System

Abstract

An object of a system according to an embodiment is to naturally and intuitively perform interaction with a generated AI in daily life.SOLUTION: A system according to an embodiment includes a terminal dedicated to generative AI, a speech recognizer, a natural language processor, and a response generator. The terminal dedicated to generated AI recognizes the user's voice. The voice recognition unit recognizes a voice of a user. The natural language processing unit analyzes the voice recognized by the voice recognition unit. The response generation unit generates a response on the basis of the content analyzed by the natural language processing unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it was difficult to interact with generative AI in a natural and intuitive way in everyday life.

[0005] The system according to the embodiment aims to enable natural and intuitive interaction with the generative AI in everyday life. [Means for solving the problem]

[0006] The system according to the embodiment includes a dedicated generation AI terminal, a speech recognition unit, a natural language processing unit, and a response generation unit. The dedicated generation AI terminal recognizes the user's speech. The speech recognition unit recognizes the user's speech. The natural language processing unit analyzes the speech recognized by the speech recognition unit. The response generation unit generates a response based on the content analyzed by the natural language processing unit. [Effects of the Invention]

[0007] The system according to the embodiment enables natural and intuitive interaction with the generative AI in everyday life. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

[0028] (Example 1) The generative AI dedicated terminal system according to an embodiment of the present invention is a system that makes interaction with generative AI a daily occurrence, providing users with a natural and intuitive AI connection. This allows users to easily access generative AI and receive the information and support they need.

[0029] A dedicated terminal system for generation AI according to an embodiment includes a dedicated terminal for generation AI, a speech recognition unit, a natural language processing unit, and a response generation unit. The dedicated terminal for generation AI includes a speech recognition unit that recognizes a user's speech. For example, the speech recognition unit uses a deep learning algorithm to recognize the user's speech with high accuracy. The speech recognition unit also uses noise canceling technology to remove environmental sounds and improve speech recognition accuracy. The speech recognition unit also uses multiple microphones and sound source localization technology to identify the user's speech. The natural language processing unit analyzes the speech recognized by the speech recognition unit. For example, the natural language processing unit performs morphological analysis to divide the speech data into words. The natural language processing unit also performs grammatical analysis to analyze the structure of the sentence. The natural language processing unit also performs semantic analysis to understand the meaning of the sentence. The response generation unit generates a response based on the content analyzed by the natural language processing unit. For example, the response generation unit uses a generation AI (e.g., a text generation AI or a multimodal generation AI) to generate an appropriate response to the user's question. The response generation unit also understands the user's intentions and suggests appropriate actions. The response generation unit also references the user's past dialogue history to generate personalized responses. This allows the dedicated terminal system for generating AI according to the embodiment to enable users to converse with the generating AI in natural language.

[0030] The generative AI dedicated terminal is equipped with an algorithm that learns a user's usage history and provides personalized responses based on that usage history. The generative AI dedicated terminal is equipped with an algorithm that learns a user's usage history and provides personalized responses. For example, the generative AI dedicated terminal learns a user's frequently asked questions and favorite content and generates appropriate responses. The generative AI dedicated terminal also analyzes a user's behavioral patterns and preferences based on the usage history and develops an algorithm that provides personalized services. For example, if a user checks the weather every morning, it will automatically provide weather information. The generative AI dedicated terminal also learns a user's usage history and builds a system that provides personalized responses. For example, if a user is interested in a specific news category, it will prioritize displaying the latest news in that category. This makes it possible to provide personalized responses based on the user's usage history.

[0031] Dedicated generative AI terminals will be developed as wearable devices so that users can carry them with them at all times. Dedicated generative AI terminals will be developed as wearable devices so that users can carry them with them at all times. For example, they could be developed as wearable devices such as smartwatches or smart glasses so that users can carry them with them at all times. For example, they could be equipped with a function that allows users to access the generative AI via voice commands. Dedicated generative AI terminals will also be developed as wearable devices so that users can use the generative AI in their daily lives. For example, they could be made accessible while walking or exercising. Dedicated generative AI terminals will also be designed as wearable devices, providing an environment where users can always connect to the generative AI. For example, generative AI functions could be integrated into a smartwatch so that they can be operated directly from the wrist. This would allow users to always carry a dedicated generative AI terminal with them.

[0032] A generative AI dedicated terminal will be integrated into a domestic robot to support the user's physical tasks. A generative AI dedicated terminal will be integrated into a domestic robot to support the user's physical tasks. For example, a generative AI dedicated terminal will be integrated into a domestic robot to develop a system that also supports physical tasks. For example, the robot will automatically perform household chores such as cleaning and cooking. A generative AI dedicated terminal will also be built into a domestic robot, allowing the user to give instructions to the robot using voice commands. For example, when the user commands "start cleaning," the robot will start cleaning. A generative AI dedicated terminal will also be integrated into a domestic robot to add functions to support physical tasks. For example, the robot will create a shopping list on behalf of the user and purchase the necessary items. This will support the user's physical tasks.

