System

Abstract

An object of the system according to the embodiment is to quickly provide an answer to a user's question by providing a generation and AI function to a set of earphones and glasses.SOLUTION: A system according to an embodiment includes earphones, a microphone, a generation AI, and a sound output unit. The microphone acquires a voice input. The generation AI analyzes the speech input acquired by the microphone and generates an appropriate answer. The sound output unit outputs the answer generated by the generation AI by sound.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] With conventional technology, there are products that combine earphones and glasses, but the problem is that they are unable to provide any added value beyond that.

[0005] The system of the embodiment aims to provide quick answers to users' questions by equipping a product set consisting of earphones and glasses with a generative AI function. [Means for solving the problem]

[0006] The system according to the embodiment includes earphones, a microphone, a generation AI, and an audio output unit. The microphone acquires audio input. The generation AI analyzes the audio input acquired by the microphone and generates an appropriate answer. The audio output unit outputs the answer generated by the generation AI by voice. [Effects of the Invention]

[0007] The system of the embodiment can provide quick answers to user questions by equipping a product set consisting of earphones and glasses with a generative AI function. [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 Eyewear system according to an embodiment of the present invention is a system that provides quick answers (A) to questions (Q) about people's problems by adding a generative AI function to the current Eyewear set consisting of earphones and glasses. As a result, when a user inputs a question by voice, the Eyewear system allows the generative AI to analyze the question and generate an appropriate answer.

[0029] An eyewear system according to an embodiment includes earphones, a microphone, a generation AI, and an audio output unit. The earphones are worn in the user's ears and output audio. For example, the earphones can be connected to a smartphone via Bluetooth. The earphones have a noise-canceling function to reduce external noise. The earphones have a built-in battery that can withstand long-term use. For example, the earphones can be used continuously for more than eight hours on a full charge. The microphone captures the user's voice. For example, the microphone uses a directional microphone to clearly capture the user's voice. The microphone can also reduce background noise using noise reduction technology. The microphone also has a function to automatically adjust the sensitivity of the voice input. For example, the microphone adjusts the sensitivity according to the volume of the user's voice to achieve optimal voice input. The generation AI analyzes the voice input captured by the microphone and generates an appropriate answer. For example, the generation AI uses a text generation AI (e.g., LLM) to generate an answer to a user's question. The generation AI can also use a multimodal generation AI to generate an answer that combines voice input and image data. The generation AI also uses natural language processing technology to analyze the user's question and generate an optimal answer. For example, the generation AI understands the intent of the user's question and generates an answer based on that. The audio output unit outputs the answer generated by the generation AI as audio. For example, the audio output unit uses a speaker to let the user hear the generated answer. The audio output unit can also provide the generated answer to the user through earphones. The audio output unit also uses speech synthesis technology to output the answer in a natural voice. For example, the audio output unit can adjust the tone and speed of the voice according to the user's preferences. As a result, the Eyewear system according to the embodiment allows the generation AI to generate an appropriate answer based on the user's voice input and output it as audio. For example, when a user asks, "What's the weather like today?", the generation AI replies, "It's sunny today" based on weather forecast data. When a user asks, "Tell me about nearby restaurants," the generation AI analyzes location information and provides audio guidance to the nearest restaurant.

[0030] The generation AI can analyze a user's past question history and automatically follow up on unresolved issues. For example, the generation AI can analyze a user's past question history and list unresolved questions. For example, it can re-suggest questions that the user previously asked but for which the answer was insufficient. The generation AI can also provide new related information based on the questions the user has previously asked. For example, it can provide the latest news on topics the user has previously asked about. The generation AI can also analyze a user's question history and automatically generate follow-up questions for unresolved issues. For example, it can request additional information on questions the user has previously asked. This allows the generation AI to automatically follow up on the user's unresolved issues.

[0031] The generation AI can analyze the user's environmental sounds and generate appropriate answers. For example, the generation AI can analyze the environmental sounds around the user using a microphone and provide concise answers if there is a lot of noise. For example, it can generate short answers in noisy places. The generation AI can also generate answers with detailed explanations if the user is in a quiet environment. For example, it can provide detailed information if the user is in a library. The generation AI can also analyze environmental sounds and suggest music or audio that will help the user relax. For example, if the user is in a quiet place, it can recommend relaxing music. This makes it possible to generate appropriate answers according to the user's environmental sounds.

[0032] Generative AI can track a user's gaze and provide information about the object in front of their eyes. For example, generative AI tracks a user's gaze with a camera and recognizes the object in front of their eyes. For example, if the user is looking at a book, it will provide information about that book. Generative AI can also analyze the user's gaze data and display related information about the object in front of their eyes. For example, if the user is looking at a restaurant menu, it will provide details about the menu. Generative AI can also make suggestions about objects that the user is interested in based on the gaze tracking data. For example, if the user is looking at a product, it will provide reviews and price information for that product. This makes it possible to provide information about the object in front of the user's eyes.

[0033] The generation AI can recognize a user's gestures and generate answers that correspond to the gestures. For example, the generation AI analyzes the user's hand movements using a camera and generates answers that correspond to the specific gesture. For example, when a user raises their hand, the generation AI accepts a question. Furthermore, when a user performs a specific gesture, the generation AI provides information corresponding to that gesture. For example, when a user points their finger, it provides information about objects in that direction. Furthermore, the generation AI learns the user's gestures and generates individually customized answers. For example, when a user performs a specific gesture, the generation AI makes suggestions based on the user's preferences. This makes it possible to generate appropriate answers that correspond to the user's gestures.

[0034] Generative AI enhances the noise canceling function of voice input, enabling accurate voice recognition even in noisy environments. For example, generative AI uses noise canceling technology to achieve accurate voice recognition even in noisy environments. For example, it removes background noise when inputting voice in the city. Generative AI also automatically adjusts the noise canceling function when a user inputs voice in a noisy environment. For example, it removes surrounding conversation sounds when inputting voice in a cafe. Generative AI also learns noise canceling technology and provides the optimal noise canceling setting according to the environment. For example, it removes the sound of trains running when inputting voice on a train. This enables accurate voice recognition even in noisy environments.

