System

Abstract

An object of the system according to the embodiment is to recognize a face, display a name, summarize conversation content, and propose next conversation content.SOLUTION: A system includes a face recognition unit, a name display unit, a voice recognition unit, and a proposal unit. The face recognition unit recognizes a face. The name display unit displays a name based on the face recognized by the face recognition unit. A voice recognition part voice-recognizes and summarizes the conversation on the basis of the name displayed by the name display part. The suggestion unit suggests the next conversation content on the basis of the conversation content summarized by the speech recognition 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 that it can be difficult to remember the names of people you haven't seen in a long time, making it difficult to properly manage the content of conversations.

[0005] The system according to the embodiment aims to recognize faces, display names, summarize conversations, and suggest conversation topics for the next conversation. [Means for solving the problem]

[0006] The system according to the embodiment includes a face recognition unit, a name display unit, a voice recognition unit, and a suggestion unit. The face recognition unit recognizes faces. The name display unit displays names based on faces recognized by the face recognition unit. The voice recognition unit recognizes voice and summarizes conversations based on the names displayed by the name display unit. The suggestion unit suggests content for the next conversation based on the content of the conversation summarized by the voice recognition unit. [Effects of the Invention]

[0007] The system according to the embodiment can recognize faces, display names, summarize conversations, and suggest conversation topics for the next conversation. [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) The AR glasses according to an embodiment of the present invention are a system that provides functions such as face recognition, name display, voice recognition and summarization, and suggestion of the next conversation topic. These AR glasses use AI to recognize the other person's face and display that person's name. They also recognize the voice of a conversation, summarize the content, and suggest a conversation topic based on the previous conversation topic for the next meeting. For example, the AR glasses have a built-in camera that captures the other person's face. This video data is analyzed by AI to recognize the other person's face. Next, based on the face recognized by the face recognition function, the person's name is displayed on the AR glasses' display. Furthermore, the AR glasses record the conversation using a built-in microphone, and the AI ​​recognizes the content of the voice. The recognized conversation content is summarized by AI, and important points are recorded. Based on the previous conversation content, appropriate conversation topics are suggested for the next meeting. This is useful in face-to-face communication situations. It is also suitable for people with conditions such as prosopagnosia, which makes it difficult to remember names. This makes the AR glasses useful in face-to-face communication situations and suitable for people with conditions such as prosopagnosia, which makes it difficult to remember names. For example, it can remember the name of someone you haven't seen in a while, allowing for smooth conversation. It can also make suggestions based on the content of previous conversations, enabling smoother communication.

[0029] The AR glasses system according to the embodiment includes a face recognition unit, a name display unit, a voice recognition unit, and a suggestion unit. The face recognition unit recognizes the face of a person. For example, the face recognition unit recognizes the face of a person by analyzing video data captured by a camera using AI. The face recognition unit can also extract facial feature points and recognize the face using a face recognition algorithm. For example, the face recognition unit uses a face recognition model based on deep learning to extract and recognize facial feature points. The face recognition unit can also recognize faces by analyzing facial features such as facial contours, eyes, nose, and mouth. The name display unit displays a name based on the face recognized by the face recognition unit. The name display unit displays the name on, for example, a display of the AR glasses. The name display unit can also adjust the display device and display format. For example, the name display unit adjusts the display resolution and font size to display the name. The name display unit can also change the color of the displayed name and background color. The voice recognition unit records conversations using a built-in microphone and uses AI to recognize and summarize the conversation. The speech recognition unit recognizes the content of the conversation using, for example, a speech recognition algorithm. The speech recognition unit can also summarize the content of the conversation using a summarization algorithm. For example, the speech recognition unit recognizes the content of the conversation using a speech recognition model using deep learning. The speech recognition unit can also summarize the content of the conversation using natural language processing technology. The suggestion unit suggests appropriate content to talk about the next time based on the content of the previous conversation. The suggestion unit, for example, analyzes the content of the previous conversation and suggests content for the next conversation. The suggestion unit can also suggest appropriate content to talk about using a proposal algorithm. For example, the suggestion unit suggests content for the next conversation using a proposal model using deep learning. The suggestion unit can also generate content for the next conversation based on the content of the previous conversation. This makes the AR glasses system according to the embodiment useful in face-to-face communication situations and suitable for people with conditions such as prosopagnosia, which makes it difficult to remember names.

[0030] The face recognition unit can analyze video data captured by a camera using AI to recognize the face of the other party. For example, the face recognition unit can analyze video data captured by a camera using AI to recognize the face of the other party. A specific method of analysis using AI can be a face recognition model using deep learning. The face recognition unit can also extract facial feature points and recognize faces using a face recognition algorithm. For example, the face recognition unit can recognize faces by analyzing facial contours and features such as the eyes, nose, and mouth. The face recognition unit can also calculate the degree of facial similarity based on the facial feature points to improve recognition accuracy. In this way, analyzing video data captured by a camera using AI improves the accuracy of face recognition. Some or all of the above-mentioned processing in the face recognition unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the face recognition unit can input video data captured by a camera to a generation AI and have the generation AI extract facial feature points and recognize faces.

[0031] The name display unit can display a name on the display of the AR glasses based on a face recognized by the face recognition unit. The name display unit displays a name on the display of the AR glasses based on, for example, a face recognized by the face recognition unit. The display of the AR glasses may use, for example, an OLED display or a liquid crystal display. The name display unit can also adjust the display device and display format. For example, the name display unit displays the name by adjusting the display resolution and font size. The name display unit can also change the color and background color of the displayed name. For example, the name display unit can use a bold font to highlight the name. The name display unit can also change the background color to make the name easier to see. In this way, displaying a name based on a face recognized by the face recognition unit allows the user to remember the name of the other person. Some or all of the above-described processing in the name display unit may be performed using, or without, a generation AI. For example, the name display unit can input data of a face recognized by the face recognition unit into the generation AI and cause the generation AI to display the name.

[0032] The speech recognition unit can record a conversation using a built-in microphone and use AI to recognize and summarize the conversation. For example, the speech recognition unit can record a conversation using a built-in microphone and use AI to recognize and summarize the conversation. The built-in microphone may have, for example, a noise canceling function. The speech recognition unit can also recognize the conversation using a speech recognition algorithm. For example, the speech recognition unit can recognize the conversation using a speech recognition model based on deep learning. The speech recognition unit can also summarize the conversation using a summarization algorithm. For example, the speech recognition unit can summarize the conversation using natural language processing technology. As a result, the conversation is recorded using a built-in microphone and the AI ​​recognizes and summarizes the conversation, recording important points. Some or all of the above-mentioned processing in the speech recognition unit may be performed using, for example, a generation AI. For example, the speech recognition unit can input recorded voice data to a generation AI and have the generation AI perform voice recognition and summarization.

[0033] The suggestion unit can suggest appropriate conversation content for the next meeting based on the content of the previous conversation. The suggestion unit can suggest appropriate conversation content for the next meeting based on, for example, the content of the previous conversation. The suggestion unit can suggest appropriate conversation content using a proposal algorithm. For example, the suggestion unit uses a proposal model using deep learning to suggest the next conversation content. The suggestion unit can also generate the next conversation content based on the content of the previous conversation. For example, the suggestion unit analyzes the content of the previous conversation and generates the next conversation content. This enables smooth communication by suggesting the next conversation content based on the content of the previous conversation. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the suggestion unit can input data of the previous conversation content into the generation AI and have the generation AI execute a suggestion for the next conversation content.

[0034] The face recognition unit can improve the accuracy of face recognition by referring to past recognition history. For example, the face recognition unit can improve the accuracy of face recognition by referring to past recognition history. The past recognition history includes, for example, data on faces previously recognized. The face recognition unit can also improve the recognition accuracy under specific environmental conditions based on the past recognition history. For example, the face recognition unit can improve the recognition accuracy of the same person by referring to data on faces previously recognized. The face recognition unit can also improve the recognition accuracy of people with specific facial expressions or movements based on the past recognition history. Furthermore, the face recognition unit can improve the recognition accuracy under specific environmental conditions based on the past recognition history. Thus, by referring to the past recognition history, the recognition accuracy is improved. Some or all of the above-described processing in the face recognition unit may be performed using, or without, a generation AI. For example, the face recognition unit can input data from the past recognition history into the generation AI and cause the generation AI to improve the recognition accuracy.

[0035] The face recognition unit can improve the accuracy of recognition by analyzing the facial expressions and movements of the other party during face recognition. For example, the face recognition unit can improve the accuracy of recognition by analyzing the facial expressions and movements of the other party during face recognition. Specific methods for analyzing facial expressions and movements include facial expression recognition algorithms and movement analysis techniques. For example, the face recognition unit analyzes the other party's facial expressions, such as smiles and anger, to improve recognition accuracy. The face recognition unit can also analyze the other party's movements (hand movements and posture) to improve recognition accuracy. Furthermore, the face recognition unit can analyze the other party's line of sight and eye movements to improve recognition accuracy. In this way, by analyzing the other party's facial expressions and movements, recognition accuracy is improved. Some or all of the above-mentioned processing in the face recognition unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the face recognition unit can input data on the other party's facial expressions and movements into the generation AI and have the generation AI analyze the facial expressions and movements.