[0033] Dedicated generative AI devices will incorporate gesture recognition technology, allowing them to be controlled with hand movements or facial expressions. Dedicated generative AI devices will incorporate gesture recognition technology, allowing them to be controlled with hand movements or facial expressions. For example, a dedicated generative AI device will incorporate gesture recognition technology to develop a system that allows users to control the generative AI with hand movements or facial expressions. For example, it will play music just by waving your hand. Dedicated generative AI devices will also use gesture recognition technology to provide an interface that allows users to intuitively control the generative AI. For example, it will be possible to give instructions to the generative AI by changing your facial expression. Dedicated generative AI devices will also integrate gesture recognition technology to add functions that allow users to control the generative AI with hand movements or facial expressions. For example, it will scroll the screen just by waving your hand. This will allow the generative AI to be controlled with hand movements or facial expressions.

[0034] A dedicated generative AI device is developed as a smart mirror, allowing users to interact with the generative AI while they are getting ready for their daily routine. A dedicated generative AI device is developed as a smart mirror, allowing users to interact with the generative AI while they are getting ready for their daily routine. For example, a dedicated generative AI device is developed as a smart mirror, and a system is built that allows users to interact with the generative AI while they are getting ready for their daily routine. For example, a user can ask the generative AI for weather information while looking at their reflection in the mirror. A dedicated generative AI device is also developed as a smart mirror, allowing users to obtain information while they are getting ready for their daily routine. For example, the mirror can display news and schedules. A dedicated generative AI device is also designed as a smart mirror, providing an environment in which users can interact with the generative AI while they are getting ready for their daily routine. For example, a user can set a reminder for the generative AI while looking at their reflection in the mirror. This allows users to interact with the generative AI while they are getting ready for their daily routine.

[0035] Dedicated generative AI terminals are developed as in-car devices to provide support while driving. Dedicated generative AI terminals are developed as in-car devices to provide support while driving. For example, a dedicated generative AI terminal is developed as an in-car device to build a system that provides support while driving. For example, the navigation system can be operated using voice commands. Dedicated generative AI terminals are also developed as in-car devices to enable information to be obtained while driving. For example, traffic and weather information can be provided in real time. Dedicated generative AI terminals are also designed as in-car devices to provide an environment in which the user can interact with the generative AI while driving. For example, music can be played or phone calls can be made using voice commands. This allows support to be provided while driving.

[0036] The generative AI dedicated device analyzes the user's health data and provides health management advice based on the health data. The generative AI dedicated device is equipped with the function of analyzing the user's health data and providing health management advice. For example, the generative AI dedicated device is equipped with a health data analysis function, and a system is developed that analyzes the user's health data and provides health management advice. For example, it analyzes the user's heart rate and sleep data and provides appropriate exercise and dietary advice. The generative AI dedicated device also develops an algorithm that monitors the user's health status based on the health data analysis and provides health management advice. For example, if the user is not getting enough exercise, it will suggest exercise. The generative AI dedicated device will also add a function that analyzes the user's health data in real time and provides health management advice. For example, if the user is feeling stressed, it will suggest relaxation methods. This makes it possible to provide health management advice based on the user's health data.

[0037] The dedicated generation AI terminal learns the user's purchase history and automatically generates a shopping list based on that purchase history. The dedicated generation AI terminal is equipped with the function of learning the user's purchase history and automatically generating a shopping list. For example, the dedicated generation AI terminal is equipped with a purchase history learning function and develops a system that learns the user's purchase history and automatically generates a shopping list. For example, it adds items that the user purchases regularly to the list. The dedicated generation AI terminal also develops an algorithm that automatically generates a user's shopping list based on the purchase history. For example, it predicts when the user will run out of an item that they previously purchased and adds it to the list. The dedicated generation AI terminal also learns the user's purchase history in real time and adds a function to automatically generate a shopping list. For example, if a user frequently purchases a particular item, it adds that item to the list preferentially. This makes it possible to automatically generate a shopping list based on the user's purchase history.

[0038] The generative AI dedicated terminal works in conjunction with smart home devices to automatically control home appliances. The generative AI dedicated terminal works in conjunction with smart home devices and has the function of automatically controlling home appliances. For example, the generative AI dedicated terminal will work in conjunction with smart home devices to develop a system that automatically controls home appliances. For example, operating lights and air conditioners with voice commands. The generative AI dedicated terminal will also work in conjunction with smart home devices to add a function for automatically controlling home appliances. For example, when a user commands, "Turn on the light in the living room," the light will turn on automatically. The generative AI dedicated terminal will also be integrated with smart home devices to build a system that automatically controls home appliances. For example, it will automatically adjust the temperature of the air conditioner based on the user's schedule. This will enable automatic control of home appliances in conjunction with smart home devices.

