System

Abstract

An object of a system according to an embodiment is to provide a voice chat service personalized based on the personality and characteristics of a user.SOLUTION: A system according to an embodiment includes a camera, a voice assistant, and a generation AI. The camera is portable. The voice assistant recognizes a facial expression or an action of the user acquired by the camera. The generative AI provides a personalized voice chat service based on the personality and characteristics of the user recognized by the voice assistant.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had a problem in that they have not adequately provided a personalized voice chat service based on the personality and characteristics of the user.

[0005] The system according to the embodiment aims to provide a voice chat service that is personalized based on the personality and characteristics of the user. [Means for solving the problem]

[0006] A system according to an embodiment includes a camera, a voice assistant, and a generation AI. The camera is portable. The voice assistant recognizes the user's facial expressions and movements captured by the camera. The generation AI provides a personalized voice chat service based on the user's personality and characteristics recognized by the voice assistant. [Effects of the Invention]

[0007] The system according to the embodiment can provide a voice chat service that is personalized based on the personality and characteristics of the user. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A voice assistant system according to an embodiment of the present invention provides users with a personalized voice chat service using a portable, camera-equipped voice assistant device. The voice assistant system functions as a unique "friend" optimized for the user's personality and characteristics, allowing users to enjoy chatting safely and securely anywhere. It also allows users to initiate conversations from the device at the appropriate time, significantly improving the quality and quantity of communication and potentially reducing or eliminating feelings of loneliness.

[0029] A voice assistant system according to an embodiment includes a camera, a voice assistant, and a generation AI. The camera recognizes a user's facial expressions and movements. For example, the camera detects facial features and analyzes the user's facial expressions. The camera can also track the user's movements and analyze their patterns. The camera can also track the user's gaze and analyze their gaze direction. The voice assistant recognizes the user's facial expressions and movements captured by the camera. For example, the voice assistant estimates the user's emotional state based on data from the camera. The voice assistant can also recognize the user's voice and engage in dialogue using natural language processing technology. The voice assistant can also analyze the user's speech and generate appropriate responses. The generation AI provides a personalized voice chat service based on the user's personality and characteristics recognized by the voice assistant. For example, the generation AI can learn the user's past conversation history and advance the conversation based on the user's interests. The generation AI can also suggest appropriate conversation topics based on the user's personality profile. The generation AI can also analyze the user's behavioral patterns and initiate conversations at appropriate times. As a result, the voice assistant system according to the embodiment can provide a personalized voice chat service based on the user's personality and characteristics. For example, it can memorize the content of past conversations and topics of interest to the user and advance the conversation based on that. Furthermore, if the user spends a long time alone, the generation AI can automatically initiate a conversation, reducing the user's sense of loneliness. Furthermore, the generation AI can also engage in positive conversations based on the user's smile. This allows the user to enjoy chatting safely and securely anywhere with a unique "friend" optimized for their personality and characteristics.

[0030] The camera reads the user's facial expressions, allowing the voice assistant to start an appropriate conversation based on the facial expression. For example, the camera can analyze the user's facial color in real time and advise the user to take a rest if they look pale. For example, the camera can detect changes in facial color and display a message encouraging the user to drink water and take a break. The camera can also track the user's eye movements and advise the user to rest if it detects eye fatigue. For example, it can suggest eye stretches after using a screen for a long period of time. The camera can also analyze the user's facial expression and suggest relaxation methods if it detects signs of stress. For example, it can encourage the user to take deep breaths or engage in a short meditation. This allows the voice assistant to start an appropriate conversation based on the user's facial expression.

[0031] The generation AI can remember what the user has talked about in the past and topics that interest them, and can advance the conversation based on that. For example, the generation AI can analyze the user's past conversation data to identify topics that the user frequently talks about. For example, it can prioritize conversations about the user's hobbies and interests that they often talk about. The generation AI can also analyze the user's past conversation data to learn the user's preferred topics. For example, it can provide topics about the user's favorite movies and music. The generation AI can also analyze the user's past conversation data to identify topics that the user wants to avoid. For example, it can advance the conversation by avoiding topics that the user dislikes. This allows the conversation to advance based on the user's past conversation content and interests.

[0032] The generation AI can automatically start a conversation when the user is alone for a long time, reducing the user's sense of loneliness. For example, the generation AI can analyze the user's behavioral patterns and automatically start a conversation when the user is alone for a long time. For example, if the user has been alone for a long time, it can ask, "How are you doing lately? Is there anything you'd like to talk about?" The generation AI can also estimate the user's emotions and provide fun topics to talk about when the user is feeling lonely. For example, it can suggest, "Let's talk about a movie you recently saw." The generation AI can also estimate the user's emotions and provide words of encouragement when the user is feeling lonely. For example, it can send a message saying, "You're not alone. Let's talk anytime." This can reduce the user's sense of loneliness.

