AI, and the generating AI then completes the purchase process. This improves the convenience of shopping by handling the purchase process for products viewed by users.

[0037] The navigation unit can create a video showing how to get from the user's current location to their destination, based on information entered by the user, such as "I want to go from XX to XX." For example, the navigation unit calculates the route based on the information entered by the user and creates the video. The navigation unit can also create videos using a generative AI. For example, the navigation unit inputs route information into the generative AI, which then creates the video. This enables visually easy-to-understand navigation by providing directions in video format based on the information entered by the user.

[0038] The data provision department can provide tourist guides and route guidance. For example, it can provide information on tourist spots and provide route guidance. The data provision department can also provide tourist guides and route guidance using generative AI. For example, the data provision department can input information on tourist spots into the generative AI, which will then provide tourist guides and route guidance. This will improve the convenience of tourism by providing tourist guides and route guidance.

[0039] The data provider can suggest recommended spots in the surrounding area. For example, the data provider can suggest recommended spots near the user's current location. The data provider can also suggest recommended spots using a generative AI. For example, the data provider can input surrounding information into the generative AI, which then suggests recommended spots. This expands the user's range of activity by suggesting nearby recommended spots.

[0040] The data provision unit can perform symptom diagnoses. For example, the data provision unit can perform diagnoses based on symptoms entered by the user. The data provision unit can also perform symptom diagnoses using a generative AI. For example, the data provision unit inputs symptom information into the generative AI, and the generative AI performs the diagnosis. This improves the user's health management by providing symptom diagnoses.

[0041] The data provision department can navigate the surgical techniques of top surgeons. For example, the data provision department provides and guides users through surgical procedures. The data provision department can also navigate surgical techniques using generative AI. For example, the data provision department inputs surgical information into the generative AI, which then navigates the surgical techniques. This improves the quality of medical care by guiding users through the surgical techniques of top surgeons.

[0042] The data provider can perform real-time translation. For example, the data provider can translate text entered by a user in real time. The data provider can also perform real-time translation using a generative AI. For example, the data provider inputs text into the generative AI, which then translates it in real time. This real-time translation facilitates smoother communication between different languages.

[0043] The data provision department can provide assistance to visually impaired individuals. For example, the data provision department provides information that helps visually impaired individuals perceive their surroundings. The data provision department can also provide assistance to visually impaired individuals using generative AI. For example, the data provision department inputs information about the surrounding environment into the generative AI, which then provides assistance to the visually impaired. This improves the quality of life for visually impaired individuals by providing them with assistance.

[0044] The data provider can suggest cooking recipes. For example, the data provider can suggest recipes based on ingredients entered by the user. The data provider can also suggest cooking recipes using generative AI. For example, the data provider can input ingredient information into the generative AI, which then suggests recipes. This enriches the user's culinary life by suggesting cooking recipes.

[0045] The data provision unit can provide alerts based on the surrounding environment. For example, the data provision unit can detect hazards in the surroundings and issue alerts to the user. The data provision unit can also use a generating AI to analyze information about the surrounding environment and issue alerts. For example, the data provision unit inputs information about the surrounding environment into the generating AI, which then issues an alert. This ensures the user's safety by providing alerts based on the surrounding environment.

[0046] The field of view recognition unit can improve recognition accuracy by tracking the user's eye movements during field of view recognition. For example, if the user's gaze is focused on a specific object, the field of view recognition unit will provide detailed information about that object. The field of view recognition unit can also improve recognition accuracy by analyzing eye movements using generative AI. For example, the field of view recognition unit inputs eye movement data into the generative AI, which analyzes the eye movements and improves recognition accuracy. In this way, tracking the user's eye movements improves the accuracy of field of view recognition.

[0047] The field of view recognition unit can optimize its recognition algorithm by referring to the user's past field of view data during field of view recognition. For example, the field of view recognition unit optimizes the recognition algorithm based on data of objects the user has seen in the past. The field of view recognition unit can also optimize the recognition algorithm by analyzing past field of view data using a generative AI. For example, the field of view recognition unit inputs past field of view data into the generative AI, and the generative AI optimizes the recognition algorithm. In this way, the recognition algorithm is optimized by referring to the user's past field of view data.

[0048] The field of view recognition unit can improve recognition accuracy by considering the user's geographical location information during field of view recognition. For example, if the user is in a specific location, the field of view recognition unit will prioritize providing information related to that location. The field of view recognition unit can also improve recognition accuracy by analyzing geographical location information using a generative AI. For example, the field of view recognition unit inputs geographical location information into the generative AI, which analyzes the geographical location information and improves recognition accuracy. As a result, the accuracy of field of view recognition is improved by considering the user's geographical location information.

[0049] The field of view recognition unit can analyze the user's social media activity during field of view recognition and provide relevant field of view information. For example, the field of view recognition unit can prioritize providing information about objects that the user has shown interest in on social media. The field of view recognition unit can also use generative AI to analyze social media activity and provide field of view information. For example, the field of view recognition unit inputs social media data into the generative AI, which analyzes the social media activity and provides field of view information. In this way, relevant field of view information is provided by analyzing the user's social media activity.

[0050] The data provision unit can provide optimal data by referring to the user's past data usage history when providing data. For example, the data provision unit can provide optimal data based on the data the user has used in the past. The data provision unit can also use a generation AI to analyze past data usage history and provide optimal data. For example, the data provision unit inputs past data usage history into the generation AI, and the generation AI provides optimal data. In this way, optimal data is provided by referring to the user's past data usage history.

[0051] The data provision unit can adjust data priorities based on the user's current situation when providing data. For example, if a user is in a specific location, the data provision unit will prioritize providing data relevant to that location. The data provision unit can also use generative AI to analyze the current situation and adjust data priorities. For example, the data provision unit inputs current situation data into the generative AI, which then adjusts the data priorities. This allows for more appropriate data provision by adjusting data priorities based on the user's current situation.

[0052] The data provision unit can provide highly relevant data by considering the user's geographical location information when providing data. For example, if the user is in a specific location, the data provision unit will prioritize providing data related to that location. The data provision unit can also use a generative AI to analyze geographical location information and provide highly relevant data. For example, the data provision unit inputs geographical location information into the generative AI, and the generative AI provides highly relevant data. In this way, highly relevant data is provided by considering the user's geographical location information.

[0053] The data provision department can analyze users' social media activity and provide relevant data when providing data. For example, the data provision department can prioritize providing data on topics that users have shown interest in on social media. The data provision department can also use generative AI to analyze social media activity and provide relevant data. For example, the data provision department can input social media data into the generative AI, which will analyze the social media activity and provide relevant data. In this way, relevant data is provided by analyzing users' social media activity.

[0054] The navigation unit can suggest the optimal navigation method during navigation by referring to the user's past travel history. For example, the navigation unit can suggest the optimal navigation method based on routes the user has used in the past. The navigation unit can also use a generative AI to analyze past travel history and suggest the optimal navigation method. For example, the navigation unit inputs past travel history into the generative AI, which then suggests the optimal navigation method. In this way, the optimal navigation method is suggested by referring to the user's past travel history.

