System

Abstract

An object of the system according to the embodiment is to allow all people including visually impaired people to enjoy works of art by utilizing senses other than vision.SOLUTION: A system includes a voice guidance unit, a tactile feedback unit, and a gesture operation unit. The voice guidance unit provides voice guidance. The haptic feedback portion provides haptic feedback. The gesture operation unit enables a gesture operation.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 not providing sufficient experiences that utilize senses other than sight when visually impaired people enjoy artworks.

[0005] The system according to the embodiment aims to enable all people, including the visually impaired, to enjoy artworks using senses other than sight. [Means for solving the problem]

[0006] The system according to the embodiment includes an audio guidance unit, a haptic feedback unit, and a gesture operation unit. The audio guidance unit provides audio guidance. The haptic feedback unit provides haptic feedback. The gesture operation unit enables gesture operation. [Effects of the Invention]

[0007] The system according to the embodiment allows all people, including the visually impaired, to enjoy artworks using senses other than sight. [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 3D museum service according to an embodiment of the present invention is a system that allows everyone, including the visually impaired, to enjoy artworks with senses beyond the visual. This system uses the AI ​​assistant "Gemini" to guide visitors through artworks through voice guidance, haptic feedback, and gesture control. This allows the 3D museum service to allow everyone, including the visually impaired, to enjoy artworks with senses beyond the visual.

[0029] The 3D museum service according to the embodiment includes an audio guidance unit, a haptic feedback unit, and a gesture operation unit. The audio guidance unit provides audio guidance. For example, when a user stands in front of a particular artwork, the AI ​​assistant "Gemini" explains, "This artwork was painted by a famous 19th-century artist and features a distinctive use of color." The generation AI generates an appropriate explanation and provides it audibly based on the user's location information and the selected artwork. The generation AI receives inputs from the user's location information and prompts regarding artwork selection, and generates audio guidance based on the prompts. The haptic feedback unit provides haptic feedback. For example, when a user touches a particular sculpture, the user can feel the unevenness and texture of the artwork's surface through a haptic device. The generation AI analyzes artwork data and provides appropriate feedback to the haptic device. The generation AI receives inputs from artwork data and the user's haptic device operation information, and the generation AI generates haptic feedback based on that information. The gesture operation unit enables gesture operation. For example, when a user moves their hand to point in a specific direction, information about the artwork in that direction is displayed. The generation AI analyzes the user's gestures and performs appropriate operations based on those gestures. The input to the generation AI is the user's gesture information, and the generation AI performs operations based on that information. As a result, the 3D museum service according to the embodiment allows everyone, including the visually impaired, to enjoy artworks with senses beyond their sense of sight. For example, the visually impaired can understand the details of the artwork through audio guidance and feel the texture of the artwork through haptic feedback. They can also freely manipulate the artwork through gesture control, resulting in a deeper understanding and appreciation.

[0030] The audio guidance unit has the ability to personalize explanations based on the user's past visit history and interests. For example, the audio guidance unit records the artworks the user has previously visited and themes that interest them, and customizes explanations for new artworks based on that information. For example, if the user is interested in Impressionist paintings, related artworks will be introduced first. Visit history and interests are obtained from the user's behavioral data and survey results, and the generation AI analyzes this data to personalize the explanation content. This allows the user to be provided with highly relevant information.

[0031] The voice guidance unit has an interactive question-answering function that answers user questions in real time. For example, when a user asks a question about a work of art, the AI ​​responds in real time. For example, if the user asks, "Who is the artist of this work?", the AI ​​will respond, "This work was painted by Picasso." The question-answering function uses natural language processing technology to analyze the user's question and generate an appropriate answer. This allows the user's question to be answered in real time.

[0032] The voice guidance unit has a multilingual support function that can accommodate users who speak different languages. The voice guidance unit uses AI to provide voice guidance based on the language selected by the user, for example. For example, it supports multiple languages, such as English, French, and Chinese. The multilingual support function uses translation technology and voice synthesis technology to provide guidance in different languages. This makes it possible to accommodate users who speak different languages.

