System

Abstract

An object of a system according to an embodiment is to immediately acquire and display information on a target entering a field of view.SOLUTION: A system includes a recognition unit, an acquisition unit, and a display unit. The recognition unit recognizes a target entering a field of view. The acquisition unit acquires information on the target recognized by the recognition unit. The display unit displays the information acquired by the acquisition unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has had the problem of making it difficult to instantly obtain and display information about objects within the field of view.

[0005] The system according to the embodiment aims to instantly obtain and display information about objects within view. [Means for solving the problem]

[0006] A system according to an embodiment includes a recognition unit, an acquisition unit, and a display unit. The recognition unit recognizes an object within its field of view. The acquisition unit acquires information about the object recognized by the recognition unit. The display unit displays the information acquired by the acquisition unit. [Effects of the Invention]

[0007] The system according to the embodiment can instantly obtain and display information about objects within its field of view. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A next-generation wearable device according to an embodiment of the present invention is a pair of glasses equipped with a generative AI that can explore any object in view at any time. When a user wears the glasses and gazes at an object in view, the generative AI recognizes the object and provides the user with the information they need. For example, when a user sees a historical building, the AI ​​displays the building's history and features. When a user sees a sign in a foreign language, the AI ​​automatically translates it and displays it in the user's field of view. Furthermore, when the user hears a foreign-language conversation, the AI ​​interprets it in real time and communicates it to the user. This device displays information on one lens or uses a text-to-speech function, allowing the user to move freely without occupying their hands or obstructing their field of view. This allows the next-generation wearable device to be used in a variety of situations, including sightseeing, travel, and business. For example, a user can check information about a historical building while viewing it at a tourist spot. When a user sees a sign in a foreign language while traveling abroad, the AI ​​instantly translates it, eliminating the need to get lost. Furthermore, when a user hears a foreign-language conversation, the AI ​​interprets it in real time, facilitating smooth communication.

[0029] A next-generation wearable device according to an embodiment includes a recognition unit, an acquisition unit, and a display unit. The recognition unit recognizes an object within its field of view. For example, the recognition unit recognizes an object within its field of view using image recognition technology. The recognition unit can also recognize an object within its field of view using voice recognition technology. The recognition unit can also recognize an object within its field of view using pattern recognition technology. For example, the recognition unit may use a camera to capture an image of an object within its field of view and identify the object using an image recognition algorithm. The recognition unit may also use a microphone to collect audio within its field of view and identify the object using a voice recognition algorithm. The acquisition unit acquires information about the object recognized by the recognition unit. For example, the acquisition unit acquires information from a database on the Internet. The acquisition unit can also acquire information from a local database. The acquisition unit can also acquire information from a cloud service. For example, the acquisition unit acquires information from a database on the Internet using an API. The acquisition unit can also acquire information by accessing a local database. The display unit displays the information acquired by the acquisition unit. For example, the display unit displays the information on a lens in one eye. The display unit can also read out information using voice synthesis technology. Furthermore, the display unit can also display information using projection technology. For example, the display unit displays information on one eye lens using AR glasses. Furthermore, the display unit can also read out information using voice synthesis technology. This allows the next-generation wearable device according to the embodiment to recognize an object within its field of view, acquire information about it, and display it.

[0030] The recognition unit can recognize objects within its field of view using generative AI. The generative AI can, for example, use deep learning technology to recognize objects within its field of view. The generative AI can also use GAN (generative artificial network) to recognize objects within its field of view. The generative AI can also use a Transformer model to recognize objects within its field of view. For example, the generative AI can use deep learning technology to learn from large amounts of image data and recognize objects within its field of view with high accuracy. The generative AI can also use GAN to generate features of objects within its field of view and improve recognition accuracy. The generative AI can also use a Transformer model to understand the context of objects within its field of view and improve recognition accuracy. As a result, the use of generative AI improves the accuracy of recognizing objects within its field of view.

[0031] The acquisition unit can acquire information about the object recognized using the generation AI. The generation AI can acquire information about the object recognized using, for example, deep learning technology. The generation AI can also acquire information about the object recognized using a GAN (generative artificial network). The generation AI can also acquire information about the object recognized using a Transformer model. For example, the generation AI can acquire information from a database on the Internet using deep learning technology. The generation AI can also generate features of the recognized object using a GAN and acquire the information. The generation AI can also understand the context of the recognized object using a Transformer model and acquire the information. As a result, the use of the generation AI improves the accuracy of acquiring information about the recognized object.

[0032] The display unit can display the acquired information on the lens for one eye. The lens for one eye can include, for example, AR glasses. The lens for one eye can also include a contact lens display. The lens for one eye can also include a head-up display. For example, the display unit can display information on the lens for one eye using AR glasses. The display unit can also display information on the lens for one eye using a contact lens display. The display unit can also display information on the lens for one eye using a head-up display. By displaying information on the lens for one eye, the information can be checked without obstructing the view.

[0033] The display unit can read out the acquired information. For example, speech synthesis technology is used for reading out the information. Natural language processing technology may also be used for reading out the information. Furthermore, speech recognition technology may also be used for reading out the information. For example, the display unit can read out the information acquired using speech synthesis technology. Furthermore, the display unit can also read out the information acquired using natural language processing technology. Furthermore, the display unit can also read out the information acquired using speech recognition technology. In this way, by reading out the information, it is possible to check the information without using one's hands.

[0034] The acquisition unit can translate signs in a foreign language and transmit the translation to the display unit. Foreign language signs include, for example, English, French, Chinese, etc. Foreign language signs may also include Spanish, German, Italian, etc. Foreign language signs may also include Korean, Russian, Arabic, etc. For example, the acquisition unit can translate English signs and transmit the translation to the display unit. The acquisition unit can also translate French signs and transmit the translation to the display unit. The acquisition unit can also translate Chinese signs and transmit the translation to the display unit. In this way, by translating foreign language signs, information can be understood across language barriers.

[0035] The acquisition unit can interpret foreign language conversations in real time and transmit them to the display unit. Foreign language conversations include, for example, English, French, Chinese, etc. Foreign language conversations may also include Spanish, German, Italian, etc. Foreign language conversations may also include Korean, Russian, Arabic, etc. For example, the acquisition unit can interpret English conversations in real time and transmit them to the display unit. The acquisition unit can also interpret French conversations in real time and transmit them to the display unit. The acquisition unit can also interpret Chinese conversations in real time and transmit them to the display unit. This allows for smooth communication by interpreting foreign language conversations in real time.

[0036] During recognition, the recognition unit can track the movement and changes of the object in real time, thereby improving recognition accuracy. The recognition unit can, for example, use motion capture technology to track the movement of the object in real time. The recognition unit can also use real-time tracking technology to track changes in the object. Furthermore, the recognition unit can also use video analysis technology to track the movement and changes of the object. For example, the recognition unit can use motion capture technology to track moving cars that come into the field of view of a walking user in real time. The recognition unit can also use real-time tracking technology to track people moving in tourist spots. Furthermore, the recognition unit can also use video analysis technology to track animals moving in natural environments. As a result, by tracking the movement and changes of the object in real time, recognition accuracy is improved.

