system

Abstract

An object of a system according to an embodiment is to directly and intuitively communicate with an IoT home appliance.SOLUTION: A system includes a sound input part, a generation AI part, and an MR-GUI part. The voice input unit receives a voice of a user. The generation AI unit analyzes the voice received by the voice receiving unit and generates an appropriate response. The MR-GUI component visually displays the response generated by the production AI component.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem that operating IoT home appliances is indirect and lacks an intuitive user interface.

[0005] The system according to the embodiment aims to communicate directly and intuitively with IoT home appliances. [Means for solving the problem]

[0006] The system according to the embodiment includes a voice input unit, a generation AI unit, and an MR-GUI unit. The voice input unit receives a user's voice. The generation AI unit analyzes the voice received by the voice input unit and generates an appropriate response. The MR-GUI unit visually displays the response generated by the generation AI unit. [Effects of the Invention]

[0007] The system according to the embodiment can communicate directly and intuitively with IoT home appliances. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The IoT home appliance operation system according to the embodiment of the present invention receives user voice, has a generating AI understand the content of the voice, and visually displays it through an MR-GUI. This allows the user to directly communicate with IoT home appliances and operate them intuitively and freely.

[0029] An IoT home appliance operation system according to an embodiment includes a voice input unit, a generation AI unit, and an MR-GUI unit. The voice input unit receives a user's voice. For example, the voice input unit collects the user's voice using a microphone. The voice input unit can also receive voice through a smartphone or smart speaker. The voice input unit can also use noise canceling technology to remove ambient noise and clearly receive the user's voice. The generation AI unit analyzes the voice received by the voice input unit and generates an appropriate response. For example, the generation AI unit converts the voice into text using a text generation AI (e.g., LLM) and understands the content. The generation AI unit can also analyze the intent of the voice and generate an appropriate response using natural language processing technology. The generation AI unit also generates specific instructions for operating an IoT home appliance based on the user's voice instruction. For example, if the user says, "Turn down the temperature of the air conditioner," the generation AI unit analyzes the instruction and generates an instruction to change the temperature setting of the air conditioner. The MR-GUI unit visually displays the response generated by the generation AI unit. For example, the MR-GUI unit uses smart glasses to display the response content in the user's field of view. The MR-GUI unit can also use AR technology to overlay the response content on the real world. The MR-GUI unit can also use VR technology to display the response content in a virtual space. For example, when an air conditioner temperature setting is changed, the MR-GUI unit displays the change in the user's field of view, allowing the user to intuitively confirm the operation result. This allows the IoT home appliance operation system according to the embodiment to allow the user to directly communicate with IoT home appliances and operate them intuitively and freely. For example, the user can issue voice instructions through the voice input unit, the generation AI unit analyzes the instructions, and the MR-GUI unit visually displays the response content, allowing the user to intuitively operate the IoT home appliance.

[0030] The generation AI unit can learn the user's past voice instruction history and provide responses based on the user's preferences and patterns. For example, the generation AI unit can learn the user's past voice instruction history and provide quick responses to frequently used instructions. For example, if a user instructs the system to "open the curtains" every morning, the system can automatically open the curtains at the same time. The generation AI unit can also learn the user's preferences and provide responses based on those preferences. For example, the system can automatically adjust the air conditioner temperature for a user who prefers a specific temperature setting. The generation AI unit can also analyze the user's patterns based on the voice instruction history and provide predictive responses. For example, if a user says "Turn on the TV" and then follows up with "Turn up the volume," the system can automatically adjust the volume with the first instruction. This makes it possible to provide responses based on the user's preferences and patterns.

[0031] The MR-GUI unit can support intuitive operation by tracking the user's gaze and highlighting the information in front of the user's eyes. For example, the MR-GUI unit tracks the user's gaze and highlights the operation menu of an IoT home appliance in front of the user's eyes. For example, it displays the operation menu of an air conditioner at which the user is looking. The MR-GUI unit also uses gaze tracking technology to highlight information at which the user is looking and confirms the operation. For example, it highlights the internal temperature of a refrigerator at which the user is looking. The MR-GUI unit also tracks the user's gaze and highlights the information at which the user is looking, supporting intuitive operation. For example, it displays the channel change menu of a television at which the user is looking. This enables intuitive operation by highlighting the information at which the user is looking.