[0036] The face recognition unit can perform corrections to accommodate different lighting conditions and backgrounds during face recognition. For example, the face recognition unit performs corrections to accommodate different lighting conditions and backgrounds during face recognition. Specific methods for accommodating different lighting conditions and backgrounds include lighting correction algorithms and background removal techniques. For example, the face recognition unit can automatically adjust the brightness of an image when the lighting is low to improve recognition accuracy. The face recognition unit can also blur a complex background to improve face recognition accuracy. Furthermore, the face recognition unit can adjust the contrast of an image when the lighting is too strong to improve recognition accuracy. This improves recognition accuracy by accommodating different lighting conditions and backgrounds. Some or all of the above-described processing in the face recognition unit may be performed using, or without, a generation AI. For example, the face recognition unit can input data on different lighting conditions and backgrounds into the generation AI and have the generation AI perform lighting correction and background removal.

[0037] The face recognition unit can prioritize recognizing highly relevant faces during face recognition by taking into account the user's geographical location information. For example, the face recognition unit prioritizes recognizing highly relevant faces during face recognition by taking into account the user's geographical location information. A specific method for acquiring geographical location information is to acquire GPS data. For example, when the user is in a specific location, the face recognition unit prioritizes recognizing faces of people the user frequently encounters at that location. Furthermore, when the user is traveling, the face recognition unit can prioritize recognizing faces of acquaintances at the travel destination. Furthermore, when the user is at work, the face recognition unit can prioritize recognizing faces of coworkers at work. In this way, highly relevant faces can be prioritized by taking into account the user's geographical location information. Some or all of the above-described processing in the face recognition unit may be performed using, or without, a generation AI. For example, the face recognition unit can input the user's geographical location information data into the generation AI and cause the generation AI to prioritize recognizing highly relevant faces.

[0038] The face recognition unit can analyze the user's social media activity and recognize related faces during face recognition. For example, the face recognition unit can analyze the user's social media activity and recognize related faces during face recognition. A specific method for analyzing social media activity is to analyze the content of posts. For example, the face recognition unit can prioritize recognizing the faces of people with whom the user frequently interacts on social media. The face recognition unit can also analyze the content of the user's social media posts and recognize the faces of related people. Furthermore, the face recognition unit can prioritize recognizing the faces of the user's social media friends. In this way, related faces can be recognized by analyzing the user's social media activity. Some or all of the above-mentioned processing in the face recognition unit may be performed using, or without, a generation AI. For example, the face recognition unit can input data on the user's social media activity into the generation AI and cause the generation AI to recognize related faces.

[0039] The face recognition unit can customize the recognition method by reflecting the user's past feedback during face recognition. For example, the face recognition unit customizes the recognition method by reflecting the user's past feedback during face recognition. Specific examples of past feedback include feedback regarding recognition accuracy. For example, if the user has expressed dissatisfaction with the recognition accuracy in the past, the face recognition unit can improve the recognition method based on that feedback. The face recognition unit can also customize the recognition method based on the user's feedback if the user wants to prioritize the recognition of a specific person. Furthermore, if the user wants to improve the recognition accuracy in a specific environment, the face recognition unit can adjust the recognition method based on that feedback. This allows the recognition method to be customized by reflecting the user's past feedback. Some or all of the above-described processing in the face recognition unit may be performed using, or without, a generation AI. For example, the face recognition unit can input the user's past feedback data into the generation AI and have the generation AI customize the recognition method.

[0040] The name display unit can adjust the level of detail of the display based on the relationship between the other party and the name display unit when displaying the name. For example, the name display unit adjusts the level of detail of the display based on the relationship between the other party and the name display unit when displaying the name. A specific method for identifying the relationship between the other party is a relationship evaluation criterion. For example, the name display unit displays the full name and nickname in the case of close friends. The name display unit can also display the full name and job title in the case of business relationships. Furthermore, the name display unit can display only the full name in the case of a first meeting. In this way, by adjusting the level of detail of the display based on the relationship between the other party, appropriate information can be displayed. Some or all of the above-mentioned processing in the name display unit may be performed using, or without, a generation AI. For example, the name display unit can input data on the relationship between the other party into the generation AI and cause the generation AI to adjust the level of detail of the display.

[0041] The name display unit can improve the accuracy of the display by referring to the user's past display history when displaying a name. For example, the name display unit can improve the accuracy of the display by referring to the user's past display history when displaying a name. The past display history includes, for example, data on names previously displayed. The name display unit can also optimize the display method under specific environmental conditions based on the past display history. For example, the name display unit prioritizes displaying names of the same person based on names previously displayed by the user. The name display unit can also optimize the display method under specific environments based on the past display history. Furthermore, the name display unit can analyze the past display history and suggest the display method with the highest visibility. By referring to the user's past display history, the accuracy of the display is improved. Some or all of the above-described processing in the name display unit may be performed using, or without, a generation AI. For example, the name display unit can input data on the past display history into the generation AI and cause the generation AI to improve the accuracy of the display.

[0042] The name display unit can improve visibility by using different fonts and colors when displaying names. For example, the name display unit can improve visibility by using different fonts and colors when displaying names. Specific types of different fonts and colors include bold fonts and changing background colors. For example, the name display unit can use bold fonts to improve visibility. The name display unit can also change the background color to highlight the name. Furthermore, the name display unit can display names using different colors to make them visually easier to distinguish. Thus, using different fonts and colors improves visibility. Some or all of the above-described processing in the name display unit may be performed using, or without, a generation AI. For example, the name display unit can input font and color data into the generation AI and cause the generation AI to improve visibility.

[0043] When displaying names, the name display unit can prioritize displaying highly relevant names by taking into account the user's geographical location information. For example, when displaying names, the name display unit prioritizes displaying highly relevant names by taking into account the user's geographical location information. A specific method for acquiring geographical location information is to acquire GPS data. For example, when the user is in a specific location, the name display unit can prioritize displaying names of people the user often meets at that location. Furthermore, when the user is traveling, the name display unit can prioritize displaying names of acquaintances at the travel destination. Furthermore, when the user is at work, the name display unit can prioritize displaying names of coworkers at work. In this way, by taking the user's geographical location information into account, highly relevant names can be prioritized. Some or all of the above-described processing in the name display unit may be performed using, or without, a generation AI. For example, the name display unit can input data of the user's geographical location information into the generation AI and cause the generation AI to prioritize displaying highly relevant names.

[0044] The name display unit can analyze the user's social media activity and display related names when displaying the name. For example, the name display unit can analyze the user's social media activity and display related names when displaying the name. A specific method for analyzing social media activity is a method for analyzing posted content. For example, the name display unit can prioritize displaying names of people with whom the user frequently interacts on social media. The name display unit can also analyze the user's social media posts and display names of related people. Furthermore, the name display unit can prioritize displaying names of the user's social media friends. In this way, related names can be displayed by analyzing the user's social media activity. Some or all of the above-described processing in the name display unit may be performed using, or without, a generation AI. For example, the name display unit can input data on the user's social media activity into the generation AI and cause the generation AI to display related names.

[0045] The name display unit can customize the display method by reflecting the user's past feedback when displaying names. For example, the name display unit customizes the display method by reflecting the user's past feedback when displaying names. Specific content of past feedback includes feedback regarding the display method. For example, if the user has expressed dissatisfaction with the display method in the past, the name display unit improves the display method based on that feedback. The name display unit can also customize the display method based on feedback if the user wants to prioritize the name of a specific person. Furthermore, if the user wants to improve the display method in a specific environment, the name display unit can adjust the display method based on that feedback. In this way, the display method can be customized by reflecting the user's past feedback. Some or all of the above-described processing in the name display unit may be performed using, or without, a generation AI. For example, the name display unit can input data of the user's past feedback into the generation AI and cause the generation AI to customize the display method.

[0046] The speech recognition unit can improve the accuracy of speech recognition by referring to past recognition history during speech recognition. For example, the speech recognition unit can improve the accuracy of speech recognition by referring to past recognition history during speech recognition. The past recognition history includes, for example, previously recognized speech data. The speech recognition unit can also improve the recognition accuracy under specific environmental conditions based on the past recognition history. For example, the speech recognition unit can improve the speech recognition accuracy of the same person by referring to previously recognized speech data. The speech recognition unit can also improve the speech recognition accuracy of a person with a specific speaking style or accent based on the past recognition history. Furthermore, the speech recognition unit can improve the speech recognition accuracy under specific environmental conditions based on the past recognition history. Thus, by referring to the past recognition history, the recognition accuracy is improved. Some or all of the above-described processing in the speech recognition unit may be performed using, or without, a generation AI. For example, the speech recognition unit can input data from the past recognition history into the generation AI and cause the generation AI to improve the recognition accuracy.

[0047] The speech recognition unit can improve the accuracy of speech recognition by removing background noise during speech recognition. For example, the speech recognition unit can improve the accuracy of speech recognition by removing background noise during speech recognition. A specific method for removing background noise is a noise removal algorithm. For example, when there is a lot of background noise, the speech recognition unit can improve the accuracy of speech recognition using noise canceling technology. The speech recognition unit can also improve the accuracy of speech recognition by removing noise in a specific frequency band. Furthermore, the speech recognition unit can also improve the accuracy of speech recognition by separating the speech signal and the noise signal. In this way, the recognition accuracy is improved by removing the background noise. Some or all of the above-mentioned processing in the speech recognition unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the speech recognition unit can input background noise data to the generation AI and have the generation AI perform noise removal.

