System

Abstract

An object of the system according to the embodiment is to allow a visually handicapped person to express a rich feeling.SOLUTION: A system includes a voice color analysis part, a face muscle analysis part, an expression generation part, and a display part. The tone of voice analysis part analyzes the tone of voice of the user. The facial muscle analyzer analyzes a degree of tension of facial muscles of the user. The expression generation part generates an appropriate expression on the basis of analysis results of the voice color analysis part and the face muscle analysis part. The display part displays the expression generated by the expression generation part on the glasses.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult for visually impaired people to express a wide range of emotions.

[0005] The system according to the embodiment aims to enable visually impaired people to express a wide range of emotions. [Means for solving the problem]

[0006] The system according to the embodiment includes a voice tone analysis unit, a facial muscle analysis unit, a facial expression generation unit, and a display unit. The voice tone analysis unit analyzes the voice tone of the user. The facial muscle analysis unit analyzes the tension of the user's facial muscles. The facial expression generation unit generates an appropriate facial expression based on the analysis results of the voice tone analysis unit and the facial muscle analysis unit. The display unit displays the facial expression generated by the facial expression generation unit on the glasses. [Effects of the Invention]

[0007] The system according to the embodiment allows visually impaired people to express a wide range of emotions. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The AI ​​glasses of the present invention are a system that analyzes the user's tone of voice and facial muscle tension, and displays appropriate facial expressions (especially eye expressions) based on the results. This allows the visually impaired to convey a richer range of emotions to others.

[0029] The AI ​​glasses according to the embodiment include a voice timbre analysis unit, a facial muscle analysis unit, a facial expression generation unit, and a display unit. The voice timbre analysis unit analyzes the user's voice timbre. For example, the voice timbre analysis unit analyzes the tone, pitch, and rhythm of the user's voice in real time to estimate the user's emotional state. The voice timbre analysis unit can also analyze the user's emotional state based on voice data using a generation AI (e.g., a text generation AI or a multimodal generation AI). For example, the generation AI receives voice data as input and outputs the user's emotional state. The facial muscle analysis unit analyzes the stiffness of the user's facial muscles. For example, the facial muscle analysis unit acquires facial image data using a camera and analyzes the muscle movement and stiffness using the generation AI. The facial muscle analysis unit can also analyze the user's emotional state based on the image data using the generation AI. For example, the generation AI receives image data as input and outputs the user's emotional state. The facial expression generation unit generates an appropriate facial expression based on the analysis results of the voice timbre analysis unit and the facial muscle analysis unit. For example, the facial expression generation unit uses the generation AI to generate eye expressions appropriate to the user's emotional state. The facial expression generation unit can also use a generation AI to generate eye expressions with a focus on the expression of the eyes. For example, the generation AI receives an emotional state as input and outputs an eye expression. The display unit displays the facial expression generated by the facial expression generation unit on the glasses. For example, the display unit uses augmented reality (AR) technology to display the facial expression on the glasses' display. The display unit can also display the facial expression in an appropriate manner depending on the type of display. For example, the display unit uses a transparent display to display the eye expression in a natural manner. This allows the AI ​​glasses according to the embodiment to enable visually impaired people to communicate richer emotions to others. For example, when a user feels joy, the AI ​​glasses can visually express that joy and convey that emotion to others. Also, when a user feels sad, the AI ​​glasses can visually express that sadness and convey that emotion to others. This is expected to enable visually impaired people to communicate with others more smoothly and deepen social connections.

[0030] The voice timbre analysis unit can simultaneously analyze the user's breathing patterns, improving the accuracy of the emotional state. For example, the voice timbre analysis unit analyzes the user's breathing patterns simultaneously with their voice timbre to more accurately estimate their emotional state. For example, shallow, rapid breathing indicates tension or excitement, while deep, slow breathing indicates relaxation or calmness. The voice timbre analysis unit also analyzes the correlation between changes in voice timbre and breathing patterns to track emotional transitions in real time. For example, rising voice tone and rapid breathing indicate excitement or joy. The voice timbre analysis unit also collects breathing sounds in addition to audio data to analyze breathing patterns, and the generation AI uses that data to analyze the emotional state. For example, it analyzes the rhythm and intensity of breathing sounds. This improves the accuracy of the emotional state.

[0031] The vocal timbre analysis unit can track changes in vocal timbre in real time and dynamically display changes in emotions. The vocal timbre analysis unit, for example, analyzes changes in a user's vocal timbre in real time and builds a system that dynamically displays changes in emotions. For example, it displays changes in vocal tone and pitch in a graph. The vocal timbre analysis unit also tracks changes in vocal timbre in real time and visually displays changes in emotions. For example, it shows changes in emotions using colors or icons. The vocal timbre analysis unit also analyzes changes in vocal timbre in real time and develops an interface for dynamically displaying changes in emotions. For example, it changes facial expression icons according to the user's vocal timbre. This makes it possible to display changes in emotions in real time.