[0035] When outputting audio, the generation AI can automatically adjust the volume and tone according to the user's hearing characteristics. For example, the generation AI analyzes the user's hearing characteristics and automatically adjusts the volume and tone of the audio output. For example, if the user has difficulty hearing high-pitched sounds, the generation AI emphasizes low-pitched sounds. The generation AI also customizes the audio output settings based on the user's hearing characteristics. For example, if the user sets the volume low, the generation AI automatically adjusts the volume. The generation AI also learns the user's hearing characteristics and provides individually customized audio output. For example, if the user has difficulty hearing a specific frequency band, the generation AI emphasizes that band. This makes it possible to automatically adjust the volume and tone according to the user's hearing characteristics.

[0036] In addition to voice input and output, the generation AI provides tactile feedback, allowing the user to feel the answer through touch. For example, the generation AI provides tactile feedback in addition to voice input and output. For example, when the user inputs a question, tactile feedback is used to confirm the question. The generation AI also provides tactile feedback when the user makes a voice input to confirm the input. For example, a vibration is used to notify the user that the voice input was successful. The generation AI also provides tactile feedback when outputting voice, allowing the user to feel the answer through touch. For example, important information is emphasized through tactile feedback. This allows the user to feel the answer through touch.

[0037] The generation AI can improve the accuracy of speech recognition by analyzing the user's mouth movements when inputting speech. For example, the generation AI can analyze the user's mouth movements using a camera to improve the accuracy of speech recognition. For example, it can analyze the shape of the user's mouth when pronouncing words. The generation AI can also analyze the user's mouth movements when inputting speech to improve the accuracy of speech recognition. For example, it can analyze the mouth movements when the user pronounces specific words. The generation AI can also learn the user's mouth movements to provide individually customized speech recognition. For example, it can analyze the mouth movements when the user pronounces specific words. This can improve the accuracy of speech recognition.

[0038] The generating AI can analyze the user's health data and make suggestions based on the health condition. The generating AI, for example, analyzes the user's health data and makes suggestions based on the health condition. For example, it provides health advice based on the user's heart rate and sleep data. The generating AI also makes individually customized health suggestions based on the user's health data. For example, it analyzes the user's exercise data and suggests an optimal exercise plan. The generating AI also learns the user's health data and provides individually customized health suggestions. For example, it analyzes the user's dietary data and suggests a nutritionally balanced meal plan. This makes it possible to make suggestions based on the user's health condition.

[0039] The generation AI can analyze the user's schedule and provide notifications and suggestions at the optimal time. For example, the generation AI can analyze the user's schedule data and provide notifications at the optimal time. For example, the user receives a reminder before a meeting. The generation AI can also provide individually customized suggestions based on the user's schedule. For example, it can suggest relaxing activities for the user when they have free time. The generation AI can also learn the user's schedule and provide notifications and suggestions at the optimal time. For example, it can make suggestions to reduce stress when the user is busy. This allows the generation AI to provide notifications and suggestions at the optimal time according to the user's schedule.

[0040] The generation AI can analyze the user's hobbies and interests and suggest related events and activities. For example, the generation AI can analyze the user's hobbies and interests and suggest related events. For example, if the user likes music, it can provide information about nearby concerts. The generation AI can also suggest individually customized activities based on the user's past behavioral patterns. For example, if the user likes the outdoors, it can suggest hiking trails. The generation AI can also learn the user's hobbies and interests and suggest related events and activities. For example, if the user likes cooking, it can provide information about cooking classes. This makes it possible to suggest events and activities that match the user's hobbies and interests.

[0041] Generative AI can analyze a user's social media activity and provide related information and news. For example, generative AI can analyze a user's social media activity and provide related news. For example, it can notify the user of the latest posts from accounts the user follows. Generative AI can also provide individually customized information based on the user's social media interests. For example, if a user is interested in a particular topic, it can provide the latest information on that topic. Generative AI can also learn the user's social media activity and provide related information and news. For example, if a user is interested in a particular event, it can provide detailed information about that event. This makes it possible to provide related information and news according to the user's social media activity.

[0042] The generation AI can analyze the user's location information and provide real-time information according to the location. The generation AI, for example, analyzes the user's location information and provides real-time information according to the location. For example, if the user is in a specific location, information about that location is provided. The generation AI also provides individually customized real-time information based on the user's location information. For example, if the user is in a tourist spot, information about that tourist spot is provided. The generation AI also learns the user's location information and provides real-time information according to the location. For example, if the user frequently visits a specific location, the latest information about that location is provided. This makes it possible to provide real-time information according to the user's location.

[0043] The generation AI can analyze a user's past behavioral patterns and provide real-time information according to predicted needs. The generation AI, for example, analyzes a user's past behavioral patterns and provides real-time information according to predicted needs. For example, if a user tends to seek specific information at a specific time, the generation AI provides that information. The generation AI also provides real-time information that is individually customized based on the user's past behavioral patterns. For example, if a user tends to seek specific information on a specific day of the week, the generation AI provides that information. The generation AI also learns a user's past behavioral patterns and provides real-time information according to predicted needs. For example, if a user tends to participate in a specific event, the generation AI provides information about that event. This makes it possible to provide real-time information according to predicted needs based on the user's past behavioral patterns.

[0044] The generating AI can analyze environmental information around the user and provide real-time information according to the environment. The generating AI can, for example, analyze environmental information around the user and provide real-time information according to the environment. For example, if the user is outdoors, it can provide weather information. The generating AI can also provide individually customized real-time information based on the environmental information around the user. For example, if the user is indoors, it can provide indoor temperature and humidity information. The generating AI can also learn environmental information around the user and provide real-time information according to the environment. For example, if the user is in a specific environment, it can provide the latest information about that environment. This makes it possible to provide real-time information according to the environmental information around the user.

