System

Abstract

A system according to an embodiment is intended to recognize a gesture of a hand of a user and operate a mobile device in a non-contact manner.SOLUTION: A system according to an embodiment includes a gesture learning unit, a gesture recognition unit, and a remote operation unit. The gesture learning unit learns a gesture of a hand of a user. The gesture recognition unit recognizes the gesture learned by the gesture learning unit. The remote operation unit operates the mobile device in a non-contact manner based on the gesture recognized by the gesture recognition unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has made it difficult to operate mobile devices without contact, which has been an inconvenience for people with disabilities and those with limited use of their hands.

[0005] The system according to the embodiment aims to recognize a user's hand gestures and operate a mobile device without contact. [Means for solving the problem]

[0006] A system according to an embodiment includes a gesture learning unit, a gesture recognition unit, and a remote operation unit. The gesture learning unit learns hand gestures of a user. The gesture recognition unit recognizes the gestures learned by the gesture learning unit. The remote operation unit operates a mobile device in a contactless manner based on the gestures recognized by the gesture recognition unit. [Effects of the Invention]

[0007] The system according to the embodiment can recognize the user's hand gestures and operate the mobile device without contact. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The gesture air control system according to an embodiment of the present invention is a system that utilizes AI deep learning technology to learn and recognize a user's hand gestures and movements, enabling contactless remote control of a mobile device. As a result, the gesture air control system allows users to perform operations such as play, pause, and volume adjustment of a media application using only hand movements without touching the screen.

[0029] A gesture air control system according to an embodiment includes a gesture learning unit, a gesture recognition unit, and a remote operation unit. The gesture learning unit learns a user's hand gestures. For example, the gesture learning unit learns a user's hand waving gesture and can recognize the same gesture from the next time onward. The gesture learning unit can also learn that a user raises their hand to start playback of a media application and lowers their hand to pause playback. The gesture learning unit can also learn that a user lowers their hand to adjust the volume. The gesture recognition unit recognizes the gestures learned by the gesture learning unit. For example, the gesture recognition unit recognizes a user's hand waving gesture and performs an appropriate operation based on the gesture. The gesture recognition unit can also recognize a user raising their hand to start playback of a media application. The gesture recognition unit can also recognize a user lowering their hand to pause playback of a media application. The remote operation unit operates the mobile device non-contact based on the gestures recognized by the gesture recognition unit. For example, the remote operation unit adjusts the volume by a user waving their hand left and right. The remote control unit can also start playing a media application when the user raises their hand, and can pause playing a media application when the user lowers their hand. This allows the gesture air control system according to the embodiment to learn and recognize the user's hand gestures and operate the mobile device without contact.

[0030] The gesture learning unit can generate an individually optimized gesture recognition model taking into account the physical characteristics of the user. The gesture learning unit optimizes the gesture recognition model taking into account, for example, the size of the user's hand. For example, the gesture movement range is adjusted according to the hand size to improve recognition accuracy. The gesture learning unit also optimizes the gesture recognition model taking into account the speed of the user's movement. For example, the gesture recognition timing is adjusted according to the speed of the movement to improve recognition accuracy. The gesture learning unit also optimizes the gesture recognition model taking into account the shape of the user's hand. For example, the gesture recognition algorithm is adjusted according to the hand shape to improve recognition accuracy. In this way, the gesture recognition model can be optimized based on the user's physical characteristics.

[0031] The gesture learning unit can refer to the user's past operation history and preferentially learn frequently used gestures. The gesture learning unit, for example, analyzes the user's past operation history and identifies frequently used gestures. For example, gestures that have been used frequently in the past are preferentially learned to improve recognition accuracy. The gesture learning unit also learns gestures that are frequently used in a specific time period based on the user's operation history. For example, gestures that are frequently used in the morning are preferentially learned to improve recognition accuracy. The gesture learning unit also learns gestures that are frequently used in a specific application based on the user's operation history. For example, gestures that are frequently used in a media application are preferentially learned to improve recognition accuracy. In this way, frequently used gestures can be preferentially learned based on the user's past operation history.