[0032] The MR-GUI unit can track the user's hand movements and provide interactive feedback based on the hand movements. The MR-GUI unit, for example, tracks the user's hand movements and provides interactive feedback based on the hand movements. For example, the user moves their hand to adjust the brightness of the lights. The MR-GUI unit also tracks the hand movements and displays an operation menu based on the hand movements. For example, the user moves their hand to adjust the temperature of the air conditioner. The MR-GUI unit also tracks the user's hand movements and provides interactive feedback based on the hand movements. For example, the user moves their hand to adjust the volume of the television. This makes it possible to provide interactive feedback based on the hand movements.

[0033] The generation AI unit can analyze user gestures in real time and execute operations based on the gestures. For example, when a user makes a gesture such as waving their hand, the generation AI analyzes the movement in real time and executes the corresponding operation. For example, waving your hand can turn the lights on / off. Also, when a user points, the generation AI understands the instruction and controls IoT appliances in the direction of the pointing finger. For example, pointing can adjust the temperature of the air conditioner. Also, when a user makes a specific gesture, the generation AI recognizes the gesture and executes a pre-set operation. For example, spreading your hand can increase the volume on a TV. This makes gesture-based operations possible.

[0034] The generation AI unit tracks the user's gaze and automatically recognizes the IoT home appliance in front of the user's gaze and suggests operations. For example, the generation AI unit automatically recognizes the IoT home appliance the user is looking at and suggests operations for that appliance. For example, it suggests adjusting the temperature of the air conditioner the user is looking at. The generation AI unit also displays the operation menu of the home appliance the user is looking at on the MR-GUI so that the user can make a selection. For example, it displays the channel change menu of the television the user is looking at. The generation AI unit also uses gaze tracking technology to display the status of the home appliance the user is looking at on the MR-GUI to confirm the operation. For example, it displays the internal temperature of the refrigerator the user is looking at. This allows the generation AI unit to automatically recognize the IoT home appliance in front of the user's gaze and suggest operations.

[0035] The generation AI unit can analyze handwritten input from the user and execute operations based on the handwritten characters and figures. For example, if a user writes "lights on" by hand, the generation AI unit analyzes the characters and executes the operation of turning on the lights. The generation AI unit also analyzes figures drawn by the user by hand and executes the operation corresponding to that figure. For example, drawing a picture of a light bulb will turn on the lights. The generation AI unit can also use handwritten input to instruct multiple operations at once. For example, if a user handwrites "air conditioner 20 degrees, lights on," the generation AI analyzes the instructions and executes both operations. This makes it possible to perform operations based on handwritten input.

[0036] The generation AI unit can analyze the environmental sounds around the user and suggest operations based on the environmental sounds. For example, the generation AI unit can analyze the surrounding environmental sounds and suggest playing relaxing music if the environment is quiet. If the surrounding environmental sounds are noisy, the generation AI unit can suggest using a device with noise canceling functionality. The generation AI unit can also analyze the environmental sounds and, if it detects a specific sound (such as a doorbell), execute a corresponding operation. For example, it can detect a doorbell and turn on the lights. This makes it possible to suggest operations based on environmental sounds.

[0037] The generation AI unit can simultaneously analyze voice instructions from multiple users and operate multiple IoT home appliances in a coordinated manner. For example, if multiple users issue different voice instructions at the same time, the generation AI unit analyzes each instruction and operates the appropriate IoT home appliance. For example, if one user says "Turn on the lights" and another says "Turn off the air conditioner," both instructions will be executed simultaneously. The generation AI unit also monitors the status of each appliance in real time and performs the optimal operation to operate multiple IoT home appliances in a coordinated manner. For example, it can adjust the air conditioner and humidifier simultaneously. The generation AI unit also analyzes voice instructions from multiple users and builds a system to operate IoT home appliances in a coordinated manner. For example, it can operate the lights, air conditioner, and TV all at once based on the instructions of all family members. This allows the system to simultaneously analyze voice instructions from multiple users and operate IoT home appliances in a coordinated manner.

[0038] The generation AI unit can analyze voice instructions in different languages ​​and provide responses in multiple languages. For example, the generation AI unit analyzes voice instructions in different languages ​​and provides a response in the corresponding language. For example, if someone says "Turn on the lights" in English, the response will be in English. The generation AI unit also uses a multilingual generation AI to provide appropriate responses to users who speak different languages ​​in the home. For example, if some family members speak Japanese and others speak English, the response will be in each language. The generation AI unit also analyzes voice instructions in different languages ​​and provides responses by automatic translation. For example, if someone says "Allume la lumiere" in French, the response will be in English. This makes it possible to provide responses that correspond to voice instructions in different languages.