[0045] The generative AI can share information between a user's devices and provide integrated real-time information. For example, the generative AI can share information between multiple user devices and provide integrated real-time information. For example, it can integrate data from a smartphone and a smartwatch. The generative AI can also share information between a user's devices and provide individually customized real-time information. For example, it can provide information in cooperation with the user's smart home devices. The generative AI can also learn information between a user's devices and provide integrated real-time information. For example, it can provide information in cooperation with the user's smart speaker. This allows information to be shared between a user's devices and provide integrated real-time information.

[0046] The generation AI can analyze the user's language proficiency level and provide answers of an appropriate level of difficulty. The generation AI, for example, analyzes the user's language proficiency level and provides answers of an appropriate level of difficulty. For example, if the user is at a beginner's level, it generates answers in simple language. The generation AI also provides answers of an individually customized level of difficulty based on the user's past question history. For example, if the user is at an intermediate level, it generates answers of a moderate level of difficulty. The generation AI also learns the user's language proficiency level and provides answers of an appropriate level of difficulty. For example, if the user is at an advanced level, it generates answers that include specialized language. This makes it possible to provide answers of an appropriate level of difficulty according to the user's language proficiency level.

[0047] The generation AI can take into account the user's cultural background and provide appropriate answers that are culturally appropriate. For example, the generation AI can analyze the user's cultural background and provide appropriate answers that are culturally appropriate. For example, if the user belongs to a particular culture, the generation AI can generate answers that take that culture into consideration. The generation AI can also provide answers that are individually customized based on the user's past question history and are appropriate for that cultural background. For example, if the user believes in a particular religion, the generation AI can generate answers that take that religion into consideration. The generation AI can also learn the user's cultural background and provide appropriate answers that are culturally appropriate. For example, if the user lives in a particular region, the generation AI can generate answers that take the culture of that region into consideration. This makes it possible to provide appropriate answers that are appropriate for the user's cultural background.

[0048] The generation AI can automatically detect the user's language settings and provide answers in the most appropriate language. For example, the generation AI analyzes the user's device settings and past language usage to provide answers in the most appropriate language. For example, if the user primarily uses English, it generates answers in English. The generation AI can also analyze the user's voice input and automatically detect the language to provide answers. For example, if the user asks a question in Japanese, it generates answers in Japanese. The generation AI can also learn the user's language settings and provide answers in the most appropriate language. For example, if the user uses multiple languages, it generates answers in the appropriate language at the time. This makes it possible to provide answers in the most appropriate language according to the user's language settings.

[0049] The generative AI can provide a language learning mode to support the user's language acquisition. The generative AI can, for example, provide a language learning mode to support the user's language acquisition. For example, when the user is learning a new language, it can teach simple phrases and vocabulary. The generative AI can also analyze the user's language acquisition level and provide individually customized language learning content. For example, if the user is at a beginner's level, it can teach basic grammar and vocabulary. The generative AI can also learn the user's language acquisition and provide an individually customized language learning mode. For example, if the user is at an intermediate level, it can provide conversation practice and listening practice. In this way, it can provide a language learning mode to support the user's language acquisition.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The Eyewear system can also analyze the user's health data and make suggestions based on their health condition. For example, it can provide health advice based on the user's heart rate and sleep data. It can also make individually customized health suggestions based on the user's health data. For example, it can analyze the user's exercise data and suggest an optimal exercise plan. It can also learn from the user's health data and provide individually customized health suggestions. For example, it can analyze the user's dietary data and suggest a nutritionally balanced meal plan. This allows it to make suggestions based on the user's health condition.

[0052] The Eyewear system can also analyze the user's schedule and provide notifications and suggestions at the optimal time. For example, the system can analyze the user's schedule data and provide notifications at the optimal time. For example, the user can receive a reminder before a meeting. The system can also provide individually customized suggestions based on the user's schedule. For example, the system can suggest relaxing activities for the user when they have free time. The system can also learn the user's schedule and provide notifications and suggestions at the optimal time. For example, the system can make suggestions to reduce stress when the user is busy. This allows the system to provide notifications and suggestions at the optimal time based on the user's schedule.

[0053] The Eyewear system can also analyze the user's hobbies and interests and suggest related events and activities. For example, it can analyze the user's hobbies and interests and suggest related events. For example, if the user likes music, it can provide information about nearby concerts. It can also suggest individually customized activities based on the user's past behavioral patterns. For example, if the user likes the outdoors, it can suggest hiking trails. It can also learn the user's hobbies and interests and suggest related events and activities. For example, if the user likes cooking, it can provide information about cooking classes. This makes it possible to suggest events and activities that match the user's hobbies and interests.

[0054] The Eyewear system can also analyze a user's social media activity and provide relevant information and news. For example, it can analyze a user's social media activity and provide relevant news. For example, it can notify the user of the latest posts from accounts the user follows. It can also provide individually customized information based on the user's social media interests. For example, if a user is interested in a particular topic, it can provide the latest information on that topic. It can also learn about the user's social media activity and provide relevant information and news. For example, if a user is interested in a particular event, it can provide detailed information about that event. This makes it possible to provide relevant information and news based on the user's social media activity.

[0055] The eyewear system can also analyze the user's location information and provide real-time information according to the location. For example, if the user is in a specific location, information about that location is provided. It can also provide individually customized real-time information based on the user's location information. For example, if the user is in a tourist spot, information about that tourist spot is provided. It can also learn the user's location information and provide real-time information according to the location. For example, if the user frequently visits a specific location, the latest information about that location is provided. This makes it possible to provide real-time information according to the user's location.

[0056] The eyewear system can also analyze a user's past behavioral patterns and provide real-time information according to predicted needs. For example, the eyewear system can analyze a user's past behavioral patterns and provide real-time information according to predicted needs. For example, if a user tends to seek specific information at a specific time, that information can be provided. The eyewear system can also provide real-time information that is individually customized based on the user's past behavioral patterns. For example, if a user tends to seek specific information on a specific day of the week, that information can be provided. The eyewear system can also learn a user's past behavioral patterns and provide real-time information according to predicted needs. For example, if a user tends to participate in a specific event, information about that event can be provided. This makes it possible to provide real-time information according to predicted needs based on the user's past behavioral patterns.