[0037] The recognition unit can improve recognition accuracy by taking into account background information of the target during recognition. The recognition unit can, for example, use environmental data to take into account background information of the target. The recognition unit can also use past history information to take into account background information of the target. Furthermore, the recognition unit can also use real-time data to take into account background information of the target. For example, when recognizing a historical building, the recognition unit can improve recognition accuracy by taking into account background information of the surrounding area. Furthermore, when recognizing a specific plant in a natural environment, the recognition unit can improve recognition accuracy by taking into account background information of the surrounding area. Furthermore, when recognizing a specific building in an urban environment, the recognition unit can improve recognition accuracy by taking into account background information of the surrounding area. In this way, recognition accuracy is improved by taking into account background information of the target.

[0038] During recognition, the recognition unit can predict a recognition target by referring to the user's past gaze history. The recognition unit, for example, acquires the user's gaze history using an eye tracking device. The recognition unit can also analyze the user's gaze history using a data analysis method. The recognition unit can also analyze the user's gaze history using a machine learning algorithm. For example, the recognition unit preferentially recognizes objects that the user has frequently looked at in the past. The recognition unit can also predict and recognize objects that the user looked at in a specific time period based on the user's past gaze history. Furthermore, the recognition unit can analyze the user's past gaze history and predict and recognize objects that the user is likely to be interested in. This makes it easier to predict a recognition target by referring to the user's past gaze history.

[0039] During recognition, the recognition unit can analyze the target's audio information and integrate it with visual information to improve recognition accuracy. For example, the recognition unit analyzes the target's audio information using speech recognition technology. The recognition unit can also integrate it with visual information using a data integration algorithm. Furthermore, the recognition unit can analyze the target's audio information using natural language processing technology. For example, the recognition unit can analyze the guide's voice at a tourist attraction and integrate it with visual information to improve recognition accuracy. The recognition unit can also analyze the speaker's voice during a meeting and integrate it with visual information to improve recognition accuracy. Furthermore, the recognition unit can analyze animal sounds in a natural environment and integrate it with visual information to improve recognition accuracy. In this way, recognition accuracy is improved by analyzing the target's audio information and integrating it with visual information.

[0040] The recognition unit can improve recognition accuracy by taking into account physical characteristics such as the temperature and texture of the object during recognition. For example, the recognition unit measures the temperature of the object using a temperature sensor. The recognition unit can also measure the texture of the object using a texture sensor. Furthermore, the recognition unit can consider the physical characteristics of the object using an algorithm that analyzes physical characteristics. For example, the recognition unit can improve recognition accuracy by taking into account the temperature and texture of a historical building. The recognition unit can also improve recognition accuracy by taking into account the temperature and texture of a specific plant in a natural environment. Furthermore, the recognition unit can improve recognition accuracy by taking into account the temperature and texture of a specific building in an urban environment. In this way, recognition accuracy is improved by taking into account the physical characteristics of the object.

[0041] During recognition, the recognition unit can narrow down the recognition targets by taking into account the user's geographical location information. The recognition unit, for example, acquires the user's geographical location information using GPS data. The recognition unit can also acquire the user's geographical location information using a location information service. The recognition unit can also narrow down the recognition targets by using an algorithm that analyzes the geographical location information. For example, if the user is in a tourist spot, the recognition unit can narrow down the recognition targets based on the geographical location information. If the user is in an urban area, the recognition unit can also narrow down the recognition targets based on the geographical location information. If the user is in a natural environment, the recognition unit can also narrow down the recognition targets based on the geographical location information. In this way, the recognition unit can narrow down the recognition targets by taking into account the user's geographical location information.

[0042] During acquisition, the acquisition unit can integrate and acquire related information of the target from multiple data sources. The acquisition unit, for example, acquires related information of the target using a database. The acquisition unit can also acquire related information of the target using an API. Furthermore, the acquisition unit can acquire related information of the target using a web service. For example, the acquisition unit integrates and acquires information on historical buildings from multiple history databases. The acquisition unit can also integrate and acquire information on specific plants in natural environments from multiple plant databases. Furthermore, the acquisition unit can integrate and acquire information on specific buildings in urban environments from multiple architectural databases. This improves the accuracy of the acquired information by integrating information from multiple data sources.

[0043] The acquisition unit can complement the acquired information by referring to past history information of the target at the time of acquisition. The acquisition unit, for example, acquires past history information of the target using log data. The acquisition unit can also acquire past history information of the target using a history database. Furthermore, the acquisition unit can complement the acquired information using an algorithm that analyzes past history information. For example, the acquisition unit complements the acquired information by referring to the past repair history of a historical building. The acquisition unit can also complement the acquired information by referring to the past growth history of a specific plant in a natural environment. Furthermore, the acquisition unit can complement the acquired information by referring to the past usage history of a specific building in an urban environment. In this way, the acquired information can be complemented by referring to the past history information of the target.

[0044] The acquisition unit can customize the acquired information by referring to the user's past search history during acquisition. The acquisition unit can acquire the user's past search history using, for example, browser history. The acquisition unit can also acquire the user's past search history using search engine data. Furthermore, the acquisition unit can customize the acquired information using an algorithm that analyzes the search history. For example, the acquisition unit can prioritize acquiring information on historical buildings that the user has previously searched for. The acquisition unit can also prioritize acquiring information on plants that the user has previously searched for. Furthermore, the acquisition unit can prioritize acquiring information on buildings that the user has previously searched for. In this way, the acquired information can be customized by referring to the user's past search history.

[0045] During acquisition, the acquisition unit can analyze the target's audio information and integrate it with visual information to provide acquired information. The acquisition unit can, for example, analyze the target's audio information using speech recognition technology. The acquisition unit can also integrate it with visual information using a data integration algorithm. The acquisition unit can also analyze the target's audio information using natural language processing technology. For example, the acquisition unit can analyze the voice of a guide at a tourist attraction and integrate it with visual information to provide acquired information. The acquisition unit can also analyze the voice of a speaker during a meeting and integrate it with visual information to provide acquired information. The acquisition unit can also analyze animal sounds in a natural environment and integrate it with visual information to provide acquired information. In this way, the accuracy of the acquired information is improved by analyzing the target's audio information and integrating it with visual information.

[0046] The acquisition unit can provide acquired information by taking into account the physical characteristics (temperature, texture, etc.) of the object during acquisition. For example, the acquisition unit measures the temperature of the object using a temperature sensor. The acquisition unit can also measure the texture of the object using a texture sensor. Furthermore, the acquisition unit can also consider the physical characteristics of the object using an algorithm that analyzes physical characteristics. For example, the acquisition unit can provide acquired information by taking into account the temperature and texture of a historical building. The acquisition unit can also provide acquired information by taking into account the temperature and texture of a specific plant in a natural environment. Furthermore, the acquisition unit can provide acquired information by taking into account the temperature and texture of a specific building in an urban environment. In this way, the accuracy of the acquired information is improved by taking into account the physical characteristics of the object.