[0048] The speech recognition unit can perform corrections to accommodate different speakers' voice qualities during speech recognition. For example, the speech recognition unit performs corrections to accommodate different speakers' voice qualities during speech recognition. Specific methods for accommodating different speakers' voice qualities include a voice quality feature extraction method and a correction algorithm. For example, the speech recognition unit adjusts the speech recognition algorithm according to the speaker's voice quality. The speech recognition unit can also improve speech recognition accuracy according to the speaker's voice pitch and tone. Furthermore, the speech recognition unit can analyze the speaker's voice characteristics and improve speech recognition accuracy. This improves recognition accuracy by accommodating different speakers' voice qualities. Some or all of the above-described processing in the speech recognition unit may be performed using, or without, a generation AI. For example, the speech recognition unit can input data on the speaker's voice quality into the generation AI and have the generation AI perform voice quality correction.

[0049] The voice recognition unit can prioritize recognizing highly relevant voices during voice recognition by taking into account the user's geographical location information. For example, the voice recognition unit prioritizes recognizing highly relevant voices by taking into account the user's geographical location information during voice recognition. A specific method for acquiring geographical location information is to acquire GPS data. For example, when the user is in a specific location, the voice recognition unit prioritizes recognizing the voices of people who frequently converse in that location. Furthermore, when the user is traveling, the voice recognition unit can prioritize recognizing the voices of acquaintances at the user's travel destination. Furthermore, when the user is at work, the voice recognition unit can prioritize recognizing the voices of coworkers at work. This allows highly relevant voices to be prioritized by taking into account the user's geographical location information. Some or all of the above-described processing in the voice recognition unit may be performed using, or without, a generation AI. For example, the voice recognition unit can input data on the user's geographical location information into the generation AI and cause the generation AI to prioritize recognizing highly relevant voices.

[0050] The voice recognition unit can analyze the user's social media activity and recognize related voices during voice recognition. For example, the voice recognition unit can analyze the user's social media activity and recognize related voices during voice recognition. A specific method for analyzing social media activity is analyzing the content of posts. For example, the voice recognition unit can prioritize recognizing the voices of people with whom the user frequently interacts on social media. The voice recognition unit can also analyze the content of the user's social media posts and recognize the voices of related people. Furthermore, the voice recognition unit can prioritize recognizing the voices of the user's friends on social media. In this way, related voices can be recognized by analyzing the user's social media activity. Some or all of the above-mentioned processing in the voice recognition unit may be performed using, or without, a generation AI. For example, the voice recognition unit can input data on the user's social media activity to the generation AI and have the generation AI recognize related voices.

[0051] The speech recognition unit can customize the recognition method by reflecting the user's past feedback during speech recognition. For example, the speech recognition unit customizes the recognition method by reflecting the user's past feedback during speech recognition. Specific content of past feedback is feedback regarding recognition accuracy. For example, if the user has expressed dissatisfaction with recognition accuracy in the past, the speech recognition unit improves the recognition method based on that feedback. The speech recognition unit can also customize the recognition method based on feedback if the user wants to prioritize the voice of a specific person. Furthermore, if the user wants to improve recognition accuracy in a specific environment, the speech recognition unit can adjust the recognition method based on that feedback. In this way, the recognition method can be customized by reflecting the user's past feedback. Some or all of the above-described processing in the speech recognition unit may be performed using, or without, a generation AI. For example, the speech recognition unit can input data of the user's past feedback into the generation AI and have the generation AI customize the recognition method.

[0052] The suggestion unit can improve the accuracy of a suggestion by referring to past proposal history when making a suggestion. For example, the suggestion unit can improve the accuracy of a suggestion by referring to past proposal history when making a suggestion. The past proposal history includes, for example, data on topics proposed in the past. The suggestion unit can also improve the accuracy of suggestions under specific environmental conditions based on the past proposal history. For example, the suggestion unit can suggest conversation content with the same person based on topics proposed in the past. The suggestion unit can also improve the accuracy of suggestions for specific topics or people with interests based on the past proposal history. The suggestion unit can also improve the accuracy of suggestions under specific environmental conditions based on the past proposal history. In this way, the accuracy of suggestions is improved by referring to the past proposal history. Some or all of the above-described processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input data on the past proposal history into the generation AI and cause the generation AI to improve the accuracy of suggestions.

[0053] The suggestion unit can adjust the level of detail of the suggestion based on the relationship with the other party when making a suggestion. For example, the suggestion unit adjusts the level of detail of the suggestion based on the relationship with the other party when making a suggestion. A specific method for identifying the relationship with the other party is a relationship evaluation criterion. For example, the suggestion unit may suggest detailed topics in the case of close friends. Furthermore, the suggestion unit may also suggest topics that focus on the main points in the case of a business relationship. Furthermore, the suggestion unit may also suggest general topics in the case of a first meeting. In this way, appropriate suggestions can be made by adjusting the level of detail of the suggestion based on the relationship with the other party. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the suggestion unit may input data on the relationship with the other party into the generation AI and cause the generation AI to adjust the level of detail of the suggestion.

[0054] The suggestion unit can customize the suggestion content based on the user's current situation and interests when making a suggestion. For example, the suggestion unit customizes the suggestion content based on the user's current situation and interests when making a suggestion. Specific methods for identifying the current situation and interests include situation evaluation criteria and interest identification methods. For example, the suggestion unit suggests appropriate topics based on the user's current situation. The suggestion unit can also suggest interesting topics based on the user's interests. Furthermore, the suggestion unit can analyze the user's current situation and interests and provide optimal suggestion content. This enables more appropriate suggestions by customizing the suggestion content based on the user's current situation and interests. Some or all of the above-described processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input data on the user's current situation and interests into the generation AI and have the generation AI customize the suggestion content.

[0055] When making a suggestion, the suggestion unit can prioritize highly relevant suggestions by taking into account the user's geographical location information. For example, when making a suggestion, the suggestion unit can prioritize highly relevant suggestions by taking into account the user's geographical location information. A specific method for acquiring geographical location information is to acquire GPS data. For example, when the user is in a specific location, the suggestion unit can prioritize suggesting topics related to the location. Furthermore, when the user is traveling, the suggestion unit can prioritize suggesting topics related to the travel destination. Furthermore, when the user is at work, the suggestion unit can prioritize suggesting topics related to the workplace. In this way, by taking the user's geographical location information into account, highly relevant suggestions can be made preferentially. Some or all of the above-described processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input data on the user's geographical location information into the generation AI and cause the generation AI to prioritize highly relevant suggestions.

[0056] The suggestion unit can analyze the user's social media activity and make related suggestions when making a suggestion. For example, the suggestion unit can analyze the user's social media activity and make related suggestions when making a suggestion. A specific method for analyzing social media activity is a method for analyzing posted content. For example, the suggestion unit can suggest topics related to people the user frequently interacts with on social media. The suggestion unit can also analyze the user's social media posts and suggest related topics. Furthermore, the suggestion unit can suggest related topics by referring to the activities of the user's friends on social media. In this way, related suggestions can be made by analyzing the user's social media activity. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input data on the user's social media activity into the generation AI and cause the generation AI to execute related suggestions.

[0057] The suggestion unit can customize the suggestion method by reflecting the user's past feedback when making a suggestion. For example, the suggestion unit customizes the suggestion method by reflecting the user's past feedback when making a suggestion. Specific content of the past feedback is feedback regarding the suggestion content. For example, if the user has been dissatisfied with the suggestion content in the past, the suggestion unit improves the suggestion method based on that feedback. The suggestion unit can also customize the suggestion method based on feedback if the user wants to prioritize a specific topic. Furthermore, if the user wants to improve the suggestion method in a specific environment, the suggestion unit can adjust the suggestion method based on that feedback. In this way, the suggestion method can be customized by reflecting the user's past feedback. Some or all of the above-described processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input data of the user's past feedback into the generation AI and cause the generation AI to customize the suggestion method.

[0058] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0059] The AR glasses system may further include a health management unit that monitors the user's health status. For example, the health management unit may measure the user's heart rate and blood pressure and display a warning if an abnormality is detected. The health management unit may also record the user's number of steps and calories burned and provide health management advice. For example, the health management unit may display a notification encouraging the user to stand up and walk if the user has been sitting for a certain period of time. The health management unit may also analyze the user's sleep patterns and make suggestions for getting quality sleep. In this way, the AR glasses system can support the user's health management and promote a healthier lifestyle.

[0060] The AR glasses system may further include a translation unit. For example, the translation unit translates the language spoken by the user in real time and displays it to the other party. The translation unit may also translate the language spoken by the other party into the user's native language and display it on the AR glasses display. For example, the translation unit may translate English into Japanese and display it in a way that is easy for the user to understand. The translation unit may also support multiple languages, facilitating communication with other parties who speak different languages. This allows the AR glasses system to support communication between people who speak different languages ​​and promote international exchange.

[0061] The AR glasses system may further include a navigation unit. The navigation unit, for example, identifies the user's current location and displays a route to the destination. The navigation unit may also provide real-time traffic information and suggest an optimal route. For example, the navigation unit may take traffic congestion information into consideration and display a route to reach the destination in the shortest time. The navigation unit may also provide route guidance for pedestrians, helping the user reach the destination without getting lost. This allows the AR glasses system to support the user's travel and provide efficient navigation.