[0057] The eyewear system can also analyze information about the user's surrounding environment and provide real-time information tailored to the environment. For example, the eyewear system can analyze information about the user's surrounding environment and provide real-time information tailored to the environment. For example, if the user is outdoors, weather information can be provided. It can also provide individually customized real-time information based on the user's surrounding environmental information. For example, if the user is indoors, indoor temperature and humidity information can be provided. It can also learn information about the user's surrounding environment and provide real-time information tailored to the environment. For example, if the user is in a specific environment, the latest information about that environment can be provided. This makes it possible to provide real-time information tailored to the user's surrounding environmental information.

[0058] The eyewear system can also share information between a user's devices to provide integrated real-time information. For example, it can share information between multiple devices and provide integrated real-time information. For example, it can integrate data from a smartphone and a smartwatch. It can also share information between a user's devices to provide individually customized real-time information. For example, it can provide information in cooperation with a user's smart home devices. It can also learn information between a user's devices and provide integrated real-time information. For example, it can provide information in cooperation with a user's smart speaker. This allows it to share information between a user's devices and provide integrated real-time information.

[0059] The processing flow of the first embodiment will be briefly explained below.

[0060] Step 1: The earphones are placed in the user's ears and output sound. For example, the earphones can be connected to a smartphone using Bluetooth. The earphones also have a noise-canceling function that can reduce external noise. The earphones also have a built-in battery that can withstand long-term use. For example, the earphones can be used continuously for more than 8 hours on a full charge. Step 2: The microphone captures the user's voice. For example, the microphone uses a directional microphone to clearly capture the user's voice. The microphone can also use noise reduction technology to reduce background noise. The microphone also has a function to automatically adjust the sensitivity of the voice input. For example, the microphone adjusts the sensitivity according to the volume of the user's voice to achieve optimal voice input. Step 3: The generation AI analyzes the voice input captured by the microphone and generates an appropriate answer. For example, the generation AI uses a text generation AI (e.g., LLM) to generate an answer to the user's question. The generation AI can also use a multimodal generation AI to generate an answer that combines the voice input and image data. The generation AI also uses natural language processing technology to analyze the user's question and generate an optimal answer. For example, the generation AI understands the intent of the user's question and generates an answer based on that. Step 4: The audio output unit outputs the answer generated by the generation AI by voice. For example, the audio output unit uses a speaker to let the user hear the generated answer. The audio output unit can also provide the generated answer to the user through earphones. The audio output unit also uses voice synthesis technology to output the answer in a natural voice. For example, the audio output unit can adjust the tone and speed of the voice according to the user's preferences.

[0061] (Example 2) The Eyewear system according to an embodiment of the present invention is a system that provides quick answers (A) to questions (Q) about people's problems by adding a generative AI function to the current Eyewear set consisting of earphones and glasses. As a result, when a user inputs a question by voice, the Eyewear system allows the generative AI to analyze the question and generate an appropriate answer.

[0062] An eyewear system according to an embodiment includes earphones, a microphone, a generation AI, and an audio output unit. The earphones are worn in the user's ears and output audio. For example, the earphones can be connected to a smartphone via Bluetooth. The earphones have a noise-canceling function to reduce external noise. The earphones have a built-in battery that can withstand long-term use. For example, the earphones can be used continuously for more than eight hours on a full charge. The microphone captures the user's voice. For example, the microphone uses a directional microphone to clearly capture the user's voice. The microphone can also reduce background noise using noise reduction technology. The microphone also has a function to automatically adjust the sensitivity of the voice input. For example, the microphone adjusts the sensitivity according to the volume of the user's voice to achieve optimal voice input. The generation AI analyzes the voice input captured by the microphone and generates an appropriate answer. For example, the generation AI uses a text generation AI (e.g., LLM) to generate an answer to a user's question. The generation AI can also use a multimodal generation AI to generate an answer that combines voice input and image data. The generation AI also uses natural language processing technology to analyze the user's question and generate an optimal answer. For example, the generation AI understands the intent of the user's question and generates an answer based on that. The audio output unit outputs the answer generated by the generation AI as audio. For example, the audio output unit uses a speaker to let the user hear the generated answer. The audio output unit can also provide the generated answer to the user through earphones. The audio output unit also uses speech synthesis technology to output the answer in a natural voice. For example, the audio output unit can adjust the tone and speed of the voice according to the user's preferences. As a result, the Eyewear system according to the embodiment allows the generation AI to generate an appropriate answer based on the user's voice input and output it as audio. For example, when a user asks, "What's the weather like today?", the generation AI replies, "It's sunny today" based on weather forecast data. When a user asks, "Tell me about nearby restaurants," the generation AI analyzes location information and provides audio guidance to the nearest restaurant.

[0063] The generation AI can analyze the user's tone of voice or phrasing to infer the user's emotions and generate answers that correspond to those emotions. For example, when a user inputs a question, the generation AI analyzes the user's tone of voice and phrasing to infer the user's emotions. For example, if the user is tired, the generation AI will make suggestions to help them relax. The generation AI also analyzes the user's facial expressions with a camera to infer emotions. For example, if the user is smiling, the generation AI will generate a positive answer. The generation AI also analyzes the user's past question history to learn emotional patterns. For example, if a user tends to ask a particular question when they are feeling stressed, the generation AI will generate an answer that corresponds to that emotion. This makes it possible to generate appropriate answers that correspond to the user's emotions.

[0064] The generation AI can analyze a user's past question history and automatically follow up on unresolved issues. For example, the generation AI can analyze a user's past question history and list unresolved questions. For example, it can re-suggest questions that the user previously asked but for which the answer was insufficient. The generation AI can also provide new related information based on the questions the user has previously asked. For example, it can provide the latest news on topics the user has previously asked about. The generation AI can also analyze a user's question history and automatically generate follow-up questions for unresolved issues. For example, it can request additional information on questions the user has previously asked. This allows the generation AI to automatically follow up on the user's unresolved issues.