[0039] Dedicated generative AI terminals are developed as educational devices to provide learning support to users. Dedicated generative AI terminals are developed as educational devices with the ability to provide learning support to users. For example, a dedicated generative AI terminal is developed as an educational device to build a system that provides learning support to users. For example, questions about learning content can be asked using voice commands, and the generative AI will provide appropriate answers. Dedicated generative AI terminals are also developed as educational devices to allow users to receive learning support. For example, the generative AI will monitor the user's learning progress and propose an appropriate learning plan. Dedicated generative AI terminals are also designed as educational devices to provide an environment in which users can receive learning support. For example, the generative AI will answer the user's questions and explain the learning content. This allows them to provide learning support as educational devices.

[0040] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0041] The generative AI dedicated terminal system can further include a gesture recognition unit that recognizes user gestures. For example, the gesture recognition unit can detect a user's hand waving and play or stop music. The gesture recognition unit can also detect a user's pointing and display specific information. Furthermore, the gesture recognition unit can detect a user's hand-waving and scroll the screen. This allows users to intuitively operate the generative AI dedicated terminal.

[0042] The generative AI dedicated terminal system can further include a health data analysis unit that analyzes the user's health data. For example, the health data analysis unit analyzes the user's heart rate and sleep data and provides appropriate exercise and dietary advice. The health data analysis unit also develops algorithms that monitor the user's health status and provide health management advice. For example, if the user is not getting enough exercise, it will suggest exercise. The health data analysis unit also adds a function that analyzes the user's health data in real time and provides health management advice. For example, if the user is feeling stressed, it will suggest relaxation methods. This allows health management advice to be provided based on the user's health data.

[0043] The generation AI dedicated terminal system can further include a purchase history learning unit that learns the user's purchase history and automatically generates a shopping list. For example, the purchase history learning unit develops a system that learns the user's purchase history and automatically generates a shopping list. For example, it adds items that the user purchases regularly to the list. The purchase history learning unit also develops an algorithm that automatically generates the user's shopping list based on the purchase history. For example, it predicts when the user will run out of an item they previously purchased and adds it to the list. The purchase history learning unit also learns the user's purchase history in real time and adds a function to automatically generate a shopping list. For example, if a user frequently purchases a particular item, it adds that item to the list preferentially. This makes it possible to automatically generate a shopping list based on the user's purchase history.

[0044] The generative AI dedicated terminal will be integrated into a domestic robot to support the user's physical tasks. For example, a system will be developed that integrates a generative AI dedicated terminal into a domestic robot to support physical tasks as well. For example, the robot will automatically perform household chores such as cleaning and cooking. The generative AI dedicated terminal will also be built into a domestic robot, allowing the user to give instructions to the robot using voice commands. For example, when the user commands "start cleaning," the robot will begin cleaning. The generative AI dedicated terminal will also be integrated into a domestic robot to add functions that support physical tasks. For example, the robot will create a shopping list on behalf of the user and purchase the necessary items. This will support the user's physical tasks.

[0045] The generative AI dedicated terminal system can further include a smart home integration unit that works with smart home devices to automatically control home appliances. For example, the smart home integration unit can be integrated with smart home devices to develop a system that automatically controls home appliances. For example, lights and air conditioners can be operated with voice commands. The smart home integration unit can also work with smart home devices to add a function to the generative AI dedicated terminal that automatically controls home appliances. For example, when a user commands, "Turn on the living room light," the light will automatically turn on. The smart home integration unit can also be integrated with smart home devices to build a system that automatically controls home appliances. For example, the air conditioner temperature can be automatically adjusted based on the user's schedule. This makes it possible to work with smart home devices to automatically control home appliances.

[0046] The generative AI dedicated terminal system can be further developed as an educational device and equipped with an educational support unit that provides learning support to users. For example, the educational support unit is developed as an educational device and builds a system that provides learning support to users. For example, questions about learning content can be asked using voice commands, and the generative AI can provide appropriate answers. The educational support unit can also develop a generative AI dedicated terminal as an educational device, allowing users to receive learning support. For example, the generative AI monitors the user's learning progress and proposes an appropriate learning plan. The educational support unit can also be designed as an educational device and provides an environment in which users can receive learning support. For example, the generative AI can answer the user's questions and explain the learning content. This allows learning support to be provided as an educational device.

[0047] The processing flow of the first embodiment will be briefly explained below.

[0048] Step 1: The voice recognition unit recognizes the user's voice. For example, the voice recognition unit uses a deep learning algorithm to recognize voices with high accuracy, noise cancellation technology to remove ambient sounds, and improve the accuracy of voice recognition. It also uses multiple microphones to identify the user's voice using sound source localization technology. Step 2: The natural language processing unit analyzes the speech recognized by the speech recognition unit. For example, the natural language processing unit performs morphological analysis to divide the speech data into words, grammatical analysis to analyze the structure of the sentence, and semantic analysis to understand the meaning of the sentence. Step 3: The response generator generates a response based on the content analyzed by the natural language processor. For example, the response generator uses a generation AI (e.g., text generation AI or multimodal generation AI) to generate an appropriate response to the user's question, understand the user's intent, suggest appropriate actions, and generate a personalized response by referring to the user's past interaction history.