[0047] The acquisition unit can acquire related information by taking into account the user's geographical location information at the time of acquisition. The acquisition unit acquires the user's geographical location information, for example, using GPS data. The acquisition unit can also acquire the user's geographical location information using a location information service. The acquisition unit can also acquire related information using an algorithm that analyzes geographical location information. For example, when the user is in a tourist spot, the acquisition unit acquires related information based on the geographical location information. When the user is in an urban area, the acquisition unit can also acquire related information based on the geographical location information. When the user is in a natural environment, the acquisition unit can also acquire related information based on the geographical location information. This makes it possible to efficiently acquire related information by taking into account the user's geographical location information.

[0048] The display unit can track the user's gaze movement during display and adjust the optimal display position. The display unit, for example, tracks the user's gaze movement using an eye-tracking device. The display unit can also analyze the user's gaze movement using a data analysis method. Furthermore, the display unit can analyze the user's gaze movement using a machine learning algorithm. For example, the display unit adjusts the display position of information in real time when the user moves their gaze. Furthermore, the display unit can display information about a specific object at an optimal position when the user gazes at that object. Furthermore, the display unit can adjust the display position of information to match the user's gaze when the user fixates their gaze. In this way, the optimal display position can be provided by tracking the user's gaze movement.

[0049] The display unit can customize the display content by referring to the user's past display history when displaying. The display unit, for example, acquires the user's past display history using log data. The display unit can also acquire the user's past display history using a history database. The display unit can also customize the display content using an algorithm that analyzes the display history. For example, the display unit customizes the display content based on information that the user has previously displayed. The display unit can also customize the display content based on information that was displayed during a specific time period from the user's past display history. The display unit can also analyze the user's past display history and customize and display information that is likely to be of interest to the user. In this way, the display content can be customized by referring to the user's past display history.

[0050] The display unit can adjust the display method according to the user's visual characteristics (color vision, eyesight, etc.) when displaying information. The display unit, for example, acquires the user's color vision characteristics using a color vision test. The display unit can also acquire the user's eyesight characteristics using a vision test. The display unit can also adjust the display method using an algorithm that analyzes the visual characteristics. For example, if the user has color vision deficiency, the display unit provides a display method that accommodates the color vision. If the user has poor eyesight, the display unit can also display information in a large font size. If the user has good eyesight, the display unit can also display detailed information in a small font size. This makes it possible to provide more appropriate information by adjusting the display method according to the user's visual characteristics.

[0051] The display unit can switch the display content based on a user's voice instruction during display. The display unit acquires the user's voice instruction using, for example, voice recognition technology. The display unit can also analyze the user's voice instruction using command analysis technology. The display unit can also switch the display content using an algorithm that analyzes the voice instruction. For example, the display unit can switch to the next information when the user issues a voice instruction such as "Show the next information." The display unit can also switch to detailed information when the user issues a voice instruction such as "Show details." The display unit can also return to the previous information when the user issues a voice instruction such as "Go back." This allows for more flexible information provision by switching the display content based on the user's voice instruction.

[0052] When displaying, the display unit can provide display content in cooperation with the user's device information (smartphone, tablet, etc.). The display unit, for example, acquires the user's device information using a smartphone. The display unit can also acquire the user's device information using a tablet. The display unit can also acquire the user's device information using a smartwatch. For example, if the user is using a smartphone, the display unit can display information on the smartphone screen. If the user is using a tablet, the display unit can also display information on the tablet screen. If the user is using a smartwatch, the display unit can also display information on the smartwatch screen. This makes it possible to provide a wider variety of information by coordinating with the user's device information.

[0053] The display unit can adjust the display method according to the user's environmental information (brightness, volume, etc.) when displaying information. The display unit acquires the user's environmental information using, for example, a brightness sensor. The display unit can also acquire the user's environmental information using a volume sensor. Furthermore, the display unit can adjust the display method using an algorithm that analyzes the environmental information. For example, when the user is in a bright place, the display unit provides a display method with high visibility. Furthermore, when the user is in a dark place, the display unit can also provide a display method that is easy on the eyes. Furthermore, when the user is in a noisy place, the display unit can increase the volume and read out information. In this way, by adjusting the display method according to the user's environmental information, more appropriate information can be provided.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] Next-generation wearable devices may also include a health management unit that monitors the user's health. The health management unit measures vital signs such as heart rate, blood pressure, and body temperature to monitor the user's health in real time. The health management unit can also record the user's exercise and sleep patterns and provide health management advice. For example, if the user's heart rate suddenly increases during exercise, the health management unit can display a warning to stop exercising. If the user has been sitting for a long time, the health management unit can also notify the user to stand up and stretch. Furthermore, the health management unit can analyze the user's sleep patterns and provide advice for better sleep. In this way, next-generation wearable devices can support users' health management and promote healthier lifestyles.

[0056] Next-generation wearable devices may further include a behavior analysis unit that analyzes a user's behavioral history and provides daily living advice based on the user's behavioral patterns. The behavior analysis unit, for example, records the user's movement history and activity history and suggests ways to improve the user's daily life. For example, if the user moves to the same place at the same time every day, the behavior analysis unit can recognize this pattern and suggest an efficient route. Also, if the user feels a lot of stress during a certain time of day, the behavior analysis unit can suggest activities to relax during that time. Furthermore, the behavior analysis unit can analyze the user's dietary history and suggest healthy meals. This allows next-generation wearable devices to improve the quality of daily life based on the user's behavioral patterns.

[0057] Next-generation wearable devices may also be equipped with a learning support unit that analyzes a user's learning history and provides advice to improve learning efficiency. The learning support unit may, for example, record the content and study time of the user in the past and suggest efficient study methods. For example, if the user has difficulty in a particular subject, the learning support unit may provide advice on focusing on that part. If the user has been studying for a long time, the learning support unit may also suggest appropriate times to take a break. Furthermore, it may be able to recommend learning materials and resources that suit the user's learning style. In this way, next-generation wearable devices may improve the user's learning efficiency and support more effective learning.

[0058] Next-generation wearable devices may also be equipped with an exercise support unit that analyzes the user's exercise history and provides advice to improve exercise efficiency. For example, the exercise support unit may record the type and duration of exercise the user has performed in the past and suggest an effective exercise plan. For example, if the user has been performing a particular exercise, the unit may provide advice to maximize the effectiveness of that exercise. Furthermore, if the user is starting a new exercise, the unit may provide the correct method and precautions for that exercise. Furthermore, the unit may suggest appropriate exercise intensity and frequency based on the user's exercise history. This allows next-generation wearable devices to improve the user's exercise efficiency and support a healthier lifestyle.