[0062] The AR glasses system may further include a reminder unit. The reminder unit may, for example, manage the user's schedule and notify the user of important appointments. The reminder unit may also remind the user of tasks set by the user and notify the user when a deadline is approaching. For example, the reminder unit may notify the user of the start time of a meeting to help the user avoid being late. The reminder unit may also manage a shopping list and remind the user to purchase necessary items. In this way, the AR glasses system may support the user in managing their schedule and ensure that important tasks are not forgotten.

[0063] The AR glasses system may further include an entertainment unit. The entertainment unit provides content for the user to enjoy, such as movies and music. The entertainment unit may also suggest recommended content based on the user's preferences. For example, the entertainment unit may suggest movies in the user's favorite genre to support viewing. The entertainment unit may also play relaxing music to support refreshing. This allows the AR glasses system to enhance the user's entertainment experience and provide relaxation and enjoyment.

[0064] The AR glasses system may further include a learning support unit. The learning support unit, for example, provides learning materials for the user and manages the user's learning progress. The learning support unit may also suggest appropriate learning content based on the user's level of understanding. For example, the learning support unit may identify areas in which the user is weak and provide learning materials related to those areas. The learning support unit may also suggest learning methods that match the user's learning style and support effective learning. In this way, the AR glasses system can support the user's learning and provide an effective learning experience.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: The face recognition unit recognizes the other person's face. For example, the face recognition unit uses AI to analyze video data captured by a camera and recognize the other person's face. The face recognition unit can also extract facial feature points and recognize the face using a face recognition algorithm. For example, the face recognition unit uses a face recognition model that uses deep learning to extract and recognize facial feature points. The face recognition unit can also recognize faces by analyzing facial contours and features such as the eyes, nose, and mouth. Step 2: The name display unit displays the name based on the face recognized by the face recognition unit. The name display unit displays the name on, for example, the display of the AR glasses. The name display unit can also adjust the display device and display format. For example, the name display unit adjusts the display resolution and font size to display the name. The name display unit can also change the color and background color of the displayed name. Step 3: The speech recognition unit uses a built-in microphone to record the conversation, and uses AI to recognize and summarize the content. The speech recognition unit, for example, uses a speech recognition algorithm to recognize the content of the conversation. The speech recognition unit can also summarize the content of the conversation using a summarization algorithm. For example, the speech recognition unit uses a speech recognition model that uses deep learning to recognize the content of the conversation. The speech recognition unit can also summarize the content of the conversation using natural language processing technology. Step 4: The suggestion unit suggests appropriate conversation content for the next meeting based on the content of the previous conversation. For example, the suggestion unit analyzes the content of the previous conversation and suggests the content of the next conversation. The suggestion unit can also suggest appropriate conversation content using a proposal algorithm. For example, the suggestion unit uses a proposal model that uses deep learning to suggest the content of the next conversation. The suggestion unit can also generate the content of the next conversation based on the content of the previous conversation.

[0067] (Example 2) The AR glasses according to an embodiment of the present invention are a system that provides functions such as face recognition, name display, voice recognition and summarization, and suggestion of the next conversation topic. These AR glasses use AI to recognize the other person's face and display that person's name. They also recognize the voice of a conversation, summarize the content, and suggest a conversation topic based on the previous conversation topic for the next meeting. For example, the AR glasses have a built-in camera that captures the other person's face. This video data is analyzed by AI to recognize the other person's face. Next, based on the face recognized by the face recognition function, the person's name is displayed on the AR glasses' display. Furthermore, the AR glasses record the conversation using a built-in microphone, and the AI ​​recognizes the content of the voice. The recognized conversation content is summarized by AI, and important points are recorded. Based on the previous conversation content, appropriate conversation topics are suggested for the next meeting. This is useful in face-to-face communication situations. It is also suitable for people with conditions such as prosopagnosia, which makes it difficult to remember names. This makes the AR glasses useful in face-to-face communication situations and suitable for people with conditions such as prosopagnosia, which makes it difficult to remember names. For example, it can remember the name of someone you haven't seen in a while, allowing for smooth conversation. It can also make suggestions based on the content of previous conversations, enabling smoother communication.

[0068] The AR glasses system according to the embodiment includes a face recognition unit, a name display unit, a voice recognition unit, and a suggestion unit. The face recognition unit recognizes the face of a person. For example, the face recognition unit recognizes the face of a person by analyzing video data captured by a camera using AI. The face recognition unit can also extract facial feature points and recognize the face using a face recognition algorithm. For example, the face recognition unit uses a face recognition model based on deep learning to extract and recognize facial feature points. The face recognition unit can also recognize faces by analyzing facial features such as facial contours, eyes, nose, and mouth. The name display unit displays a name based on the face recognized by the face recognition unit. The name display unit displays the name on, for example, a display of the AR glasses. The name display unit can also adjust the display device and display format. For example, the name display unit adjusts the display resolution and font size to display the name. The name display unit can also change the color of the displayed name and background color. The voice recognition unit records conversations using a built-in microphone and uses AI to recognize and summarize the conversation. The speech recognition unit recognizes the content of the conversation using, for example, a speech recognition algorithm. The speech recognition unit can also summarize the content of the conversation using a summarization algorithm. For example, the speech recognition unit recognizes the content of the conversation using a speech recognition model using deep learning. The speech recognition unit can also summarize the content of the conversation using natural language processing technology. The suggestion unit suggests appropriate content to talk about the next time based on the content of the previous conversation. The suggestion unit, for example, analyzes the content of the previous conversation and suggests content for the next conversation. The suggestion unit can also suggest appropriate content to talk about using a proposal algorithm. For example, the suggestion unit suggests content for the next conversation using a proposal model using deep learning. The suggestion unit can also generate content for the next conversation based on the content of the previous conversation. This makes the AR glasses system according to the embodiment useful in face-to-face communication situations and suitable for people with conditions such as prosopagnosia, which makes it difficult to remember names.

[0069] The face recognition unit can analyze video data captured by a camera using AI to recognize the face of the other party. For example, the face recognition unit can analyze video data captured by a camera using AI to recognize the face of the other party. A specific method of analysis using AI can be a face recognition model using deep learning. The face recognition unit can also extract facial feature points and recognize faces using a face recognition algorithm. For example, the face recognition unit can recognize faces by analyzing facial contours and features such as the eyes, nose, and mouth. The face recognition unit can also calculate the degree of facial similarity based on the facial feature points to improve recognition accuracy. In this way, analyzing video data captured by a camera using AI improves the accuracy of face recognition. Some or all of the above-mentioned processing in the face recognition unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the face recognition unit can input video data captured by a camera to a generation AI and have the generation AI extract facial feature points and recognize faces.

[0070] The name display unit can display a name on the display of the AR glasses based on a face recognized by the face recognition unit. The name display unit displays a name on the display of the AR glasses based on, for example, a face recognized by the face recognition unit. The display of the AR glasses may use, for example, an OLED display or a liquid crystal display. The name display unit can also adjust the display device and display format. For example, the name display unit displays the name by adjusting the display resolution and font size. The name display unit can also change the color and background color of the displayed name. For example, the name display unit can use a bold font to highlight the name. The name display unit can also change the background color to make the name easier to see. In this way, displaying a name based on a face recognized by the face recognition unit allows the user to remember the name of the other person. Some or all of the above-described processing in the name display unit may be performed using, or without, a generation AI. For example, the name display unit can input data of a face recognized by the face recognition unit into the generation AI and cause the generation AI to display the name.

[0071] The speech recognition unit can record a conversation using a built-in microphone and use AI to recognize and summarize the conversation. For example, the speech recognition unit can record a conversation using a built-in microphone and use AI to recognize and summarize the conversation. The built-in microphone may have, for example, a noise canceling function. The speech recognition unit can also recognize the conversation using a speech recognition algorithm. For example, the speech recognition unit can recognize the conversation using a speech recognition model based on deep learning. The speech recognition unit can also summarize the conversation using a summarization algorithm. For example, the speech recognition unit can summarize the conversation using natural language processing technology. As a result, the conversation is recorded using a built-in microphone and the AI ​​recognizes and summarizes the conversation, recording important points. Some or all of the above-mentioned processing in the speech recognition unit may be performed using, for example, a generation AI. For example, the speech recognition unit can input recorded voice data to a generation AI and have the generation AI perform voice recognition and summarization.

[0072] The suggestion unit can suggest appropriate conversation content for the next meeting based on the content of the previous conversation. The suggestion unit can suggest appropriate conversation content for the next meeting based on, for example, the content of the previous conversation. The suggestion unit can suggest appropriate conversation content using a proposal algorithm. For example, the suggestion unit uses a proposal model using deep learning to suggest the next conversation content. The suggestion unit can also generate the next conversation content based on the content of the previous conversation. For example, the suggestion unit analyzes the content of the previous conversation and generates the next conversation content. This enables smooth communication by suggesting the next conversation content based on the content of the previous conversation. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the suggestion unit can input data of the previous conversation content into the generation AI and have the generation AI execute a suggestion for the next conversation content.