[0032] The gesture learning unit can combine voice commands to enable operation through combined voice and gesture input. For example, the gesture learning unit starts playback of a media application when the user raises their hand and simultaneously issues the voice command "play." This enables operation through combined voice and gesture input. The gesture learning unit also adjusts the volume when the user waves their hand left and right and simultaneously issues the voice command "volume up." This enables operation through combined voice and gesture input. The gesture learning unit also pauses playback of a media application when the user lowers their hand and simultaneously issues the voice command "stop." This enables operation through combined voice and gesture input.

[0033] The gesture learning unit can learn gestures from different cultural spheres or regions to build a gesture recognition system that is compatible with globalization. The gesture learning unit, for example, learns gestures from different cultural spheres to build a gesture recognition system that is compatible with globalization. For example, it learns gestures that are common in Asia and gestures that are common in Europe and the United States to improve recognition accuracy. The gesture learning unit can also learn gestures from different regions to build a gesture recognition system that is compatible with globalization. For example, it learns gestures that are common in North America and gestures that are common in South America to improve recognition accuracy. The gesture learning unit can also learn gestures from different cultural spheres or regions to build a gesture recognition system that is compatible with globalization. For example, it learns gestures that are common in Europe and gestures that are common in Africa to improve recognition accuracy. In this way, it is possible to learn gestures from different cultural spheres or regions to build a gesture recognition system that is compatible with globalization.

[0034] The remote operation unit can combine user gaze tracking technology to realize operations by linking gaze and gestures. The remote operation unit, for example, tracks the user's gaze and realizes operations by linking gaze and gestures. For example, an item selected with the gaze is operated with a gesture. The remote operation unit also uses gaze tracking technology to perform operations according to the direction the user is looking. For example, an item selected with the gaze is operated with a hand movement. The remote operation unit also combines gaze tracking technology and gesture recognition to realize operations by linking gaze and gestures. For example, an item selected with the gaze is operated with a hand movement. This makes it possible to realize operations by linking gaze and gestures.

[0035] The remote control unit can be expanded to include smart devices in the home, allowing the entire home to be controlled with gestures. For example, the remote control unit will develop a system that allows smart devices in the home (lights, air conditioners, televisions, etc.) to be controlled with gestures. For example, turning on the lights by raising your hand. The remote control unit will also develop a system that allows smart devices in the home to be controlled with gestures. For example, adjusting the temperature of the air conditioner by waving your hand from side to side. The remote control unit will also develop a system that allows smart devices in the home to be controlled with gestures. For example, turning off the television by lowering your hand. This allows smart devices in the home to be controlled with gestures.

[0036] The remote operation unit can be applied to an in-vehicle system to enable operation of a navigation or entertainment system with hand gestures to enhance safety while driving. The remote operation unit, for example, introduces gesture operation into the in-vehicle system to enhance safety while driving. For example, starting navigation with a hand-raising motion. The remote operation unit also enables operation of the in-vehicle entertainment system with gestures. For example, skipping music with a hand-waving motion. The remote operation unit also introduces gesture operation into the in-vehicle system to enhance safety while driving. For example, stopping the entertainment system with a hand-down motion. In this way, the in-vehicle system can be operated with hand gestures.

[0037] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0038] The gesture learning unit can learn gestures customized based on the user's hobbies and preferences. For example, for a user who likes music, the unit learns gestures specialized for music playback. For example, waving your hand from side to side skips to the next song. Also, for a user who likes movies, the unit learns gestures specialized for movie playback. For example, raising your hand up starts playback and lowering your hand down pauses. Also, for a user who likes sports, the unit learns gestures specialized for operating sports-related applications. For example, sticking your hand out in front displays the score. This makes it possible to learn gestures customized based on the user's hobbies and preferences.

[0039] The gesture learning unit can learn gestures according to the age and gender of the user. For example, it can learn simple and intuitive gestures for older users, such as adjusting the volume with a gentle wave of the hand. It can also learn complex and multi-functional gestures for younger users, such as scrolling through a menu with a rotating hand. It can also learn gestures according to gender, such as graceful hand movements for female users and powerful movements for male users. This makes it possible to learn gestures according to the age and gender of the user.