[0039] The MR-GUI unit can analyze environmental information around the user and provide visual feedback according to the environment. For example, the MR-GUI unit analyzes environmental information around the user and provides visual feedback according to the environment. For example, it suggests adjusting the lighting according to the brightness of the room. The MR-GUI unit also analyzes environmental information around the user and provides visual feedback according to the environment. For example, it suggests air conditioner settings according to the room temperature. The MR-GUI unit also analyzes environmental information around the user and provides visual feedback according to the environment. For example, it suggests a noise canceling function according to the noise level. This makes it possible to provide visual feedback based on environmental information.

[0040] The MR-GUI unit can analyze the user's physical movements and provide feedback based on the movements. For example, the MR-GUI unit analyzes the user's physical movements and provides feedback based on the movements. For example, when the user stands up, the lights are turned on. The MR-GUI unit also analyzes the user's physical movements and provides feedback based on the movements. For example, when the user raises their hand, the temperature of the air conditioner is adjusted. The MR-GUI unit also analyzes the user's physical movements and provides feedback based on the movements. For example, when the user sits down, the volume of the television is adjusted. This makes it possible to provide feedback based on physical movements.

[0041] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0042] An IoT home appliance operation system can also be equipped with a gesture recognition unit that recognizes user gestures. The gesture recognition unit detects the user's hand and body movements using a camera or sensor, and operates home appliances based on those movements. For example, a user can turn lights on and off by waving their hand. Or, when a user points, they can operate home appliances in that direction. Furthermore, by pre-setting specific gestures, the corresponding operation will be automatically executed when the user performs that gesture. This enables intuitive operation using gestures in addition to voice input.

[0043] The IoT home appliance operation system can further include an eye-gaze tracking unit that tracks the user's gaze. The eye-gaze tracking unit detects the direction of the user's gaze and automatically recognizes the home appliance in front of the user's gaze. For example, it can display the operation menu of an air conditioner where the user is looking, allowing the user to make a selection. It can also display the channel change menu of a television where the user is looking. It can also display the internal temperature of a refrigerator where the user is looking. This allows for intuitive operation by highlighting the information in front of the user's gaze.

[0044] The IoT home appliance operation system can further include a handwriting recognition unit that analyzes handwritten input by the user. The handwriting recognition unit analyzes the characters and figures handwritten by the user and operates the home appliance based on the content. For example, if the user writes "lights on," the operation to turn on the lights can be executed. The system can also analyze figures handwritten by the user and perform the operation corresponding to that figure. For example, if the user draws a picture of a light bulb, the lights can be turned on. Furthermore, handwritten input can be used to instruct multiple operations at once. This makes it possible to perform operations based on handwritten input.

[0045] The IoT home appliance operation system can further include an environmental sound analysis unit that analyzes the environmental sounds around the user. The environmental sound analysis unit analyzes the surrounding environmental sounds and suggests home appliance operations based on those sounds. For example, if the environment is quiet, it can suggest playing relaxing music. If the surrounding environmental sounds are noisy, it can suggest using a device with noise-canceling functionality. Furthermore, if a specific sound (e.g., a doorbell) is detected, it can execute a corresponding operation. This makes it possible to suggest operations based on environmental sounds.

[0046] The IoT home appliance operation system can further include a body movement analysis unit that analyzes the user's body movements. The body movement analysis unit detects the user's body movements using cameras and sensors and operates home appliances based on those movements. For example, the unit can turn on the lights when the user stands up, adjust the air conditioner temperature when the user raises their hand, and adjust the TV volume when the user sits down. This makes it possible to provide feedback based on body movements.

[0047] The IoT home appliance operation system can also analyze the voice instructions of multiple users simultaneously and operate multiple IoT home appliances in a coordinated manner. For example, if multiple users issue different voice instructions at the same time, it can analyze each instruction and operate the appropriate IoT home appliance. In addition, to operate multiple IoT home appliances in a coordinated manner, it can monitor the status of each appliance in real time and perform the optimal operation. Furthermore, it is possible to build a system that analyzes the voice instructions of multiple users and operates IoT home appliances in a coordinated manner. This makes it possible to analyze the voice instructions of multiple users simultaneously and operate IoT home appliances in a coordinated manner.