[0055] The navigation unit can optimize the route based on the user's current traffic conditions during navigation. For example, the navigation unit can suggest the optimal route based on real-time traffic congestion information. The navigation unit can also analyze traffic conditions and optimize the route using generative AI. For example, the navigation unit inputs traffic condition data into the generative AI, which then optimizes the route. This enables more efficient navigation by optimizing the route based on the user's current traffic conditions.

[0056] The navigation unit can provide the optimal route during navigation, taking into account the user's geographical location. For example, if the user is in a specific location, the navigation unit will prioritize providing routes related to that location. The navigation unit can also use generative AI to analyze geographical location information and provide the optimal route. For example, the navigation unit inputs geographical location information into the generative AI, which then provides the optimal route. In this way, the optimal route is provided by taking the user's geographical location information into consideration.

[0057] The navigation unit can analyze the user's social media activity during navigation and provide relevant route information. For example, the navigation unit can prioritize providing route information related to places the user has shown interest in on social media. The navigation unit can also use generative AI to analyze social media activity and provide relevant route information. For example, the navigation unit inputs social media data into the generative AI, which analyzes the social media activity and provides relevant route information. In this way, relevant route information is provided by analyzing the user's social media activity.

[0058] The shopping department can suggest the most suitable products by referring to the user's past purchase history during shopping. For example, the shopping department can suggest the most suitable products based on the products the user has purchased in the past. The shopping department can also use a generative AI to analyze past purchase history and suggest the most suitable products. For example, the shopping department can input past purchase history into the generative AI, which will then suggest the most suitable products. In this way, the most suitable products are suggested by referring to the user's past purchase history.

[0059] The shopping department can adjust product priorities based on the user's current purchasing intent during the shopping process. For example, if a user shows strong interest in a particular product, the shopping department will prioritize and suggest that product. The shopping department can also use generative AI to analyze purchasing intent and adjust product priorities. For example, the shopping department inputs purchasing intent data into the generative AI, which then adjusts product priorities. This allows for more appropriate product suggestions by adjusting product priorities based on the user's current purchasing intent.

[0060] The shopping function can suggest highly relevant products by considering the user's geographical location during shopping. For example, if the user is in a specific location, the shopping function will prioritize suggesting products related to that location. The shopping function can also use generative AI to analyze geographical location information and suggest highly relevant products. For example, the shopping function inputs geographical location information into the generative AI, which then suggests highly relevant products. In this way, highly relevant products are suggested by considering the user's geographical location.

[0061] The shopping department can analyze a user's social media activity during shopping and suggest relevant products. For example, the shopping department can prioritize suggesting products that the user has shown interest in on social media. The shopping department can also use generative AI to analyze social media activity and suggest relevant products. For example, the shopping department inputs social media data into the generative AI, which analyzes social media activity and suggests relevant products. In this way, relevant products are suggested by analyzing the user's social media activity.

[0062] The voice conversation unit can provide the optimal response during a voice conversation by referring to the user's past conversation history. For example, the voice conversation unit can provide the optimal response based on conversations the user has had in the past. The voice conversation unit can also use a generative AI to analyze past conversation history and provide the optimal response. For example, the voice conversation unit inputs past conversation history into the generative AI, and the generative AI provides the optimal response. In this way, the optimal response is provided by referring to the user's past conversation history.

[0063] The voice conversation unit can optimize conversation content based on the user's current situation during a voice conversation. For example, if the user is in a specific location, the voice conversation unit will provide conversation content relevant to that location. The voice conversation unit can also use generative AI to analyze the current situation and optimize conversation content. For example, the voice conversation unit inputs current situation data into the generative AI, which then optimizes the conversation content. This allows for more appropriate conversations by optimizing conversation content based on the user's current situation.

[0064] The voice conversation unit can provide highly relevant conversation content by considering the user's geographical location during a voice conversation. For example, if the user is in a specific location, the voice conversation unit will provide conversation content related to that location. The voice conversation unit can also use a generative AI to analyze geographical location information and provide highly relevant conversation content. For example, the voice conversation unit inputs geographical location information into the generative AI, and the generative AI provides highly relevant conversation content. In this way, highly relevant conversation content is provided by considering the user's geographical location.

[0065] The voice conversation unit can analyze the user's social media activity during a voice conversation and provide relevant conversation content. For example, the voice conversation unit can provide conversation content related to topics the user has shown interest in on social media. The voice conversation unit can also use generative AI to analyze social media activity and provide relevant conversation content. For example, the voice conversation unit inputs social media data into the generative AI, which analyzes the social media activity and provides relevant conversation content. In this way, relevant conversation content is provided by analyzing the user's social media activity.

[0066] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0067] The eye-tracking unit tracks the user's gaze and, when their gaze is focused on a specific object, can provide detailed information about that object. For example, if a user is looking at a particular painting in a museum, it can provide information about the painting's history and artist. Similarly, if a user is looking at a product in a store, it can display detailed specifications and reviews of that product. Furthermore, by analyzing eye movements, it can identify objects the user is interested in and prioritize providing relevant information. This improves the accuracy of eye-tracking and enables the provision of more appropriate information.

[0068] The data provision department can provide optimal data by referring to the user's past data usage history. For example, it can prioritize providing relevant data based on the user's past search history and website browsing history. It can also provide information on related products based on the user's past purchase history. Furthermore, it can provide information related to places the user has visited in the past. By referring to the user's past data usage history, optimal data is provided, improving user convenience.

[0069] The navigation system can optimize routes based on the user's current traffic conditions. For example, it can suggest the optimal route based on real-time traffic congestion information. It can also provide the best transfer routes considering the operating status of public transportation. Furthermore, it can suggest the best route based on weather information. By optimizing routes based on the user's current traffic conditions, more efficient navigation becomes possible.

[0070] The shopping section can suggest the most suitable products by referring to the user's past purchase history. For example, it can provide information on related products based on the user's past purchase history. It can also prioritize providing information on products that the user might be interested in based on the products they have previously viewed. Furthermore, it can provide information on products that other users have given high ratings to, based on the user's past purchase reviews. In this way, by referring to the user's past purchase history, the most suitable products are suggested, improving the convenience of shopping.

[0071] The voice conversation unit can provide optimal responses by referring to the user's past conversation history. For example, it can provide relevant information based on the content of past conversations the user has had. It can also quickly respond to similar questions based on questions the user has asked in the past. Furthermore, it can prioritize providing information on topics the user has shown interest in in the past. As a result, by referring to the user's past conversation history, optimal responses are provided, improving the convenience of voice conversations.

[0072] The following briefly describes the processing flow for example form 1.