[0033] When the user smiles, the generation AI-4 can engage in positive conversation in response to the smile. For example, the generation AI-4 detects the user's smile and engages in positive conversation in response to the smile. For example, when the user smiles, it sends a positive message such as, "That smile is lovely!" The generation AI-4 can also detect the user's smile and provide fun topics in response to the smile. For example, it can ask, "Has anything fun happened recently?" The generation AI-4 can also detect the user's smile and provide encouraging words in response to the smile. For example, it can send a message such as, "With that smile, everything will surely go well!" This allows for positive conversation in response to the user's smile.

[0034] The generation AI can suggest new topics based on what the user has talked about in the past. For example, the generation AI analyzes the user's past conversation data to identify topics that the user has recently been interested in. For example, it generates conversations based on the accounts the user has recently followed and the content of their posts. The generation AI also analyzes the user's past conversation data to generate conversations based on the latest trends. For example, it provides the latest news and topics that the user is likely to be interested in. The generation AI also analyzes the user's past conversation data to generate conversations based on the communities and groups the user participates in. For example, it provides topics related to groups the user belongs to. This makes it possible to suggest new topics based on the user's past conversations.

[0035] The generation AI can respond to what the user says with a moderate amount of humor. For example, if the user makes a joke, the generation AI will respond with a humorous tone, such as, "That's funny!" The generation AI will also provide fun topics with a moderate amount of humor in response to what the user says. For example, it might ask, "Has anything interesting happened recently?" The generation AI will also provide encouraging words with a moderate amount of humor in response to what the user says. For example, it might send a message saying, "With that kind of humor, everything will surely be fine!" This allows the generation AI to respond to what the user says with a moderate amount of humor.

[0036] The camera can monitor the user's health condition in real time and provide health advice. For example, the camera can analyze the user's facial color in real time and advise the user to take a rest if the user's complexion is poor. For example, the camera can detect changes in facial color and display a message encouraging the user to hydrate and take a break. The camera can also track the user's eye movements and advise the user to rest if it detects eye fatigue. For example, the camera can suggest eye stretches after using a screen for a long period of time. The camera can also analyze the user's facial expressions and suggest relaxation methods if it detects signs of stress. For example, the camera can encourage the user to take deep breaths or engage in a short meditation. This allows the user's health condition to be monitored in real time and health advice to be provided.

[0037] The camera can suggest appropriate conversation topics based on the user's clothing and background. For example, the camera analyzes the user's clothing and suggests conversation topics according to the season and weather. For example, if the user is wearing winter clothing, the camera can suggest topics about hot drinks. The camera can also analyze the user's background and suggest conversation topics based on the objects and scenery that appear in the background. For example, if a bookshelf appears in the background, the camera can suggest topics related to reading. The camera can also analyze the user's accessories and belongings and suggest conversation topics related to them. For example, if the user is wearing sportswear, the camera can suggest topics related to exercise and sports. This makes it possible to suggest appropriate conversation topics based on the user's clothing and background.

[0038] Camera-equipped voice assistant devices can also be used as pet monitoring and communication tools. For example, camera-equipped voice assistant devices can be used as pet surveillance cameras to monitor pet movements in real time. For example, you can check what your pet is doing around the house. You can also use camera-equipped voice assistant devices to give voice instructions to your pet. For example, you can give your pet commands such as "sit" or "stay." Camera-equipped voice assistant devices can also be used to communicate with your pet remotely. For example, you can talk to your pet and check on how it's doing while you're away. This makes them useful as pet monitoring and communication tools.

[0039] The camera can generate conversations based on the user's surrounding environment. For example, the camera analyzes the weather around the user and generates conversations based on the weather. For example, if it is raining, it will provide topics about how to spend a rainy day. The camera can also analyze the scenery around the user and generate conversations based on the scenery. For example, if the user is in a park, it will provide topics about nature and plants. The camera can also analyze the environmental sounds around the user and generate conversations based on the environmental sounds. For example, if birds can be heard singing, it will provide topics about the types of birds and their ecology. In this way, it is possible to generate conversations based on the user's surrounding environment.

[0040] The generation AI analyzes the user's past conversation data and can provide more personalized conversations based on a deep understanding of the user's preferences and interests. For example, the generation AI analyzes the user's past conversation data to identify topics that the user frequently talks about. For example, it can prioritize conversations about the hobbies and interests that the user often talks about. The generation AI also analyzes the user's past conversation data to learn the trends in topics that the user likes. For example, it can provide topics about the user's favorite movies and music. The generation AI also analyzes the user's past conversation data to identify topics that the user wants to avoid. For example, it can advance the conversation by avoiding topics that the user dislikes. This allows the generation AI to provide more personalized conversations based on a deep understanding of the user's preferences and interests.