[0048] The IoT home appliance operation system can further analyze voice instructions in different languages ​​and provide multilingual responses. For example, it can analyze voice instructions in different languages ​​and provide responses in the corresponding language. It can also use multilingual generation AI to provide appropriate responses to users in the home who speak different languages. It can also analyze voice instructions in different languages ​​and provide responses through automatic translation. This makes it possible to provide responses that correspond to voice instructions in different languages.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The voice input unit accepts the user's voice. For example, the voice input unit may collect the user's voice using a microphone. The voice input unit may also accept the voice through a smartphone or smart speaker. Furthermore, the voice input unit may use noise canceling technology to remove ambient noise and clearly accept the user's voice. Step 2: The generation AI unit analyzes the voice received by the voice input unit and generates an appropriate response. For example, the generation AI unit converts the voice into text using a text generation AI (e.g., LLM) and understands its content. The generation AI unit can also use natural language processing technology to analyze the intent of the voice and generate an appropriate response. Furthermore, the generation AI unit generates specific instructions for operating the IoT home appliance based on the user's voice instructions. Step 3: The MR-GUI unit visually displays the response generated by the generation AI unit. For example, the MR-GUI unit uses smart glasses to display the response content in the user's field of view. The MR-GUI unit can also use AR technology to overlay the response content on the real world. Furthermore, the MR-GUI unit can also use VR technology to display the response content in a virtual space.

[0051] (Example 2) The IoT home appliance operation system according to the embodiment of the present invention receives user voice, has a generating AI understand the content of the voice, and visually displays it through an MR-GUI. This allows the user to directly communicate with IoT home appliances and operate them intuitively and freely.

[0052] An IoT home appliance operation system according to an embodiment includes a voice input unit, a generation AI unit, and an MR-GUI unit. The voice input unit receives a user's voice. For example, the voice input unit collects the user's voice using a microphone. The voice input unit can also receive voice through a smartphone or smart speaker. The voice input unit can also use noise canceling technology to remove ambient noise and clearly receive the user's voice. The generation AI unit analyzes the voice received by the voice input unit and generates an appropriate response. For example, the generation AI unit converts the voice into text using a text generation AI (e.g., LLM) and understands the content. The generation AI unit can also analyze the intent of the voice and generate an appropriate response using natural language processing technology. The generation AI unit also generates specific instructions for operating an IoT home appliance based on the user's voice instruction. For example, if the user says, "Turn down the temperature of the air conditioner," the generation AI unit analyzes the instruction and generates an instruction to change the temperature setting of the air conditioner. The MR-GUI unit visually displays the response generated by the generation AI unit. For example, the MR-GUI unit uses smart glasses to display the response content in the user's field of view. The MR-GUI unit can also use AR technology to overlay the response content on the real world. The MR-GUI unit can also use VR technology to display the response content in a virtual space. For example, when an air conditioner temperature setting is changed, the MR-GUI unit displays the change in the user's field of view, allowing the user to intuitively confirm the operation result. This allows the IoT home appliance operation system according to the embodiment to allow the user to directly communicate with IoT home appliances and operate them intuitively and freely. For example, the user can issue voice instructions through the voice input unit, the generation AI unit analyzes the instructions, and the MR-GUI unit visually displays the response content, allowing the user to intuitively operate the IoT home appliance.

[0053] The voice input unit can analyze the user's voice tone and speed and generate a response according to the user's level of urgency and emotional state. For example, if the user's voice tone is high and the speed is fast, the voice input unit determines that the level of urgency is high and generates a quick response. For example, the voice input unit responds immediately to an instruction such as "Turn off the air conditioner immediately." Furthermore, if the user's voice tone is low and the speed is slow, the voice input unit generates a relaxed response. For example, the voice input unit responds slowly to an instruction such as "Turn off the lights a little." Furthermore, the voice input unit uses voice analysis to estimate the user's emotional state and generate a response according to the emotion. For example, if the user is angry, the voice input unit generates a calm response. This makes it possible to generate an appropriate response according to the user's level of urgency and emotional state.