[0032] The vocal tone analysis unit can use the emotion estimation function to suggest appropriate music or environmental sounds based on emotions estimated from the user's vocal tone. The vocal tone analysis unit, for example, builds a system that suggests appropriate music based on emotions estimated from the user's vocal tone. For example, if the user has a relaxed vocal tone, it suggests calm music. The vocal tone analysis unit also suggests environmental sounds according to the user's emotional state based on the results of the vocal tone analysis. For example, if the user is feeling stressed, it suggests natural sounds. The vocal tone analysis unit also uses the emotion estimation function to develop a system that automatically plays appropriate music or environmental sounds based on emotions estimated from the user's vocal tone. For example, if the user is feeling happy, it plays cheerful music. This makes it possible to suggest music or environmental sounds according to emotions.

[0033] The voice tone analysis unit can display the voice tone analysis results in association with the user's health condition. The voice tone analysis unit, for example, builds a system that displays the user's health condition (stress level and fatigue level) based on the voice tone analysis results. For example, it estimates the stress level from changes in voice tone. The voice tone analysis unit also displays the voice tone analysis results in association with the health condition. For example, a lower voice tone indicates a higher stress level. The voice tone analysis unit also develops a system that monitors the user's health condition in real time based on the voice tone analysis results and displays the stress level and fatigue level. For example, the health condition is displayed according to changes in voice tone. This allows the user to understand the health condition.

[0034] The vocal tone analysis unit can use the emotion estimation function to automatically adjust aromas and lighting based on emotions estimated from the user's vocal tone. The vocal tone analysis unit, for example, builds a system that automatically adjusts appropriate aromas based on emotions estimated from the user's vocal tone. For example, a lavender aroma is used for a relaxed vocal tone. The vocal tone analysis unit also automatically adjusts lighting according to the user's emotional state based on the results of the vocal tone analysis. For example, warm lighting is used for stress. The vocal tone analysis unit also uses the emotion estimation function to develop a system that automatically adjusts appropriate aromas and lighting based on emotions estimated from the user's vocal tone. For example, bright lighting is used for joy. This makes it possible to automatically adjust aromas and lighting according to emotions.

[0035] The facial muscle analysis unit can use a high-resolution camera to capture subtle changes in facial expression. For example, the facial muscle analysis unit uses a high-resolution camera to build a system that captures subtle changes in facial expression. For example, it analyzes subtle movements of the eyebrows and changes in the corners of the mouth. The facial muscle analysis unit also uses a high-resolution camera to analyze in detail the degree of tension in facial muscles. For example, it tracks facial muscle movements in real time. The facial muscle analysis unit also uses a high-resolution camera to develop an algorithm for capturing subtle changes in facial expression. For example, it analyzes facial muscle movements with high accuracy. This makes it possible to capture subtle changes in facial expression with high accuracy.

[0036] The facial muscle analysis unit reconstructs facial muscle movements as a 3D model, enabling more detailed emotion analysis. The facial muscle analysis unit, for example, reconstructs facial muscle movements as a 3D model and builds a system for performing detailed emotion analysis. For example, the facial muscle movements are visualized using a 3D model. The facial muscle analysis unit also reconstructs facial muscle movements as a 3D model to improve the accuracy of emotion analysis. For example, the facial muscle movements are analyzed using a 3D model. The facial muscle analysis unit also reconstructs facial muscle movements as a 3D model and develops an algorithm for performing detailed emotion analysis. For example, the facial muscle movements are analyzed using a 3D model. This enables detailed emotion analysis.

[0037] The facial muscle analysis unit can use the emotion estimation function to suggest a relaxation method based on emotions estimated from the degree of tension in the user's facial muscles. The facial muscle analysis unit, for example, builds a system that suggests relaxation methods based on emotions estimated from the degree of tension in the user's facial muscles. For example, if the user is feeling stressed, it suggests deep breathing. The facial muscle analysis unit also analyzes the degree of tension in the facial muscles and suggests a relaxation method according to the user's emotional state. For example, if the user is feeling tense, it suggests meditation. The facial muscle analysis unit also uses the emotion estimation function to develop a system that automatically suggests relaxation methods based on emotions estimated from the degree of tension in the user's facial muscles. For example, if the user is feeling tired, it suggests stretching. This makes it possible to suggest relaxation methods.

[0038] The facial muscle analysis unit can utilize the analysis results of the facial muscle tension in facial expression training for the user to improve their emotional expression skills. The facial muscle analysis unit, for example, builds a system for facial expression training for the user based on the analysis results of the facial muscle tension. For example, it proposes a facial muscle training method. The facial muscle analysis unit also utilizes the analysis results of the facial muscle tension to develop a training program for improving the user's emotional expression skills. For example, it proposes a facial muscle training method. The facial muscle analysis unit also develops an interface for facial expression training for the user based on the analysis results of the facial muscle tension. For example, it proposes a facial muscle training method. This improves the emotional expression skills.