[0041] The generation AI can analyze a user's social media account and generate conversations based on the user's latest interests and trends. For example, the generation AI can analyze a user's social media account and identify topics that the user has recently been interested in. For example, it can generate conversations based on accounts the user has recently followed and the content of their posts. The generation AI can also analyze a user's social media account and generate conversations based on the latest trends. For example, it can provide the latest news and topics that the user is likely to be interested in. The generation AI can also analyze a user's social media account and generate conversations based on the communities and groups the user participates in. For example, it can provide topics related to groups the user belongs to. This makes it possible to generate conversations based on the user's latest interests and trends.

[0042] The generating AI can learn information about the user's family and friends and provide topics related to them. For example, the generating AI can learn information about the user's family and friends and provide topics related to them. For example, it can ask questions such as, "How was your mother's birthday?" The generating AI can also learn information about the user's family and friends and provide topics based on memories with them. For example, it can ask questions such as, "Did you enjoy your trip last year?" The generating AI can also learn information about the user's family and friends and provide topics based on their recent activities. For example, it can ask questions such as, "How's your friend's new job going?" This allows it to provide topics related to the user's family and friends.

[0043] The generative AI can suggest new hobbies and activities based on the user's hobbies and special skills. For example, the generative AI can learn the user's hobbies and special skills and suggest new hobbies based on them. For example, it could suggest, "If you've recently become fond of drawing, why not try taking an art class?" The generative AI can also learn the user's hobbies and special skills and suggest new activities based on them. For example, it could suggest, "If you're good at cooking, why not try a new recipe?" The generative AI can also learn the user's hobbies and special skills and suggest new events or workshops based on them. For example, it could suggest, "If you like music, why not go to a concert?" This makes it possible to suggest new hobbies and activities based on the user's hobbies and special skills.

[0044] Generative AI can analyze a user's schedule and send reminders and notifications at the appropriate time. For example, generative AI can analyze a user's schedule and send a reminder before an important appointment. For example, it can notify the user, "Are you ready for tomorrow's meeting?". Generative AI can also analyze a user's schedule and make suggestions for relaxing during breaks. For example, it can suggest, "Why not take a short walk during your next break?". Generative AI can also analyze a user's schedule and send notifications at the appropriate time. For example, it can notify the user, "Let's check your schedule for this weekend." This allows it to send reminders and notifications at the appropriate time based on the user's schedule.

[0045] The generation AI can analyze the user's biometric data and start a conversation when relaxation is needed. For example, the generation AI can analyze the user's heart rate and start a conversation to help them relax if their heart rate increases. For example, it can suggest, "Take a deep breath and relax." The generation AI can also analyze the user's body temperature and start a conversation to help them relax if their body temperature rises. For example, it can suggest, "Drink a cold drink and refresh yourself." The generation AI can also analyze the user's biometric data and start a conversation to help them relax if it determines that stress is increasing. For example, it can suggest, "Take a short break and relax." This makes it possible to start a conversation when relaxation is needed based on the user's biometric data.

[0046] The generation AI can analyze the sound environment around the user and start a conversation in a quiet place. For example, the generation AI can analyze the sound environment around the user and start a conversation in a quiet place. For example, when the surroundings become quiet, it can suggest, "Shall we talk for a bit now?" The generation AI can also analyze the sound environment around the user and refrain from conversation in noisy places. For example, if the surroundings are noisy, it can notify the user, "Let's talk later." The generation AI can also analyze the sound environment around the user and provide a relaxing conversation in a quiet place. For example, it can suggest, "Let's talk about your recent hobbies" in a quiet place. This allows a conversation to start in a quiet place based on the sound environment around the user.

[0047] The generative AI can learn the user's travel patterns and provide appropriate conversations while traveling. For example, the generative AI can learn the user's travel patterns and provide relaxing conversations during the commute. For example, it might suggest, "We'll provide you with topics to talk about that will help you relax during your commute." The generative AI can also learn the user's travel patterns and provide useful information during the journey. For example, it might notify you by saying, "We'll show you the directions to your next destination." The generative AI can also learn the user's travel patterns and provide enjoyable conversations while traveling. For example, it might suggest, "We'll suggest music and podcasts that you might want to listen to while traveling." This makes it possible to provide appropriate conversations while traveling based on the user's travel patterns.

[0048] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0049] The voice assistant system can also include a health management unit that monitors the user's health status. For example, the health management unit can measure the user's heart rate and body temperature in real time and issue an alert if an abnormality is detected. The health management unit can also analyze the user's sleep patterns and provide appropriate sleep advice. Furthermore, the health management unit can manage the user's dietary records and suggest nutritionally balanced meals. This allows the system to comprehensively manage the user's health status and support health maintenance.

[0050] The voice assistant system may further include a hobby suggestion unit that suggests new hobbies and activities based on the user's hobbies and interests. For example, the hobby suggestion unit may analyze the user's past conversation data and suggest new hobbies that the user may be interested in. The hobby suggestion unit may also suggest events or workshops related to the user's current hobbies. Furthermore, the hobby suggestion unit may recommend new books or movies based on the user's interests. This provides new enjoyment to the user's life and allows them to spend their time more fulfillingly.