[0054] The generation AI unit can learn the user's past voice instruction history and provide responses based on the user's preferences and patterns. For example, the generation AI unit can learn the user's past voice instruction history and provide quick responses to frequently used instructions. For example, if a user instructs the system to "open the curtains" every morning, the system can automatically open the curtains at the same time. The generation AI unit can also learn the user's preferences and provide responses based on those preferences. For example, the system can automatically adjust the air conditioner temperature for a user who prefers a specific temperature setting. The generation AI unit can also analyze the user's patterns based on the voice instruction history and provide predictive responses. For example, if a user says "Turn on the TV" and then follows up with "Turn up the volume," the system can automatically adjust the volume with the first instruction. This makes it possible to provide responses based on the user's preferences and patterns.

[0055] The MR-GUI unit can support intuitive operation by tracking the user's gaze and highlighting the information in front of the user's eyes. For example, the MR-GUI unit tracks the user's gaze and highlights the operation menu of an IoT home appliance in front of the user's eyes. For example, it displays the operation menu of an air conditioner at which the user is looking. The MR-GUI unit also uses gaze tracking technology to highlight information at which the user is looking and confirms the operation. For example, it highlights the internal temperature of a refrigerator at which the user is looking. The MR-GUI unit also tracks the user's gaze and highlights the information at which the user is looking, supporting intuitive operation. For example, it displays the channel change menu of a television at which the user is looking. This enables intuitive operation by highlighting the information at which the user is looking.

[0056] The MR-GUI unit can track the user's hand movements and provide interactive feedback based on the hand movements. The MR-GUI unit, for example, tracks the user's hand movements and provides interactive feedback based on the hand movements. For example, the user moves their hand to adjust the brightness of the lights. The MR-GUI unit also tracks the hand movements and displays an operation menu based on the hand movements. For example, the user moves their hand to adjust the temperature of the air conditioner. The MR-GUI unit also tracks the user's hand movements and provides interactive feedback based on the hand movements. For example, the user moves their hand to adjust the volume of the television. This makes it possible to provide interactive feedback based on the hand movements.

[0057] The MR-GUI unit can provide feedback according to the user's emotional state, thereby improving satisfaction with the operation. For example, the MR-GUI unit analyzes the user's emotional state in real time and provides feedback according to the emotion. For example, if the user is tired, it suggests an operation that will help the user relax. The MR-GUI unit also uses an emotion estimation function to provide feedback according to the user's emotional state. For example, if the user is angry, it suggests a calm response and an operation that will reduce stress. The MR-GUI unit also analyzes the user's emotional state in real time and provides feedback according to the emotion. For example, if the user is happy, it suggests a positive operation. As a result, satisfaction with the operation is improved by feedback according to the emotional state.

[0058] The generation AI unit can analyze user gestures in real time and execute operations based on the gestures. For example, when a user makes a gesture such as waving their hand, the generation AI analyzes the movement in real time and executes the corresponding operation. For example, waving your hand can turn the lights on / off. Also, when a user points, the generation AI understands the instruction and controls IoT appliances in the direction of the pointing finger. For example, pointing can adjust the temperature of the air conditioner. Also, when a user makes a specific gesture, the generation AI recognizes the gesture and executes a pre-set operation. For example, spreading your hand can increase the volume on a TV. This makes gesture-based operations possible.

[0059] The generation AI unit tracks the user's gaze and automatically recognizes the IoT home appliance in front of the user's gaze and suggests operations. For example, the generation AI unit automatically recognizes the IoT home appliance the user is looking at and suggests operations for that appliance. For example, it suggests adjusting the temperature of the air conditioner the user is looking at. The generation AI unit also displays the operation menu of the home appliance the user is looking at on the MR-GUI so that the user can make a selection. For example, it displays the channel change menu of the television the user is looking at. The generation AI unit also uses gaze tracking technology to display the status of the home appliance the user is looking at on the MR-GUI to confirm the operation. For example, it displays the internal temperature of the refrigerator the user is looking at. This allows the generation AI unit to automatically recognize the IoT home appliance in front of the user's gaze and suggest operations.

[0060] The generation AI unit uses the emotion estimation function to suggest operations according to the user's emotional state, thereby providing an interface that reduces stress. For example, the generation AI unit estimates the user's emotions from their facial expressions and voice, and suggests operations that will help them relax if they are highly stressed. For example, it might suggest changing the lighting to a warmer color. The generation AI unit also uses the emotion estimation function to suggest simple operations if the user is irritated. For example, it might suggest adjusting the air conditioner temperature with one touch. The generation AI unit also changes the priority of operations according to the user's emotional state. For example, if the user is tired, it might suggest playing relaxing music. This makes it possible to provide an interface that reduces stress by suggesting operations according to the user's emotional state.