[0040] The gesture learning unit can learn gestures that suit the user's environment. For example, it can learn gestures that are suitable for operation in a dark place, for example, operations that require large hand movements. It can also learn gestures that are suitable for operation in a noisy place, for example, operations that require quick hand movements. It can also learn gestures that are suitable for operation in a narrow space, for example, operations that require small hand movements. This makes it possible to learn gestures that suit the user's environment.

[0041] The gesture learning unit can learn gestures according to the activity level of the user. For example, for a user who is exercising, it learns gestures that are suitable for exercise. For example, operations are performed by waving the hands widely. For a user who is sitting, it learns gestures that can be performed while sitting. For example, operations are performed by gently moving the hands. For a user who is relaxed, it learns gestures that can be performed in a relaxed state. For example, operations are performed by slowly moving the hands. This makes it possible to learn gestures according to the activity level of the user.

[0042] The remote operation unit can combine the user's gaze tracking technology to realize operations by linking gaze and gestures. For example, an item selected with the gaze can be operated with a gesture. The remote operation unit also uses gaze tracking technology to perform operations according to the direction the user is looking. For example, an item selected with the gaze can be operated with a hand movement. The remote operation unit also combines gaze tracking technology and gesture recognition to realize operations by linking gaze and gestures. For example, an item selected with the gaze can be operated with a hand movement. This makes it possible to realize operations by linking gaze and gestures.

[0043] The remote control unit can be expanded to include smart devices in the home, allowing the entire house to be controlled with gestures. For example, a system will be developed that allows smart devices in the home (lights, air conditioners, televisions, etc.) to be controlled with gestures. For example, a light can be turned on by raising your hand. The remote control unit will also develop a system that allows smart devices in the home to be controlled with gestures. For example, the air conditioner temperature can be adjusted by waving your hand from side to side. The remote control unit will also develop a system that allows smart devices in the home to be controlled with gestures. For example, a TV can be turned off by lowering your hand. This allows smart devices in the home to be controlled with gestures.

[0044] The remote operation unit can be applied to an in-vehicle system to enable operation of a navigation or entertainment system with hand gestures to enhance safety while driving. For example, gesture operation can be introduced into an in-vehicle system to enhance safety while driving. For example, a gesture of raising your hand can start navigation. The remote operation unit can also enable operation of an in-vehicle entertainment system with gestures. For example, a gesture of waving your hand left and right can skip music. The remote operation unit can also introduce gesture operation into an in-vehicle system to enhance safety while driving. For example, a gesture of lowering your hand can stop the entertainment system. In this way, the in-vehicle system can be operated with hand gestures.

[0045] The processing flow of the first embodiment will be briefly explained below.

[0046] Step 1: The gesture learning unit learns the user's hand gestures, such as waving their hand from side to side, raising their hand, and lowering their hand, and learns the operations corresponding to each gesture (adjusting the volume, starting playback of a media application, pausing playback). Step 2: The gesture recognition unit recognizes the gestures learned by the gesture learning unit, such as a user waving their hand from side to side, raising their hand, or lowering their hand, and performs an appropriate operation based on each gesture. Step 3: The remote control unit operates the mobile device contactlessly based on the gestures recognized by the gesture recognition unit, such as adjusting the volume by waving your hand left and right, starting playback of a media application by raising your hand, and pausing playback by lowering your hand.

[0047] (Example 2) The gesture air control system according to an embodiment of the present invention is a system that utilizes AI deep learning technology to learn and recognize a user's hand gestures and movements, enabling contactless remote control of a mobile device. As a result, the gesture air control system allows users to perform operations such as play, pause, and volume adjustment of a media application using only hand movements without touching the screen.