[0051] The voice assistant system can also include a mobility support unit that learns the user's travel patterns and provides useful information during travel. For example, the mobility support unit can analyze the user's commute route and suggest the optimal means of transportation and route. The mobility support unit can also recommend music or podcasts that will help the user relax during their travel. The mobility support unit can also provide information about the user's destination and recommend tourist spots and restaurants. This makes the user's travel more comfortable and meaningful.

[0052] The voice assistant system may further include a communication support unit that supports communication with the user's family and friends. For example, the communication support unit may remind the user of their family and friends' birthdays and anniversaries and suggest sending them a message. The communication support unit may also analyze past conversations with the user's family and friends and provide reminiscences. Furthermore, the communication support unit may provide topics based on the recent activities of the user's family and friends. This may deepen the user's relationships and realize richer communication.

[0053] The voice assistant system can further include a learning support unit that supports the user's learning. For example, the learning support unit can manage the user's learning progress and propose an appropriate learning plan. The learning support unit can also provide learning resources based on the user's interests. Furthermore, the learning support unit can provide appropriate answers to the user's questions about their studies. This can help the user's learning progress more efficiently and improve their knowledge.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The camera recognizes the user's facial expressions and movements. For example, the camera detects the user's facial features and analyzes their facial expressions. The camera can also track the user's movements and analyze their movement patterns. Furthermore, the camera can track the user's gaze and analyze their gaze direction. Step 2: The voice assistant recognizes the user's facial expressions and actions captured by the camera. For example, the voice assistant can estimate the user's emotional state based on the data from the camera. The voice assistant can also recognize the user's voice and use natural language processing technology to engage in dialogue. Furthermore, the voice assistant can analyze the content of the user's speech and generate appropriate responses. Step 3: The generation AI provides a personalized voice chat service based on the user's personality and characteristics recognized by the voice assistant. For example, the generation AI learns the user's past conversation history and advances the conversation based on the user's interests. The generation AI can also suggest appropriate conversation topics based on the user's personality profile. Furthermore, the generation AI can analyze the user's behavioral patterns and start a conversation at the appropriate time.

[0056] (Example 2) A voice assistant system according to an embodiment of the present invention provides users with a personalized voice chat service using a portable, camera-equipped voice assistant device. The voice assistant system functions as a unique "friend" optimized for the user's personality and characteristics, allowing users to enjoy chatting safely and securely anywhere. It also allows users to initiate conversations from the device at the appropriate time, significantly improving the quality and quantity of communication and potentially reducing or eliminating feelings of loneliness.

[0057] A voice assistant system according to an embodiment includes a camera, a voice assistant, and a generation AI. The camera recognizes a user's facial expressions and movements. For example, the camera detects facial features and analyzes the user's facial expressions. The camera can also track the user's movements and analyze their patterns. The camera can also track the user's gaze and analyze their gaze direction. The voice assistant recognizes the user's facial expressions and movements captured by the camera. For example, the voice assistant estimates the user's emotional state based on data from the camera. The voice assistant can also recognize the user's voice and engage in dialogue using natural language processing technology. The voice assistant can also analyze the user's speech and generate appropriate responses. The generation AI provides a personalized voice chat service based on the user's personality and characteristics recognized by the voice assistant. For example, the generation AI can learn the user's past conversation history and advance the conversation based on the user's interests. The generation AI can also suggest appropriate conversation topics based on the user's personality profile. The generation AI can also analyze the user's behavioral patterns and initiate conversations at appropriate times. As a result, the voice assistant system according to the embodiment can provide a personalized voice chat service based on the user's personality and characteristics. For example, it can memorize the content of past conversations and topics of interest to the user and advance the conversation based on that. Furthermore, if the user spends a long time alone, the generation AI can automatically initiate a conversation, reducing the user's sense of loneliness. Furthermore, the generation AI can also engage in positive conversations based on the user's smile. This allows the user to enjoy chatting safely and securely anywhere with a unique "friend" optimized for their personality and characteristics.

[0058] The camera reads the user's facial expressions, allowing the voice assistant to start an appropriate conversation based on the facial expression. For example, the camera can analyze the user's facial color in real time and advise the user to take a rest if they look pale. For example, the camera can detect changes in facial color and display a message encouraging the user to drink water and take a break. The camera can also track the user's eye movements and advise the user to rest if it detects eye fatigue. For example, it can suggest eye stretches after using a screen for a long period of time. The camera can also analyze the user's facial expression and suggest relaxation methods if it detects signs of stress. For example, it can encourage the user to take deep breaths or engage in a short meditation. This allows the voice assistant to start an appropriate conversation based on the user's facial expression.

[0059] The generation AI can remember what the user has talked about in the past and topics that interest them, and can advance the conversation based on that. For example, the generation AI can analyze the user's past conversation data to identify topics that the user frequently talks about. For example, it can prioritize conversations about the user's hobbies and interests that they often talk about. The generation AI can also analyze the user's past conversation data to learn the user's preferred topics. For example, it can provide topics about the user's favorite movies and music. The generation AI can also analyze the user's past conversation data to identify topics that the user wants to avoid. For example, it can advance the conversation by avoiding topics that the user dislikes. This allows the conversation to advance based on the user's past conversation content and interests.