[0073] Step 1: The field of view recognition unit recognizes the field of view. The field of view recognition unit captures the user's field of view using a camera, for example, and analyzes the field of view using a recognition algorithm. The field of view recognition unit can also improve the accuracy of field of view recognition using a generative AI. For example, the field of view recognition unit can input video data acquired by the camera into the generative AI and have the generative AI perform field of view recognition. Step 2: The data provision unit provides data in real time based on the field of view recognized by the field of view recognition unit. For example, the data provision unit provides information about objects the user is looking at. The data provision unit can also use a generating AI to provide optimal data based on the user's field of view. For example, the data provision unit inputs the field of view data acquired from the field of view recognition unit into the generating AI, and the generating AI provides the data. Step 3: The navigation unit performs navigation based on the data provided by the data provision unit. For example, the navigation unit calculates and displays the route from the user's current location to the destination. The navigation unit can also optimize the route in real time using a generative AI. For example, the navigation unit inputs the data obtained from the data provision unit into the generative AI, which then calculates the route. Step 4: The Shopping Department performs online shopping based on the data provided by the Data Provision Department. For example, the Shopping Department displays the lowest price for the product the user is viewing and handles the purchase process. The Shopping Department can also use Generative AI to improve the user's shopping experience. For example, the Shopping Department inputs data obtained from the Data Provision Department into the Generative AI, which then displays the lowest price and handles the purchase process. Step 5: The voice conversation unit converses using voice based on the data provided by the data provision unit. For example, the voice conversation unit interacts with the user via voice and provides necessary information. The voice conversation unit can also achieve natural conversation using generative AI. For example, the voice conversation unit inputs data obtained from the data provision unit into the generative AI, which then converses using voice.

[0074] (Example of form 2) The smart glasses system according to an embodiment of the present invention incorporates a generating AI into the smart glasses, realizing an AI agent that recognizes the user's field of vision and provides various data in real time. This smart glasses system allows for voice conversation with the AI ​​agent installed in the smart glasses and provides life assistance and services using a vast amount of up-to-date data. Examples include navigation, displaying the lowest prices for online shopping and in physical stores, purchasing, and displaying images after trying on clothes. For example, if a user inputs "I want to go from XX to XX," the generating AI creates a video showing the route from the current location to the destination and displays it on the smart glasses. Furthermore, the system can display the lowest price for a product the user is viewing through the smart glasses and handle the purchase process on their behalf. In addition, it provides a wide range of functions, including tourist guides and route guidance, suggestions for nearby recommended spots, symptom diagnosis, navigation to top surgeons, real-time translation, assistance for the visually impaired, cooking recipe suggestions, and alerts from the surrounding environment. As a result, the smart glasses system can support the user's life in many ways and improve convenience.

[0075] The smart glasses system according to this embodiment comprises a field of view recognition unit, a data provision unit, a navigation unit, a shopping unit, and a voice conversation unit. The field of view recognition unit recognizes the user's field of view. The field of view recognition unit captures the user's field of view using a camera, for example, and analyzes the field of view using a recognition algorithm. The field of view recognition unit can also improve the accuracy of field of view recognition using a generation AI. For example, the field of view recognition unit can input video data acquired by the camera into the generation AI and have the generation AI perform field of view recognition. The data provision unit provides data in real time based on the field of view recognized by the field of view recognition unit. The data provision unit provides, for example, information about objects the user is looking at. The data provision unit can also provide optimal data based on the user's field of view using a generation AI. For example, the data provision unit inputs field of view data acquired from the field of view recognition unit into the generation AI, and the generation AI provides the data. The navigation unit performs navigation based on the data provided by the data provision unit. The navigation unit calculates and displays, for example, a route from the user's current location to the destination. The navigation unit can also optimize the route in real time using a generation AI. For example, the navigation unit inputs data obtained from the data provision unit into a generating AI, which then calculates a route. The shopping unit performs online shopping based on the data provided by the data provision unit. The shopping unit, for example, displays the lowest price for the product the user is viewing and handles the purchase process. The shopping unit can also use the generating AI to improve the user's shopping experience. For example, the shopping unit inputs data obtained from the data provision unit into a generating AI, which displays the lowest price and handles the purchase process. The voice conversation unit engages in voice conversation based on the data provided by the data provision unit. The voice conversation unit, for example, interacts with the user via voice and provides necessary information. The voice conversation unit can also use the generating AI to achieve natural conversation. For example, the voice conversation unit inputs data obtained from the data provision unit into a generating AI, which then engages in voice conversation. As a result, the smart glasses system according to this embodiment enables vision recognition, data provision, navigation, online shopping, and voice conversation.

[0076] The field of view recognition unit recognizes the user's field of view. For example, the field of view recognition unit captures the user's field of view using a camera and analyzes the field of view using a recognition algorithm. The field of view recognition unit can also improve the accuracy of field of view recognition using generative AI. Specifically, the field of view recognition unit can input video data acquired by the camera into the generative AI and have the generative AI perform field of view recognition. The generative AI analyzes the video data using a deep learning model and recognizes objects and the environment the user is looking at with high accuracy. For example, if the user is walking in a city, the field of view recognition unit identifies buildings, signs, vehicles, pedestrians, etc. from the video captured by the camera and understands their respective positions and movements. Based on this information, the generative AI optimizes the information displayed in the user's field of view. Furthermore, by combining this with user eye-tracking technology, the field of view recognition unit can identify specific objects or areas that the user is focusing on and prioritize the analysis of that information. As a result, the field of view recognition unit can analyze the user's field of view in real time and quickly provide the necessary information. In addition, the field of view recognition unit can dynamically adjust the recognition algorithm in response to changes in the environment and maintain high recognition accuracy at all times. For example, the camera settings and recognition algorithms are automatically optimized in response to changes in day / night cycles and weather conditions. This enables the field of view recognition unit to achieve high-precision field of view recognition in any environment and provide the user with optimal information.

[0077] The data provision unit provides data in real time based on the field of view recognized by the field of view recognition unit. For example, the data provision unit provides information about objects that the user is looking at. Specifically, the data provision unit inputs the field of view data acquired from the field of view recognition unit into a generating AI, which then provides the data. The generating AI generates detailed information about the objects and places the user is looking at and overlays it onto the user's field of view. For example, if a user is looking at an exhibit in a museum, the data provision unit can display historical background and related information about that exhibit in real time. Also, if a user is looking at a restaurant menu, the data provision unit can provide detailed descriptions of each dish, calorie information, allergy information, etc. Furthermore, the data provision unit can select and provide the most relevant information based on the user's interests and preferences. For example, if a user is interested in a particular brand or product, the data provision unit will prioritize displaying the latest information and promotional information about that brand or product. This allows the data provision unit to provide personalized information tailored to the user's needs and improve the user experience. The data provision unit can also collect user feedback and continuously improve the accuracy and relevance of the information it provides. This allows the data provision department to always provide users with the latest and most relevant information, thereby increasing user satisfaction.