[0065] The generation AI can analyze the user's environmental sounds and generate appropriate answers. For example, the generation AI can analyze the environmental sounds around the user using a microphone and provide concise answers if there is a lot of noise. For example, it can generate short answers in noisy places. The generation AI can also generate answers with detailed explanations if the user is in a quiet environment. For example, it can provide detailed information if the user is in a library. The generation AI can also analyze environmental sounds and suggest music or audio that will help the user relax. For example, if the user is in a quiet place, it can recommend relaxing music. This makes it possible to generate appropriate answers according to the user's environmental sounds.

[0066] Generative AI can track a user's gaze and provide information about the object in front of their eyes. For example, generative AI tracks a user's gaze with a camera and recognizes the object in front of their eyes. For example, if the user is looking at a book, it will provide information about that book. Generative AI can also analyze the user's gaze data and display related information about the object in front of their eyes. For example, if the user is looking at a restaurant menu, it will provide details about the menu. Generative AI can also make suggestions about objects that the user is interested in based on the gaze tracking data. For example, if the user is looking at a product, it will provide reviews and price information for that product. This makes it possible to provide information about the object in front of the user's eyes.

[0067] The generation AI can recognize a user's gestures and generate answers that correspond to the gestures. For example, the generation AI analyzes the user's hand movements using a camera and generates answers that correspond to the specific gesture. For example, when a user raises their hand, the generation AI accepts a question. Furthermore, when a user performs a specific gesture, the generation AI provides information corresponding to that gesture. For example, when a user points their finger, it provides information about objects in that direction. Furthermore, the generation AI learns the user's gestures and generates individually customized answers. For example, when a user performs a specific gesture, the generation AI makes suggestions based on the user's preferences. This makes it possible to generate appropriate answers that correspond to the user's gestures.

[0068] The generative AI can estimate a user's emotions and recommend music or podcasts that correspond to those emotions. For example, the generative AI analyzes the user's tone of voice and language to estimate emotions. For example, if the user is tired, it can recommend relaxing music. The generative AI can also analyze the user's facial expressions with a camera to estimate emotions. For example, if the user is sad, it can recommend an uplifting podcast. The generative AI can also analyze the user's past music and podcast history to make recommendations that correspond to emotions. For example, if the user is feeling stressed, it can recommend relaxing music. This makes it possible to recommend music and podcasts that correspond to the user's emotions.

[0069] The generation AI can estimate the user's emotions when they speak and generate answers in a tone that corresponds to their emotions. For example, the generation AI analyzes the user's voice tone to estimate their emotions. For example, if the user is angry, the generation AI generates answers in a calm tone. The generation AI can also estimate the user's emotions when they speak and generate answers in a tone that corresponds to their emotions. For example, if the user is sad, the generation AI generates answers in a gentle tone. The generation AI can also learn the user's voice tone and generate answers in an individually customized tone. For example, if the user is relaxed, the generation AI generates answers in a relaxed tone. This makes it possible to generate answers in a tone that corresponds to the user's emotions.

[0070] Generative AI enhances the noise canceling function of voice input, enabling accurate voice recognition even in noisy environments. For example, generative AI uses noise canceling technology to achieve accurate voice recognition even in noisy environments. For example, it removes background noise when inputting voice in the city. Generative AI also automatically adjusts the noise canceling function when a user inputs voice in a noisy environment. For example, it removes surrounding conversation sounds when inputting voice in a cafe. Generative AI also learns noise canceling technology and provides the optimal noise canceling setting according to the environment. For example, it removes the sound of trains running when inputting voice on a train. This enables accurate voice recognition even in noisy environments.

[0071] When outputting audio, the generation AI can automatically adjust the volume and tone according to the user's hearing characteristics. For example, the generation AI analyzes the user's hearing characteristics and automatically adjusts the volume and tone of the audio output. For example, if the user has difficulty hearing high-pitched sounds, the generation AI emphasizes low-pitched sounds. The generation AI also customizes the audio output settings based on the user's hearing characteristics. For example, if the user sets the volume low, the generation AI automatically adjusts the volume. The generation AI also learns the user's hearing characteristics and provides individually customized audio output. For example, if the user has difficulty hearing a specific frequency band, the generation AI emphasizes that band. This makes it possible to automatically adjust the volume and tone according to the user's hearing characteristics.

[0072] In addition to voice input and output, the generation AI provides tactile feedback, allowing the user to feel the answer through touch. For example, the generation AI provides tactile feedback in addition to voice input and output. For example, when the user inputs a question, tactile feedback is used to confirm the question. The generation AI also provides tactile feedback when the user makes a voice input to confirm the input. For example, a vibration is used to notify the user that the voice input was successful. The generation AI also provides tactile feedback when outputting voice, allowing the user to feel the answer through touch. For example, important information is emphasized through tactile feedback. This allows the user to feel the answer through touch.

[0073] The generation AI can improve the accuracy of speech recognition by analyzing the user's mouth movements when inputting speech. For example, the generation AI can analyze the user's mouth movements using a camera to improve the accuracy of speech recognition. For example, it can analyze the shape of the user's mouth when pronouncing words. The generation AI can also analyze the user's mouth movements when inputting speech to improve the accuracy of speech recognition. For example, it can analyze the mouth movements when the user pronounces specific words. The generation AI can also learn the user's mouth movements to provide individually customized speech recognition. For example, it can analyze the mouth movements when the user pronounces specific words. This can improve the accuracy of speech recognition.

[0074] The generation AI can estimate the user's emotions when outputting voice and add music and sound effects that correspond to the emotions. For example, the generation AI analyzes the user's voice tone to estimate the emotions. For example, if the user is relaxed, relaxing music is added. The generation AI can also estimate the emotions when the user inputs voice and add music and sound effects that correspond to the emotions. For example, if the user is excited, energetic music is added. The generation AI can also learn the user's voice tone and provide individually customized music and sound effects. For example, if the user is sad, uplifting music is added. This makes it possible to add music and sound effects that correspond to the user's emotions.