[0039] The facial muscle analysis unit can utilize the analysis results of the facial muscle stiffness for psychological counseling of the user, thereby improving the emotional state. The facial muscle analysis unit, for example, builds a system for psychological counseling of the user based on the analysis results of the facial muscle stiffness. For example, it proposes methods for reducing stress and anxiety. The facial muscle analysis unit also utilizes the analysis results of the facial muscle stiffness to develop a counseling program for improving the user's emotional state. For example, it proposes relaxation methods. The facial muscle analysis unit also develops an interface for psychological counseling of the user based on the analysis results of the facial muscle stiffness. For example, it proposes methods for reducing stress and anxiety. This improves the emotional state.

[0040] The facial muscle analysis unit can use the emotion estimation function to provide appropriate feedback based on emotions estimated from the degree of tension in the user's facial muscles. The facial muscle analysis unit, for example, builds a system that provides appropriate feedback based on emotions estimated from the degree of tension in the user's facial muscles. For example, it provides advice on how to relax. The facial muscle analysis unit also analyzes the degree of tension in the facial muscles and provides feedback according to the user's emotional state. For example, it suggests relaxation methods if the user is feeling stressed. The facial muscle analysis unit also uses the emotion estimation function to develop a system that automatically provides appropriate feedback based on emotions estimated from the degree of tension in the user's facial muscles. For example, it suggests rest if the user is feeling tired. This makes it possible to provide appropriate feedback.

[0041] The facial expression generation unit can learn the user's individual facial expression patterns and generate more personalized facial expressions. The facial expression generation unit, for example, learns the user's individual facial expression patterns and builds a system that generates personalized facial expressions. For example, facial expressions are generated based on the user's past facial expression data. The facial expression generation unit also learns the individual facial expression patterns and generates personalized facial expressions according to the user's emotional state. For example, it reproduces the user's characteristic smile or eyebrow movements. The facial expression generation unit also learns the user's individual facial expression patterns so that the generated facial expressions more accurately reflect the user's emotions. For example, it generates facial expressions that correspond to the user's specific emotions. This makes it possible to generate personalized facial expressions.

[0042] The facial expression generation unit can automatically select an appropriate facial expression according to the background and environment when generating a facial expression. For example, the facial expression generation unit builds a system that automatically selects an appropriate facial expression according to the user's background and environment when generating a facial expression. For example, if the user is outdoors, a bright facial expression is selected. The facial expression generation unit also develops an algorithm for automatically selecting an facial expression according to the background and environment. For example, if the user is in a meeting, a serious facial expression is selected. The facial expression generation unit also develops a system that analyzes background and environmental information when generating a facial expression and automatically selects an appropriate facial expression. For example, if the user is in a relaxed environment, a calm facial expression is selected. This makes it possible to automatically select an appropriate facial expression according to the background and environment.

[0043] The facial expression generation unit can link with other devices (smartphones and tablets) and display the facial expression generation results on multiple devices. For example, the facial expression generation unit builds a system that links with smartphones and tablets and displays the facial expression generation results on multiple devices. For example, the generated facial expression is displayed on a smartphone screen. The facial expression generation unit also links with other devices to allow users to check the facial expression on multiple devices. For example, the generated facial expression is displayed on a tablet screen. The facial expression generation unit also develops an interface for linking with other devices and displaying the facial expression generation results on multiple devices. For example, the generated facial expression is displayed on a smartphone or tablet in real time. This allows the facial expressions to be displayed on multiple devices.

[0044] The facial expression generation unit reflects the facial expression generation result in the user's avatar, thereby improving emotional expression in the virtual reality environment. The facial expression generation unit, for example, builds a system that reflects the facial expression generation result in the user's avatar and improves emotional expression in the virtual reality environment. For example, the facial expression generation unit changes the avatar's facial expression in real time in VR chat. The facial expression generation unit also develops an algorithm for reflecting the facial expression generation result in the avatar and improving emotional expression in the virtual reality environment. For example, the facial expression generation unit changes the avatar's facial expression according to the user's emotion. The facial expression generation unit also develops an interface for reflecting the facial expression generation result in the user's avatar and improving emotional expression in the virtual reality environment. For example, the avatar's facial expression is displayed in real time through a VR headset. This improves emotional expression in the virtual reality environment.

[0045] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0046] The AI ​​glasses can also be equipped with a temperature measurement unit that measures the user's body temperature. For example, the temperature measurement unit can measure the temperature of the user's forehead or ears without contact and acquire body temperature data. The temperature measurement unit can monitor the user's health condition based on the body temperature data and display an alert if an abnormality is detected. For example, if the body temperature is high, it can indicate the possibility of a fever and encourage the user to rest. The temperature measurement unit can also combine the body temperature data with other health data to evaluate the user's overall health condition. This can support the user's health management.