[0073] The face recognition unit can estimate a user's emotions and adjust the accuracy of face recognition based on the estimated user emotions. For example, the face recognition unit estimates a user's emotions and adjusts the accuracy of face recognition based on the estimated user emotions. Specific methods for estimating a user's emotions include facial expression analysis and voice analysis. For example, the face recognition unit analyzes a user's facial expressions to estimate emotions. The face recognition unit can also analyze a user's voice to estimate emotions. For example, if a user is nervous, the face recognition unit analyzes more detailed facial features to improve face recognition accuracy. Furthermore, if a user is relaxed, the face recognition unit can return the face recognition accuracy to a normal setting and prioritize processing speed. Furthermore, if a user is in a hurry, the face recognition unit can temporarily reduce the face recognition accuracy to improve recognition speed. This improves recognition accuracy by adjusting the face recognition accuracy according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the face recognition unit may be performed using the generation AI, for example, or may be performed without using the generation AI. For example, the face recognition unit may input user facial expression data into the generation AI and cause the generation AI to estimate emotions and adjust the accuracy of face recognition.

[0074] The face recognition unit can improve the accuracy of face recognition by referring to past recognition history. For example, the face recognition unit can improve the accuracy of face recognition by referring to past recognition history. The past recognition history includes, for example, data on faces previously recognized. The face recognition unit can also improve the recognition accuracy under specific environmental conditions based on the past recognition history. For example, the face recognition unit can improve the recognition accuracy of the same person by referring to data on faces previously recognized. The face recognition unit can also improve the recognition accuracy of people with specific facial expressions or movements based on the past recognition history. Furthermore, the face recognition unit can improve the recognition accuracy under specific environmental conditions based on the past recognition history. Thus, by referring to the past recognition history, the recognition accuracy is improved. Some or all of the above-described processing in the face recognition unit may be performed using, or without, a generation AI. For example, the face recognition unit can input data from the past recognition history into the generation AI and cause the generation AI to improve the recognition accuracy.

[0075] The face recognition unit can improve the accuracy of recognition by analyzing the facial expressions and movements of the other party during face recognition. For example, the face recognition unit can improve the accuracy of recognition by analyzing the facial expressions and movements of the other party during face recognition. Specific methods for analyzing facial expressions and movements include facial expression recognition algorithms and movement analysis techniques. For example, the face recognition unit analyzes the other party's facial expressions, such as smiles and anger, to improve recognition accuracy. The face recognition unit can also analyze the other party's movements (hand movements and posture) to improve recognition accuracy. Furthermore, the face recognition unit can analyze the other party's line of sight and eye movements to improve recognition accuracy. In this way, by analyzing the other party's facial expressions and movements, recognition accuracy is improved. Some or all of the above-mentioned processing in the face recognition unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the face recognition unit can input data on the other party's facial expressions and movements into the generation AI and have the generation AI analyze the facial expressions and movements.

[0076] The face recognition unit can perform corrections to accommodate different lighting conditions and backgrounds during face recognition. For example, the face recognition unit performs corrections to accommodate different lighting conditions and backgrounds during face recognition. Specific methods for accommodating different lighting conditions and backgrounds include lighting correction algorithms and background removal techniques. For example, the face recognition unit can automatically adjust the brightness of an image when the lighting is low to improve recognition accuracy. The face recognition unit can also blur a complex background to improve face recognition accuracy. Furthermore, the face recognition unit can adjust the contrast of an image when the lighting is too strong to improve recognition accuracy. This improves recognition accuracy by accommodating different lighting conditions and backgrounds. Some or all of the above-described processing in the face recognition unit may be performed using, or without, a generation AI. For example, the face recognition unit can input data on different lighting conditions and backgrounds into the generation AI and have the generation AI perform lighting correction and background removal.

[0077] The face recognition unit can estimate the user's emotions and determine the priority of faces to be recognized based on the estimated user emotions. The face recognition unit, for example, estimates the user's emotions and determines the priority of faces to be recognized based on the estimated user emotions. Specific methods for estimating the user's emotions include facial expression analysis and voice analysis. For example, the face recognition unit analyzes the user's facial expressions to estimate the emotions. The face recognition unit can also analyze the user's voice to estimate the emotions. For example, the face recognition unit can prioritize recognizing the faces of important people when the user is nervous. The face recognition unit can also recognize all faces equally when the user is relaxed. Furthermore, the face recognition unit can prioritize recognizing the faces of people the user has frequently met in the past when the user is in a hurry. In this way, by determining the priority of faces to be recognized according to the user's emotions, the faces of important people can be prioritized. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the face recognition unit may be performed using, for example, a generation AI. For example, the face recognition unit may input user facial expression data to the generation AI and have the generation AI estimate emotions and determine the priority of faces.

[0078] The face recognition unit can prioritize recognizing highly relevant faces during face recognition by taking into account the user's geographical location information. For example, the face recognition unit prioritizes recognizing highly relevant faces during face recognition by taking into account the user's geographical location information. A specific method for acquiring geographical location information is to acquire GPS data. For example, when the user is in a specific location, the face recognition unit prioritizes recognizing faces of people the user frequently encounters at that location. Furthermore, when the user is traveling, the face recognition unit can prioritize recognizing faces of acquaintances at the travel destination. Furthermore, when the user is at work, the face recognition unit can prioritize recognizing faces of coworkers at work. In this way, highly relevant faces can be prioritized by taking into account the user's geographical location information. Some or all of the above-described processing in the face recognition unit may be performed using, or without, a generation AI. For example, the face recognition unit can input the user's geographical location information data into the generation AI and cause the generation AI to prioritize recognizing highly relevant faces.

[0079] The face recognition unit can analyze the user's social media activity and recognize related faces during face recognition. For example, the face recognition unit can analyze the user's social media activity and recognize related faces during face recognition. A specific method for analyzing social media activity is to analyze the content of posts. For example, the face recognition unit can prioritize recognizing the faces of people with whom the user frequently interacts on social media. The face recognition unit can also analyze the content of the user's social media posts and recognize the faces of related people. Furthermore, the face recognition unit can prioritize recognizing the faces of the user's social media friends. In this way, related faces can be recognized by analyzing the user's social media activity. Some or all of the above-mentioned processing in the face recognition unit may be performed using, or without, a generation AI. For example, the face recognition unit can input data on the user's social media activity into the generation AI and cause the generation AI to recognize related faces.

[0080] The face recognition unit can customize the recognition method by reflecting the user's past feedback during face recognition. For example, the face recognition unit customizes the recognition method by reflecting the user's past feedback during face recognition. Specific examples of past feedback include feedback regarding recognition accuracy. For example, if the user has expressed dissatisfaction with the recognition accuracy in the past, the face recognition unit can improve the recognition method based on that feedback. The face recognition unit can also customize the recognition method based on the user's feedback if the user wants to prioritize the recognition of a specific person. Furthermore, if the user wants to improve the recognition accuracy in a specific environment, the face recognition unit can adjust the recognition method based on that feedback. This allows the recognition method to be customized by reflecting the user's past feedback. Some or all of the above-described processing in the face recognition unit may be performed using, or without, a generation AI. For example, the face recognition unit can input the user's past feedback data into the generation AI and have the generation AI customize the recognition method.

[0081] The name display unit can estimate the user's emotion and adjust the name display method based on the estimated user's emotion. The name display unit, for example, estimates the user's emotion and adjusts the name display method based on the estimated user's emotion. Specific methods for estimating the user's emotion include facial expression analysis and voice analysis. For example, the name display unit analyzes the user's facial expression to estimate the emotion. The name display unit can also analyze the user's voice to estimate the emotion. For example, if the user is nervous, the name display unit can display the name in a larger size to improve visibility. If the user is relaxed, the name display unit can also display the name in a normal size. Furthermore, if the user is in a hurry, the name display unit can shorten the name to allow quick confirmation. This improves visibility by adjusting the name display method according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the name display unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the name display unit may input the user's facial expression data into the generation AI and have the generation AI estimate the emotion and adjust the name display method.

[0082] The name display unit can adjust the level of detail of the display based on the relationship between the other party and the name display unit when displaying the name. For example, the name display unit adjusts the level of detail of the display based on the relationship between the other party and the name display unit when displaying the name. A specific method for identifying the relationship between the other party is a relationship evaluation criterion. For example, the name display unit displays the full name and nickname in the case of close friends. The name display unit can also display the full name and job title in the case of business relationships. Furthermore, the name display unit can display only the full name in the case of a first meeting. In this way, by adjusting the level of detail of the display based on the relationship between the other party, appropriate information can be displayed. Some or all of the above-mentioned processing in the name display unit may be performed using, or without, a generation AI. For example, the name display unit can input data on the relationship between the other party into the generation AI and cause the generation AI to adjust the level of detail of the display.

[0083] The name display unit can improve the accuracy of the display by referring to the user's past display history when displaying a name. For example, the name display unit can improve the accuracy of the display by referring to the user's past display history when displaying a name. The past display history includes, for example, data on names previously displayed. The name display unit can also optimize the display method under specific environmental conditions based on the past display history. For example, the name display unit prioritizes displaying names of the same person based on names previously displayed by the user. The name display unit can also optimize the display method under specific environments based on the past display history. Furthermore, the name display unit can analyze the past display history and suggest the display method with the highest visibility. By referring to the user's past display history, the accuracy of the display is improved. Some or all of the above-described processing in the name display unit may be performed using, or without, a generation AI. For example, the name display unit can input data on the past display history into the generation AI and cause the generation AI to improve the accuracy of the display.