[0078] The navigation unit performs navigation based on data provided by the data provision unit. For example, the navigation unit calculates and displays a route from the user's current location to their destination. Specifically, the navigation unit inputs data obtained from the data provision unit into a generating AI, which then calculates the route. The generating AI analyzes traffic conditions and road congestion in real time to provide the optimal route. For example, if the user is driving a car, the navigation unit considers traffic congestion and accident information to calculate and present the most efficient route. For pedestrians, the navigation unit provides a safe and comfortable walking route, helping the user reach their destination without getting lost. Furthermore, the navigation unit can respond to unexpected situations that may arise during the user's journey. For example, if road construction or an event causes a road closure, the navigation unit immediately calculates a new route and notifies the user. This allows the navigation unit to always provide optimal navigation based on the latest information, smoothly supporting the user's journey. The navigation unit can also learn the user's past travel history and preferences to provide more personalized navigation. This allows the navigation unit to improve the user's travel experience and achieve comfortable and efficient travel.

[0079] The Shopping Department performs online shopping based on data provided by the Data Provision Department. For example, the Shopping Department displays the lowest price for the product the user is viewing and handles the purchase process on their behalf. Specifically, the Shopping Department inputs data obtained from the Data Provision Department into a Generating AI, which displays the lowest price and handles the purchase process. The Generating AI searches multiple online shopping sites in real time to identify the lowest price for the product the user is viewing. For example, if a user is viewing home appliances, the Shopping Department displays the lowest price for that product, making it easy for the user to purchase. The Shopping Department can also learn the user's purchase history and preferences and recommend the most suitable products to the user. This allows the Shopping Department to improve the user's shopping experience. Furthermore, the Shopping Department simplifies the purchase process, allowing users to purchase products smoothly. For example, it enhances user convenience by offering features such as one-click purchase completion and multiple payment methods. The Shopping Department can also track the delivery status of products in real time and notify the user. This allows the Shopping Department to provide users with a fast and reliable shopping experience, increasing their satisfaction.

[0080] The voice conversation unit engages in voice conversations based on data provided by the data provision unit. For example, the voice conversation unit interacts with the user via voice and provides necessary information. Specifically, the voice conversation unit inputs data obtained from the data provision unit into a generating AI, which then engages in voice conversation. The generating AI uses natural language processing technology to analyze the user's statements and generate appropriate responses. For instance, if a user asks for information about a specific location, the voice conversation unit provides detailed information about that location via voice. Similarly, if a user wants to know more about a product, the voice conversation unit can explain its features, price, reviews, and other information via voice. Furthermore, the voice conversation unit can learn the user's speech history and preferences to provide more personalized responses. This enables the voice conversation unit to achieve natural conversations with users and increase user satisfaction. The voice conversation unit also supports multiple languages ​​and can accommodate users who speak different languages. This allows the voice conversation unit to provide high-quality voice conversations to a global user base. Additionally, the voice conversation unit can collect user feedback and continuously improve the accuracy and naturalness of its responses. This allows the voice conversation unit to always provide the latest and most optimal voice dialogue, improving the user experience.

[0081] The data provider can display the lowest price for the product the user is viewing. For example, the data provider collects price information for the product the user is viewing and displays the lowest price. The data provider can also use a generating AI to analyze price information and display the lowest price. For example, the data provider inputs price information into the generating AI, and the generating AI displays the lowest price. This enables efficient shopping by displaying the lowest price for the product the user is viewing.

[0082] The data provision department can handle the purchase process for products viewed by users. For example, the data provision department can automatically complete the purchase process for products viewed by users. The data provision department can also handle the purchase process using a generating AI. For example, the data provision department inputs the purchase information into the generating AI, and the generating AI then completes the purchase process. This improves the convenience of shopping by handling the purchase process for products viewed by users.

[0083] The navigation unit can create a video showing how to get from the user's current location to their destination, based on information entered by the user, such as "I want to go from XX to XX." For example, the navigation unit calculates the route based on the information entered by the user and creates the video. The navigation unit can also create videos using a generative AI. For example, the navigation unit inputs route information into the generative AI, which then creates the video. This enables visually easy-to-understand navigation by providing directions in video format based on the information entered by the user.

[0084] The data provision department can provide tourist guides and route guidance. For example, it can provide information on tourist spots and provide route guidance. The data provision department can also provide tourist guides and route guidance using generative AI. For example, the data provision department can input information on tourist spots into the generative AI, which will then provide tourist guides and route guidance. This will improve the convenience of tourism by providing tourist guides and route guidance.

[0085] The data provider can suggest recommended spots in the surrounding area. For example, the data provider can suggest recommended spots near the user's current location. The data provider can also suggest recommended spots using a generative AI. For example, the data provider can input surrounding information into the generative AI, which then suggests recommended spots. This expands the user's range of activity by suggesting nearby recommended spots.

[0086] The data provision unit can perform symptom diagnoses. For example, the data provision unit can perform diagnoses based on symptoms entered by the user. The data provision unit can also perform symptom diagnoses using a generative AI. For example, the data provision unit inputs symptom information into the generative AI, and the generative AI performs the diagnosis. This improves the user's health management by providing symptom diagnoses.

[0087] The data provision department can navigate the surgical techniques of top surgeons. For example, the data provision department provides and guides users through surgical procedures. The data provision department can also navigate surgical techniques using generative AI. For example, the data provision department inputs surgical information into the generative AI, which then navigates the surgical techniques. This improves the quality of medical care by guiding users through the surgical techniques of top surgeons.

[0088] The data provider can perform real-time translation. For example, the data provider can translate text entered by a user in real time. The data provider can also perform real-time translation using a generative AI. For example, the data provider inputs text into the generative AI, which then translates it in real time. This real-time translation facilitates smoother communication between different languages.

[0089] The data provision department can provide assistance to visually impaired individuals. For example, the data provision department provides information that helps visually impaired individuals perceive their surroundings. The data provision department can also provide assistance to visually impaired individuals using generative AI. For example, the data provision department inputs information about the surrounding environment into the generative AI, which then provides assistance to the visually impaired. This improves the quality of life for visually impaired individuals by providing them with assistance.

[0090] The data provider can suggest cooking recipes. For example, the data provider can suggest recipes based on ingredients entered by the user. The data provider can also suggest cooking recipes using generative AI. For example, the data provider can input ingredient information into the generative AI, which then suggests recipes. This enriches the user's culinary life by suggesting cooking recipes.

[0091] The data provision unit can provide alerts based on the surrounding environment. For example, the data provision unit can detect hazards in the surroundings and issue alerts to the user. The data provision unit can also use a generating AI to analyze information about the surrounding environment and issue alerts. For example, the data provision unit inputs information about the surrounding environment into the generating AI, which then issues an alert. This ensures the user's safety by providing alerts based on the surrounding environment.