[0047] AI glasses can also be equipped with a gait analysis unit that analyzes the user's walking pattern. For example, the gait analysis unit analyzes the user's walking speed, stride length, and balance in real time to acquire walking data. The gait analysis unit can monitor the user's exercise status based on the walking data and display an alert if an abnormality is detected. For example, if the user's gait is unstable, it can indicate the risk of falling and warn the user. The gait analysis unit can also combine the walking data with other health data to evaluate the user's overall exercise status. This can support the user's exercise management.

[0048] The AI ​​glasses can also be equipped with a diet analysis unit that analyzes the user's eating patterns. For example, the diet analysis unit analyzes the content and amount of food the user eats to obtain dietary data. The diet analysis unit can monitor the user's nutritional status based on the dietary data and suggest balanced meals. For example, if a user is deficient in a particular nutrient, the diet analysis unit can suggest foods containing that nutrient. The diet analysis unit can also combine the dietary data with other health data to evaluate the user's overall nutritional status. This can support the user's nutritional management.

[0049] AI glasses can also be equipped with a sleep analysis unit that analyzes the user's sleep patterns. The sleep analysis unit, for example, analyzes the user's sleep time and quality to acquire sleep data. The sleep analysis unit can monitor the user's sleep state based on the sleep data and provide advice if improvement is necessary. For example, if the user's sleep is shallow, it can suggest relaxation methods. The sleep analysis unit can also combine the sleep data with other health data to evaluate the user's overall sleep state. This can support the user's sleep management.

[0050] The AI ​​glasses can also be equipped with a stress analysis unit that analyzes the user's stress level. The stress analysis unit acquires stress data by, for example, analyzing the user's heart rate and electrodermal activity. The stress analysis unit can monitor the user's stress state based on the stress data and suggest relaxation methods. For example, if stress is high, the stress analysis unit can suggest deep breathing or meditation. The stress analysis unit can also combine the stress data with other health data to evaluate the user's overall stress state. This can support the user's stress management.

[0051] The processing flow of the first embodiment will be briefly explained below.

[0052] Step 1: The voice analysis unit analyzes the user's voice. For example, the voice analysis unit analyzes the user's voice tone, pitch, and rhythm in real time to estimate their emotional state. Generative AI can also be used to analyze their emotional state based on voice data. Step 2: The facial muscle analysis unit analyzes the tension of the user's facial muscles. For example, a camera is used to capture image data of the face, and the generation AI is used to analyze the muscle movement and tension. The generation AI can also be used to analyze the user's emotional state based on the image data. Step 3: The facial expression generator generates an appropriate facial expression based on the analysis results of the voice analysis unit and facial muscle analysis unit. For example, it uses a generation AI to generate eye expressions that suit the user's emotional state. Step 4: The display unit displays the facial expression generated by the facial expression generation unit on the glasses. For example, the facial expression is displayed on the display of the glasses using AR technology.

[0053] (Example 2) The AI ​​glasses of the present invention are a system that analyzes the user's tone of voice and facial muscle tension, and displays appropriate facial expressions (especially eye expressions) based on the results. This allows the visually impaired to convey a richer range of emotions to others.

[0054] The AI ​​glasses according to the embodiment include a voice timbre analysis unit, a facial muscle analysis unit, a facial expression generation unit, and a display unit. The voice timbre analysis unit analyzes the user's voice timbre. For example, the voice timbre analysis unit analyzes the tone, pitch, and rhythm of the user's voice in real time to estimate the user's emotional state. The voice timbre analysis unit can also analyze the user's emotional state based on voice data using a generation AI (e.g., a text generation AI or a multimodal generation AI). For example, the generation AI receives voice data as input and outputs the user's emotional state. The facial muscle analysis unit analyzes the stiffness of the user's facial muscles. For example, the facial muscle analysis unit acquires facial image data using a camera and analyzes the muscle movement and stiffness using the generation AI. The facial muscle analysis unit can also analyze the user's emotional state based on the image data using the generation AI. For example, the generation AI receives image data as input and outputs the user's emotional state. The facial expression generation unit generates an appropriate facial expression based on the analysis results of the voice timbre analysis unit and the facial muscle analysis unit. For example, the facial expression generation unit uses the generation AI to generate eye expressions appropriate to the user's emotional state. The facial expression generation unit can also use a generation AI to generate eye expressions with a focus on the expression of the eyes. For example, the generation AI receives an emotional state as input and outputs an eye expression. The display unit displays the facial expression generated by the facial expression generation unit on the glasses. For example, the display unit uses augmented reality (AR) technology to display the facial expression on the glasses' display. The display unit can also display the facial expression in an appropriate manner depending on the type of display. For example, the display unit uses a transparent display to display the eye expression in a natural manner. This allows the AI ​​glasses according to the embodiment to enable visually impaired people to communicate richer emotions to others. For example, when a user feels joy, the AI ​​glasses can visually express that joy and convey that emotion to others. Also, when a user feels sad, the AI ​​glasses can visually express that sadness and convey that emotion to others. This is expected to enable visually impaired people to communicate with others more smoothly and deepen social connections.