[0060] The generation AI can automatically start a conversation when the user is alone for a long time, reducing the user's sense of loneliness. For example, the generation AI can analyze the user's behavioral patterns and automatically start a conversation when the user is alone for a long time. For example, if the user has been alone for a long time, it can ask, "How are you doing lately? Is there anything you'd like to talk about?" The generation AI can also estimate the user's emotions and provide fun topics to talk about when the user is feeling lonely. For example, it can suggest, "Let's talk about a movie you recently saw." The generation AI can also estimate the user's emotions and provide words of encouragement when the user is feeling lonely. For example, it can send a message saying, "You're not alone. Let's talk anytime." This can reduce the user's sense of loneliness.

[0061] When the user smiles, the generation AI-4 can engage in positive conversation in response to the smile. For example, the generation AI-4 detects the user's smile and engages in positive conversation in response to the smile. For example, when the user smiles, it sends a positive message such as, "That smile is lovely!" The generation AI-4 can also detect the user's smile and provide fun topics in response to the smile. For example, it can ask, "Has anything fun happened recently?" The generation AI-4 can also detect the user's smile and provide encouraging words in response to the smile. For example, it can send a message such as, "With that smile, everything will surely go well!" This allows for positive conversation in response to the user's smile.

[0062] The generation AI can suggest new topics based on what the user has talked about in the past. For example, the generation AI analyzes the user's past conversation data to identify topics that the user has recently been interested in. For example, it generates conversations based on the accounts the user has recently followed and the content of their posts. The generation AI also analyzes the user's past conversation data to generate conversations based on the latest trends. For example, it provides the latest news and topics that the user is likely to be interested in. The generation AI also analyzes the user's past conversation data to generate conversations based on the communities and groups the user participates in. For example, it provides topics related to groups the user belongs to. This makes it possible to suggest new topics based on the user's past conversations.

[0063] The generation AI can respond to what the user says with a moderate amount of humor. For example, if the user makes a joke, the generation AI will respond with a humorous tone, such as, "That's funny!" The generation AI will also provide fun topics with a moderate amount of humor in response to what the user says. For example, it might ask, "Has anything interesting happened recently?" The generation AI will also provide encouraging words with a moderate amount of humor in response to what the user says. For example, it might send a message saying, "With that kind of humor, everything will surely be fine!" This allows the generation AI to respond to what the user says with a moderate amount of humor.

[0064] The camera can monitor the user's health condition in real time and provide health advice. For example, the camera can analyze the user's facial color in real time and advise the user to take a rest if the user's complexion is poor. For example, the camera can detect changes in facial color and display a message encouraging the user to hydrate and take a break. The camera can also track the user's eye movements and advise the user to rest if it detects eye fatigue. For example, the camera can suggest eye stretches after using a screen for a long period of time. The camera can also analyze the user's facial expressions and suggest relaxation methods if it detects signs of stress. For example, the camera can encourage the user to take deep breaths or engage in a short meditation. This allows the user's health condition to be monitored in real time and health advice to be provided.

[0065] The camera can suggest appropriate conversation topics based on the user's clothing and background. For example, the camera analyzes the user's clothing and suggests conversation topics according to the season and weather. For example, if the user is wearing winter clothing, the camera can suggest topics about hot drinks. The camera can also analyze the user's background and suggest conversation topics based on the objects and scenery that appear in the background. For example, if a bookshelf appears in the background, the camera can suggest topics related to reading. The camera can also analyze the user's accessories and belongings and suggest conversation topics related to them. For example, if the user is wearing sportswear, the camera can suggest topics related to exercise and sports. This makes it possible to suggest appropriate conversation topics based on the user's clothing and background.

[0066] The emotion estimation function can estimate the emotion from the user's facial expression and suggest music or videos that match the emotion. For example, the emotion estimation function uses a camera to analyze the user's facial expression, estimate the emotion, and suggest music that matches that emotion. For example, if the user looks sad, it plays relaxing music. The emotion estimation function also uses a camera to analyze the user's facial expression, estimate the emotion, and suggest videos that match that emotion. For example, if the user looks tired, it plays a video of a relaxing landscape. The emotion estimation function also uses a camera to analyze the user's facial expression, estimate the emotion, and suggest entertainment that matches that emotion. For example, if the user looks happy, it suggests a comedy movie. In this way, it is possible to suggest music or videos that match the user's emotions.