[0049] (Example 2) The generative AI dedicated terminal system according to an embodiment of the present invention is a system that makes interaction with generative AI a daily occurrence, providing users with a natural and intuitive AI connection. This allows users to easily access generative AI and receive the information and support they need.

[0050] A dedicated terminal system for generation AI according to an embodiment includes a dedicated terminal for generation AI, a speech recognition unit, a natural language processing unit, and a response generation unit. The dedicated terminal for generation AI includes a speech recognition unit that recognizes a user's speech. For example, the speech recognition unit uses a deep learning algorithm to recognize the user's speech with high accuracy. The speech recognition unit also uses noise canceling technology to remove environmental sounds and improve speech recognition accuracy. The speech recognition unit also uses multiple microphones and sound source localization technology to identify the user's speech. The natural language processing unit analyzes the speech recognized by the speech recognition unit. For example, the natural language processing unit performs morphological analysis to divide the speech data into words. The natural language processing unit also performs grammatical analysis to analyze the structure of the sentence. The natural language processing unit also performs semantic analysis to understand the meaning of the sentence. The response generation unit generates a response based on the content analyzed by the natural language processing unit. For example, the response generation unit uses a generation AI (e.g., a text generation AI or a multimodal generation AI) to generate an appropriate response to the user's question. The response generation unit also understands the user's intentions and suggests appropriate actions. The response generation unit also references the user's past dialogue history to generate personalized responses. This allows the dedicated terminal system for generating AI according to the embodiment to enable users to converse with the generating AI in natural language.

[0051] The generative AI terminal is equipped with an emotion estimation function that estimates the user's emotional state and generates a response based on the emotional state estimated by the emotion estimation function. The generative AI terminal is equipped with an emotion estimation function and estimates the user's emotional state. For example, the emotion estimation function captures the user's facial expressions with a camera and analyzes their emotions using an expression recognition algorithm. The emotion estimation function also analyzes the user's vocal tone and estimates their emotions using voice analysis technology. The emotion estimation function also collects the user's biometric data (heart rate and electrodermal activity) with a sensor and estimates their emotions using biometric technology. Based on the emotional state estimated by the emotion estimation function, the response generation unit generates an appropriate response. For example, if the user is tired, the response generation unit recommends relaxing music. If the user is stressed, the response generation unit provides encouraging words and relaxation advice. If the user is happy, the response generation unit provides positive news and entertainment information. This allows the system to provide an appropriate response according to the user's emotions.

[0052] The generative AI dedicated terminal is equipped with an algorithm that learns a user's usage history and provides personalized responses based on that usage history. The generative AI dedicated terminal is equipped with an algorithm that learns a user's usage history and provides personalized responses. For example, the generative AI dedicated terminal learns a user's frequently asked questions and favorite content and generates appropriate responses. The generative AI dedicated terminal also analyzes a user's behavioral patterns and preferences based on the usage history and develops an algorithm that provides personalized services. For example, if a user checks the weather every morning, it will automatically provide weather information. The generative AI dedicated terminal also learns a user's usage history and builds a system that provides personalized responses. For example, if a user is interested in a specific news category, it will prioritize displaying the latest news in that category. This makes it possible to provide personalized responses based on the user's usage history.

[0053] The generative AI dedicated device analyzes the user's voice tone or speed, detects stress or fatigue based on the tone or speed, and provides appropriate advice. The generative AI dedicated device has the ability to analyze the user's voice tone and speed, detect stress or fatigue, and provide appropriate advice. For example, the generative AI dedicated device is equipped with a voice analysis function and analyzes the user's voice tone and speed to detect stress or fatigue. For example, if the user's voice becomes lower or the speed becomes slower, it will suggest taking a break. The generative AI dedicated device will also develop a system that detects the user's stress or fatigue based on voice analysis and provides appropriate advice. For example, if the user is tired, it will suggest relaxation methods. The generative AI dedicated device will also add a function to analyze the user's voice tone and speed in real time to detect stress or fatigue. For example, if the user is feeling stressed, it will suggest activities to relieve stress. This makes it possible to detect the user's stress or fatigue and provide appropriate advice.

[0054] Dedicated generative AI terminals will be developed as wearable devices so that users can carry them with them at all times. Dedicated generative AI terminals will be developed as wearable devices so that users can carry them with them at all times. For example, they could be developed as wearable devices such as smartwatches or smart glasses so that users can carry them with them at all times. For example, they could be equipped with a function that allows users to access the generative AI via voice commands. Dedicated generative AI terminals will also be developed as wearable devices so that users can use the generative AI in their daily lives. For example, they could be made accessible while walking or exercising. Dedicated generative AI terminals will also be designed as wearable devices, providing an environment where users can always connect to the generative AI. For example, generative AI functions could be integrated into a smartwatch so that they can be operated directly from the wrist. This would allow users to always carry a dedicated generative AI terminal with them.