[0055] The voice timbre analysis unit can simultaneously analyze the user's breathing patterns, improving the accuracy of the emotional state. For example, the voice timbre analysis unit analyzes the user's breathing patterns simultaneously with their voice timbre to more accurately estimate their emotional state. For example, shallow, rapid breathing indicates tension or excitement, while deep, slow breathing indicates relaxation or calmness. The voice timbre analysis unit also analyzes the correlation between changes in voice timbre and breathing patterns to track emotional transitions in real time. For example, rising voice tone and rapid breathing indicate excitement or joy. The voice timbre analysis unit also collects breathing sounds in addition to audio data to analyze breathing patterns, and the generation AI uses that data to analyze the emotional state. For example, it analyzes the rhythm and intensity of breathing sounds. This improves the accuracy of the emotional state.

[0056] The vocal timbre analysis unit can track changes in vocal timbre in real time and dynamically display changes in emotions. The vocal timbre analysis unit, for example, analyzes changes in a user's vocal timbre in real time and builds a system that dynamically displays changes in emotions. For example, it displays changes in vocal tone and pitch in a graph. The vocal timbre analysis unit also tracks changes in vocal timbre in real time and visually displays changes in emotions. For example, it shows changes in emotions using colors or icons. The vocal timbre analysis unit also analyzes changes in vocal timbre in real time and develops an interface for dynamically displaying changes in emotions. For example, it changes facial expression icons according to the user's vocal timbre. This makes it possible to display changes in emotions in real time.

[0057] The vocal tone analysis unit can use the emotion estimation function to suggest appropriate music or environmental sounds based on emotions estimated from the user's vocal tone. The vocal tone analysis unit, for example, builds a system that suggests appropriate music based on emotions estimated from the user's vocal tone. For example, if the user has a relaxed vocal tone, it suggests calm music. The vocal tone analysis unit also suggests environmental sounds according to the user's emotional state based on the results of the vocal tone analysis. For example, if the user is feeling stressed, it suggests natural sounds. The vocal tone analysis unit also uses the emotion estimation function to develop a system that automatically plays appropriate music or environmental sounds based on emotions estimated from the user's vocal tone. For example, if the user is feeling happy, it plays cheerful music. This makes it possible to suggest music or environmental sounds according to emotions.

[0058] The vocal tone analysis unit can add a social function that shares the results of vocal tone analysis with other users and promotes emotional empathy. The vocal tone analysis unit, for example, develops a social function that shares the results of vocal tone analysis with other users and promotes emotional empathy. For example, by sharing one's emotional state, one can receive empathy and encouragement from other users. The vocal tone analysis unit also adds a function that posts the results of vocal tone analysis on social media and shares emotions with other users. For example, one can share one's emotional state in real time. The vocal tone analysis unit also builds a platform for sharing emotions with other users based on the results of vocal tone analysis. For example, by sharing one's emotional state, one can receive empathy and support. This can promote emotional empathy.

[0059] The voice tone analysis unit can display the voice tone analysis results in association with the user's health condition. The voice tone analysis unit, for example, builds a system that displays the user's health condition (stress level and fatigue level) based on the voice tone analysis results. For example, it estimates the stress level from changes in voice tone. The voice tone analysis unit also displays the voice tone analysis results in association with the health condition. For example, a lower voice tone indicates a higher stress level. The voice tone analysis unit also develops a system that monitors the user's health condition in real time based on the voice tone analysis results and displays the stress level and fatigue level. For example, the health condition is displayed according to changes in voice tone. This allows the user to understand the health condition.

[0060] The vocal tone analysis unit can use the emotion estimation function to automatically adjust aromas and lighting based on emotions estimated from the user's vocal tone. The vocal tone analysis unit, for example, builds a system that automatically adjusts appropriate aromas based on emotions estimated from the user's vocal tone. For example, a lavender aroma is used for a relaxed vocal tone. The vocal tone analysis unit also automatically adjusts lighting according to the user's emotional state based on the results of the vocal tone analysis. For example, warm lighting is used for stress. The vocal tone analysis unit also uses the emotion estimation function to develop a system that automatically adjusts appropriate aromas and lighting based on emotions estimated from the user's vocal tone. For example, bright lighting is used for joy. This makes it possible to automatically adjust aromas and lighting according to emotions.