[0061] The generation AI unit can analyze handwritten input from the user and execute operations based on the handwritten characters and figures. For example, if a user writes "lights on" by hand, the generation AI unit analyzes the characters and executes the operation of turning on the lights. The generation AI unit also analyzes figures drawn by the user by hand and executes the operation corresponding to that figure. For example, drawing a picture of a light bulb will turn on the lights. The generation AI unit can also use handwritten input to instruct multiple operations at once. For example, if a user handwrites "air conditioner 20 degrees, lights on," the generation AI analyzes the instructions and executes both operations. This makes it possible to perform operations based on handwritten input.

[0062] The generation AI unit can analyze the environmental sounds around the user and suggest operations based on the environmental sounds. For example, the generation AI unit can analyze the surrounding environmental sounds and suggest playing relaxing music if the environment is quiet. If the surrounding environmental sounds are noisy, the generation AI unit can suggest using a device with noise canceling functionality. The generation AI unit can also analyze the environmental sounds and, if it detects a specific sound (such as a doorbell), execute a corresponding operation. For example, it can detect a doorbell and turn on the lights. This makes it possible to suggest operations based on environmental sounds.

[0063] The generation AI unit can monitor the emotions felt by the user during operation in real time and provide feedback to elicit positive emotions. For example, the generation AI unit monitors the user's emotions in real time and displays an encouraging message if positive emotions decrease. The generation AI unit also uses an emotion estimation function to provide feedback to reduce the stress felt by the user during operation. For example, if an operation does not go well, it displays a simple operation guide. The generation AI unit also adjusts the difficulty of the operation according to the user's emotional state. For example, if the user's emotions are unstable, it will only suggest simple operations. This makes it possible to provide feedback to elicit positive emotions.

[0064] The generation AI unit can simultaneously analyze voice instructions from multiple users and operate multiple IoT home appliances in a coordinated manner. For example, if multiple users issue different voice instructions at the same time, the generation AI unit analyzes each instruction and operates the appropriate IoT home appliance. For example, if one user says "Turn on the lights" and another says "Turn off the air conditioner," both instructions will be executed simultaneously. The generation AI unit also monitors the status of each appliance in real time and performs the optimal operation to operate multiple IoT home appliances in a coordinated manner. For example, it can adjust the air conditioner and humidifier simultaneously. The generation AI unit also analyzes voice instructions from multiple users and builds a system to operate IoT home appliances in a coordinated manner. For example, it can operate the lights, air conditioner, and TV all at once based on the instructions of all family members. This allows the system to simultaneously analyze voice instructions from multiple users and operate IoT home appliances in a coordinated manner.

[0065] The generation AI unit can analyze voice instructions in different languages ​​and provide responses in multiple languages. For example, the generation AI unit analyzes voice instructions in different languages ​​and provides a response in the corresponding language. For example, if someone says "Turn on the lights" in English, the response will be in English. The generation AI unit also uses a multilingual generation AI to provide appropriate responses to users who speak different languages ​​in the home. For example, if some family members speak Japanese and others speak English, the response will be in each language. The generation AI unit also analyzes voice instructions in different languages ​​and provides responses by automatic translation. For example, if someone says "Allume la lumiere" in French, the response will be in English. This makes it possible to provide responses that correspond to voice instructions in different languages.

[0066] The generation AI unit can analyze the user's emotional response to voice instructions and suggest operations based on their emotions. For example, if the user is tired, the generation AI unit can suggest lighting settings that will help them relax. The generation AI unit also uses an emotion estimation function to analyze the user's emotional response to voice instructions and suggest operations based on their emotions. For example, if the user is angry, the generation AI unit can suggest operations that will reduce stress along with a calm response. The generation AI unit also analyzes the user's emotional response to voice instructions in real time and suggest operations based on their emotions. For example, if the user is happy, the generation AI unit can suggest positive operations. This makes it possible to suggest operations based on emotional responses.