[0055] A generative AI dedicated terminal will be integrated into a domestic robot to support the user's physical tasks. A generative AI dedicated terminal will be integrated into a domestic robot to support the user's physical tasks. For example, a generative AI dedicated terminal will be integrated into a domestic robot to develop a system that also supports physical tasks. For example, the robot will automatically perform household chores such as cleaning and cooking. A generative AI dedicated terminal will also be built into a domestic robot, allowing the user to give instructions to the robot using voice commands. For example, when the user commands "start cleaning," the robot will start cleaning. A generative AI dedicated terminal will also be integrated into a domestic robot to add functions to support physical tasks. For example, the robot will create a shopping list on behalf of the user and purchase the necessary items. This will support the user's physical tasks.

[0056] The generative AI dedicated terminal is equipped with an emotion estimation function that estimates the user's emotions and recommends music or video content based on the user's emotions. The generative AI dedicated terminal is equipped with an emotion estimation function and recommends music or video content based on the user's emotions. For example, the generative AI dedicated terminal is equipped with an emotion estimation function and develops a system that recommends music and video content based on the user's emotional state. For example, if the user is feeling sad, it would recommend uplifting music. The generative AI dedicated terminal also uses the emotion estimation function to develop an algorithm that recommends content based on the user's emotions. For example, if the user wants to relax, it would recommend relaxation videos. The generative AI dedicated terminal also analyzes the user's emotional state in real time and builds a system that recommends optimal music and video content based on the user's emotions. For example, if the user is excited, it would recommend action movies. This makes it possible to recommend appropriate music and video content based on the user's emotions.

[0057] The generative AI dedicated device is equipped with an emotion estimation function that estimates the user's emotions and customizes the interface based on those emotions. The generative AI dedicated device is equipped with an emotion estimation function and customizes the interface according to the user's emotions. For example, the generative AI dedicated device will be equipped with an emotion estimation function and develop a system that customizes the interface according to the user's emotional state. For example, if the user is feeling stressed, the design will be changed to a simple and calm one. The generative AI dedicated device will also use the emotion estimation function to develop an algorithm that customizes the interface according to the user's emotions. For example, if the user is happy, bright colors and animations will be added. The generative AI dedicated device will also analyze the user's emotional state in real time and build a system that customizes the interface based on emotions. For example, if the user is tired, visually relaxing elements will be added. This makes it possible to customize the interface according to the user's emotions.

[0058] Dedicated generative AI devices will incorporate gesture recognition technology, allowing them to be controlled with hand movements or facial expressions. Dedicated generative AI devices will incorporate gesture recognition technology, allowing them to be controlled with hand movements or facial expressions. For example, a dedicated generative AI device will incorporate gesture recognition technology to develop a system that allows users to control the generative AI with hand movements or facial expressions. For example, it will play music just by waving your hand. Dedicated generative AI devices will also use gesture recognition technology to provide an interface that allows users to intuitively control the generative AI. For example, it will be possible to give instructions to the generative AI by changing your facial expression. Dedicated generative AI devices will also integrate gesture recognition technology to add functions that allow users to control the generative AI with hand movements or facial expressions. For example, it will scroll the screen just by waving your hand. This will allow the generative AI to be controlled with hand movements or facial expressions.

[0059] A dedicated generative AI device is developed as a smart mirror, allowing users to interact with the generative AI while they are getting ready for their daily routine. A dedicated generative AI device is developed as a smart mirror, allowing users to interact with the generative AI while they are getting ready for their daily routine. For example, a dedicated generative AI device is developed as a smart mirror, and a system is built that allows users to interact with the generative AI while they are getting ready for their daily routine. For example, a user can ask the generative AI for weather information while looking at their reflection in the mirror. A dedicated generative AI device is also developed as a smart mirror, allowing users to obtain information while they are getting ready for their daily routine. For example, the mirror can display news and schedules. A dedicated generative AI device is also designed as a smart mirror, providing an environment in which users can interact with the generative AI while they are getting ready for their daily routine. For example, a user can set a reminder for the generative AI while looking at their reflection in the mirror. This allows users to interact with the generative AI while they are getting ready for their daily routine.

[0060] Dedicated generative AI terminals are developed as in-car devices to provide support while driving. Dedicated generative AI terminals are developed as in-car devices to provide support while driving. For example, a dedicated generative AI terminal is developed as an in-car device to build a system that provides support while driving. For example, the navigation system can be operated using voice commands. Dedicated generative AI terminals are also developed as in-car devices to enable information to be obtained while driving. For example, traffic and weather information can be provided in real time. Dedicated generative AI terminals are also designed as in-car devices to provide an environment in which the user can interact with the generative AI while driving. For example, music can be played or phone calls can be made using voice commands. This allows support to be provided while driving.