[0061] The facial muscle analysis unit can use a high-resolution camera to capture subtle changes in facial expression. For example, the facial muscle analysis unit uses a high-resolution camera to build a system that captures subtle changes in facial expression. For example, it analyzes subtle movements of the eyebrows and changes in the corners of the mouth. The facial muscle analysis unit also uses a high-resolution camera to analyze in detail the degree of tension in facial muscles. For example, it tracks facial muscle movements in real time. The facial muscle analysis unit also uses a high-resolution camera to develop an algorithm for capturing subtle changes in facial expression. For example, it analyzes facial muscle movements with high accuracy. This makes it possible to capture subtle changes in facial expression with high accuracy.

[0062] The facial muscle analysis unit reconstructs facial muscle movements as a 3D model, enabling more detailed emotion analysis. The facial muscle analysis unit, for example, reconstructs facial muscle movements as a 3D model and builds a system for performing detailed emotion analysis. For example, the facial muscle movements are visualized using a 3D model. The facial muscle analysis unit also reconstructs facial muscle movements as a 3D model to improve the accuracy of emotion analysis. For example, the facial muscle movements are analyzed using a 3D model. The facial muscle analysis unit also reconstructs facial muscle movements as a 3D model and develops an algorithm for performing detailed emotion analysis. For example, the facial muscle movements are analyzed using a 3D model. This enables detailed emotion analysis.

[0063] The facial muscle analysis unit can use the emotion estimation function to suggest a relaxation method based on emotions estimated from the degree of tension in the user's facial muscles. The facial muscle analysis unit, for example, builds a system that suggests relaxation methods based on emotions estimated from the degree of tension in the user's facial muscles. For example, if the user is feeling stressed, it suggests deep breathing. The facial muscle analysis unit also analyzes the degree of tension in the facial muscles and suggests a relaxation method according to the user's emotional state. For example, if the user is feeling tense, it suggests meditation. The facial muscle analysis unit also uses the emotion estimation function to develop a system that automatically suggests relaxation methods based on emotions estimated from the degree of tension in the user's facial muscles. For example, if the user is feeling tired, it suggests stretching. This makes it possible to suggest relaxation methods.

[0064] The facial muscle analysis unit can utilize the analysis results of the facial muscle tension in facial expression training for the user to improve their emotional expression skills. The facial muscle analysis unit, for example, builds a system for facial expression training for the user based on the analysis results of the facial muscle tension. For example, it proposes a facial muscle training method. The facial muscle analysis unit also utilizes the analysis results of the facial muscle tension to develop a training program for improving the user's emotional expression skills. For example, it proposes a facial muscle training method. The facial muscle analysis unit also develops an interface for facial expression training for the user based on the analysis results of the facial muscle tension. For example, it proposes a facial muscle training method. This improves the emotional expression skills.

[0065] The facial muscle analysis unit can utilize the analysis results of the facial muscle stiffness for psychological counseling of the user, thereby improving the emotional state. The facial muscle analysis unit, for example, builds a system for psychological counseling of the user based on the analysis results of the facial muscle stiffness. For example, it proposes methods for reducing stress and anxiety. The facial muscle analysis unit also utilizes the analysis results of the facial muscle stiffness to develop a counseling program for improving the user's emotional state. For example, it proposes relaxation methods. The facial muscle analysis unit also develops an interface for psychological counseling of the user based on the analysis results of the facial muscle stiffness. For example, it proposes methods for reducing stress and anxiety. This improves the emotional state.

[0066] The facial muscle analysis unit can use the emotion estimation function to provide appropriate feedback based on emotions estimated from the degree of tension in the user's facial muscles. The facial muscle analysis unit, for example, builds a system that provides appropriate feedback based on emotions estimated from the degree of tension in the user's facial muscles. For example, it provides advice on how to relax. The facial muscle analysis unit also analyzes the degree of tension in the facial muscles and provides feedback according to the user's emotional state. For example, it suggests relaxation methods if the user is feeling stressed. The facial muscle analysis unit also uses the emotion estimation function to develop a system that automatically provides appropriate feedback based on emotions estimated from the degree of tension in the user's facial muscles. For example, it suggests rest if the user is feeling tired. This makes it possible to provide appropriate feedback.

[0067] The facial expression generation unit can learn the user's individual facial expression patterns and generate more personalized facial expressions. The facial expression generation unit, for example, learns the user's individual facial expression patterns and builds a system that generates personalized facial expressions. For example, facial expressions are generated based on the user's past facial expression data. The facial expression generation unit also learns the individual facial expression patterns and generates personalized facial expressions according to the user's emotional state. For example, it reproduces the user's characteristic smile or eyebrow movements. The facial expression generation unit also learns the user's individual facial expression patterns so that the generated facial expressions more accurately reflect the user's emotions. For example, it generates facial expressions that correspond to the user's specific emotions. This makes it possible to generate personalized facial expressions.