[0048] A gesture air control system according to an embodiment includes a gesture learning unit, a gesture recognition unit, and a remote operation unit. The gesture learning unit learns a user's hand gestures. For example, the gesture learning unit learns a user's hand waving gesture and can recognize the same gesture from the next time onward. The gesture learning unit can also learn that a user raises their hand to start playback of a media application and lowers their hand to pause playback. The gesture learning unit can also learn that a user lowers their hand to adjust the volume. The gesture recognition unit recognizes the gestures learned by the gesture learning unit. For example, the gesture recognition unit recognizes a user's hand waving gesture and performs an appropriate operation based on the gesture. The gesture recognition unit can also recognize a user raising their hand to start playback of a media application. The gesture recognition unit can also recognize a user lowering their hand to pause playback of a media application. The remote operation unit operates the mobile device non-contact based on the gestures recognized by the gesture recognition unit. For example, the remote operation unit adjusts the volume by a user waving their hand left and right. The remote control unit can also start playing a media application when the user raises their hand, and can pause playing a media application when the user lowers their hand. This allows the gesture air control system according to the embodiment to learn and recognize the user's hand gestures and operate the mobile device without contact.

[0049] The gesture learning unit may include an emotion estimation unit that estimates the emotional state of the user and learns gestures according to the emotional state. The gesture learning unit estimates the emotional state of the user using, for example, a generation AI and learns gestures according to the emotion. For example, if the user is feeling stressed, the gesture learning unit learns gestures for relaxation. For example, the gesture of moving the hands slowly may be learned as a relaxation gesture and this gesture may be recommended to a user in a stressed state. Also, if the user is excited, the gesture learning unit learns gestures for regaining composure. For example, the gesture of placing the hands on the chest may be learned as a calming gesture and this gesture may be recommended to a user in an excited state. Also, if the user is tired, the gesture learning unit learns gestures for restoring energy. For example, the gesture of raising the hands above the head may be learned as an energy gesture and this gesture may be recommended to a user in a fatigued state. This makes it possible to learn gestures according to the emotional state of the user.

[0050] The gesture learning unit can generate an individually optimized gesture recognition model taking into account the physical characteristics of the user. The gesture learning unit optimizes the gesture recognition model taking into account, for example, the size of the user's hand. For example, the gesture movement range is adjusted according to the hand size to improve recognition accuracy. The gesture learning unit also optimizes the gesture recognition model taking into account the speed of the user's movement. For example, the gesture recognition timing is adjusted according to the speed of the movement to improve recognition accuracy. The gesture learning unit also optimizes the gesture recognition model taking into account the shape of the user's hand. For example, the gesture recognition algorithm is adjusted according to the hand shape to improve recognition accuracy. In this way, the gesture recognition model can be optimized based on the user's physical characteristics.

[0051] The gesture learning unit can refer to the user's past operation history and preferentially learn frequently used gestures. The gesture learning unit, for example, analyzes the user's past operation history and identifies frequently used gestures. For example, gestures that have been used frequently in the past are preferentially learned to improve recognition accuracy. The gesture learning unit also learns gestures that are frequently used in a specific time period based on the user's operation history. For example, gestures that are frequently used in the morning are preferentially learned to improve recognition accuracy. The gesture learning unit also learns gestures that are frequently used in a specific application based on the user's operation history. For example, gestures that are frequently used in a media application are preferentially learned to improve recognition accuracy. In this way, frequently used gestures can be preferentially learned based on the user's past operation history.

[0052] The gesture learning unit can combine voice commands to enable operation through combined voice and gesture input. For example, the gesture learning unit starts playback of a media application when the user raises their hand and simultaneously issues the voice command "play." This enables operation through combined voice and gesture input. The gesture learning unit also adjusts the volume when the user waves their hand left and right and simultaneously issues the voice command "volume up." This enables operation through combined voice and gesture input. The gesture learning unit also pauses playback of a media application when the user lowers their hand and simultaneously issues the voice command "stop." This enables operation through combined voice and gesture input.