[0084] The name display unit can improve visibility by using different fonts and colors when displaying names. For example, the name display unit can improve visibility by using different fonts and colors when displaying names. Specific types of different fonts and colors include bold fonts and changing background colors. For example, the name display unit can use bold fonts to improve visibility. The name display unit can also change the background color to highlight the name. Furthermore, the name display unit can display names using different colors to make them visually easier to distinguish. Thus, using different fonts and colors improves visibility. Some or all of the above-described processing in the name display unit may be performed using, or without, a generation AI. For example, the name display unit can input font and color data into the generation AI and cause the generation AI to improve visibility.

[0085] The name display unit can estimate the user's emotions and determine the priority of names to display based on the estimated user emotions. The name display unit, for example, estimates the user's emotions and determines the priority of names to display based on the estimated user emotions. Specific methods for estimating the user's emotions include facial expression analysis and voice analysis. For example, the name display unit analyzes the user's facial expressions to estimate the emotions. The name display unit can also analyze the user's voice to estimate the emotions. For example, when the user is nervous, the name display unit can prioritize displaying names of important people. When the user is relaxed, the name display unit can also display all names equally. Furthermore, when the user is in a hurry, the name display unit can prioritize displaying names of people the user has frequently met in the past. In this way, by determining the priority of names to display based on the user's emotions, the names of important people can be prioritized. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the name display unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the name display unit may input the user's facial expression data into the generation AI and have the generation AI estimate emotions and determine the priority of names.

[0086] When displaying names, the name display unit can prioritize displaying highly relevant names by taking into account the user's geographical location information. For example, when displaying names, the name display unit prioritizes displaying highly relevant names by taking into account the user's geographical location information. A specific method for acquiring geographical location information is to acquire GPS data. For example, when the user is in a specific location, the name display unit can prioritize displaying names of people the user often meets at that location. Furthermore, when the user is traveling, the name display unit can prioritize displaying names of acquaintances at the travel destination. Furthermore, when the user is at work, the name display unit can prioritize displaying names of coworkers at work. In this way, by taking the user's geographical location information into account, highly relevant names can be prioritized. Some or all of the above-described processing in the name display unit may be performed using, or without, a generation AI. For example, the name display unit can input data of the user's geographical location information into the generation AI and cause the generation AI to prioritize displaying highly relevant names.

[0087] The name display unit can analyze the user's social media activity and display related names when displaying the name. For example, the name display unit can analyze the user's social media activity and display related names when displaying the name. A specific method for analyzing social media activity is a method for analyzing posted content. For example, the name display unit can prioritize displaying names of people with whom the user frequently interacts on social media. The name display unit can also analyze the user's social media posts and display names of related people. Furthermore, the name display unit can prioritize displaying names of the user's social media friends. In this way, related names can be displayed by analyzing the user's social media activity. Some or all of the above-described processing in the name display unit may be performed using, or without, a generation AI. For example, the name display unit can input data on the user's social media activity into the generation AI and cause the generation AI to display related names.

[0088] The name display unit can customize the display method by reflecting the user's past feedback when displaying names. For example, the name display unit customizes the display method by reflecting the user's past feedback when displaying names. Specific content of past feedback includes feedback regarding the display method. For example, if the user has expressed dissatisfaction with the display method in the past, the name display unit improves the display method based on that feedback. The name display unit can also customize the display method based on feedback if the user wants to prioritize the name of a specific person. Furthermore, if the user wants to improve the display method in a specific environment, the name display unit can adjust the display method based on that feedback. In this way, the display method can be customized by reflecting the user's past feedback. Some or all of the above-described processing in the name display unit may be performed using, or without, a generation AI. For example, the name display unit can input data of the user's past feedback into the generation AI and cause the generation AI to customize the display method.

[0089] The voice recognition unit can estimate the user's emotions and adjust the accuracy of voice recognition based on the estimated user emotions. For example, the voice recognition unit estimates the user's emotions and adjusts the accuracy of voice recognition based on the estimated user emotions. Specific methods for estimating the user's emotions include facial expression analysis and voice analysis. For example, the voice recognition unit analyzes the user's facial expressions to estimate the emotions. The voice recognition unit can also analyze the user's voice to estimate the emotions. For example, if the user is nervous, the voice recognition unit can perform more detailed voice analysis to improve the accuracy of voice recognition. If the user is relaxed, the voice recognition unit can return the voice recognition accuracy to a normal setting and prioritize processing speed. Furthermore, if the user is in a hurry, the voice recognition unit can temporarily reduce the voice recognition accuracy and improve the recognition speed. This improves recognition accuracy by adjusting the voice recognition accuracy according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the speech recognition unit may be performed using the generation AI, for example, or may be performed without using the generation AI. For example, the speech recognition unit may input user facial expression data into the generation AI and cause the generation AI to estimate emotions and adjust the accuracy of speech recognition.

[0090] The speech recognition unit can improve the accuracy of speech recognition by referring to past recognition history during speech recognition. For example, the speech recognition unit can improve the accuracy of speech recognition by referring to past recognition history during speech recognition. The past recognition history includes, for example, previously recognized speech data. The speech recognition unit can also improve the recognition accuracy under specific environmental conditions based on the past recognition history. For example, the speech recognition unit can improve the speech recognition accuracy of the same person by referring to previously recognized speech data. The speech recognition unit can also improve the speech recognition accuracy of a person with a specific speaking style or accent based on the past recognition history. Furthermore, the speech recognition unit can improve the speech recognition accuracy under specific environmental conditions based on the past recognition history. Thus, by referring to the past recognition history, the recognition accuracy is improved. Some or all of the above-described processing in the speech recognition unit may be performed using, or without, a generation AI. For example, the speech recognition unit can input data from the past recognition history into the generation AI and cause the generation AI to improve the recognition accuracy.

[0091] The speech recognition unit can improve the accuracy of speech recognition by removing background noise during speech recognition. For example, the speech recognition unit can improve the accuracy of speech recognition by removing background noise during speech recognition. A specific method for removing background noise is a noise removal algorithm. For example, when there is a lot of background noise, the speech recognition unit can improve the accuracy of speech recognition using noise canceling technology. The speech recognition unit can also improve the accuracy of speech recognition by removing noise in a specific frequency band. Furthermore, the speech recognition unit can also improve the accuracy of speech recognition by separating the speech signal and the noise signal. In this way, the recognition accuracy is improved by removing the background noise. Some or all of the above-mentioned processing in the speech recognition unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the speech recognition unit can input background noise data to the generation AI and have the generation AI perform noise removal.

[0092] The speech recognition unit can perform corrections to accommodate different speakers' voice qualities during speech recognition. For example, the speech recognition unit performs corrections to accommodate different speakers' voice qualities during speech recognition. Specific methods for accommodating different speakers' voice qualities include a voice quality feature extraction method and a correction algorithm. For example, the speech recognition unit adjusts the speech recognition algorithm according to the speaker's voice quality. The speech recognition unit can also improve speech recognition accuracy according to the speaker's voice pitch and tone. Furthermore, the speech recognition unit can analyze the speaker's voice characteristics and improve speech recognition accuracy. This improves recognition accuracy by accommodating different speakers' voice qualities. Some or all of the above-described processing in the speech recognition unit may be performed using, or without, a generation AI. For example, the speech recognition unit can input data on the speaker's voice quality into the generation AI and have the generation AI perform voice quality correction.

[0093] The voice recognition unit can estimate the user's emotions and determine the priority of voices to be recognized based on the estimated user emotions. The voice recognition unit, for example, estimates the user's emotions and determines the priority of voices to be recognized based on the estimated user emotions. Specific methods for estimating the user's emotions include facial expression analysis and voice analysis. For example, the voice recognition unit analyzes the user's facial expressions to estimate the emotions. The voice recognition unit can also analyze the user's voice to estimate the emotions. For example, if the user is nervous, the voice recognition unit can prioritize recognizing voices of important conversations. Furthermore, if the user is relaxed, the voice recognition unit can also recognize all voices equally. Furthermore, if the user is in a hurry, the voice recognition unit can prioritize recognizing voices of people with whom the user has frequently spoken in the past. Thus, by determining the priority of voices to be recognized based on the user's emotions, voices of important conversations can be prioritized. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the speech recognition unit may be performed using the generation AI, for example, or may be performed without using the generation AI. For example, the speech recognition unit may input user facial expression data into the generation AI and cause the generation AI to estimate emotions and determine the priority of speech.

[0094] The voice recognition unit can prioritize recognizing highly relevant voices during voice recognition by taking into account the user's geographical location information. For example, the voice recognition unit prioritizes recognizing highly relevant voices by taking into account the user's geographical location information during voice recognition. A specific method for acquiring geographical location information is to acquire GPS data. For example, when the user is in a specific location, the voice recognition unit prioritizes recognizing the voices of people who frequently converse in that location. Furthermore, when the user is traveling, the voice recognition unit can prioritize recognizing the voices of acquaintances at the user's travel destination. Furthermore, when the user is at work, the voice recognition unit can prioritize recognizing the voices of coworkers at work. This allows highly relevant voices to be prioritized by taking into account the user's geographical location information. Some or all of the above-described processing in the voice recognition unit may be performed using, or without, a generation AI. For example, the voice recognition unit can input data on the user's geographical location information into the generation AI and cause the generation AI to prioritize recognizing highly relevant voices.