[0067] Camera-equipped voice assistant devices can also be used as pet monitoring and communication tools. For example, camera-equipped voice assistant devices can be used as pet surveillance cameras to monitor pet movements in real time. For example, you can check what your pet is doing around the house. You can also use camera-equipped voice assistant devices to give voice instructions to your pet. For example, you can give your pet commands such as "sit" or "stay." Camera-equipped voice assistant devices can also be used to communicate with your pet remotely. For example, you can talk to your pet and check on how it's doing while you're away. This makes them useful as pet monitoring and communication tools.

[0068] The camera can generate conversations based on the user's surrounding environment. For example, the camera analyzes the weather around the user and generates conversations based on the weather. For example, if it is raining, it will provide topics about how to spend a rainy day. The camera can also analyze the scenery around the user and generate conversations based on the scenery. For example, if the user is in a park, it will provide topics about nature and plants. The camera can also analyze the environmental sounds around the user and generate conversations based on the environmental sounds. For example, if birds can be heard singing, it will provide topics about the types of birds and their ecology. In this way, it is possible to generate conversations based on the user's surrounding environment.

[0069] The emotion estimation function can suggest relaxation methods if the user is feeling stressed. For example, the emotion estimation function uses a camera to analyze the user's facial expression and suggests relaxation methods if it is estimated that the user is feeling stressed. For example, it can teach deep breathing or meditation techniques. The emotion estimation function can also analyze the user's movements and play relaxation music if it is estimated that the user is feeling stressed. For example, it can select and play relaxing music. The emotion estimation function can also analyze the user's facial expression and play relaxation videos if it is estimated that the user is feeling stressed. For example, it can play videos of natural landscapes or the ocean. This makes it possible to suggest relaxation methods if the user is feeling stressed.

[0070] The generation AI analyzes the user's past conversation data and can provide more personalized conversations based on a deep understanding of the user's preferences and interests. For example, the generation AI analyzes the user's past conversation data to identify topics that the user frequently talks about. For example, it can prioritize conversations about the hobbies and interests that the user often talks about. The generation AI also analyzes the user's past conversation data to learn the trends in topics that the user likes. For example, it can provide topics about the user's favorite movies and music. The generation AI also analyzes the user's past conversation data to identify topics that the user wants to avoid. For example, it can advance the conversation by avoiding topics that the user dislikes. This allows the generation AI to provide more personalized conversations based on a deep understanding of the user's preferences and interests.

[0071] The generation AI can analyze a user's social media account and generate conversations based on the user's latest interests and trends. For example, the generation AI can analyze a user's social media account and identify topics that the user has recently been interested in. For example, it can generate conversations based on accounts the user has recently followed and the content of their posts. The generation AI can also analyze a user's social media account and generate conversations based on the latest trends. For example, it can provide the latest news and topics that the user is likely to be interested in. The generation AI can also analyze a user's social media account and generate conversations based on the communities and groups the user participates in. For example, it can provide topics related to groups the user belongs to. This makes it possible to generate conversations based on the user's latest interests and trends.

[0072] The emotion estimation function can provide words of encouragement and comfort according to the user's emotions. For example, the generation AI can estimate the user's emotions and provide words of encouragement if the user is feeling down. For example, it can send a message such as, "Don't worry, everything will be fine." The emotion estimation function can also estimate the user's emotions and provide words of comfort if the user is sad. For example, it can send a message such as, "Don't push yourself if you're feeling down, just take a rest." The emotion estimation function can also estimate the user's emotions and provide advice to help the user relax if they are feeling stressed. For example, it can send a message such as, "Take a deep breath and relax." This makes it possible to provide words of encouragement and comfort according to the user's emotions.

[0073] The generating AI can learn information about the user's family and friends and provide topics related to them. For example, the generating AI can learn information about the user's family and friends and provide topics related to them. For example, it can ask questions such as, "How was your mother's birthday?" The generating AI can also learn information about the user's family and friends and provide topics based on memories with them. For example, it can ask questions such as, "Did you enjoy your trip last year?" The generating AI can also learn information about the user's family and friends and provide topics based on their recent activities. For example, it can ask questions such as, "How's your friend's new job going?" This allows it to provide topics related to the user's family and friends.

[0074] The generative AI can suggest new hobbies and activities based on the user's hobbies and special skills. For example, the generative AI can learn the user's hobbies and special skills and suggest new hobbies based on them. For example, it could suggest, "If you've recently become fond of drawing, why not try taking an art class?" The generative AI can also learn the user's hobbies and special skills and suggest new activities based on them. For example, it could suggest, "If you're good at cooking, why not try a new recipe?" The generative AI can also learn the user's hobbies and special skills and suggest new events or workshops based on them. For example, it could suggest, "If you like music, why not go to a concert?" This makes it possible to suggest new hobbies and activities based on the user's hobbies and special skills.

[0075] The emotion estimation function can suggest events and activities that the user might be interested in. For example, the generation AI estimates the user's emotions and suggests events that the user might be interested in. For example, it might suggest, "Why not go to a festival nearby this weekend?" The emotion estimation function also estimates the user's emotions and suggests activities that the user might be interested in. For example, it might suggest, "Why not try a new sport?" The emotion estimation function also estimates the user's emotions and suggests workshops and seminars that the user might be interested in. For example, it might suggest, "Why not take part in a cooking class that's being held this weekend?" This makes it possible to suggest events and activities that the user might be interested in.