[0059] Next-generation wearable devices may also include a hobby recommendation unit that analyzes a user's hobbies and interests and recommends related events and activities. For example, the hobby recommendation unit may record the history of events and activities the user has participated in in the past and recommend new related events. For example, if a user frequently attends music concerts, the hobby recommendation unit may provide information about nearby concerts. If a user enjoys outdoor activities, the hobby recommendation unit may provide information about nearby hiking trails and campsites. Furthermore, if a user searches for information about a specific hobby, the hobby recommendation unit may recommend events and activities related to that hobby. This allows next-generation wearable devices to provide information based on a user's hobbies and interests, supporting a more fulfilling life.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The recognition unit recognizes objects within its field of view. The recognition unit can recognize objects using image recognition technology, voice recognition technology, or pattern recognition technology. For example, a camera can be used to capture an image of an object within its field of view, and an image recognition algorithm can be used to identify the object. A microphone can also be used to collect audio within its field of view, and a voice recognition algorithm can be used to identify the object. Step 2: The acquisition unit acquires information about the object recognized by the recognition unit. The acquisition unit can acquire information from an online database, a local database, or a cloud service. For example, the acquisition unit can acquire information from an online database using an API. It can also acquire information by accessing a local database. Step 3: The display unit displays the information acquired by the acquisition unit. The display unit can display the information on a lens in one eye, read the information aloud using voice synthesis technology, or display the information using projection technology. For example, the display unit can display the information on a lens in one eye using AR glasses. It can also read the information aloud using voice synthesis technology.

[0062] (Example 2) A next-generation wearable device according to an embodiment of the present invention is a pair of glasses equipped with a generative AI that can explore any object in view at any time. When a user wears the glasses and gazes at an object in view, the generative AI recognizes the object and provides the user with the information they need. For example, when a user sees a historical building, the AI ​​displays the building's history and features. When a user sees a sign in a foreign language, the AI ​​automatically translates it and displays it in the user's field of view. Furthermore, when the user hears a foreign-language conversation, the AI ​​interprets it in real time and communicates it to the user. This device displays information on one lens or uses a text-to-speech function, allowing the user to move freely without occupying their hands or obstructing their field of view. This allows the next-generation wearable device to be used in a variety of situations, including sightseeing, travel, and business. For example, a user can check information about a historical building while viewing it at a tourist spot. When a user sees a sign in a foreign language while traveling abroad, the AI ​​instantly translates it, eliminating the need to get lost. Furthermore, when a user hears a foreign-language conversation, the AI ​​interprets it in real time, facilitating smooth communication.

[0063] A next-generation wearable device according to an embodiment includes a recognition unit, an acquisition unit, and a display unit. The recognition unit recognizes an object within its field of view. For example, the recognition unit recognizes an object within its field of view using image recognition technology. The recognition unit can also recognize an object within its field of view using voice recognition technology. The recognition unit can also recognize an object within its field of view using pattern recognition technology. For example, the recognition unit may use a camera to capture an image of an object within its field of view and identify the object using an image recognition algorithm. The recognition unit may also use a microphone to collect audio within its field of view and identify the object using a voice recognition algorithm. The acquisition unit acquires information about the object recognized by the recognition unit. For example, the acquisition unit acquires information from a database on the Internet. The acquisition unit can also acquire information from a local database. The acquisition unit can also acquire information from a cloud service. For example, the acquisition unit acquires information from a database on the Internet using an API. The acquisition unit can also acquire information by accessing a local database. The display unit displays the information acquired by the acquisition unit. For example, the display unit displays the information on a lens in one eye. The display unit can also read out information using voice synthesis technology. Furthermore, the display unit can also display information using projection technology. For example, the display unit displays information on one eye lens using AR glasses. Furthermore, the display unit can also read out information using voice synthesis technology. This allows the next-generation wearable device according to the embodiment to recognize an object within its field of view, acquire information about it, and display it.

[0064] The recognition unit can recognize objects within its field of view using generative AI. The generative AI can, for example, use deep learning technology to recognize objects within its field of view. The generative AI can also use GAN (generative artificial network) to recognize objects within its field of view. The generative AI can also use a Transformer model to recognize objects within its field of view. For example, the generative AI can use deep learning technology to learn from large amounts of image data and recognize objects within its field of view with high accuracy. The generative AI can also use GAN to generate features of objects within its field of view and improve recognition accuracy. The generative AI can also use a Transformer model to understand the context of objects within its field of view and improve recognition accuracy. As a result, the use of generative AI improves the accuracy of recognizing objects within its field of view.

[0065] The acquisition unit can acquire information about the object recognized using the generation AI. The generation AI can acquire information about the object recognized using, for example, deep learning technology. The generation AI can also acquire information about the object recognized using a GAN (generative artificial network). The generation AI can also acquire information about the object recognized using a Transformer model. For example, the generation AI can acquire information from a database on the Internet using deep learning technology. The generation AI can also generate features of the recognized object using a GAN and acquire the information. The generation AI can also understand the context of the recognized object using a Transformer model and acquire the information. As a result, the use of the generation AI improves the accuracy of acquiring information about the recognized object.

[0066] The display unit can display the acquired information on the lens for one eye. The lens for one eye can include, for example, AR glasses. The lens for one eye can also include a contact lens display. The lens for one eye can also include a head-up display. For example, the display unit can display information on the lens for one eye using AR glasses. The display unit can also display information on the lens for one eye using a contact lens display. The display unit can also display information on the lens for one eye using a head-up display. By displaying information on the lens for one eye, the information can be checked without obstructing the view.

[0067] The display unit can read out the acquired information. For example, speech synthesis technology is used for reading out the information. Natural language processing technology may also be used for reading out the information. Furthermore, speech recognition technology may also be used for reading out the information. For example, the display unit can read out the information acquired using speech synthesis technology. Furthermore, the display unit can also read out the information acquired using natural language processing technology. Furthermore, the display unit can also read out the information acquired using speech recognition technology. In this way, by reading out the information, it is possible to check the information without using one's hands.

[0068] The acquisition unit can translate signs in a foreign language and transmit the translation to the display unit. Foreign language signs include, for example, English, French, Chinese, etc. Foreign language signs may also include Spanish, German, Italian, etc. Foreign language signs may also include Korean, Russian, Arabic, etc. For example, the acquisition unit can translate English signs and transmit the translation to the display unit. The acquisition unit can also translate French signs and transmit the translation to the display unit. The acquisition unit can also translate Chinese signs and transmit the translation to the display unit. In this way, by translating foreign language signs, information can be understood across language barriers.

[0069] The acquisition unit can interpret foreign language conversations in real time and transmit them to the display unit. Foreign language conversations include, for example, English, French, Chinese, etc. Foreign language conversations may also include Spanish, German, Italian, etc. Foreign language conversations may also include Korean, Russian, Arabic, etc. For example, the acquisition unit can interpret English conversations in real time and transmit them to the display unit. The acquisition unit can also interpret French conversations in real time and transmit them to the display unit. The acquisition unit can also interpret Chinese conversations in real time and transmit them to the display unit. This allows for smooth communication by interpreting foreign language conversations in real time.