[0095] The voice recognition unit can analyze the user's social media activity and recognize related voices during voice recognition. For example, the voice recognition unit can analyze the user's social media activity and recognize related voices during voice recognition. A specific method for analyzing social media activity is analyzing the content of posts. For example, the voice recognition unit can prioritize recognizing the voices of people with whom the user frequently interacts on social media. The voice recognition unit can also analyze the content of the user's social media posts and recognize the voices of related people. Furthermore, the voice recognition unit can prioritize recognizing the voices of the user's friends on social media. In this way, related voices can be recognized by analyzing the user's social media activity. Some or all of the above-mentioned processing in the voice recognition unit may be performed using, or without, a generation AI. For example, the voice recognition unit can input data on the user's social media activity to the generation AI and have the generation AI recognize related voices.

[0096] The speech recognition unit can customize the recognition method by reflecting the user's past feedback during speech recognition. For example, the speech recognition unit customizes the recognition method by reflecting the user's past feedback during speech recognition. Specific content of past feedback is feedback regarding recognition accuracy. For example, if the user has expressed dissatisfaction with recognition accuracy in the past, the speech recognition unit improves the recognition method based on that feedback. The speech recognition unit can also customize the recognition method based on feedback if the user wants to prioritize the voice of a specific person. Furthermore, if the user wants to improve recognition accuracy in a specific environment, the speech recognition unit can adjust the recognition method based on that feedback. In this way, the recognition method can be customized by reflecting the user's past feedback. Some or all of the above-described processing in the speech recognition unit may be performed using, or without, a generation AI. For example, the speech recognition unit can input data of the user's past feedback into the generation AI and have the generation AI customize the recognition method.

[0097] The suggestion unit can estimate the user's emotion and adjust the suggestion content based on the estimated user's emotion. For example, the suggestion unit estimates the user's emotion and adjusts the suggestion content based on the estimated user's emotion. Specific methods for estimating the user's emotion include facial expression analysis and voice analysis. For example, the suggestion unit analyzes the user's facial expression to estimate the emotion. The suggestion unit can also analyze the user's voice to estimate the emotion. For example, the suggestion unit can suggest a topic that will help the user relax if the user is nervous. The suggestion unit can also suggest an interesting topic if the user is relaxed. Furthermore, the suggestion unit can suggest a short, to-the-point topic if the user is in a hurry. This enables more appropriate suggestions by adjusting the suggestion content according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the suggestion unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the suggestion unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion and adjust the suggestion content.

[0098] The suggestion unit can improve the accuracy of a suggestion by referring to past proposal history when making a suggestion. For example, the suggestion unit can improve the accuracy of a suggestion by referring to past proposal history when making a suggestion. The past proposal history includes, for example, data on topics proposed in the past. The suggestion unit can also improve the accuracy of suggestions under specific environmental conditions based on the past proposal history. For example, the suggestion unit can suggest conversation content with the same person based on topics proposed in the past. The suggestion unit can also improve the accuracy of suggestions for specific topics or people with interests based on the past proposal history. The suggestion unit can also improve the accuracy of suggestions under specific environmental conditions based on the past proposal history. In this way, the accuracy of suggestions is improved by referring to the past proposal history. Some or all of the above-described processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input data on the past proposal history into the generation AI and cause the generation AI to improve the accuracy of suggestions.

[0099] The suggestion unit can adjust the level of detail of the suggestion based on the relationship with the other party when making a suggestion. For example, the suggestion unit adjusts the level of detail of the suggestion based on the relationship with the other party when making a suggestion. A specific method for identifying the relationship with the other party is a relationship evaluation criterion. For example, the suggestion unit may suggest detailed topics in the case of close friends. Furthermore, the suggestion unit may also suggest topics that focus on the main points in the case of a business relationship. Furthermore, the suggestion unit may also suggest general topics in the case of a first meeting. In this way, appropriate suggestions can be made by adjusting the level of detail of the suggestion based on the relationship with the other party. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the suggestion unit may input data on the relationship with the other party into the generation AI and cause the generation AI to adjust the level of detail of the suggestion.

[0100] The suggestion unit can customize the suggestion content based on the user's current situation and interests when making a suggestion. For example, the suggestion unit customizes the suggestion content based on the user's current situation and interests when making a suggestion. Specific methods for identifying the current situation and interests include situation evaluation criteria and interest identification methods. For example, the suggestion unit suggests appropriate topics based on the user's current situation. The suggestion unit can also suggest interesting topics based on the user's interests. Furthermore, the suggestion unit can analyze the user's current situation and interests and provide optimal suggestion content. This enables more appropriate suggestions by customizing the suggestion content based on the user's current situation and interests. Some or all of the above-described processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input data on the user's current situation and interests into the generation AI and have the generation AI customize the suggestion content.

[0101] The suggestion unit can estimate the user's emotions and determine the priority of suggested content based on the estimated user emotions. The suggestion unit, for example, estimates the user's emotions and determines the priority of suggested content based on the estimated user emotions. Specific methods for estimating the user's emotions include facial expression analysis and voice analysis. For example, the suggestion unit analyzes the user's facial expressions to estimate the emotions. The suggestion unit can also analyze the user's voice to estimate the emotions. For example, if the user is nervous, the suggestion unit can prioritize suggesting topics that will help the user relax. If the user is relaxed, the suggestion unit can prioritize suggesting interesting topics. Furthermore, if the user is in a hurry, the suggestion unit can prioritize suggesting short, to-the-point topics. In this way, by determining the priority of suggested content according to the user's emotions, important topics can be prioritized. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the suggestion unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the suggestion unit may input the user's facial expression data into the generation AI and have the generation AI estimate the emotion and determine the priority of the suggestion content.

[0102] When making a suggestion, the suggestion unit can prioritize highly relevant suggestions by taking into account the user's geographical location information. For example, when making a suggestion, the suggestion unit can prioritize highly relevant suggestions by taking into account the user's geographical location information. A specific method for acquiring geographical location information is to acquire GPS data. For example, when the user is in a specific location, the suggestion unit can prioritize suggesting topics related to the location. Furthermore, when the user is traveling, the suggestion unit can prioritize suggesting topics related to the travel destination. Furthermore, when the user is at work, the suggestion unit can prioritize suggesting topics related to the workplace. In this way, by taking the user's geographical location information into account, highly relevant suggestions can be made preferentially. Some or all of the above-described processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input data on the user's geographical location information into the generation AI and cause the generation AI to prioritize highly relevant suggestions.

[0103] The suggestion unit can analyze the user's social media activity and make related suggestions when making a suggestion. For example, the suggestion unit can analyze the user's social media activity and make related suggestions when making a suggestion. A specific method for analyzing social media activity is a method for analyzing posted content. For example, the suggestion unit can suggest topics related to people the user frequently interacts with on social media. The suggestion unit can also analyze the user's social media posts and suggest related topics. Furthermore, the suggestion unit can suggest related topics by referring to the activities of the user's friends on social media. In this way, related suggestions can be made by analyzing the user's social media activity. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input data on the user's social media activity into the generation AI and cause the generation AI to execute related suggestions.

[0104] The suggestion unit can customize the suggestion method by reflecting the user's past feedback when making a suggestion. For example, the suggestion unit customizes the suggestion method by reflecting the user's past feedback when making a suggestion. Specific content of the past feedback is feedback regarding the suggestion content. For example, if the user has been dissatisfied with the suggestion content in the past, the suggestion unit improves the suggestion method based on that feedback. The suggestion unit can also customize the suggestion method based on feedback if the user wants to prioritize a specific topic. Furthermore, if the user wants to improve the suggestion method in a specific environment, the suggestion unit can adjust the suggestion method based on that feedback. In this way, the suggestion method can be customized by reflecting the user's past feedback. Some or all of the above-described processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input data of the user's past feedback into the generation AI and cause the generation AI to customize the suggestion method. === Hard Collateral 1-1 === Each of the multiple elements including the face recognition unit, name display unit, voice recognition unit, and suggestion unit described above is realized, for example, in at least one of the smart device 14 and the data processing device 12. For example, the face recognition unit can capture a picture of the other person's face using the camera 42 of the smart device 14 and recognize the face using the specific processing unit 290 of the data processing device 12. The name display unit can display a name on the display 40A of the smart device 14, for example. The voice recognition unit can record a conversation using the microphone 38B of the smart device 14 and recognize and summarize the voice using the specific processing unit 290 of the data processing device 12. The suggestion unit can analyze the content of the previous conversation using the specific processing unit 290 of the data processing device 12 and suggest the content of the next conversation, for example. === Hard Collateral 1-2 === Each of the multiple elements including the face recognition unit, name display unit, voice recognition unit, and suggestion unit described above is realized, for example, in at least one of the smart glasses 214 and the data processing device 12. For example, the face recognition unit can capture a picture of the other person's face using the camera 42 of the smart glasses 214 and recognize the face using the specific processing unit 290 of the data processing device 12. The name display unit can display a name on the display of the smart glasses 214, for example. The voice recognition unit can record a conversation using the microphone 238 of the smart glasses 214 and recognize and summarize the voice using the specific processing unit 290 of the data processing device 12. The suggestion unit can analyze the content of the previous conversation using the specific processing unit 290 of the data processing device 12 and suggest the content of the next conversation, for example. === Hard Collateral 1-3 === Each of the multiple elements including the face recognition unit, name display unit, voice recognition unit, and suggestion unit described above is realized, for example, in at least one of the headset-type terminal 314 and the data processing device 12. For example, the face recognition unit can photograph the face of the other party using the camera 42 of the headset-type terminal 314 and recognize the face using the specific processing unit 290 of the data processing device 12. The name display unit can display the name on the display 343 of the headset-type terminal 314, for example. The voice recognition unit can record a conversation using the microphone 238 of the headset-type terminal 314 and recognize and summarize the voice using the specific processing unit 290 of the data processing device 12. The suggestion unit can analyze the content of the previous conversation using the specific processing unit 290 of the data processing device 12 and suggest the content of the next conversation, for example. === Hard Collateral 1-4 === Each of the multiple elements including the face recognition unit, name display unit, voice recognition unit, and suggestion unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the face recognition unit can photograph the face of the other party using the camera 42 of the robot 414 and recognize the face using the specific processing unit 290 of the data processing device 12. The name display unit can display the name on the display of the robot 414, for example. The voice recognition unit can record a conversation using the microphone 238 of the robot 414 and recognize and summarize the voice using the specific processing unit 290 of the data processing device 12. The suggestion unit can analyze the content of the previous conversation using the specific processing unit 290 of the data processing device 12 and suggest the content of the next conversation, for example.