[0053] The gesture learning unit can learn gestures from different cultural spheres or regions to build a gesture recognition system that is compatible with globalization. The gesture learning unit, for example, learns gestures from different cultural spheres to build a gesture recognition system that is compatible with globalization. For example, it learns gestures that are common in Asia and gestures that are common in Europe and the United States to improve recognition accuracy. The gesture learning unit can also learn gestures from different regions to build a gesture recognition system that is compatible with globalization. For example, it learns gestures that are common in North America and gestures that are common in South America to improve recognition accuracy. The gesture learning unit can also learn gestures from different cultural spheres or regions to build a gesture recognition system that is compatible with globalization. For example, it learns gestures that are common in Europe and gestures that are common in Africa to improve recognition accuracy. In this way, it is possible to learn gestures from different cultural spheres or regions to build a gesture recognition system that is compatible with globalization.

[0054] The gesture learning unit can use the emotion estimation function to learn the gestures that the user most naturally makes when feeling a specific emotion and use the gestures for operation. The gesture learning unit, for example, uses the emotion estimation function to learn the gestures that the user most naturally makes when feeling a specific emotion and use the gestures for operation. For example, the gesture learning unit learns the gestures that the user most naturally makes when feeling joy and uses the gestures for operation. For example, the gesture of spreading hands is learned as a gesture of joy and used for operation. Also, the gesture that the user most naturally makes when feeling sad and uses the gestures for operation. For example, the gesture of lowering hands is learned as a gesture of sadness and used for operation. Also, the gesture that the user most naturally makes when feeling surprise and uses the gestures for operation. For example, the gesture of putting hands to face is learned as a gesture of surprise and used for operation. In this way, the gestures that the user most naturally makes when feeling a specific emotion can be learned and used for operation.

[0055] The remote operation unit can monitor the user's emotional state in real time and provide operation feedback according to the emotional state. The remote operation unit can, for example, use a generation AI to monitor the user's emotional state in real time and provide operation feedback according to the emotion. For example, if the user is irritated, feedback to simplify the operation can be provided, for example, by replacing a complex operation with a simple gesture. Also, if the user is relaxed, feedback to perform the operation in detail can be provided, for example, by suggesting gestures that enable fine operation. Also, if the user is concentrating, feedback to perform the operation efficiently can be provided, for example, by suggesting to perform multiple operations with a single gesture. In this way, operation feedback according to the user's emotional state can be provided.

[0056] The remote operation unit can combine user gaze tracking technology to realize operations by linking gaze and gestures. The remote operation unit, for example, tracks the user's gaze and realizes operations by linking gaze and gestures. For example, an item selected with the gaze is operated with a gesture. The remote operation unit also uses gaze tracking technology to perform operations according to the direction the user is looking. For example, an item selected with the gaze is operated with a hand movement. The remote operation unit also combines gaze tracking technology and gesture recognition to realize operations by linking gaze and gestures. For example, an item selected with the gaze is operated with a hand movement. This makes it possible to realize operations by linking gaze and gestures.

[0057] The remote control unit can be expanded to include smart devices in the home, allowing the entire home to be controlled with gestures. For example, the remote control unit will develop a system that allows smart devices in the home (lights, air conditioners, televisions, etc.) to be controlled with gestures. For example, turning on the lights by raising your hand. The remote control unit will also develop a system that allows smart devices in the home to be controlled with gestures. For example, adjusting the temperature of the air conditioner by waving your hand from side to side. The remote control unit will also develop a system that allows smart devices in the home to be controlled with gestures. For example, turning off the television by lowering your hand. This allows smart devices in the home to be controlled with gestures.

[0058] The remote operation unit can be applied to an in-vehicle system to enable operation of a navigation or entertainment system with hand gestures to enhance safety while driving. The remote operation unit, for example, introduces gesture operation into the in-vehicle system to enhance safety while driving. For example, starting navigation with a hand-raising motion. The remote operation unit also enables operation of the in-vehicle entertainment system with gestures. For example, skipping music with a hand-waving motion. The remote operation unit also introduces gesture operation into the in-vehicle system to enhance safety while driving. For example, stopping the entertainment system with a hand-down motion. In this way, the in-vehicle system can be operated with hand gestures.