[0076] Generative AI can analyze a user's schedule and send reminders and notifications at the appropriate time. For example, generative AI can analyze a user's schedule and send a reminder before an important appointment. For example, it can notify the user, "Are you ready for tomorrow's meeting?". Generative AI can also analyze a user's schedule and make suggestions for relaxing during breaks. For example, it can suggest, "Why not take a short walk during your next break?". Generative AI can also analyze a user's schedule and send notifications at the appropriate time. For example, it can notify the user, "Let's check your schedule for this weekend." This allows it to send reminders and notifications at the appropriate time based on the user's schedule.

[0077] The generation AI can analyze the user's biometric data and start a conversation when relaxation is needed. For example, the generation AI can analyze the user's heart rate and start a conversation to help them relax if their heart rate increases. For example, it can suggest, "Take a deep breath and relax." The generation AI can also analyze the user's body temperature and start a conversation to help them relax if their body temperature rises. For example, it can suggest, "Drink a cold drink and refresh yourself." The generation AI can also analyze the user's biometric data and start a conversation to help them relax if it determines that stress is increasing. For example, it can suggest, "Take a short break and relax." This makes it possible to start a conversation when relaxation is needed based on the user's biometric data.

[0078] The emotion estimation function can provide a conversation that helps the user relax when they are feeling stressed. For example, the generation AI estimates the user's emotions and provides a conversation that helps the user relax when they are feeling stressed. For example, it might ask, "How are you doing lately? Is there anything you'd like to talk about?" The emotion estimation function also estimates the user's emotions and provides a topic that helps the user relax when they are feeling stressed. For example, it might suggest, "Let's talk about a movie you recently saw." The emotion estimation function also estimates the user's emotions and provides advice that helps the user relax when they are feeling stressed. For example, it might suggest, "Try taking a short break and trying to relax." This makes it possible to provide a conversation that helps the user relax when they are feeling stressed.

[0079] The generation AI can analyze the sound environment around the user and start a conversation in a quiet place. For example, the generation AI can analyze the sound environment around the user and start a conversation in a quiet place. For example, when the surroundings become quiet, it can suggest, "Shall we talk for a bit now?" The generation AI can also analyze the sound environment around the user and refrain from conversation in noisy places. For example, if the surroundings are noisy, it can notify the user, "Let's talk later." The generation AI can also analyze the sound environment around the user and provide a relaxing conversation in a quiet place. For example, it can suggest, "Let's talk about your recent hobbies" in a quiet place. This allows a conversation to start in a quiet place based on the sound environment around the user.

[0080] The generative AI can learn the user's travel patterns and provide appropriate conversations while traveling. For example, the generative AI can learn the user's travel patterns and provide relaxing conversations during the commute. For example, it might suggest, "We'll provide you with topics to talk about that will help you relax during your commute." The generative AI can also learn the user's travel patterns and provide useful information during the journey. For example, it might notify you by saying, "We'll show you the directions to your next destination." The generative AI can also learn the user's travel patterns and provide enjoyable conversations while traveling. For example, it might suggest, "We'll suggest music and podcasts that you might want to listen to while traveling." This makes it possible to provide appropriate conversations while traveling based on the user's travel patterns.

[0081] The emotion estimation function can start a conversation when the user is feeling lonely, thereby reducing the sense of loneliness. For example, the generation AI estimates the user's emotions and starts a conversation when the user is feeling lonely. For example, it might ask, "How are you doing lately? Is there anything you'd like to talk about?" The emotion estimation function also estimates the user's emotions and offers fun topics to talk about when the user is feeling lonely. For example, it might suggest, "Let's talk about a movie you recently saw." The emotion estimation function also estimates the user's emotions and offers words of encouragement when the user is feeling lonely. For example, it might send a message saying, "You're not alone. Let's talk anytime." This allows the user to start a conversation when the user is feeling lonely, thereby reducing the sense of loneliness.

[0082] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0083] The voice assistant system can also include a health management unit that monitors the user's health status. For example, the health management unit can measure the user's heart rate and body temperature in real time and issue an alert if an abnormality is detected. The health management unit can also analyze the user's sleep patterns and provide appropriate sleep advice. Furthermore, the health management unit can manage the user's dietary records and suggest nutritionally balanced meals. This allows the system to comprehensively manage the user's health status and support health maintenance.

[0084] The voice assistant system may further include a hobby suggestion unit that suggests new hobbies and activities based on the user's hobbies and interests. For example, the hobby suggestion unit may analyze the user's past conversation data and suggest new hobbies that the user may be interested in. The hobby suggestion unit may also suggest events or workshops related to the user's current hobbies. Furthermore, the hobby suggestion unit may recommend new books or movies based on the user's interests. This provides new enjoyment to the user's life and allows them to spend their time more fulfillingly.