[0067] The MR-GUI unit can analyze environmental information around the user and provide visual feedback according to the environment. For example, the MR-GUI unit analyzes environmental information around the user and provides visual feedback according to the environment. For example, it suggests adjusting the lighting according to the brightness of the room. The MR-GUI unit also analyzes environmental information around the user and provides visual feedback according to the environment. For example, it suggests air conditioner settings according to the room temperature. The MR-GUI unit also analyzes environmental information around the user and provides visual feedback according to the environment. For example, it suggests a noise canceling function according to the noise level. This makes it possible to provide visual feedback based on environmental information.

[0068] The MR-GUI unit can analyze the user's physical movements and provide feedback based on the movements. For example, the MR-GUI unit analyzes the user's physical movements and provides feedback based on the movements. For example, when the user stands up, the lights are turned on. The MR-GUI unit also analyzes the user's physical movements and provides feedback based on the movements. For example, when the user raises their hand, the temperature of the air conditioner is adjusted. The MR-GUI unit also analyzes the user's physical movements and provides feedback based on the movements. For example, when the user sits down, the volume of the television is adjusted. This makes it possible to provide feedback based on physical movements.

[0069] The MR-GUI unit can use the emotion estimation function to provide customizable feedback based on the user's emotional state. For example, the MR-GUI unit uses the emotion estimation function to provide customizable feedback based on the user's emotional state. For example, if the user is tired, the MR-GUI unit suggests an operation that will help the user relax. The MR-GUI unit also analyzes the user's emotional state in real time and provides customizable feedback based on the emotion. For example, if the user is angry, the MR-GUI unit suggests an operation that will reduce stress along with a calm response. The MR-GUI unit also uses the emotion estimation function to provide customizable feedback based on the user's emotional state. For example, if the user is happy, the MR-GUI unit suggests a positive operation. This makes it possible to provide customizable feedback based on the emotional state.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] An IoT home appliance operation system can also be equipped with a gesture recognition unit that recognizes user gestures. The gesture recognition unit detects the user's hand and body movements using a camera or sensor, and operates home appliances based on those movements. For example, a user can turn lights on and off by waving their hand. Or, when a user points, they can operate home appliances in that direction. Furthermore, by pre-setting specific gestures, the corresponding operation will be automatically executed when the user performs that gesture. This enables intuitive operation using gestures in addition to voice input.

[0072] The IoT home appliance operation system can further include an emotion estimation unit that estimates the user's emotional state. The emotion estimation unit analyzes the user's facial expression, voice tone, speed, etc. to estimate the user's emotional state. For example, if the user is tired, it can suggest operations that will help them relax. If the user is angry, it can suggest operations that will reduce stress along with a calm response. Furthermore, if the user is happy, it can suggest positive operations. This makes it possible to respond appropriately according to the user's emotional state.

[0073] The IoT home appliance operation system can further include an eye-gaze tracking unit that tracks the user's gaze. The eye-gaze tracking unit detects the direction of the user's gaze and automatically recognizes the home appliance in front of the user's gaze. For example, it can display the operation menu of an air conditioner where the user is looking, allowing the user to make a selection. It can also display the channel change menu of a television where the user is looking. It can also display the internal temperature of a refrigerator where the user is looking. This allows for intuitive operation by highlighting the information in front of the user's gaze.

[0074] The IoT home appliance operation system can further include a handwriting recognition unit that analyzes handwritten input by the user. The handwriting recognition unit analyzes the characters and figures handwritten by the user and operates the home appliance based on the content. For example, if the user writes "lights on," the operation to turn on the lights can be executed. The system can also analyze figures handwritten by the user and perform the operation corresponding to that figure. For example, if the user draws a picture of a light bulb, the lights can be turned on. Furthermore, handwritten input can be used to instruct multiple operations at once. This makes it possible to perform operations based on handwritten input.

[0075] The IoT home appliance operation system can further include an environmental sound analysis unit that analyzes the environmental sounds around the user. The environmental sound analysis unit analyzes the surrounding environmental sounds and suggests home appliance operations based on those sounds. For example, if the environment is quiet, it can suggest playing relaxing music. If the surrounding environmental sounds are noisy, it can suggest using a device with noise-canceling functionality. Furthermore, if a specific sound (e.g., a doorbell) is detected, it can execute a corresponding operation. This makes it possible to suggest operations based on environmental sounds.