[0092] The visual recognition unit can estimate the user's emotions and adjust the accuracy of visual recognition based on the estimated emotions. For example, the visual recognition unit can analyze the user's facial expressions and voice to estimate emotions. The visual recognition unit can also use generative AI to estimate emotions and adjust the accuracy of visual recognition. For example, the visual recognition unit inputs the user's facial expression data into the generative AI, which estimates emotions and adjusts the accuracy of visual recognition. This allows for the provision of more appropriate information by adjusting the accuracy of visual recognition based on the user's emotions.

[0093] The field of view recognition unit can improve recognition accuracy by tracking the user's eye movements during field of view recognition. For example, if the user's gaze is focused on a specific object, the field of view recognition unit will provide detailed information about that object. The field of view recognition unit can also improve recognition accuracy by analyzing eye movements using generative AI. For example, the field of view recognition unit inputs eye movement data into the generative AI, which analyzes the eye movements and improves recognition accuracy. In this way, tracking the user's eye movements improves the accuracy of field of view recognition.

[0094] The field of view recognition unit can optimize its recognition algorithm by referring to the user's past field of view data during field of view recognition. For example, the field of view recognition unit optimizes the recognition algorithm based on data of objects the user has seen in the past. The field of view recognition unit can also optimize the recognition algorithm by analyzing past field of view data using a generative AI. For example, the field of view recognition unit inputs past field of view data into the generative AI, and the generative AI optimizes the recognition algorithm. In this way, the recognition algorithm is optimized by referring to the user's past field of view data.

[0095] The visual recognition unit can estimate the user's emotions and determine the priority of visual recognition based on the estimated emotions. For example, if the user is excited, the visual recognition unit will increase the priority of visual recognition to provide information quickly. The visual recognition unit can also use generative AI to estimate emotions and determine the priority of visual recognition. For example, the visual recognition unit inputs user emotion data into the generative AI, which estimates the emotions and determines the priority of visual recognition. This allows for more appropriate information to be provided by determining the priority of visual recognition based on the user's emotions.

[0096] The field of view recognition unit can improve recognition accuracy by considering the user's geographical location information during field of view recognition. For example, if the user is in a specific location, the field of view recognition unit will prioritize providing information related to that location. The field of view recognition unit can also improve recognition accuracy by analyzing geographical location information using a generative AI. For example, the field of view recognition unit inputs geographical location information into the generative AI, which analyzes the geographical location information and improves recognition accuracy. As a result, the accuracy of field of view recognition is improved by considering the user's geographical location information.

[0097] The field of view recognition unit can analyze the user's social media activity during field of view recognition and provide relevant field of view information. For example, the field of view recognition unit can prioritize providing information about objects that the user has shown interest in on social media. The field of view recognition unit can also use generative AI to analyze social media activity and provide field of view information. For example, the field of view recognition unit inputs social media data into the generative AI, which analyzes the social media activity and provides field of view information. In this way, relevant field of view information is provided by analyzing the user's social media activity.

[0098] The data provision unit can estimate the user's emotions and adjust the data provision method based on the estimated emotions. For example, if the user is relaxed, the data provision unit will provide data at a relaxed pace. The data provision unit can also use generative AI to estimate emotions and adjust the data provision method. For example, the data provision unit inputs the user's emotion data into the generative AI, which estimates the emotions and adjusts the data provision method. This allows for more appropriate data provision by adjusting the data provision method based on the user's emotions.

[0099] The data provision unit can provide optimal data by referring to the user's past data usage history when providing data. For example, the data provision unit can provide optimal data based on the data the user has used in the past. The data provision unit can also use a generation AI to analyze past data usage history and provide optimal data. For example, the data provision unit inputs past data usage history into the generation AI, and the generation AI provides optimal data. In this way, optimal data is provided by referring to the user's past data usage history.

[0100] The data provision unit can adjust data priorities based on the user's current situation when providing data. For example, if a user is in a specific location, the data provision unit will prioritize providing data relevant to that location. The data provision unit can also use generative AI to analyze the current situation and adjust data priorities. For example, the data provision unit inputs current situation data into the generative AI, which then adjusts the data priorities. This allows for more appropriate data provision by adjusting data priorities based on the user's current situation.

[0101] The data provision unit can estimate the user's emotions and adjust the timing of data provision based on those emotions. For example, if the user is relaxed, the data provision unit will provide data at a relaxed pace. The data provision unit can also use generative AI to estimate emotions and adjust the timing of data provision. For example, the data provision unit inputs the user's emotion data into the generative AI, which estimates the emotions and adjusts the timing of data provision. This allows for more appropriate data provision at the right time by adjusting the timing based on the user's emotions.

[0102] The data provision unit can provide highly relevant data by considering the user's geographical location information when providing data. For example, if the user is in a specific location, the data provision unit will prioritize providing data related to that location. The data provision unit can also use a generative AI to analyze geographical location information and provide highly relevant data. For example, the data provision unit inputs geographical location information into the generative AI, and the generative AI provides highly relevant data. In this way, highly relevant data is provided by considering the user's geographical location information.

[0103] The data provision department can analyze users' social media activity and provide relevant data when providing data. For example, the data provision department can prioritize providing data on topics that users have shown interest in on social media. The data provision department can also use generative AI to analyze social media activity and provide relevant data. For example, the data provision department can input social media data into the generative AI, which will analyze the social media activity and provide relevant data. In this way, relevant data is provided by analyzing users' social media activity.

[0104] The navigation unit can estimate the user's emotions and adjust the navigation display method based on the estimated emotions. For example, if the user is feeling anxious, the navigation unit can provide a simple and highly visible display method. The navigation unit can also use generative AI to estimate emotions and adjust the navigation display method. For example, the navigation unit inputs user emotion data into the generative AI, which estimates the emotions and adjusts the navigation display method. This allows for more appropriate navigation by adjusting the navigation display method based on the user's emotions.

[0105] The navigation unit can suggest the optimal navigation method during navigation by referring to the user's past travel history. For example, the navigation unit can suggest the optimal navigation method based on routes the user has used in the past. The navigation unit can also use a generative AI to analyze past travel history and suggest the optimal navigation method. For example, the navigation unit inputs past travel history into the generative AI, which then suggests the optimal navigation method. In this way, the optimal navigation method is suggested by referring to the user's past travel history.

[0106] The navigation unit can optimize the route based on the user's current traffic conditions during navigation. For example, the navigation unit can suggest the optimal route based on real-time traffic congestion information. The navigation unit can also analyze traffic conditions and optimize the route using generative AI. For example, the navigation unit inputs traffic condition data into the generative AI, which then optimizes the route. This enables more efficient navigation by optimizing the route based on the user's current traffic conditions.

[0107] The navigation unit can estimate the user's emotions and determine navigation priorities based on those emotions. For example, if the user is excited, the navigation unit will prioritize navigation to provide information more quickly. The navigation unit can also use generative AI to estimate emotions and determine navigation priorities. For example, the navigation unit inputs user emotion data into the generative AI, which estimates the emotions and determines navigation priorities. This allows for more appropriate navigation by determining navigation priorities based on the user's emotions.