[0075] The generation AI can estimate the user's emotions and provide customized answers based on those emotions. For example, the generation AI analyzes the user's tone of voice to estimate emotions. For example, if the user is tired, it can make suggestions to help them relax. The generation AI can also analyze the user's facial expressions with a camera to estimate emotions. For example, if the user is smiling, the generation AI can generate a positive answer. The generation AI can also analyze the user's past question history and learn emotional patterns. For example, if the user tends to ask a specific question when they are feeling stressed, it can generate an answer based on that emotion. This makes it possible to provide customized answers based on the user's emotions.

[0076] The generating AI can analyze the user's health data and make suggestions based on the health condition. The generating AI, for example, analyzes the user's health data and makes suggestions based on the health condition. For example, it provides health advice based on the user's heart rate and sleep data. The generating AI also makes individually customized health suggestions based on the user's health data. For example, it analyzes the user's exercise data and suggests an optimal exercise plan. The generating AI also learns the user's health data and provides individually customized health suggestions. For example, it analyzes the user's dietary data and suggests a nutritionally balanced meal plan. This makes it possible to make suggestions based on the user's health condition.

[0077] The generation AI can analyze the user's schedule and provide notifications and suggestions at the optimal time. For example, the generation AI can analyze the user's schedule data and provide notifications at the optimal time. For example, the user receives a reminder before a meeting. The generation AI can also provide individually customized suggestions based on the user's schedule. For example, it can suggest relaxing activities for the user when they have free time. The generation AI can also learn the user's schedule and provide notifications and suggestions at the optimal time. For example, it can make suggestions to reduce stress when the user is busy. This allows the generation AI to provide notifications and suggestions at the optimal time according to the user's schedule.

[0078] The generation AI can analyze the user's hobbies and interests and suggest related events and activities. For example, the generation AI can analyze the user's hobbies and interests and suggest related events. For example, if the user likes music, it can provide information about nearby concerts. The generation AI can also suggest individually customized activities based on the user's past behavioral patterns. For example, if the user likes the outdoors, it can suggest hiking trails. The generation AI can also learn the user's hobbies and interests and suggest related events and activities. For example, if the user likes cooking, it can provide information about cooking classes. This makes it possible to suggest events and activities that match the user's hobbies and interests.

[0079] Generative AI can analyze a user's social media activity and provide related information and news. For example, generative AI can analyze a user's social media activity and provide related news. For example, it can notify the user of the latest posts from accounts the user follows. Generative AI can also provide individually customized information based on the user's social media interests. For example, if a user is interested in a particular topic, it can provide the latest information on that topic. Generative AI can also learn the user's social media activity and provide related information and news. For example, if a user is interested in a particular event, it can provide detailed information about that event. This makes it possible to provide related information and news according to the user's social media activity.

[0080] The generative AI can estimate the user's emotions and suggest relaxation methods and exercises that correspond to the emotions. For example, the generative AI analyzes the user's voice tone to estimate emotions. For example, if the user is feeling stressed, it will suggest relaxation methods. The generative AI can also analyze the user's facial expressions with a camera to estimate emotions. For example, if the user is tired, it will suggest relaxing exercises. The generative AI can also analyze the user's past behavioral patterns to suggest relaxation methods and exercises that correspond to the emotions. For example, if the user tends to do a particular exercise when feeling stressed, it will suggest that exercise. This makes it possible to suggest relaxation methods and exercises that correspond to the user's emotions.

[0081] The generation AI can estimate the user's emotions and provide real-time information according to the emotions. For example, the generation AI analyzes the user's voice tone to estimate the emotions. For example, if the user is excited, entertainment information is provided. The generation AI can also analyze the user's facial expressions with a camera to estimate the emotions. For example, if the user is relaxed, relaxing news is provided. The generation AI can also analyze the user's past behavioral patterns to provide real-time information according to the emotions. For example, when the user is feeling stressed, information that will help them relax is provided. This makes it possible to provide real-time information according to the user's emotions.

[0082] The generation AI can analyze the user's location information and provide real-time information according to the location. The generation AI, for example, analyzes the user's location information and provides real-time information according to the location. For example, if the user is in a specific location, information about that location is provided. The generation AI also provides individually customized real-time information based on the user's location information. For example, if the user is in a tourist spot, information about that tourist spot is provided. The generation AI also learns the user's location information and provides real-time information according to the location. For example, if the user frequently visits a specific location, the latest information about that location is provided. This makes it possible to provide real-time information according to the user's location.

[0083] The generation AI can analyze a user's past behavioral patterns and provide real-time information according to predicted needs. The generation AI, for example, analyzes a user's past behavioral patterns and provides real-time information according to predicted needs. For example, if a user tends to seek specific information at a specific time, the generation AI provides that information. The generation AI also provides real-time information that is individually customized based on the user's past behavioral patterns. For example, if a user tends to seek specific information on a specific day of the week, the generation AI provides that information. The generation AI also learns a user's past behavioral patterns and provides real-time information according to predicted needs. For example, if a user tends to participate in a specific event, the generation AI provides information about that event. This makes it possible to provide real-time information according to predicted needs based on the user's past behavioral patterns.

[0084] The generating AI can analyze environmental information around the user and provide real-time information according to the environment. The generating AI can, for example, analyze environmental information around the user and provide real-time information according to the environment. For example, if the user is outdoors, it can provide weather information. The generating AI can also provide individually customized real-time information based on the environmental information around the user. For example, if the user is indoors, it can provide indoor temperature and humidity information. The generating AI can also learn environmental information around the user and provide real-time information according to the environment. For example, if the user is in a specific environment, it can provide the latest information about that environment. This makes it possible to provide real-time information according to the environmental information around the user.

[0085] The generative AI can share information between a user's devices and provide integrated real-time information. For example, the generative AI can share information between multiple user devices and provide integrated real-time information. For example, it can integrate data from a smartphone and a smartwatch. The generative AI can also share information between a user's devices and provide individually customized real-time information. For example, it can provide information in cooperation with the user's smart home devices. The generative AI can also learn information between a user's devices and provide integrated real-time information. For example, it can provide information in cooperation with the user's smart speaker. This allows information to be shared between a user's devices and provide integrated real-time information.