[0085] The voice assistant system can also include a mobility support unit that learns the user's travel patterns and provides useful information during travel. For example, the mobility support unit can analyze the user's commute route and suggest the optimal means of transportation and route. The mobility support unit can also recommend music or podcasts that will help the user relax during their travel. The mobility support unit can also provide information about the user's destination and recommend tourist spots and restaurants. This makes the user's travel more comfortable and meaningful.

[0086] The voice assistant system may further include a communication support unit that supports communication with the user's family and friends. For example, the communication support unit may remind the user of their family and friends' birthdays and anniversaries and suggest sending them a message. The communication support unit may also analyze past conversations with the user's family and friends and provide reminiscences. Furthermore, the communication support unit may provide topics based on the recent activities of the user's family and friends. This may deepen the user's relationships and realize richer communication.

[0087] The voice assistant system can further include a learning support unit that supports the user's learning. For example, the learning support unit can manage the user's learning progress and propose an appropriate learning plan. The learning support unit can also provide learning resources based on the user's interests. Furthermore, the learning support unit can provide appropriate answers to the user's questions about their studies. This can help the user's learning progress more efficiently and improve their knowledge.

[0088] The voice assistant system can also estimate the user's emotions and suggest appropriate relaxation methods based on the estimated emotions. For example, if the user is feeling stressed, it can teach deep breathing or meditation techniques. If the user is tired, it can play relaxing music. Furthermore, if the user is feeling down, it can provide words of encouragement. In this way, it can suggest relaxation methods according to the user's emotions and support their mental health.

[0089] The voice assistant system can also estimate the user's emotions and provide appropriate entertainment based on the estimated emotions. For example, if the user is sad, it can suggest a comedy movie to lighten the mood. If the user wants to relax, it can play relaxing music. Furthermore, if the user is excited, it can suggest an action movie. In this way, it can provide entertainment that matches the user's emotions and help them change their mood.

[0090] The voice assistant system can also estimate the user's emotions and suggest appropriate exercises based on the estimated emotions. For example, if the user is feeling stressed, it can suggest light stretching or yoga. If the user is feeling energetic, it can suggest running or dancing. If the user wants to relax, it can suggest walking or meditation. In this way, it can suggest exercises that correspond to the user's emotions and support physical and mental health.

[0091] The voice assistant system can also estimate the user's emotions and suggest appropriate meals based on the estimated emotions. For example, if the user is tired, it can suggest nutritious meals. If the user wants to relax, it can suggest herbal tea with a relaxing effect. Furthermore, if the user wants to feel energized, it can suggest meals that will replenish energy. In this way, it can suggest meals that correspond to the user's emotions and support a healthy eating lifestyle.

[0092] The voice assistant system can also estimate the user's emotions and suggest appropriate communication methods based on the estimated emotions. For example, if the user feels lonely, it can suggest contacting friends or family. If the user feels stressed, it can offer relaxing conversations. Furthermore, if the user is happy, it can suggest ways to share that joy. In this way, it can suggest communication methods that correspond to the user's emotions and support mental health.

[0093] The processing flow of the second embodiment will be briefly explained below.

[0094] Step 1: The camera recognizes the user's facial expressions and movements. For example, the camera detects the user's facial features and analyzes their facial expressions. The camera can also track the user's movements and analyze their movement patterns. Furthermore, the camera can track the user's gaze and analyze their gaze direction. Step 2: The voice assistant recognizes the user's facial expressions and actions captured by the camera. For example, the voice assistant can estimate the user's emotional state based on the data from the camera. The voice assistant can also recognize the user's voice and use natural language processing technology to engage in dialogue. Furthermore, the voice assistant can analyze the content of the user's speech and generate appropriate responses. Step 3: The generation AI provides a personalized voice chat service based on the user's personality and characteristics recognized by the voice assistant. For example, the generation AI learns the user's past conversation history and advances the conversation based on the user's interests. The generation AI can also suggest appropriate conversation topics based on the user's personality profile. Furthermore, the generation AI can analyze the user's behavioral patterns and start a conversation at the appropriate time.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0099] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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).

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0114] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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."

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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]

[0162] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A portable camera and A voice assistant that recognizes the user's facial expressions and actions captured by the camera; A generation AI that provides a personalized voice chat service based on the personality and characteristics of the user recognized by the voice assistant. A system characterized by:

2. The camera is Read the user's facial expression, The voice assistant: Start an appropriate conversation based on the facial expression 2. The system of claim 1.

3. The generated AI is Remembering what the user has said in the past and topics of interest; Build a conversation based on that 2. The system of claim 1.

4. The generated AI is automatically initiate conversations when the user is alone for an extended period of time; Reducing the user's sense of loneliness 2. The system of claim 1.

5. The product AI-4 is If the user smiles, Have a positive conversation in response to the smile 2. The system of claim 1.

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