[0061] The generative AI dedicated device is equipped with an emotion estimation function that estimates the user's emotions and provides exercise and relaxation advice based on the user's emotions. The generative AI dedicated device is equipped with an emotion estimation function and provides exercise and relaxation advice based on the user's emotions. For example, the generative AI dedicated device will be equipped with an emotion estimation function and develop a system that provides exercise and relaxation advice based on the user's emotional state. For example, if the user is feeling stressed, it will suggest yoga poses. The generative AI dedicated device will also use the emotion estimation function to develop an algorithm that provides exercise and relaxation advice based on the user's emotions. For example, if the user is tired, it will suggest light stretching. The generative AI dedicated device will also analyze the user's emotional state in real time and build a system that provides optimal exercise and relaxation advice based on the user's emotions. For example, if the user feels like relaxing, it will suggest a meditation method. This makes it possible to provide exercise and relaxation advice based on the user's emotions.

[0062] The generative AI dedicated device is equipped with an emotion estimation function that estimates the user's emotions and manages schedules or sets reminders based on the user's emotions. The generative AI dedicated device is equipped with an emotion estimation function and manages schedules and sets reminders based on the user's emotions. For example, the generative AI dedicated device will be equipped with an emotion estimation function and develop a system that manages schedules and sets reminders based on the user's emotional state. For example, if the user is feeling stressed, longer break times will be set. The generative AI dedicated device will also use the emotion estimation function to develop an algorithm that manages schedules and sets reminders based on the user's emotions. For example, if the user is tired, important tasks will be postponed. The generative AI dedicated device will also analyze the user's emotional state in real time and build a system that manages schedules and sets reminders based on emotions. For example, if the user feels like relaxing, additional relaxation time will be set. This makes it possible to manage schedules and set reminders based on the user's emotions.

[0063] The generative AI dedicated device analyzes the user's health data and provides health management advice based on the health data. The generative AI dedicated device is equipped with the function of analyzing the user's health data and providing health management advice. For example, the generative AI dedicated device is equipped with a health data analysis function, and a system is developed that analyzes the user's health data and provides health management advice. For example, it analyzes the user's heart rate and sleep data and provides appropriate exercise and dietary advice. The generative AI dedicated device also develops an algorithm that monitors the user's health status based on the health data analysis and provides health management advice. For example, if the user is not getting enough exercise, it will suggest exercise. The generative AI dedicated device will also add a function that analyzes the user's health data in real time and provides health management advice. For example, if the user is feeling stressed, it will suggest relaxation methods. This makes it possible to provide health management advice based on the user's health data.

[0064] The dedicated generation AI terminal learns the user's purchase history and automatically generates a shopping list based on that purchase history. The dedicated generation AI terminal is equipped with the function of learning the user's purchase history and automatically generating a shopping list. For example, the dedicated generation AI terminal is equipped with a purchase history learning function and develops a system that learns the user's purchase history and automatically generates a shopping list. For example, it adds items that the user purchases regularly to the list. The dedicated generation AI terminal also develops an algorithm that automatically generates a user's shopping list based on the purchase history. For example, it predicts when the user will run out of an item that they previously purchased and adds it to the list. The dedicated generation AI terminal also learns the user's purchase history in real time and adds a function to automatically generate a shopping list. For example, if a user frequently purchases a particular item, it adds that item to the list preferentially. This makes it possible to automatically generate a shopping list based on the user's purchase history.

[0065] The generative AI dedicated terminal works in conjunction with smart home devices to automatically control home appliances. The generative AI dedicated terminal works in conjunction with smart home devices and has the function of automatically controlling home appliances. For example, the generative AI dedicated terminal will work in conjunction with smart home devices to develop a system that automatically controls home appliances. For example, operating lights and air conditioners with voice commands. The generative AI dedicated terminal will also work in conjunction with smart home devices to add a function for automatically controlling home appliances. For example, when a user commands, "Turn on the light in the living room," the light will turn on automatically. The generative AI dedicated terminal will also be integrated with smart home devices to build a system that automatically controls home appliances. For example, it will automatically adjust the temperature of the air conditioner based on the user's schedule. This will enable automatic control of home appliances in conjunction with smart home devices.

[0066] Dedicated generative AI terminals are developed as educational devices to provide learning support to users. Dedicated generative AI terminals are developed as educational devices with the ability to provide learning support to users. For example, a dedicated generative AI terminal is developed as an educational device to build a system that provides learning support to users. For example, questions about learning content can be asked using voice commands, and the generative AI will provide appropriate answers. Dedicated generative AI terminals are also developed as educational devices to allow users to receive learning support. For example, the generative AI will monitor the user's learning progress and propose an appropriate learning plan. Dedicated generative AI terminals are also designed as educational devices to provide an environment in which users can receive learning support. For example, the generative AI will answer the user's questions and explain the learning content. This allows them to provide learning support as educational devices.