[0086] The generation AI can estimate the user's emotions and provide news and entertainment information according to the emotions. For example, the generation AI analyzes the user's voice tone to estimate emotions. For example, if the user is relaxed, it provides relaxing news. The generation AI also analyzes the user's facial expressions with a camera to estimate emotions. For example, if the user is excited, it provides entertainment information. The generation AI also analyzes the user's past behavioral patterns to provide news and entertainment information according to the emotions. For example, it provides information that will help the user relax when they are feeling stressed. This makes it possible to provide news and entertainment information according to the user's emotions.

[0087] The generation AI can estimate the user's emotions and generate multilingual answers in a tone that corresponds to the emotion. The generation AI, for example, analyzes the user's voice tone to estimate the emotion. For example, if the user is relaxed, it generates multilingual answers in a relaxed tone. The generation AI also analyzes the user's facial expressions with a camera to estimate the emotion. For example, if the user is excited, it generates multilingual answers in an energetic tone. The generation AI also analyzes the user's past question history and generates multilingual answers in a tone that corresponds to the emotion. For example, if the user is stressed, it generates multilingual answers in a calm tone. This makes it possible to generate multilingual answers in a tone that corresponds to the user's emotions.

[0088] The generation AI can analyze the user's language proficiency level and provide answers of an appropriate level of difficulty. The generation AI, for example, analyzes the user's language proficiency level and provides answers of an appropriate level of difficulty. For example, if the user is at a beginner's level, it generates answers in simple language. The generation AI also provides answers of an individually customized level of difficulty based on the user's past question history. For example, if the user is at an intermediate level, it generates answers of a moderate level of difficulty. The generation AI also learns the user's language proficiency level and provides answers of an appropriate level of difficulty. For example, if the user is at an advanced level, it generates answers that include specialized language. This makes it possible to provide answers of an appropriate level of difficulty according to the user's language proficiency level.

[0089] The generation AI can take into account the user's cultural background and provide appropriate answers that are culturally appropriate. For example, the generation AI can analyze the user's cultural background and provide appropriate answers that are culturally appropriate. For example, if the user belongs to a particular culture, the generation AI can generate answers that take that culture into consideration. The generation AI can also provide answers that are individually customized based on the user's past question history and are appropriate for that cultural background. For example, if the user believes in a particular religion, the generation AI can generate answers that take that religion into consideration. The generation AI can also learn the user's cultural background and provide appropriate answers that are culturally appropriate. For example, if the user lives in a particular region, the generation AI can generate answers that take the culture of that region into consideration. This makes it possible to provide appropriate answers that are appropriate for the user's cultural background.

[0090] The generation AI can automatically detect the user's language settings and provide answers in the most appropriate language. For example, the generation AI analyzes the user's device settings and past language usage to provide answers in the most appropriate language. For example, if the user primarily uses English, it generates answers in English. The generation AI can also analyze the user's voice input and automatically detect the language to provide answers. For example, if the user asks a question in Japanese, it generates answers in Japanese. The generation AI can also learn the user's language settings and provide answers in the most appropriate language. For example, if the user uses multiple languages, it generates answers in the appropriate language at the time. This makes it possible to provide answers in the most appropriate language according to the user's language settings.

[0091] The generative AI can provide a language learning mode to support the user's language acquisition. The generative AI can, for example, provide a language learning mode to support the user's language acquisition. For example, when the user is learning a new language, it can teach simple phrases and vocabulary. The generative AI can also analyze the user's language acquisition level and provide individually customized language learning content. For example, if the user is at a beginner's level, it can teach basic grammar and vocabulary. The generative AI can also learn the user's language acquisition and provide an individually customized language learning mode. For example, if the user is at an intermediate level, it can provide conversation practice and listening practice. In this way, it can provide a language learning mode to support the user's language acquisition.

[0092] The generative AI can estimate the user's emotions and provide language learning content that corresponds to the emotions. For example, the generative AI analyzes the user's voice tone to estimate the emotions. For example, if the user is relaxed, it provides relaxing language learning content. The generative AI also analyzes the user's facial expressions with a camera to estimate the emotions. For example, if the user is excited, it provides energetic language learning content. The generative AI also analyzes the user's past behavioral patterns to provide language learning content that corresponds to the emotions. For example, it provides relaxing language learning content when the user is feeling stressed. This makes it possible to provide language learning content that corresponds to the user's emotions.

[0093] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0094] The Eyewear system can also analyze the user's health data and make suggestions based on their health condition. For example, it can provide health advice based on the user's heart rate and sleep data. It can also make individually customized health suggestions based on the user's health data. For example, it can analyze the user's exercise data and suggest an optimal exercise plan. It can also learn from the user's health data and provide individually customized health suggestions. For example, it can analyze the user's dietary data and suggest a nutritionally balanced meal plan. This allows it to make suggestions based on the user's health condition.

[0095] The Eyewear system can also analyze the user's schedule and provide notifications and suggestions at the optimal time. For example, the system can analyze the user's schedule data and provide notifications at the optimal time. For example, the user can receive a reminder before a meeting. The system can also provide individually customized suggestions based on the user's schedule. For example, the system can suggest relaxing activities for the user when they have free time. The system can also learn the user's schedule and provide notifications and suggestions at the optimal time. For example, the system can make suggestions to reduce stress when the user is busy. This allows the system to provide notifications and suggestions at the optimal time based on the user's schedule.

[0096] The Eyewear system can also analyze the user's hobbies and interests and suggest related events and activities. For example, it can analyze the user's hobbies and interests and suggest related events. For example, if the user likes music, it can provide information about nearby concerts. It can also suggest individually customized activities based on the user's past behavioral patterns. For example, if the user likes the outdoors, it can suggest hiking trails. It can also learn the user's hobbies and interests and suggest related events and activities. For example, if the user likes cooking, it can provide information about cooking classes. This makes it possible to suggest events and activities that match the user's hobbies and interests.