[0059] The remote operation unit can use the emotion estimation function to propose an operation method that is most comfortable for the user when the user has a specific emotion, and apply the operation method to contactless operation. The remote operation unit, for example, uses the emotion estimation function to propose an operation method that is most comfortable for the user when the user has a specific emotion, and apply the operation method to contactless operation. For example, the remote operation unit proposes an operation method that is most comfortable for the user when the user is relaxed, and applies the operation method to contactless operation. For example, it proposes a motion of moving the hands slowly. The remote operation unit also proposes an operation method that is most comfortable for the user when the user is concentrating, and applies the operation method to contactless operation. For example, it proposes a motion of moving the hands quickly. The remote operation unit also proposes an operation method that is most comfortable for the user when the user is tired, and applies the operation method to contactless operation. For example, it proposes a motion of moving the hands lightly. In this way, the remote operation unit can propose an operation method that is most comfortable for the user when the user has a specific emotion, and apply the operation method to contactless operation.

[0060] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0061] The gesture learning unit can monitor the user's health state and learn gestures according to the health state. For example, if the user feels tired, the unit learns gestures for relaxation. For example, the unit learns the action of moving the hands slowly as a relaxation gesture and recommends this action to a user who is tired. Also, if the user feels stressed, the unit learns gestures for reducing stress. For example, the unit learns the action of placing the hands on the chest as a stress-reducing gesture and recommends this action to a user who is stressed. Also, if the user wants to improve their concentration, the unit learns gestures for improving concentration. For example, the unit learns the action of raising the hands above the head as a gesture for improving concentration and recommends this action to a user who wants to improve their concentration. This makes it possible to learn gestures according to the user's health state.

[0062] The gesture learning unit can learn gestures customized based on the user's hobbies and preferences. For example, for a user who likes music, the unit learns gestures specialized for music playback. For example, waving your hand from side to side skips to the next song. Also, for a user who likes movies, the unit learns gestures specialized for movie playback. For example, raising your hand up starts playback and lowering your hand down pauses. Also, for a user who likes sports, the unit learns gestures specialized for operating sports-related applications. For example, sticking your hand out in front displays the score. This makes it possible to learn gestures customized based on the user's hobbies and preferences.

[0063] The gesture learning unit can learn gestures according to the age and gender of the user. For example, it can learn simple and intuitive gestures for older users, such as adjusting the volume with a gentle wave of the hand. It can also learn complex and multi-functional gestures for younger users, such as scrolling through a menu with a rotating hand. It can also learn gestures according to gender, such as graceful hand movements for female users and powerful movements for male users. This makes it possible to learn gestures according to the age and gender of the user.

[0064] The gesture learning unit can learn gestures that suit the user's environment. For example, it can learn gestures that are suitable for operation in a dark place, for example, operations that require large hand movements. It can also learn gestures that are suitable for operation in a noisy place, for example, operations that require quick hand movements. It can also learn gestures that are suitable for operation in a narrow space, for example, operations that require small hand movements. This makes it possible to learn gestures that suit the user's environment.

[0065] The gesture learning unit can learn gestures according to the activity level of the user. For example, for a user who is exercising, it learns gestures that are suitable for exercise. For example, operations are performed by waving the hands widely. For a user who is sitting, it learns gestures that can be performed while sitting. For example, operations are performed by gently moving the hands. For a user who is relaxed, it learns gestures that can be performed in a relaxed state. For example, operations are performed by slowly moving the hands. This makes it possible to learn gestures according to the activity level of the user.

[0066] The gesture learning unit can estimate the emotional state of the user and learn gestures according to the emotional state. For example, if the user is feeling stressed, it learns gestures for relaxation. For example, it learns the action of moving the hands slowly as a relaxation gesture and recommends that action to a user who is stressed. Also, if the user is excited, it learns gestures for regaining composure. For example, it learns the action of placing the hands on the chest as a calm gesture and recommends that action to a user who is excited. Also, if the user is tired, it learns gestures for restoring energy. For example, it learns the action of raising the hands above the head as an energy gesture and recommends that action to a user who is tired. This makes it possible to learn gestures according to the emotional state of the user.