[0067] The generative AI dedicated terminal is equipped with an emotion estimation function that estimates the user's emotions and adjusts the priority of reminders or tasks based on the user's emotions. The generative AI dedicated terminal is equipped with an emotion estimation function and has a function to adjust the priority of reminders and tasks based on the user's emotions. For example, the generative AI dedicated terminal is equipped with an emotion estimation function and a system is developed that adjusts the priority of reminders and tasks based on the user's emotional state. For example, if the user is feeling stressed, important tasks are postponed. The generative AI dedicated terminal also uses the emotion estimation function to develop an algorithm that adjusts the priority of reminders and tasks based on the user's emotions. For example, if the user is tired, it prioritizes rest. The generative AI dedicated terminal also analyzes the user's emotional state in real time and builds a system that adjusts the priority of reminders and tasks based on emotions. For example, if the user feels like relaxing, it prioritizes relaxation tasks. This makes it possible to adjust the priority of reminders and tasks based on the user's emotions.

[0068] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0069] The generative AI dedicated terminal system can further include a gesture recognition unit that recognizes user gestures. For example, the gesture recognition unit can detect a user's hand waving and play or stop music. The gesture recognition unit can also detect a user's pointing and display specific information. Furthermore, the gesture recognition unit can detect a user's hand-waving and scroll the screen. This allows users to intuitively operate the generative AI dedicated terminal.

[0070] The generative AI dedicated terminal is equipped with an emotion estimation function that estimates the user's emotions and recommends music or video content based on the emotions. For example, the generative AI dedicated terminal will be equipped with an emotion estimation function and develop a system that recommends music and video content based on the user's emotional state. For example, if the user is feeling sad, it will recommend uplifting music. The generative AI dedicated terminal will also use the emotion estimation function to develop an algorithm that recommends content based on the user's emotions. For example, if the user wants to relax, it will recommend relaxation videos. The generative AI dedicated terminal will also analyze the user's emotional state in real time and build a system that recommends optimal music and video content based on the emotions. For example, if the user is excited, it will recommend action movies. This makes it possible to recommend appropriate music and video content based on the user's emotions.

[0071] The generative AI dedicated terminal system can further include a health data analysis unit that analyzes the user's health data. For example, the health data analysis unit analyzes the user's heart rate and sleep data and provides appropriate exercise and dietary advice. The health data analysis unit also develops algorithms that monitor the user's health status and provide health management advice. For example, if the user is not getting enough exercise, it will suggest exercise. The health data analysis unit also adds a function that analyzes the user's health data in real time and provides health management advice. For example, if the user is feeling stressed, it will suggest relaxation methods. This allows health management advice to be provided based on the user's health data.

[0072] The generative AI dedicated device analyzes the tone or speed of the user's voice, detects stress or fatigue based on the tone or speed, and provides appropriate advice. For example, the generative AI dedicated device is equipped with a voice analysis function and analyzes the tone and speed of the user's voice to detect stress or fatigue. For example, if the voice becomes lower or the speed slows, it will suggest taking a break. In addition, the generative AI dedicated device will develop a system that detects the user's stress or fatigue based on voice analysis and provides appropriate advice. For example, if the user is tired, it will suggest relaxation methods. In addition, the generative AI dedicated device will analyze the user's tone and speed in real time to add a function to detect stress and fatigue. For example, if the user is feeling stressed, it will suggest activities to relieve stress. This will enable the device to detect the user's stress and fatigue and provide appropriate advice.

[0073] The generation AI dedicated terminal system can further include a purchase history learning unit that learns the user's purchase history and automatically generates a shopping list. For example, the purchase history learning unit develops a system that learns the user's purchase history and automatically generates a shopping list. For example, it adds items that the user purchases regularly to the list. The purchase history learning unit also develops an algorithm that automatically generates the user's shopping list based on the purchase history. For example, it predicts when the user will run out of an item they previously purchased and adds it to the list. The purchase history learning unit also learns the user's purchase history in real time and adds a function to automatically generate a shopping list. For example, if a user frequently purchases a particular item, it adds that item to the list preferentially. This makes it possible to automatically generate a shopping list based on the user's purchase history.

[0074] The generative AI dedicated terminal will be integrated into a domestic robot to support the user's physical tasks. For example, a system will be developed that integrates a generative AI dedicated terminal into a domestic robot to support physical tasks as well. For example, the robot will automatically perform household chores such as cleaning and cooking. The generative AI dedicated terminal will also be built into a domestic robot, allowing the user to give instructions to the robot using voice commands. For example, when the user commands "start cleaning," the robot will begin cleaning. The generative AI dedicated terminal will also be integrated into a domestic robot to add functions that support physical tasks. For example, the robot will create a shopping list on behalf of the user and purchase the necessary items. This will support the user's physical tasks.