[0076] The IoT home appliance operation system can also provide customizable feedback based on the user's emotional state. For example, if the user is tired, it can suggest actions to help them relax. If the user is angry, it can suggest actions to reduce stress along with a calm response. Furthermore, if the user is happy, it can suggest positive actions. This makes customizable feedback based on the user's emotional state possible.

[0077] The IoT home appliance operation system can further include a body movement analysis unit that analyzes the user's body movements. The body movement analysis unit detects the user's body movements using cameras and sensors and operates home appliances based on those movements. For example, the unit can turn on the lights when the user stands up, adjust the air conditioner temperature when the user raises their hand, and adjust the TV volume when the user sits down. This makes it possible to provide feedback based on body movements.

[0078] The IoT home appliance operation system can also monitor the user's emotional state in real time and provide feedback to elicit positive emotions. For example, if the user's emotions drop, an encouraging message can be displayed. Also, if the user is having trouble operating the device, a simple operation guide can be displayed. Furthermore, the difficulty of the operation can be adjusted according to the user's emotional state. This makes it possible to provide feedback to elicit positive emotions.

[0079] The IoT home appliance operation system can also analyze the voice instructions of multiple users simultaneously and operate multiple IoT home appliances in a coordinated manner. For example, if multiple users issue different voice instructions at the same time, it can analyze each instruction and operate the appropriate IoT home appliance. In addition, to operate multiple IoT home appliances in a coordinated manner, it can monitor the status of each appliance in real time and perform the optimal operation. Furthermore, it is possible to build a system that analyzes the voice instructions of multiple users and operates IoT home appliances in a coordinated manner. This makes it possible to analyze the voice instructions of multiple users simultaneously and operate IoT home appliances in a coordinated manner.

[0080] The IoT home appliance operation system can further analyze voice instructions in different languages ​​and provide multilingual responses. For example, it can analyze voice instructions in different languages ​​and provide responses in the corresponding language. It can also use multilingual generation AI to provide appropriate responses to users in the home who speak different languages. It can also analyze voice instructions in different languages ​​and provide responses through automatic translation. This makes it possible to provide responses that correspond to voice instructions in different languages.

[0081] The processing flow of the second embodiment will be briefly explained below.

[0082] Step 1: The voice input unit accepts the user's voice. For example, the voice input unit may collect the user's voice using a microphone. The voice input unit may also accept the voice through a smartphone or smart speaker. Furthermore, the voice input unit may use noise canceling technology to remove ambient noise and clearly accept the user's voice. Step 2: The generation AI unit analyzes the voice received by the voice input unit and generates an appropriate response. For example, the generation AI unit converts the voice into text using a text generation AI (e.g., LLM) and understands its content. The generation AI unit can also use natural language processing technology to analyze the intent of the voice and generate an appropriate response. Furthermore, the generation AI unit generates specific instructions for operating the IoT home appliance based on the user's voice instructions. Step 3: The MR-GUI unit visually displays the response generated by the generation AI unit. For example, the MR-GUI unit uses smart glasses to display the response content in the user's field of view. The MR-GUI unit can also use AR technology to overlay the response content on the real world. Furthermore, the MR-GUI unit can also use VR technology to display the response content in a virtual space.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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).

[0092] 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.

[0093] 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.

[0094] 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.

[0095] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0096] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0111] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0117] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0127] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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."

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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]

[0150] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a voice input unit that accepts a user's voice; a generation AI unit that analyzes the voice received by the voice input unit and generates an appropriate response; an MR-GUI unit that visually displays the response generated by the generation AI unit; A system characterized by:

2. The voice input unit Analyzes the user's tone and speed of speech to generate responses that correspond to the user's urgency and emotional state 2. The system of claim 1.

3. The generation AI unit Learns from a user's past voice commands and provides responses based on the user's preferences and patterns 2. The system of claim 1.

4. The MR-GUI unit Supports intuitive operation by tracking the user's gaze and highlighting the information in front of the gaze.

2. The system of claim 1.

5. The MR-GUI unit Tracks the user's hand movements and provides interactive feedback based on hand movements 2. The system of claim 1.

6. The MR-GUI unit Providing feedback according to the user's emotional state to improve operational satisfaction 2. The system of claim 1.

7. The generation AI unit Analyze user gestures in real time and perform gesture-based operations 2. The system of claim 1.

8. The generation AI unit Tracks the user's gaze, automatically recognizes the IoT appliance in front of the user's eyes, and suggests operations.

2. The system of claim 1.

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