[0070] The recognition unit can estimate the user's emotions and prioritize the objects to be recognized based on the estimated user's emotions. The recognition unit can estimate the user's emotions using, for example, facial expression recognition technology. The recognition unit can also estimate the user's emotions using voice analysis technology. Furthermore, the recognition unit can estimate the user's emotions using biometrics technology. For example, the recognition unit can analyze the user's facial expressions using facial expression recognition technology to estimate the user's emotions. The recognition unit can also analyze the tone and speed of the user's voice using voice analysis technology to estimate the user's emotions. Furthermore, the recognition unit can analyze the user's heart rate and electrodermal activity using biometrics technology to estimate the user's emotions. This allows for more appropriate information to be provided by prioritizing the objects to be recognized based on the user's emotions. For example, if the user is excited, the generation AI can prioritize recognizing all objects within its field of view and providing information. Alternatively, if the user is relaxed, the generation AI can prioritize recognizing objects within its field of view that are likely to be of particular interest. Alternatively, if the user is tired, the generation AI can prioritize recognizing only the most important information within its field of view.

[0071] During recognition, the recognition unit can track the movement and changes of the object in real time, thereby improving recognition accuracy. The recognition unit can, for example, use motion capture technology to track the movement of the object in real time. The recognition unit can also use real-time tracking technology to track changes in the object. Furthermore, the recognition unit can also use video analysis technology to track the movement and changes of the object. For example, the recognition unit can use motion capture technology to track moving cars that come into the field of view of a walking user in real time. The recognition unit can also use real-time tracking technology to track people moving in tourist spots. Furthermore, the recognition unit can also use video analysis technology to track animals moving in natural environments. As a result, by tracking the movement and changes of the object in real time, recognition accuracy is improved.

[0072] The recognition unit can improve recognition accuracy by taking into account background information of the target during recognition. The recognition unit can, for example, use environmental data to take into account background information of the target. The recognition unit can also use past history information to take into account background information of the target. Furthermore, the recognition unit can also use real-time data to take into account background information of the target. For example, when recognizing a historical building, the recognition unit can improve recognition accuracy by taking into account background information of the surrounding area. Furthermore, when recognizing a specific plant in a natural environment, the recognition unit can improve recognition accuracy by taking into account background information of the surrounding area. Furthermore, when recognizing a specific building in an urban environment, the recognition unit can improve recognition accuracy by taking into account background information of the surrounding area. In this way, recognition accuracy is improved by taking into account background information of the target.

[0073] During recognition, the recognition unit can predict a recognition target by referring to the user's past gaze history. The recognition unit, for example, acquires the user's gaze history using an eye tracking device. The recognition unit can also analyze the user's gaze history using a data analysis method. The recognition unit can also analyze the user's gaze history using a machine learning algorithm. For example, the recognition unit preferentially recognizes objects that the user has frequently looked at in the past. The recognition unit can also predict and recognize objects that the user looked at in a specific time period based on the user's past gaze history. Furthermore, the recognition unit can analyze the user's past gaze history and predict and recognize objects that the user is likely to be interested in. This makes it easier to predict a recognition target by referring to the user's past gaze history.

[0074] The recognition unit can estimate the user's emotion and adjust the display method of the recognition result based on the estimated user's emotion. The recognition unit can estimate the user's emotion using, for example, facial expression recognition technology. The recognition unit can also estimate the user's emotion using voice analysis technology. Furthermore, the recognition unit can estimate the user's emotion using biometrics technology. For example, the recognition unit can analyze the user's facial expression using facial expression recognition technology to estimate the emotion. The recognition unit can also analyze the tone and speed of the user's voice using voice analysis technology to estimate the emotion. Furthermore, the recognition unit can analyze the user's heart rate and electrodermal activity using biometrics technology to estimate the emotion. This allows the display method to be adjusted based on the user's emotion, thereby providing more appropriate information. For example, if the user is nervous, a simple, highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. If the user is in a hurry, a display method that focuses on the main points can be provided.

[0075] During recognition, the recognition unit can analyze the target's audio information and integrate it with visual information to improve recognition accuracy. For example, the recognition unit analyzes the target's audio information using speech recognition technology. The recognition unit can also integrate it with visual information using a data integration algorithm. Furthermore, the recognition unit can analyze the target's audio information using natural language processing technology. For example, the recognition unit can analyze the guide's voice at a tourist attraction and integrate it with visual information to improve recognition accuracy. The recognition unit can also analyze the speaker's voice during a meeting and integrate it with visual information to improve recognition accuracy. Furthermore, the recognition unit can analyze animal sounds in a natural environment and integrate it with visual information to improve recognition accuracy. In this way, recognition accuracy is improved by analyzing the target's audio information and integrating it with visual information.

[0076] The recognition unit can improve recognition accuracy by taking into account physical characteristics such as the temperature and texture of the object during recognition. For example, the recognition unit measures the temperature of the object using a temperature sensor. The recognition unit can also measure the texture of the object using a texture sensor. Furthermore, the recognition unit can consider the physical characteristics of the object using an algorithm that analyzes physical characteristics. For example, the recognition unit can improve recognition accuracy by taking into account the temperature and texture of a historical building. The recognition unit can also improve recognition accuracy by taking into account the temperature and texture of a specific plant in a natural environment. Furthermore, the recognition unit can improve recognition accuracy by taking into account the temperature and texture of a specific building in an urban environment. In this way, recognition accuracy is improved by taking into account the physical characteristics of the object.

[0077] During recognition, the recognition unit can narrow down the recognition targets by taking into account the user's geographical location information. The recognition unit, for example, acquires the user's geographical location information using GPS data. The recognition unit can also acquire the user's geographical location information using a location information service. The recognition unit can also narrow down the recognition targets by using an algorithm that analyzes the geographical location information. For example, if the user is in a tourist spot, the recognition unit can narrow down the recognition targets based on the geographical location information. If the user is in an urban area, the recognition unit can also narrow down the recognition targets based on the geographical location information. If the user is in a natural environment, the recognition unit can also narrow down the recognition targets based on the geographical location information. In this way, the recognition unit can narrow down the recognition targets by taking into account the user's geographical location information.

[0078] The acquisition unit can estimate the user's emotions and determine the priority of information to be acquired based on the estimated user's emotions. The acquisition unit can estimate the user's emotions using, for example, facial expression recognition technology. The acquisition unit can also estimate the user's emotions using voice analysis technology. Furthermore, the acquisition unit can estimate the user's emotions using biometrics technology. For example, the acquisition unit can analyze the user's facial expressions using facial expression recognition technology to estimate emotions. The acquisition unit can also analyze the tone and speed of the user's voice using voice analysis technology to estimate emotions. Furthermore, the acquisition unit can analyze the user's heart rate and electrodermal activity using biometrics technology to estimate emotions. This allows more appropriate information to be acquired by determining the priority of information based on the user's emotions. For example, if the user is excited, the generation AI can prioritize acquiring information about all objects within its field of view. Alternatively, if the user is relaxed, the generation AI can prioritize acquiring information about objects within its field of view that are likely to be of particular interest. Alternatively, if the user is tired, the generation AI can prioritize acquiring only the most important information from among the objects within its field of view.