[0068] The facial expression generation unit can automatically select an appropriate facial expression according to the background and environment when generating a facial expression. For example, the facial expression generation unit builds a system that automatically selects an appropriate facial expression according to the user's background and environment when generating a facial expression. For example, if the user is outdoors, a bright facial expression is selected. The facial expression generation unit also develops an algorithm for automatically selecting an facial expression according to the background and environment. For example, if the user is in a meeting, a serious facial expression is selected. The facial expression generation unit also develops a system that analyzes background and environmental information when generating a facial expression and automatically selects an appropriate facial expression. For example, if the user is in a relaxed environment, a calm facial expression is selected. This makes it possible to automatically select an appropriate facial expression according to the background and environment.

[0069] The facial expression generation unit uses the emotion estimation function to generate a facial expression according to the emotional state of the user, thereby facilitating communication with others. The facial expression generation unit, for example, uses the emotion estimation function to build a system that generates a facial expression according to the emotional state of the user. For example, if the user is feeling happy, a smile is generated. The facial expression generation unit also generates a facial expression according to the emotional state of the user, thereby developing an interface for facilitating communication with others. For example, a facial expression icon according to the emotion is displayed. The facial expression generation unit also uses the emotion estimation function to automatically generate a facial expression according to the emotional state of the user, thereby developing a system for facilitating communication with others. For example, a facial expression according to the emotion is displayed in real time. This facilitates communication with others.

[0070] The facial expression generation unit can link with other devices (smartphones and tablets) and display the facial expression generation results on multiple devices. For example, the facial expression generation unit builds a system that links with smartphones and tablets and displays the facial expression generation results on multiple devices. For example, the generated facial expression is displayed on a smartphone screen. The facial expression generation unit also links with other devices to allow users to check the facial expression on multiple devices. For example, the generated facial expression is displayed on a tablet screen. The facial expression generation unit also develops an interface for linking with other devices and displaying the facial expression generation results on multiple devices. For example, the generated facial expression is displayed on a smartphone or tablet in real time. This allows the facial expressions to be displayed on multiple devices.

[0071] The facial expression generation unit reflects the facial expression generation result in the user's avatar, thereby improving emotional expression in the virtual reality environment. The facial expression generation unit, for example, builds a system that reflects the facial expression generation result in the user's avatar and improves emotional expression in the virtual reality environment. For example, the facial expression generation unit changes the avatar's facial expression in real time in VR chat. The facial expression generation unit also develops an algorithm for reflecting the facial expression generation result in the avatar and improving emotional expression in the virtual reality environment. For example, the facial expression generation unit changes the avatar's facial expression according to the user's emotion. The facial expression generation unit also develops an interface for reflecting the facial expression generation result in the user's avatar and improving emotional expression in the virtual reality environment. For example, the avatar's facial expression is displayed in real time through a VR headset. This improves emotional expression in the virtual reality environment.

[0072] The facial expression generation unit uses the emotion estimation function to generate facial expressions based on the user's emotional state, thereby supporting emotional expression in online conferences and video chats. The facial expression generation unit, for example, uses the emotion estimation function to generate facial expressions based on the user's emotional state, thereby building a system that supports emotional expression in online conferences and video chats. For example, the facial expressions generated during a video chat are displayed. The facial expression generation unit also generates facial expressions based on the user's emotional state, thereby developing an interface for supporting emotional expression in online conferences and video chats. For example, a facial expression icon corresponding to the emotion is displayed. The facial expression generation unit also uses the emotion estimation function to automatically generate facial expressions based on the user's emotional state, thereby developing a system that supports emotional expression in online conferences and video chats. For example, a facial expression corresponding to the emotion is displayed in real time. This makes it possible to support emotional expression in online conferences and video chats.

[0073] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0074] The AI ​​glasses can also be equipped with a temperature measurement unit that measures the user's body temperature. For example, the temperature measurement unit can measure the temperature of the user's forehead or ears without contact and acquire body temperature data. The temperature measurement unit can monitor the user's health condition based on the body temperature data and display an alert if an abnormality is detected. For example, if the body temperature is high, it can indicate the possibility of a fever and encourage the user to rest. The temperature measurement unit can also combine the body temperature data with other health data to evaluate the user's overall health condition. This can support the user's health management.

[0075] AI glasses can also be equipped with a gait analysis unit that analyzes the user's walking pattern. For example, the gait analysis unit analyzes the user's walking speed, stride length, and balance in real time to acquire walking data. The gait analysis unit can monitor the user's exercise status based on the walking data and display an alert if an abnormality is detected. For example, if the user's gait is unstable, it can indicate the risk of falling and warn the user. The gait analysis unit can also combine the walking data with other health data to evaluate the user's overall exercise status. This can support the user's exercise management.

[0076] The AI ​​glasses can also be equipped with a diet analysis unit that analyzes the user's eating patterns. For example, the diet analysis unit analyzes the content and amount of food the user eats to obtain dietary data. The diet analysis unit can monitor the user's nutritional status based on the dietary data and suggest balanced meals. For example, if a user is deficient in a particular nutrient, the diet analysis unit can suggest foods containing that nutrient. The diet analysis unit can also combine the dietary data with other health data to evaluate the user's overall nutritional status. This can support the user's nutritional management.