[0105] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0106] The AR glasses system may further include a health management unit that monitors the user's health status. For example, the health management unit may measure the user's heart rate and blood pressure and display a warning if an abnormality is detected. The health management unit may also record the user's number of steps and calories burned and provide health management advice. For example, the health management unit may display a notification encouraging the user to stand up and walk if the user has been sitting for a certain period of time. The health management unit may also analyze the user's sleep patterns and make suggestions for getting quality sleep. In this way, the AR glasses system can support the user's health management and promote a healthier lifestyle.

[0107] The face recognition unit can estimate the user's emotions and adjust the accuracy of face recognition based on the estimated user emotions. For example, if the user is nervous, the face recognition unit can analyze more detailed facial features to improve the accuracy of face recognition. Also, if the user is relaxed, the face recognition unit can return the accuracy of face recognition to a normal setting and prioritize processing speed. Furthermore, if the user is in a hurry, the face recognition unit can temporarily lower the accuracy of face recognition to improve the recognition speed. In this way, by adjusting the accuracy of face recognition according to the user's emotions, recognition accuracy is improved.

[0108] The name display unit can estimate the user's emotions and adjust the name display method based on the estimated user emotions. For example, if the user is nervous, the name display unit can display the name in a larger size to improve visibility. If the user is relaxed, the name display unit can also display the name in a normal size. Furthermore, if the user is in a hurry, the name display unit can shorten the name to enable quick confirmation. In this way, visibility is improved by adjusting the name display method according to the user's emotions.

[0109] The voice recognition unit can estimate the user's emotions and adjust the accuracy of voice recognition based on the estimated user's emotions. For example, if the user is nervous, the voice recognition unit can perform more detailed voice analysis to improve the accuracy of voice recognition. Also, if the user is relaxed, the voice recognition unit can return the voice recognition accuracy to a normal setting and prioritize processing speed. Furthermore, if the user is in a hurry, the voice recognition unit can temporarily lower the voice recognition accuracy and improve the recognition speed. In this way, by adjusting the voice recognition accuracy according to the user's emotions, the recognition accuracy is improved.

[0110] The suggestion unit can estimate the user's emotions and adjust the suggestion content based on the estimated user's emotions. For example, if the user is nervous, the suggestion unit can suggest a topic that will help the user relax. Also, if the user is relaxed, the suggestion unit can suggest an interesting topic. Furthermore, if the user is in a hurry, the suggestion unit can suggest a short and to-the-point topic. This allows for more appropriate suggestions by adjusting the suggestion content according to the user's emotions.

[0111] The AR glasses system may further include a translation unit. For example, the translation unit translates the language spoken by the user in real time and displays it to the other party. The translation unit may also translate the language spoken by the other party into the user's native language and display it on the AR glasses display. For example, the translation unit may translate English into Japanese and display it in a way that is easy for the user to understand. The translation unit may also support multiple languages, facilitating communication with other parties who speak different languages. This allows the AR glasses system to support communication between people who speak different languages ​​and promote international exchange.

[0112] The AR glasses system may further include a navigation unit. The navigation unit, for example, identifies the user's current location and displays a route to the destination. The navigation unit may also provide real-time traffic information and suggest an optimal route. For example, the navigation unit may take traffic congestion information into consideration and display a route to reach the destination in the shortest time. The navigation unit may also provide route guidance for pedestrians, helping the user reach the destination without getting lost. This allows the AR glasses system to support the user's travel and provide efficient navigation.

[0113] The AR glasses system may further include a reminder unit. The reminder unit may, for example, manage the user's schedule and notify the user of important appointments. The reminder unit may also remind the user of tasks set by the user and notify the user when a deadline is approaching. For example, the reminder unit may notify the user of the start time of a meeting to help the user avoid being late. The reminder unit may also manage a shopping list and remind the user to purchase necessary items. In this way, the AR glasses system may support the user in managing their schedule and ensure that important tasks are not forgotten.

[0114] The AR glasses system may further include an entertainment unit. The entertainment unit provides content for the user to enjoy, such as movies and music. The entertainment unit may also suggest recommended content based on the user's preferences. For example, the entertainment unit may suggest movies in the user's favorite genre to support viewing. The entertainment unit may also play relaxing music to support refreshing. This allows the AR glasses system to enhance the user's entertainment experience and provide relaxation and enjoyment.

[0115] The AR glasses system may further include a learning support unit. The learning support unit, for example, provides learning materials for the user and manages the user's learning progress. The learning support unit may also suggest appropriate learning content based on the user's level of understanding. For example, the learning support unit may identify areas in which the user is weak and provide learning materials related to those areas. The learning support unit may also suggest learning methods that match the user's learning style and support effective learning. In this way, the AR glasses system can support the user's learning and provide an effective learning experience.

[0116] The processing flow of the second embodiment will be briefly explained below.

[0117] Step 1: The face recognition unit recognizes the other person's face. For example, the face recognition unit uses AI to analyze video data captured by a camera and recognize the other person's face. The face recognition unit can also extract facial feature points and recognize the face using a face recognition algorithm. For example, the face recognition unit uses a face recognition model that uses deep learning to extract and recognize facial feature points. The face recognition unit can also recognize faces by analyzing facial contours and features such as the eyes, nose, and mouth. Step 2: The name display unit displays the name based on the face recognized by the face recognition unit. The name display unit displays the name on, for example, the display of the AR glasses. The name display unit can also adjust the display device and display format. For example, the name display unit adjusts the display resolution and font size to display the name. The name display unit can also change the color and background color of the displayed name. Step 3: The speech recognition unit uses a built-in microphone to record the conversation, and uses AI to recognize and summarize the content. The speech recognition unit, for example, uses a speech recognition algorithm to recognize the content of the conversation. The speech recognition unit can also summarize the content of the conversation using a summarization algorithm. For example, the speech recognition unit uses a speech recognition model that uses deep learning to recognize the content of the conversation. The speech recognition unit can also summarize the content of the conversation using natural language processing technology. Step 4: The suggestion unit suggests appropriate conversation content for the next meeting based on the content of the previous conversation. For example, the suggestion unit analyzes the content of the previous conversation and suggests the content of the next conversation. The suggestion unit can also suggest appropriate conversation content using a proposal algorithm. For example, the suggestion unit uses a proposal model that uses deep learning to suggest the content of the next conversation. The suggestion unit can also generate the content of the next conversation based on the content of the previous conversation.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0122] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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 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.

[0136] 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.

[0137] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0138] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0139] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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 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.

[0152] 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.

[0153] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0154] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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).

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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 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.

[0169] 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.

[0170] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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).

[0175] 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 area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0176] 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."

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] [Explanation of symbols]

[0190] 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 face recognition unit that recognizes a face; a name display unit that displays a name based on the face recognized by the face recognition unit; a speech recognition unit that recognizes and summarizes speech based on the name displayed by the name display unit; a suggestion unit that suggests a next conversation content based on the conversation content summarized by the speech recognition unit; Equipped with A system characterized by:

2. The face recognition unit The video data captured by the camera is analyzed using AI to recognize the other person's face.

2. The system of claim 1.

3. The name display unit is A name is displayed on the display of the AR glasses based on the face recognized by the face recognition unit.

2. The system of claim 1.

4. The voice recognition unit The built-in microphone records conversations, and the content is recognized and summarized by AI.

2. The system of claim 1.

5. The proposal unit Based on the content of the previous conversation, suggest appropriate conversation topics for the next meeting 2. The system of claim 1.

6. The face recognition unit Estimate the user's emotions and adjust the accuracy of facial recognition based on the estimated user emotions.

2. The system of claim 1.

7. The face recognition unit When recognizing faces, past recognition history is referenced to improve the accuracy of recognition.

2. The system of claim 1.

8. The face recognition unit When recognizing faces, the accuracy of recognition is improved by analyzing the other person's facial expressions and movements.

2. The system of claim 1.

Images