[0067] The remote operation unit can monitor the user's emotional state in real time and provide operation feedback according to the emotional state. For example, if the user is irritated, feedback to simplify the operation is provided, for example, by replacing a complicated operation with a simple gesture. Also, if the user is relaxed, feedback to perform the operation in detail is provided, for example, by suggesting gestures that enable detailed operations. Also, if the user is concentrating, feedback to perform the operation efficiently is provided, for example, by suggesting performing multiple operations with a single gesture. In this way, operation feedback according to the user's emotional state can be provided.

[0068] The remote operation unit can combine the user's gaze tracking technology to realize operations by linking gaze and gestures. For example, an item selected with the gaze can be operated with a gesture. The remote operation unit also uses gaze tracking technology to perform operations according to the direction the user is looking. For example, an item selected with the gaze can be operated with a hand movement. The remote operation unit also combines gaze tracking technology and gesture recognition to realize operations by linking gaze and gestures. For example, an item selected with the gaze can be operated with a hand movement. This makes it possible to realize operations by linking gaze and gestures.

[0069] The remote control unit can be expanded to include smart devices in the home, allowing the entire house to be controlled with gestures. For example, a system will be developed that allows smart devices in the home (lights, air conditioners, televisions, etc.) to be controlled with gestures. For example, a light can be turned on by raising your hand. The remote control unit will also develop a system that allows smart devices in the home to be controlled with gestures. For example, the air conditioner temperature can be adjusted by waving your hand from side to side. The remote control unit will also develop a system that allows smart devices in the home to be controlled with gestures. For example, a TV can be turned off by lowering your hand. This allows smart devices in the home to be controlled with gestures.

[0070] The remote operation unit can be applied to an in-vehicle system to enable operation of a navigation or entertainment system with hand gestures to enhance safety while driving. For example, gesture operation can be introduced into an in-vehicle system to enhance safety while driving. For example, a gesture of raising your hand can start navigation. The remote operation unit can also enable operation of an in-vehicle entertainment system with gestures. For example, a gesture of waving your hand left and right can skip music. The remote operation unit can also introduce gesture operation into an in-vehicle system to enhance safety while driving. For example, a gesture of lowering your hand can stop the entertainment system. In this way, the in-vehicle system can be operated with hand gestures.

[0071] The processing flow of the second embodiment will be briefly explained below.

[0072] Step 1: The gesture learning unit learns the user's hand gestures, such as waving their hand from side to side, raising their hand, and lowering their hand, and learns the operations corresponding to each gesture (adjusting the volume, starting playback of a media application, pausing playback). Step 2: The gesture recognition unit recognizes the gestures learned by the gesture learning unit, such as a user waving their hand from side to side, raising their hand, or lowering their hand, and performs an appropriate operation based on each gesture. Step 3: The remote control unit operates the mobile device contactlessly based on the gestures recognized by the gesture recognition unit, such as adjusting the volume by waving your hand left and right, starting playback of a media application by raising your hand, and pausing playback by lowering your hand.

[0073] 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.

[0074] 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.

[0075] 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.

[0076] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0077] 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.

[0078] 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.

[0079] 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.

[0080] 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.

[0081] 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).

[0082] 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.

[0083] 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.

[0084] 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.

[0085] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0086] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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).

[0097] 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.

[0098] 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.

[0099] 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.

[0100] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0101] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0107] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0117] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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.

[0127] 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."

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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]

[0140] 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 gesture learning unit that learns hand gestures of a user; a gesture recognition unit that recognizes the gesture learned by the gesture learning unit; a remote operation unit that operates the mobile device in a non-contact manner based on the gesture recognized by the gesture recognition unit. A system characterized by:

2. The gesture learning unit An emotion estimation unit is provided that estimates the user's emotional state and learns gestures according to the emotional state.

2. The system of claim 1.

3. The gesture learning unit Generate an individually optimized gesture recognition model taking into account the user's physical characteristics 2. The system of claim 1.

4. The gesture learning unit Refers to the user's past gesture history and prioritizes learning frequently used gestures.

2. The system of claim 1.

5. The gesture learning unit Combining voice commands allows for combined voice and gesture input for operation.

2. The system of claim 1.

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