[0079] During acquisition, the acquisition unit can integrate and acquire related information of the target from multiple data sources. The acquisition unit, for example, acquires related information of the target using a database. The acquisition unit can also acquire related information of the target using an API. Furthermore, the acquisition unit can acquire related information of the target using a web service. For example, the acquisition unit integrates and acquires information on historical buildings from multiple history databases. The acquisition unit can also integrate and acquire information on specific plants in natural environments from multiple plant databases. Furthermore, the acquisition unit can integrate and acquire information on specific buildings in urban environments from multiple architectural databases. This improves the accuracy of the acquired information by integrating information from multiple data sources.

[0080] The acquisition unit can complement the acquired information by referring to past history information of the target at the time of acquisition. The acquisition unit, for example, acquires past history information of the target using log data. The acquisition unit can also acquire past history information of the target using a history database. Furthermore, the acquisition unit can complement the acquired information using an algorithm that analyzes past history information. For example, the acquisition unit complements the acquired information by referring to the past repair history of a historical building. The acquisition unit can also complement the acquired information by referring to the past growth history of a specific plant in a natural environment. Furthermore, the acquisition unit can complement the acquired information by referring to the past usage history of a specific building in an urban environment. In this way, the acquired information can be complemented by referring to the past history information of the target.

[0081] The acquisition unit can customize the acquired information by referring to the user's past search history during acquisition. The acquisition unit can acquire the user's past search history using, for example, browser history. The acquisition unit can also acquire the user's past search history using search engine data. Furthermore, the acquisition unit can customize the acquired information using an algorithm that analyzes the search history. For example, the acquisition unit can prioritize acquiring information on historical buildings that the user has previously searched for. The acquisition unit can also prioritize acquiring information on plants that the user has previously searched for. Furthermore, the acquisition unit can prioritize acquiring information on buildings that the user has previously searched for. In this way, the acquired information can be customized by referring to the user's past search history.

[0082] The acquisition unit can estimate the user's emotion and adjust the display method of the acquired information based on the estimated user's emotion. The acquisition unit can estimate the user's emotion using, for example, facial expression recognition technology. The acquisition unit can also estimate the user's emotion using voice analysis technology. Furthermore, the acquisition unit can estimate the user's emotion using biometrics technology. For example, the acquisition unit can analyze the user's facial expression using facial expression recognition technology to estimate the emotion. The acquisition unit can also analyze the tone and speed of the user's voice using voice analysis technology to estimate the emotion. Furthermore, the acquisition unit can analyze the user's heart rate and electrodermal activity using biometrics technology to estimate the emotion. This allows the display method to be adjusted based on the user's emotion, thereby providing more appropriate information. For example, if the user is nervous, a simple, highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. If the user is in a hurry, a display method that focuses on the main points can be provided.

[0083] During acquisition, the acquisition unit can analyze the target's audio information and integrate it with visual information to provide acquired information. The acquisition unit can, for example, analyze the target's audio information using speech recognition technology. The acquisition unit can also integrate it with visual information using a data integration algorithm. The acquisition unit can also analyze the target's audio information using natural language processing technology. For example, the acquisition unit can analyze the voice of a guide at a tourist attraction and integrate it with visual information to provide acquired information. The acquisition unit can also analyze the voice of a speaker during a meeting and integrate it with visual information to provide acquired information. The acquisition unit can also analyze animal sounds in a natural environment and integrate it with visual information to provide acquired information. In this way, the accuracy of the acquired information is improved by analyzing the target's audio information and integrating it with visual information.

[0084] The acquisition unit can provide acquired information by taking into account the physical characteristics (temperature, texture, etc.) of the object during acquisition. For example, the acquisition unit measures the temperature of the object using a temperature sensor. The acquisition unit can also measure the texture of the object using a texture sensor. Furthermore, the acquisition unit can also consider the physical characteristics of the object using an algorithm that analyzes physical characteristics. For example, the acquisition unit can provide acquired information by taking into account the temperature and texture of a historical building. The acquisition unit can also provide acquired information by taking into account the temperature and texture of a specific plant in a natural environment. Furthermore, the acquisition unit can provide acquired information by taking into account the temperature and texture of a specific building in an urban environment. In this way, the accuracy of the acquired information is improved by taking into account the physical characteristics of the object.

[0085] The acquisition unit can acquire related information by taking into account the user's geographical location information at the time of acquisition. The acquisition unit acquires the user's geographical location information, for example, using GPS data. The acquisition unit can also acquire the user's geographical location information using a location information service. The acquisition unit can also acquire related information using an algorithm that analyzes geographical location information. For example, when the user is in a tourist spot, the acquisition unit acquires related information based on the geographical location information. When the user is in an urban area, the acquisition unit can also acquire related information based on the geographical location information. When the user is in a natural environment, the acquisition unit can also acquire related information based on the geographical location information. This makes it possible to efficiently acquire related information by taking into account the user's geographical location information.

[0086] The display unit can estimate the user's emotions and prioritize display content based on the estimated user's emotions. The display unit can estimate the user's emotions using, for example, facial expression recognition technology. The display unit can also estimate the user's emotions using voice analysis technology. Furthermore, the display unit can estimate the user's emotions using biometrics technology. For example, the display unit can analyze the user's facial expressions using facial expression recognition technology to estimate emotions. The display unit can also analyze the tone and speed of the user's voice using voice analysis technology to estimate emotions. Furthermore, the display unit can analyze the user's heart rate and electrodermal activity using biometrics technology to estimate emotions. This allows the display unit to prioritize display content based on the user's emotions, thereby providing more appropriate information. For example, if the user is excited, the generation AI can prioritize displaying all information within its field of view. Alternatively, if the user is relaxed, the generation AI can prioritize displaying information that is likely to be of particular interest from among the information within its field of view. Alternatively, if the user is tired, the generation AI can prioritize displaying only the most important information from among the information within its field of view.

[0087] The display unit can track the user's gaze movement during display and adjust the optimal display position. The display unit, for example, tracks the user's gaze movement using an eye-tracking device. The display unit can also analyze the user's gaze movement using a data analysis method. Furthermore, the display unit can analyze the user's gaze movement using a machine learning algorithm. For example, the display unit adjusts the display position of information in real time when the user moves their gaze. Furthermore, the display unit can display information about a specific object at an optimal position when the user gazes at that object. Furthermore, the display unit can adjust the display position of information to match the user's gaze when the user fixates their gaze. In this way, the optimal display position can be provided by tracking the user's gaze movement.

[0088] The display unit can customize the display content by referring to the user's past display history when displaying. The display unit, for example, acquires the user's past display history using log data. The display unit can also acquire the user's past display history using a history database. The display unit can also customize the display content using an algorithm that analyzes the display history. For example, the display unit customizes the display content based on information that the user has previously displayed. The display unit can also customize the display content based on information that was displayed during a specific time period from the user's past display history. The display unit can also analyze the user's past display history and customize and display information that is likely to be of interest to the user. In this way, the display content can be customized by referring to the user's past display history.