[0097] The Eyewear system can also analyze a user's social media activity and provide relevant information and news. For example, it can analyze a user's social media activity and provide relevant news. For example, it can notify the user of the latest posts from accounts the user follows. It can also provide individually customized information based on the user's social media interests. For example, if a user is interested in a particular topic, it can provide the latest information on that topic. It can also learn about the user's social media activity and provide relevant information and news. For example, if a user is interested in a particular event, it can provide detailed information about that event. This makes it possible to provide relevant information and news based on the user's social media activity.

[0098] The eyewear system can also estimate the user's emotions and suggest relaxation methods and exercises according to their emotions. For example, it can analyze the user's voice tone to estimate their emotions. For example, if the user is feeling stressed, it can suggest relaxation methods. It can also analyze the user's facial expressions with a camera to estimate their emotions. For example, if the user is tired, it can suggest relaxing exercises. It can also analyze the user's past behavioral patterns to suggest relaxation methods and exercises according to their emotions. For example, if the user tends to do a particular exercise when they are feeling stressed, it can suggest that exercise. This makes it possible to suggest relaxation methods and exercises according to the user's emotions.

[0099] The eyewear system can also estimate the user's emotions and provide real-time information according to the emotions. For example, it can analyze the user's voice tone to estimate the emotions. For example, if the user is excited, entertainment information can be provided. It can also analyze the user's facial expressions with a camera to estimate the emotions. For example, if the user is relaxed, relaxing news can be provided. It can also analyze the user's past behavioral patterns to provide real-time information according to the emotions. For example, if the user is feeling stressed, information that will help them relax can be provided. This makes it possible to provide real-time information according to the user's emotions.

[0100] The eyewear system can also analyze the user's location information and provide real-time information according to the location. For example, if the user is in a specific location, information about that location is provided. It can also provide individually customized real-time information based on the user's location information. For example, if the user is in a tourist spot, information about that tourist spot is provided. It can also learn the user's location information and provide real-time information according to the location. For example, if the user frequently visits a specific location, the latest information about that location is provided. This makes it possible to provide real-time information according to the user's location.

[0101] The eyewear system can also analyze a user's past behavioral patterns and provide real-time information according to predicted needs. For example, the eyewear system can analyze a user's past behavioral patterns and provide real-time information according to predicted needs. For example, if a user tends to seek specific information at a specific time, that information can be provided. The eyewear system can also provide real-time information that is individually customized based on the user's past behavioral patterns. For example, if a user tends to seek specific information on a specific day of the week, that information can be provided. The eyewear system can also learn a user's past behavioral patterns and provide real-time information according to predicted needs. For example, if a user tends to participate in a specific event, information about that event can be provided. This makes it possible to provide real-time information according to predicted needs based on the user's past behavioral patterns.

[0102] The eyewear system can also analyze information about the user's surrounding environment and provide real-time information tailored to the environment. For example, the eyewear system can analyze information about the user's surrounding environment and provide real-time information tailored to the environment. For example, if the user is outdoors, weather information can be provided. It can also provide individually customized real-time information based on the user's surrounding environmental information. For example, if the user is indoors, indoor temperature and humidity information can be provided. It can also learn information about the user's surrounding environment and provide real-time information tailored to the environment. For example, if the user is in a specific environment, the latest information about that environment can be provided. This makes it possible to provide real-time information tailored to the user's surrounding environmental information.

[0103] The eyewear system can also share information between a user's devices to provide integrated real-time information. For example, it can share information between multiple devices and provide integrated real-time information. For example, it can integrate data from a smartphone and a smartwatch. It can also share information between a user's devices to provide individually customized real-time information. For example, it can provide information in cooperation with a user's smart home devices. It can also learn information between a user's devices and provide integrated real-time information. For example, it can provide information in cooperation with a user's smart speaker. This allows it to share information between a user's devices and provide integrated real-time information.

[0104] The processing flow of the second embodiment will be briefly explained below.

[0105] Step 1: The earphones are placed in the user's ears and output sound. For example, the earphones can be connected to a smartphone using Bluetooth. The earphones also have a noise-canceling function that can reduce external noise. The earphones also have a built-in battery that can withstand long-term use. For example, the earphones can be used continuously for more than 8 hours on a full charge. Step 2: The microphone captures the user's voice. For example, the microphone uses a directional microphone to clearly capture the user's voice. The microphone can also use noise reduction technology to reduce background noise. The microphone also has a function to automatically adjust the sensitivity of the voice input. For example, the microphone adjusts the sensitivity according to the volume of the user's voice to achieve optimal voice input. Step 3: The generation AI analyzes the voice input captured by the microphone and generates an appropriate answer. For example, the generation AI uses a text generation AI (e.g., LLM) to generate an answer to the user's question. The generation AI can also use a multimodal generation AI to generate an answer that combines the voice input and image data. The generation AI also uses natural language processing technology to analyze the user's question and generate an optimal answer. For example, the generation AI understands the intent of the user's question and generates an answer based on that. Step 4: The audio output unit outputs the answer generated by the generation AI by voice. For example, the audio output unit uses a speaker to let the user hear the generated answer. The audio output unit can also provide the generated answer to the user through earphones. The audio output unit also uses voice synthesis technology to output the answer in a natural voice. For example, the audio output unit can adjust the tone and speed of the voice according to the user's preferences.

[0106] 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.

[0107] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<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.

[0108] 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.

[0109] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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).

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0125] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] In the headset type terminal 314, 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. 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 specific processing unit 290 using these models.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0140] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] In the robot 414, 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 robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] 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."

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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. [Explanation of symbols]

[0173] 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. Equipped with earphones and a microphone, A generation AI that analyzes voice input acquired by the microphone and generates an appropriate answer; and a voice output unit that outputs the answer generated by the generation AI by voice. A system characterized by:

2. The generated AI is Analyzing a user's tone of voice or phrasing, inferring the user's emotions, and generating a response according to the emotions 2. The system of claim 1.

3. The generated AI is Analyze users' past question history and automatically follow up on unresolved issues 2. The system of claim 1.

4. The generated AI is Analyzes the user's ambient sounds and generates appropriate answers 2. The system of claim 1.

5. The generated AI is Tracks the user's gaze and provides information about the object in front of them 2. The system of claim 1.

Images