[0077] AI glasses can also be equipped with a sleep analysis unit that analyzes the user's sleep patterns. The sleep analysis unit, for example, analyzes the user's sleep time and quality to acquire sleep data. The sleep analysis unit can monitor the user's sleep state based on the sleep data and provide advice if improvement is necessary. For example, if the user's sleep is shallow, it can suggest relaxation methods. The sleep analysis unit can also combine the sleep data with other health data to evaluate the user's overall sleep state. This can support the user's sleep management.

[0078] The AI ​​glasses can also be equipped with a stress analysis unit that analyzes the user's stress level. The stress analysis unit acquires stress data by, for example, analyzing the user's heart rate and electrodermal activity. The stress analysis unit can monitor the user's stress state based on the stress data and suggest relaxation methods. For example, if stress is high, the stress analysis unit can suggest deep breathing or meditation. The stress analysis unit can also combine the stress data with other health data to evaluate the user's overall stress state. This can support the user's stress management.

[0079] The AI ​​glasses can also provide appropriate feedback based on the user's emotional state. For example, if the user is tense, they can provide advice on how to relax. If the user is feeling happy, they can suggest ways to share that joy. Furthermore, if the user is feeling sad, they can suggest activities to improve their mood. In this way, they can provide feedback according to the user's emotional state and maintain emotional balance.

[0080] The AI ​​glasses can also suggest appropriate music and environmental sounds based on the user's emotional state. For example, if the user wants to relax, they can suggest calming music. If the user wants to concentrate, they can suggest environmental sounds to help with concentration. Furthermore, if the user wants to cheer up, they can suggest lively music. This allows them to suggest music and environmental sounds that match the user's emotional state and maintain emotional balance.

[0081] The AI ​​glasses can also automatically adjust the appropriate aroma and lighting based on the user's emotional state. For example, if the user wants to relax, they can use lavender aroma. If the user wants to concentrate, they can use lighting to enhance concentration. Furthermore, if the user wants to feel energized, they can use bright lighting. In this way, the glasses can automatically adjust the aroma and lighting according to the user's emotional state and maintain emotional balance.

[0082] The AI ​​glasses can also suggest appropriate relaxation methods based on the user's emotional state. For example, if the user is feeling stressed, they will suggest deep breathing. If the user is tense, they will suggest meditation. If the user is feeling tired, they can even suggest stretching. This allows the glasses to suggest relaxation methods according to the user's emotional state and maintain emotional balance.

[0083] The AI ​​glasses can also provide appropriate feedback based on the user's emotional state. For example, they can provide advice on how to relax. If the user is feeling stressed, they can suggest relaxation methods. If the user is feeling tired, they can even suggest resting. This allows them to provide feedback according to the user's emotional state and maintain emotional balance.

[0084] The processing flow of the second embodiment will be briefly explained below.

[0085] Step 1: The voice analysis unit analyzes the user's voice. For example, the voice analysis unit analyzes the user's voice tone, pitch, and rhythm in real time to estimate their emotional state. Generative AI can also be used to analyze their emotional state based on voice data. Step 2: The facial muscle analysis unit analyzes the tension of the user's facial muscles. For example, a camera is used to capture image data of the face, and the generation AI is used to analyze the muscle movement and tension. The generation AI can also be used to analyze the user's emotional state based on the image data. Step 3: The facial expression generator generates an appropriate facial expression based on the analysis results of the voice analysis unit and facial muscle analysis unit. For example, it uses a generation AI to generate eye expressions that suit the user's emotional state. Step 4: The display unit displays the facial expression generated by the facial expression generation unit on the glasses. For example, the facial expression is displayed on the display of the glasses using AR technology.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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).

[0095] 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.

[0096] 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.

[0097] 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.

[0098] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0099] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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).

[0110] 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.

[0111] 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.

[0112] 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.

[0113] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0114] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0120] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0130] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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."

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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]

[0153] 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 tone analysis unit that analyzes the voice tone of a user; a facial muscle analysis unit that analyzes the degree of tension in the user's facial muscles; a facial expression generating unit that generates an appropriate facial expression based on the analysis results of the voice timbre analyzing unit and the facial muscle analyzing unit; a display unit that displays the facial expression generated by the facial expression generation unit on glasses. A system characterized by:

2. The voice timbre analysis unit The user's breathing patterns are also analyzed simultaneously to improve the accuracy of their emotional state.

2. The system of claim 1.

3. The facial muscle analysis unit Use a high-resolution camera to capture subtle changes in facial expressions 2. The system of claim 1.

4. The facial expression generation unit Learning the individual facial expression patterns of the user and generating more personalized facial expressions 2. The system of claim 1.

5. The voice timbre analysis unit Add a social function that allows users to share the results of the tone of voice analysis with other users and promote empathy.

2. The system of claim 1.

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