[0089] The display unit can adjust the display method according to the user's visual characteristics (color vision, eyesight, etc.) when displaying information. The display unit, for example, acquires the user's color vision characteristics using a color vision test. The display unit can also acquire the user's eyesight characteristics using a vision test. The display unit can also adjust the display method using an algorithm that analyzes the visual characteristics. For example, if the user has color vision deficiency, the display unit provides a display method that accommodates the color vision. If the user has poor eyesight, the display unit can also display information in a large font size. If the user has good eyesight, the display unit can also display detailed information in a small font size. This makes it possible to provide more appropriate information by adjusting the display method according to the user's visual characteristics.

[0090] The display unit can estimate the user's emotion and adjust the length of the display content based on the estimated user's emotion. The display unit can estimate the user's emotion using, for example, facial expression recognition technology. The display unit can also estimate the user's emotion using voice analysis technology. Furthermore, the display unit can estimate the user's emotion using biometrics technology. For example, the display unit can analyze the user's facial expression using facial expression recognition technology to estimate the emotion. The display unit can also analyze the tone and speed of the user's voice using voice analysis technology to estimate the emotion. Furthermore, the display unit can analyze the user's heart rate and electrodermal activity using biometrics technology to estimate the emotion. This allows the display unit to provide more appropriate information by adjusting the length of the display content based on the user's emotion. For example, if the user is in a hurry, short, to-the-point display content can be provided. If the user is relaxed, longer display content including detailed explanations can be provided. If the user is excited, display content with visually stimulating effects can be provided.

[0091] The display unit can switch the display content based on a user's voice instruction during display. The display unit acquires the user's voice instruction using, for example, voice recognition technology. The display unit can also analyze the user's voice instruction using command analysis technology. The display unit can also switch the display content using an algorithm that analyzes the voice instruction. For example, the display unit can switch to the next information when the user issues a voice instruction such as "Show the next information." The display unit can also switch to detailed information when the user issues a voice instruction such as "Show details." The display unit can also return to the previous information when the user issues a voice instruction such as "Go back." This allows for more flexible information provision by switching the display content based on the user's voice instruction.

[0092] When displaying, the display unit can provide display content in cooperation with the user's device information (smartphone, tablet, etc.). The display unit, for example, acquires the user's device information using a smartphone. The display unit can also acquire the user's device information using a tablet. The display unit can also acquire the user's device information using a smartwatch. For example, if the user is using a smartphone, the display unit can display information on the smartphone screen. If the user is using a tablet, the display unit can also display information on the tablet screen. If the user is using a smartwatch, the display unit can also display information on the smartwatch screen. This makes it possible to provide a wider variety of information by coordinating with the user's device information.

[0093] The display unit can adjust the display method according to the user's environmental information (brightness, volume, etc.) when displaying information. The display unit acquires the user's environmental information using, for example, a brightness sensor. The display unit can also acquire the user's environmental information using a volume sensor. Furthermore, the display unit can adjust the display method using an algorithm that analyzes the environmental information. For example, when the user is in a bright place, the display unit provides a display method with high visibility. Furthermore, when the user is in a dark place, the display unit can also provide a display method that is easy on the eyes. Furthermore, when the user is in a noisy place, the display unit can increase the volume and read out information. In this way, by adjusting the display method according to the user's environmental information, more appropriate information can be provided. === Hard Collateral 1-1 === Each of the multiple elements including the recognition unit, acquisition unit, and display unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the recognition unit can recognize objects within its field of view using the camera 42 and microphone 38B of the smart device 14, and perform image recognition and voice recognition using the control unit 46A. The acquisition unit can acquire information from an internet database or a local database using the specific processing unit 290 of the data processing device 12. The display unit can display information using the display 40A and speaker 40B of the smart device 14, and read the information aloud using voice synthesis technology. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned recognition unit, acquisition unit, and display unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the recognition unit can recognize objects in the field of view using the camera 42 and microphone 238 of the smart glasses 214, and perform image recognition and voice recognition using the control unit 46A. The acquisition unit can acquire information from a database on the Internet or a local database using the specific processing unit 290 of the data processing device 12. The display unit can display information using the display and speaker 240 of the smart glasses 214 and read the information aloud using voice synthesis technology. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned recognition unit, acquisition unit, and display unit is realized, for example, in at least one of the headset type terminal 314 and the data processing device 12. For example, the recognition unit can recognize objects within its field of view using the camera 42 and microphone 238 of the headset type terminal 314, and perform image recognition and voice recognition using the control unit 46A. The acquisition unit can acquire information from a database on the Internet or a local database using the specific processing unit 290 of the data processing device 12. The display unit can display information using the display 343 and speaker 240 of the headset type terminal 314, and read out the information using voice synthesis technology. === Hard Collateral 1-4 === Each of the multiple elements including the recognition unit, acquisition unit, and display unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the recognition unit can recognize objects within its field of view using the camera 42 and microphone 238 of the robot 414, and perform image recognition and voice recognition using the control unit 46A. The acquisition unit can acquire information from a database on the Internet or a local database using the specific processing unit 290 of the data processing device 12. The display unit can display information using the display and speaker 240 of the robot 414, and read the information aloud using voice synthesis technology.

[0094] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0095] Next-generation wearable devices may also include a health management unit that monitors the user's health. The health management unit measures vital signs such as heart rate, blood pressure, and body temperature to monitor the user's health in real time. The health management unit can also record the user's exercise and sleep patterns and provide health management advice. For example, if the user's heart rate suddenly increases during exercise, the health management unit can display a warning to stop exercising. If the user has been sitting for a long time, the health management unit can also notify the user to stand up and stretch. Furthermore, the health management unit can analyze the user's sleep patterns and provide advice for better sleep. In this way, next-generation wearable devices can support users' health management and promote healthier lifestyles.

[0096] The next-generation wearable device may further include a music recommendation unit that estimates the user's emotions and recommends music based on the estimated emotions. The music recommendation unit may estimate the user's emotions from, for example, the user's facial expressions or voice, and automatically play music that matches those emotions. For example, if the user is relaxed, it may recommend relaxing music. Also, if the user is feeling stressed, it may recommend music to relieve stress. Furthermore, if the user is exercising, it may recommend fast-paced music that is suitable for exercise. This allows the next-generation wearable device to provide music that matches the user's emotions, providing a more comfortable experience.

[0097] Next-generation wearable devices may further include a behavior analysis unit that analyzes a user's behavioral history and provides daily living advice based on the user's behavioral patterns. The behavior analysis unit, for example, records the user's movement history and activity history and suggests ways to improve the user's daily life. For example, if the user moves to the same place at the same time every day, the behavior analysis unit can recognize this pattern and suggest an efficient route. Also, if the user feels a lot of stress during a certain time of day, the behavior analysis unit can suggest activities to relax during that time. Furthermore, the behavior analysis unit can analyze the user's dietary history and suggest healthy meals. This allows next-generation wearable devices to improve the quality of daily life based on the user's behavioral patterns.