[0108] The navigation unit can provide the optimal route during navigation, taking into account the user's geographical location. For example, if the user is in a specific location, the navigation unit will prioritize providing routes related to that location. The navigation unit can also use generative AI to analyze geographical location information and provide the optimal route. For example, the navigation unit inputs geographical location information into the generative AI, which then provides the optimal route. In this way, the optimal route is provided by taking the user's geographical location information into consideration.

[0109] The navigation unit can analyze the user's social media activity during navigation and provide relevant route information. For example, the navigation unit can prioritize providing route information related to places the user has shown interest in on social media. The navigation unit can also use generative AI to analyze social media activity and provide relevant route information. For example, the navigation unit inputs social media data into the generative AI, which analyzes the social media activity and provides relevant route information. In this way, relevant route information is provided by analyzing the user's social media activity.

[0110] The shopping department can estimate the user's emotions and adjust its shopping recommendations based on those emotions. For example, if the user is relaxed, the shopping department will suggest products at a relaxed pace. The shopping department can also use generative AI to estimate emotions and adjust its shopping recommendations. For example, the shopping department inputs user emotion data into the generative AI, which estimates the emotions and adjusts the shopping recommendations. This allows for more appropriate product recommendations by adjusting the shopping recommendations based on the user's emotions.

[0111] The shopping department can suggest the most suitable products by referring to the user's past purchase history during shopping. For example, the shopping department can suggest the most suitable products based on the products the user has purchased in the past. The shopping department can also use a generative AI to analyze past purchase history and suggest the most suitable products. For example, the shopping department can input past purchase history into the generative AI, which will then suggest the most suitable products. In this way, the most suitable products are suggested by referring to the user's past purchase history.

[0112] The shopping department can adjust product priorities based on the user's current purchasing intent during the shopping process. For example, if a user shows strong interest in a particular product, the shopping department will prioritize and suggest that product. The shopping department can also use generative AI to analyze purchasing intent and adjust product priorities. For example, the shopping department inputs purchasing intent data into the generative AI, which then adjusts product priorities. This allows for more appropriate product suggestions by adjusting product priorities based on the user's current purchasing intent.

[0113] The shopping function can estimate the user's emotions and adjust the timing of shopping based on those emotions. For example, if the user is relaxed, the shopping function will suggest products at a relaxed pace. The shopping function can also use generative AI to estimate emotions and adjust the timing of shopping. For example, the shopping function inputs user emotion data into the generative AI, which estimates the emotions and adjusts the timing of shopping. This allows for more appropriate product suggestions by adjusting the timing of shopping based on the user's emotions.

[0114] The shopping function can suggest highly relevant products by considering the user's geographical location during shopping. For example, if the user is in a specific location, the shopping function will prioritize suggesting products related to that location. The shopping function can also use generative AI to analyze geographical location information and suggest highly relevant products. For example, the shopping function inputs geographical location information into the generative AI, which then suggests highly relevant products. In this way, highly relevant products are suggested by considering the user's geographical location.

[0115] The shopping department can analyze a user's social media activity during shopping and suggest relevant products. For example, the shopping department can prioritize suggesting products that the user has shown interest in on social media. The shopping department can also use generative AI to analyze social media activity and suggest relevant products. For example, the shopping department inputs social media data into the generative AI, which analyzes social media activity and suggests relevant products. In this way, relevant products are suggested by analyzing the user's social media activity.

[0116] The voice conversation unit can estimate the user's emotions and adjust the tone of the voice conversation based on those emotions. For example, if the user is nervous, the voice conversation unit will converse in a calm tone. The voice conversation unit can also use generative AI to estimate emotions and adjust the tone of the voice conversation. For example, the voice conversation unit inputs the user's emotion data into the generative AI, which estimates the emotions and adjusts the tone of the voice conversation. This allows for more appropriate conversations by adjusting the tone of the voice conversation based on the user's emotions.

[0117] The voice conversation unit can provide the optimal response during a voice conversation by referring to the user's past conversation history. For example, the voice conversation unit can provide the optimal response based on conversations the user has had in the past. The voice conversation unit can also use a generative AI to analyze past conversation history and provide the optimal response. For example, the voice conversation unit inputs past conversation history into the generative AI, and the generative AI provides the optimal response. In this way, the optimal response is provided by referring to the user's past conversation history.

[0118] The voice conversation unit can optimize conversation content based on the user's current situation during a voice conversation. For example, if the user is in a specific location, the voice conversation unit will provide conversation content relevant to that location. The voice conversation unit can also use generative AI to analyze the current situation and optimize conversation content. For example, the voice conversation unit inputs current situation data into the generative AI, which then optimizes the conversation content. This allows for more appropriate conversations by optimizing conversation content based on the user's current situation.

[0119] The voice conversation unit can estimate the user's emotions and prioritize voice conversations based on those emotions. For example, if the user is excited, the voice conversation unit will prioritize the conversation and respond quickly. The voice conversation unit can also use generative AI to estimate emotions and prioritize voice conversations. For example, the voice conversation unit inputs user emotion data into the generative AI, which estimates the emotions and determines the priority of voice conversations. This allows for more appropriate conversations by prioritizing voice conversations based on the user's emotions.

[0120] The voice conversation unit can provide highly relevant conversation content by considering the user's geographical location during a voice conversation. For example, if the user is in a specific location, the voice conversation unit will provide conversation content related to that location. The voice conversation unit can also use a generative AI to analyze geographical location information and provide highly relevant conversation content. For example, the voice conversation unit inputs geographical location information into the generative AI, and the generative AI provides highly relevant conversation content. In this way, highly relevant conversation content is provided by considering the user's geographical location.

[0121] The voice conversation unit can analyze the user's social media activity during a voice conversation and provide relevant conversation content. For example, the voice conversation unit can provide conversation content related to topics the user has shown interest in on social media. The voice conversation unit can also use generative AI to analyze social media activity and provide relevant conversation content. For example, the voice conversation unit inputs social media data into the generative AI, which analyzes the social media activity and provides relevant conversation content. In this way, relevant conversation content is provided by analyzing the user's social media activity.

[0122] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0123] The eye-tracking unit tracks the user's gaze and, when their gaze is focused on a specific object, can provide detailed information about that object. For example, if a user is looking at a particular painting in a museum, it can provide information about the painting's history and artist. Similarly, if a user is looking at a product in a store, it can display detailed specifications and reviews of that product. Furthermore, by analyzing eye movements, it can identify objects the user is interested in and prioritize providing relevant information. This improves the accuracy of eye-tracking and enables the provision of more appropriate information.

[0124] The data provision department can provide optimal data by referring to the user's past data usage history. For example, it can prioritize providing relevant data based on the user's past search history and website browsing history. It can also provide information on related products based on the user's past purchase history. Furthermore, it can provide information related to places the user has visited in the past. By referring to the user's past data usage history, optimal data is provided, improving user convenience.