[0075] The generative AI dedicated device will be equipped with an emotion estimation function that can estimate the user's emotions and customize the interface based on those emotions. For example, the generative AI dedicated device will be equipped with an emotion estimation function and a system will be developed that customizes the interface according to the user's emotional state. For example, if the user is feeling stressed, the design will be changed to a simple and calm one. The generative AI dedicated device will also use the emotion estimation function to develop an algorithm that customizes the interface according to the user's emotions. For example, if the user is happy, bright colors and animations will be added. The generative AI dedicated device will also analyze the user's emotional state in real time and build a system that customizes the interface based on emotions. For example, if the user is tired, visually relaxing elements will be added. This will make it possible to customize the interface according to the user's emotions.

[0076] The generative AI dedicated terminal system can further include a smart home integration unit that works with smart home devices to automatically control home appliances. For example, the smart home integration unit can be integrated with smart home devices to develop a system that automatically controls home appliances. For example, lights and air conditioners can be operated with voice commands. The smart home integration unit can also work with smart home devices to add a function to the generative AI dedicated terminal that automatically controls home appliances. For example, when a user commands, "Turn on the living room light," the light will automatically turn on. The smart home integration unit can also be integrated with smart home devices to build a system that automatically controls home appliances. For example, the air conditioner temperature can be automatically adjusted based on the user's schedule. This makes it possible to work with smart home devices to automatically control home appliances.

[0077] The generative AI dedicated device will be equipped with an emotion estimation function that can estimate a user's emotions and provide exercise and relaxation advice based on those emotions. For example, a system will be developed in which the generative AI dedicated device is equipped with an emotion estimation function and provides exercise and relaxation advice based on the user's emotional state. For example, if the user is feeling stressed, yoga poses will be suggested. The generative AI dedicated device will also use the emotion estimation function to develop an algorithm that provides exercise and relaxation advice based on the user's emotions. For example, if the user is tired, light stretching will be suggested. The generative AI dedicated device will also analyze the user's emotional state in real time and build a system that provides optimal exercise and relaxation advice based on emotions. For example, if the user feels like relaxing, it will suggest a meditation method. This will enable the device to provide exercise and relaxation advice based on the user's emotions.

[0078] The generative AI dedicated terminal system can be further developed as an educational device and equipped with an educational support unit that provides learning support to users. For example, the educational support unit is developed as an educational device and builds a system that provides learning support to users. For example, questions about learning content can be asked using voice commands, and the generative AI can provide appropriate answers. The educational support unit can also develop a generative AI dedicated terminal as an educational device, allowing users to receive learning support. For example, the generative AI monitors the user's learning progress and proposes an appropriate learning plan. The educational support unit can also be designed as an educational device and provides an environment in which users can receive learning support. For example, the generative AI can answer the user's questions and explain the learning content. This allows learning support to be provided as an educational device.

[0079] The processing flow of the second embodiment will be briefly explained below.

[0080] Step 1: The voice recognition unit recognizes the user's voice. For example, the voice recognition unit uses a deep learning algorithm to recognize voices with high accuracy, noise cancellation technology to remove ambient sounds, and improve the accuracy of voice recognition. It also uses multiple microphones to identify the user's voice using sound source localization technology. Step 2: The natural language processing unit analyzes the speech recognized by the speech recognition unit. For example, the natural language processing unit performs morphological analysis to divide the speech data into words, grammatical analysis to analyze the structure of the sentence, and semantic analysis to understand the meaning of the sentence. Step 3: The response generator generates a response based on the content analyzed by the natural language processor. For example, the response generator uses a generation AI (e.g., text generation AI or multimodal generation AI) to generate an appropriate response to the user's question, understand the user's intent, suggest appropriate actions, and generate a personalized response by referring to the user's past interaction history.

[0081] 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.

[0082] 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.

[0083] 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.

[0084] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0085] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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).

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0094] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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).

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0109] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0115] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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).

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0125] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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."

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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]

[0148] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. Equipped with a dedicated device for generating AI, The generation AI dedicated terminal is: a speech recognition unit that recognizes a user's speech; a natural language processing unit that analyzes the speech recognized by the speech recognition unit; a response generation unit that generates a response based on the content analyzed by the natural language processing unit. A system characterized by:

2. The generation AI dedicated terminal is: Developed as a wearable device, allowing the user to carry it with them at all times 2. The system of claim 1.

3. The generation AI dedicated terminal is: Introducing gesture recognition technology to enable operation with hand movements or facial expressions 2. The system of claim 1.

4. The generation AI dedicated terminal is: Equipped with an emotion estimation function that estimates the user's emotions and provides advice on exercise and relaxation based on those emotions 2. The system of claim 1.

5. The generation AI dedicated terminal is: Links with smart home devices to automatically control home appliances 2. The system of claim 1.

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