[0098] Next-generation wearable devices may further include a stress management unit that estimates a user's emotions and provides stress management advice based on the estimated emotions. The stress management unit estimates the user's emotions from, for example, their facial expressions and voice and evaluates their stress level. For example, if the user is feeling high stress, the stress management unit can provide advice on breathing techniques or meditation to help them relax. If the user is relaxed, the stress management unit can also suggest activities to help maintain that state. Furthermore, the stress level of the user can be monitored over the long term, the causes of stress can be identified, and improvements can be suggested. In this way, next-generation wearable devices can support users' stress management and promote healthier lifestyles.

[0099] Next-generation wearable devices may also be equipped with a learning support unit that analyzes a user's learning history and provides advice to improve learning efficiency. The learning support unit may, for example, record the content and study time of the user in the past and suggest efficient study methods. For example, if the user has difficulty in a particular subject, the learning support unit may provide advice on focusing on that part. If the user has been studying for a long time, the learning support unit may also suggest appropriate times to take a break. Furthermore, it may be able to recommend learning materials and resources that suit the user's learning style. In this way, next-generation wearable devices may improve the user's learning efficiency and support more effective learning.

[0100] Next-generation wearable devices may further include a communication support unit that estimates the user's emotions and provides communication advice based on the estimated emotions. The communication support unit may estimate the user's emotions from, for example, the user's facial expressions and voice and suggest appropriate communication methods. For example, if the user is nervous, the unit may provide advice on how to relax, supporting smooth communication. If the user is angry, the unit may provide advice on how to stay calm. Furthermore, if the user is happy, the unit may suggest ways to share those emotions. In this way, next-generation wearable devices can provide communication support according to the user's emotions and build better human relationships.

[0101] Next-generation wearable devices may also be equipped with an exercise support unit that analyzes the user's exercise history and provides advice to improve exercise efficiency. For example, the exercise support unit may record the type and duration of exercise the user has performed in the past and suggest an effective exercise plan. For example, if the user has been performing a particular exercise, the unit may provide advice to maximize the effectiveness of that exercise. Furthermore, if the user is starting a new exercise, the unit may provide the correct method and precautions for that exercise. Furthermore, the unit may suggest appropriate exercise intensity and frequency based on the user's exercise history. This allows next-generation wearable devices to improve the user's exercise efficiency and support a healthier lifestyle.

[0102] The next-generation wearable device may further include a reminder unit that estimates the user's emotions and provides reminders based on the estimated emotions. The reminder unit estimates the user's emotions from, for example, the user's facial expressions and voice, and displays reminders at appropriate times. For example, if the user is busy, the reminder unit can provide reminders to ensure that the user does not forget important tasks. If the user is relaxed, the reminder unit can also display reminders more subtly. Furthermore, if the user is feeling stressed, the reminder unit can display reminders preferentially to encourage the user to complete tasks. This allows the next-generation wearable device to provide reminders according to the user's emotions and support efficient task management.

[0103] Next-generation wearable devices may also include a hobby recommendation unit that analyzes a user's hobbies and interests and recommends related events and activities. For example, the hobby recommendation unit may record the history of events and activities the user has participated in in the past and recommend new related events. For example, if a user frequently attends music concerts, the hobby recommendation unit may provide information about nearby concerts. If a user enjoys outdoor activities, the hobby recommendation unit may provide information about nearby hiking trails and campsites. Furthermore, if a user searches for information about a specific hobby, the hobby recommendation unit may recommend events and activities related to that hobby. This allows next-generation wearable devices to provide information based on a user's hobbies and interests, supporting a more fulfilling life.

[0104] The next-generation wearable device may further include a feedback unit that estimates the user's emotions and provides feedback based on the estimated emotions. The feedback unit may estimate the user's emotions from, for example, the user's facial expressions or voice, and provide feedback according to the emotions. For example, if the user is satisfied, the feedback unit may provide positive feedback to reinforce the emotion. If the user is dissatisfied, the feedback unit may identify the cause and suggest a solution. Furthermore, if the user is confused, the feedback unit may provide advice to solve the problem. In this way, the next-generation wearable device can provide feedback according to the user's emotions and support a better experience.

[0105] The processing flow of the second embodiment will be briefly explained below.

[0106] Step 1: The recognition unit recognizes objects within its field of view. The recognition unit can recognize objects using image recognition technology, voice recognition technology, or pattern recognition technology. For example, a camera can be used to capture an image of an object within its field of view, and an image recognition algorithm can be used to identify the object. A microphone can also be used to collect audio within its field of view, and a voice recognition algorithm can be used to identify the object. Step 2: The acquisition unit acquires information about the object recognized by the recognition unit. The acquisition unit can acquire information from an online database, a local database, or a cloud service. For example, the acquisition unit can acquire information from an online database using an API. It can also acquire information by accessing a local database. Step 3: The display unit displays the information acquired by the acquisition unit. The display unit can display the information on a lens in one eye, read the information aloud using voice synthesis technology, or display the information using projection technology. For example, the display unit can display the information on a lens in one eye using AR glasses. It can also read the information aloud using voice synthesis technology.

[0107] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0108] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0109] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0110] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0111] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0112] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0113] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0114] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0115] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0116] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0117] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0118] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0119] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0120] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0121] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0122] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0123] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0124] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0125] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0126] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0127] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0128] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0129] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0130] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0131] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0133] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0134] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0135] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0136] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0137] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0138] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0139] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0140] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0141] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0142] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0143] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0144] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0145] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0146] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0147] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0148] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0149] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0150] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0151] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0152] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0153] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0154] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0155] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0156] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0157] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0158] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0159] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0160] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0161] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0162] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0163] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0164] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0165] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0166] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0167] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0168] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0169] 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.

[0170] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0171] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0172] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0173] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0174] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0175] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0176] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0177] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0178] [Explanation of symbols]

[0179] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a recognition unit that recognizes an object in its field of view; an acquisition unit that acquires information about the object recognized by the recognition unit; a display unit that displays the information acquired by the acquisition unit; Equipped with A system characterized by:

2. The recognition unit Using generative AI to recognize objects in view 2. The system of claim 1.

3. The acquisition unit Obtaining information about recognized objects using generative AI 2. The system of claim 1.

4. The display unit The acquired information is displayed in one eye lens.

2. The system of claim 1.

5. The display unit Read out the acquired information 2. The system of claim 1.

6. The acquisition unit Translates signs in foreign languages ​​and transmits them to the display unit 2. The system of claim 1.

7. The acquisition unit Real-time translation of foreign language conversations and transmission to the display 2. The system of claim 1.

8. The recognition unit Estimate the user's emotions and determine the priority of recognition targets based on the estimated user emotions.

2. The system of claim 1.

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