[0125] The navigation system can optimize routes based on the user's current traffic conditions. For example, it can suggest the optimal route based on real-time traffic congestion information. It can also provide the best transfer routes considering the operating status of public transportation. Furthermore, it can suggest the best route based on weather information. By optimizing routes based on the user's current traffic conditions, more efficient navigation becomes possible.

[0126] The shopping section can suggest the most suitable products by referring to the user's past purchase history. For example, it can provide information on related products based on the user's past purchase history. It can also prioritize providing information on products that the user might be interested in based on the products they have previously viewed. Furthermore, it can provide information on products that other users have given high ratings to, based on the user's past purchase reviews. In this way, by referring to the user's past purchase history, the most suitable products are suggested, improving the convenience of shopping.

[0127] The voice conversation unit can provide optimal responses by referring to the user's past conversation history. For example, it can provide relevant information based on the content of past conversations the user has had. It can also quickly respond to similar questions based on questions the user has asked in the past. Furthermore, it can prioritize providing information on topics the user has shown interest in in the past. As a result, by referring to the user's past conversation history, optimal responses are provided, improving the convenience of voice conversations.

[0128] The visual recognition unit can estimate the user's emotions and adjust the accuracy of visual recognition based on those emotions. For example, if the user is excited, the accuracy of visual recognition can be increased to provide information quickly. If the user is relaxed, the accuracy of visual recognition can be adjusted to provide information at a more relaxed pace. Furthermore, if the user is feeling anxious, it is possible to prioritize providing information that corresponds to that emotion. In this way, by adjusting the accuracy of visual recognition based on the user's emotions, more appropriate information can be provided.

[0129] The data delivery unit can estimate the user's emotions and adjust the data delivery method based on the estimated emotions. For example, if the user is relaxed, data can be delivered at a leisurely pace. If the user is excited, data can be delivered quickly. Furthermore, if the user is feeling anxious, data corresponding to that emotion can be prioritized. In this way, by adjusting the data delivery method based on the user's emotions, more appropriate data can be delivered.

[0130] The navigation unit can estimate the user's emotions and adjust the display method of the navigation based on those emotions. For example, if the user is tense, it can provide a simple and highly visible display method. If the user is relaxed, it can provide a display method that includes detailed information. Furthermore, if the user is excited, it can provide a display method that matches that emotion. By adjusting the display method of the navigation based on the user's emotions, more appropriate navigation becomes possible.

[0131] The shopping function can estimate the user's emotions and adjust its shopping recommendations based on those emotions. For example, if the user is relaxed, it will suggest products at a leisurely pace. If the user is excited, it can suggest products quickly. Furthermore, if the user is feeling anxious, it can prioritize suggesting products that match that emotion. By adjusting the shopping recommendation method based on the user's emotions, it becomes possible to make more appropriate product suggestions.

[0132] The voice conversation unit can estimate the user's emotions and adjust the tone of the voice conversation based on those emotions. For example, if the user is nervous, the conversation will be conducted in a calm tone. If the user is relaxed, the conversation can be conducted in a friendly tone. Furthermore, if the user is excited, the conversation can be conducted in a tone appropriate to that emotion. By adjusting the tone of the voice conversation based on the user's emotions, more appropriate conversations become possible.

[0133] The following briefly describes the processing flow for example form 2.

[0134] Step 1: The field of view recognition unit recognizes the field of view. The field of view recognition unit captures the user's field of view using a camera, for example, and analyzes the field of view using a recognition algorithm. The field of view recognition unit can also improve the accuracy of field of view recognition using a generative AI. For example, the field of view recognition unit can input video data acquired by the camera into the generative AI and have the generative AI perform field of view recognition. Step 2: The data provision unit provides data in real time based on the field of view recognized by the field of view recognition unit. For example, the data provision unit provides information about objects the user is looking at. The data provision unit can also use a generating AI to provide optimal data based on the user's field of view. For example, the data provision unit inputs the field of view data acquired from the field of view recognition unit into the generating AI, and the generating AI provides the data. Step 3: The navigation unit performs navigation based on the data provided by the data provision unit. For example, the navigation unit calculates and displays the route from the user's current location to the destination. The navigation unit can also optimize the route in real time using a generative AI. For example, the navigation unit inputs the data obtained from the data provision unit into the generative AI, which then calculates the route. Step 4: The Shopping Department performs online shopping based on the data provided by the Data Provision Department. For example, the Shopping Department displays the lowest price for the product the user is viewing and handles the purchase process. The Shopping Department can also use Generative AI to improve the user's shopping experience. For example, the Shopping Department inputs data obtained from the Data Provision Department into the Generative AI, which then displays the lowest price and handles the purchase process. Step 5: The voice conversation unit converses using voice based on the data provided by the data provision unit. For example, the voice conversation unit interacts with the user via voice and provides necessary information. The voice conversation unit can also achieve natural conversation using generative AI. For example, the voice conversation unit inputs data obtained from the data provision unit into the generative AI, which then converses using voice.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] Each of the multiple elements described above, including the field of view recognition unit, data provision unit, navigation unit, shopping unit, and voice conversation unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the field of view recognition unit captures the user's field of view using the camera 42 of the smart device 14 and analyzes the field of view using the control unit 46A. The data provision unit is implemented in real time based on the field of view data acquired from the field of view recognition unit, for example, by the identification processing unit 290 of the data processing unit 12. The navigation unit is implemented in real time based on the user's current location and destination, for example, by the identification processing unit 290 of the data processing unit 12. The navigation unit is implemented in real time based on the user's current location and displays the route on the display 40A of the smart device 14. The shopping unit is implemented in real time based on the control unit 46A of the smart device 14. The shopping unit displays the lowest price of the product the user is looking at and handles the purchase process. The voice conversation unit communicates with the user by voice using the microphone 38B and speaker 40B of the smart device 14 and provides necessary information. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0139] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] Each of the multiple elements described above, including the field of view recognition unit, data provision unit, navigation unit, shopping unit, and voice conversation unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the field of view recognition unit captures the user's field of view using the camera 42 of the smart glasses 214 and analyzes the field of view using the control unit 46A. The data provision unit is implemented in real time based on the field of view data acquired from the field of view recognition unit, for example, by the identification processing unit 290 of the data processing unit 12. The navigation unit is implemented in real time based on the user's current location and destination, for example, by the identification processing unit 290 of the data processing unit 12. The navigation unit is implemented in real time based on the user's current location and displays the route on the display of the smart glasses 214. The shopping unit is implemented in real time based on the control unit 46A of the smart glasses 214. The shopping unit displays the lowest price of the product the user is looking at and handles the purchase process. The voice conversation unit communicates with the user by voice using the microphone 238 and speaker 240 of the smart glasses 214 and provides necessary information. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0155] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.).

[0167] 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.

[0168] 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.

[0169] 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.

[0170] Each of the multiple elements described above, including the field of view recognition unit, data provision unit, navigation unit, shopping unit, and voice conversation unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the field of view recognition unit captures the user's field of view using the camera 42 of the headset terminal 314 and analyzes the field of view using the control unit 46A. The data provision unit is implemented in real time based on the field of view data acquired from the field of view recognition unit, for example, by the identification processing unit 290 of the data processing unit 12. The navigation unit is implemented in real time based on the field of view data acquired from the field of view recognition unit, for example, by the identification processing unit 290 of the data processing unit 12. It calculates the route from the user's current location to the destination and displays it on the display 343 of the headset terminal 314. The shopping unit is implemented in real time based on the control unit 46A of the headset terminal 314. It displays the lowest price of the product the user is looking at and handles the purchase procedure. The voice conversation unit communicates with the user by voice using the microphone 238 and speaker 240 of the headset terminal 314 and provides necessary information. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0171] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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).

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.).

[0184] 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.

[0185] 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.

[0186] 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.

[0187] Each of the multiple elements described above, including the field of view recognition unit, data provision unit, navigation unit, shopping unit, and voice conversation unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the field of view recognition unit captures the user's field of view using the camera 42 of the robot 414 and analyzes the field of view using the control unit 46A. The data provision unit is implemented in real time based on the field of view data acquired from the field of view recognition unit, for example, by the specific processing unit 290 of the data processing unit 12. The navigation unit is implemented in real time based on the specific processing unit 290 of the data processing unit 12 and calculates the route from the user's current location to the destination and displays it on the robot 414's display. The shopping unit is implemented in real time based on the control unit 46A of the robot 414 and displays the lowest price of the product the user is looking at and handles the purchase procedure. The voice conversation unit communicates with the user by voice using the microphone 238 and speaker 240 of the robot 414 and provides necessary information. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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."

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] (Note 1) A field recognition unit that recognizes the field of view, A data provision unit provides data in real time based on the field of view recognized by the field of view recognition unit, A navigation unit that performs navigation based on the data provided by the data provision unit, A shopping unit that performs online shopping based on the data provided by the aforementioned data provision unit, The system includes a voice conversation unit that engages in voice conversation based on the data provided by the data provision unit. A system characterized by the following features. (Note 2) The aforementioned data provision unit, Display the lowest price for the product the user is viewing. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned data provision unit, We handle the purchase process for products that users are viewing. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned navigation unit is Based on the destination information entered by the user, a video is created showing how to get from the current location to the destination. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned data provision unit, Provide tourist guides and route directions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned data provision unit, Suggest nearby recommended spots. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned data provision unit, Diagnose the symptoms The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned data provision unit, Navigating the surgical techniques of top surgeons The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned data provision unit, Perform real-time translation The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned data provision unit, Providing assistance to visually impaired people The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned data provision unit, Suggesting cooking recipes The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned data provision unit, Receive alerts from the surrounding environment. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned field recognition unit is The system estimates the user's emotions and adjusts the accuracy of visual recognition based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned field recognition unit is During visual recognition, tracking the user's eye movements improves recognition accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned field recognition unit is During visual recognition, the recognition algorithm is optimized by referencing the user's past visual data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned field recognition unit is The system estimates the user's emotions and determines the priority of visual recognition based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned field recognition unit is When recognizing a user's field of view, the system improves recognition accuracy by taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned field recognition unit is During visual recognition, the system analyzes the user's social media activity and provides relevant visual information. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned data provision unit, We estimate the user's emotions and adjust the data delivery method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned data provision unit, When providing data, we refer to the user's past data usage history to provide the most suitable data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned data provision unit, When providing data, we adjust data priorities based on the user's current situation. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned data provision unit, We estimate the user's emotions and adjust the timing of data delivery based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned data provision unit, When providing data, we will consider the user's geographical location to provide highly relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned data provision unit, When providing data, we analyze users' social media activity and provide relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned navigation unit is It estimates the user's emotions and adjusts how navigation is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned navigation unit is During navigation, the system suggests the optimal navigation method by referring to the user's past travel history. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned navigation unit is During navigation, the route is optimized based on the user's current traffic conditions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned navigation unit is It estimates the user's emotions and determines navigation priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned navigation unit is During navigation, the system provides the optimal route by taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned navigation unit is During navigation, the system analyzes the user's social media activity and provides relevant route information. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned shopping department is It estimates the user's emotions and adjusts shopping suggestions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned shopping department is When shopping, the system suggests the most suitable products by referring to the user's past purchase history. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned shopping department is When shopping, the system adjusts product priorities based on the user's current purchasing intent. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned shopping department is It estimates the user's emotions and adjusts the timing of purchases based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned shopping department is When shopping, the system suggests highly relevant products based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned shopping department is When users are shopping, the system analyzes their social media activity and suggests relevant products. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned voice conversation unit is It estimates the user's emotions and adjusts the tone of the voice conversation based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned voice conversation unit is During voice conversations, the system provides the most appropriate response by referring to the user's past conversation history. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned voice conversation unit is During voice conversations, the conversation content is optimized based on the user's current situation. The system described in Appendix 1, characterized by the features described herein. (Note 40) The aforementioned voice conversation unit is It estimates the user's emotions and prioritizes voice conversations based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 41) The aforementioned voice conversation unit is During voice conversations, the system takes the user's geographical location into consideration to provide more relevant conversation content. The system described in Appendix 1, characterized by the features described herein. (Note 42) The aforementioned voice conversation unit is During voice conversations, the system analyzes the user's social media activity and provides relevant conversation content. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0207] 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 field recognition unit that recognizes the field of view, A data provision unit provides data in real time based on the field of view recognized by the field of view recognition unit, A navigation unit that performs navigation based on the data provided by the data provision unit, A shopping unit that performs online shopping based on the data provided by the aforementioned data provision unit, The system includes a voice conversation unit that engages in voice conversation based on the data provided by the data provision unit. A system characterized by the following features.

2. The aforementioned data provision unit, Display the lowest price for the product the user is viewing. The system according to feature 1.

3. The aforementioned data provision unit, We handle the purchase process for products that users are viewing. The system according to feature 1.

4. The aforementioned navigation unit is Based on the destination information entered by the user, a video is created showing how to get from the current location to the destination. The system according to feature 1.

5. The aforementioned data provision unit, Provide tourist guides and route directions. The system according to feature 1.

6. The aforementioned data provision unit, Suggest nearby recommended spots. The system according to feature 1.

7. The aforementioned data provision unit, Diagnose the symptoms The system according to feature 1.

8. The aforementioned data provision unit, Navigating the surgical techniques of top surgeons The system according to feature 1.

9. The aforementioned data provision unit, Perform real-time translation The system according to feature 1.

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