[0033] The audio guidance unit has the function of providing information in combination with visual auxiliary information. For example, when a user stands in front of a work of art, the audio guidance unit uses AI to provide a visual guide using AR technology along with audio guidance. For example, detailed information about the work and related images can be displayed via a smartphone or tablet. Visual auxiliary information includes images, videos, text information, etc. and is provided to deepen the user's understanding. This allows it to be provided in combination with visual auxiliary information.

[0034] The haptic feedback unit has the function of providing detailed haptic information based on the historical background and production process of a work. For example, when a user touches a painting, the haptic feedback unit provides haptic feedback in which the AI ​​reproduces the movement of the brush. For example, it allows the user to feel the brush strokes and the thickness of the paint. The haptic information based on the historical background and production process reproduces the texture and shape of the work in detail and is provided to the user. This makes it possible to provide haptic information based on the historical background and production process of a work.

[0035] The haptic feedback unit has the ability to analyze the user's hand movement and pressure in real time, providing a more precise haptic experience. For example, when a user touches a work of art, the haptic feedback unit uses AI to analyze the hand movement and pressure in real time and adjust the haptic feedback. For example, it changes the strength and pattern of the haptic sensation according to the hand movement. Hand movement and pressure are analyzed in real time using sensor technology, and haptic feedback is provided based on the results. This makes it possible to provide a precise haptic experience that corresponds to the user's hand movement and pressure.

[0036] The haptic feedback unit has a function to provide haptic feedback in combination with other sensory feedback such as temperature and vibration. For example, the haptic feedback unit combines temperature feedback with haptic feedback when a user touches a work of art. For example, touching a metal work of art provides a cool feeling, and touching a wooden work of art provides a warm feeling. Sensory feedback such as temperature and vibration is provided in combination with haptic feedback to enrich the user's experience. This allows it to be provided in combination with other sensory feedback such as temperature and vibration.

[0037] The haptic feedback unit has a function of providing haptic feedback using a wearable device that extends over the user's entire body. The haptic feedback unit, for example, builds a system in which the user wears the wearable device and feels haptic feedback throughout the entire body. For example, the user feels the texture and shape of the artwork with their entire body. The wearable device provides haptic feedback that extends over the user's entire body, realizing the experience of feeling the artwork with their entire body. This makes it possible to provide haptic feedback that extends over the user's entire body.

[0038] The gesture operation unit has a multi-user support function that allows multiple users to operate the unit simultaneously. The gesture operation unit, for example, can be used to build a system that allows multiple users to perform gesture operations simultaneously, providing the experience of operating a work collaboratively. For example, operating a work together with family and friends. The multi-user support function is designed to allow multiple users to operate the work simultaneously, and each user's operation is reflected in real time. This allows multiple users to operate the work simultaneously.

[0039] The gesture operation unit has a function of analyzing the user's body movements and posture in real time to provide a more natural operation feel. The gesture operation unit, for example, analyzes the user's body movements and posture in real time to improve the accuracy of gesture operations. For example, it analyzes hand movements and body tilt to adjust the operation response. Body movements and posture are analyzed in real time using sensor technology, and the operation feel is adjusted based on the results. This makes it possible to provide a natural operation feel that matches the user's body movements and posture.

[0040] The gesture operation unit has functions that are provided in combination with voice commands and gaze tracking. The gesture operation unit builds a system in which, for example, a user can operate artworks by combining gesture operations and voice commands. For example, a work can be selected with hand movements, and detailed information displayed by voice. Voice commands and gaze tracking are provided in combination with gesture operations to realize a composite operation method. This enables operations that are combined with voice commands and gaze tracking.

[0041] The gesture operation unit is equipped with a remote interaction function that allows users in remote locations to operate the device collaboratively. The gesture operation unit, for example, builds a system that allows users in remote locations to perform gesture operations collaboratively. For example, operating a work of art together with friends or family in different locations. The remote interaction function synchronizes the operations of users in remote locations via the Internet, providing a collaborative experience. This allows users in remote locations to operate the work collaboratively.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The audio guidance unit has the ability to personalize explanations based on the user's past visit history and interests. For example, it records the artworks the user has previously visited and themes that interest them, and customizes explanations for new artworks based on that information. If a user is interested in Impressionist paintings, it can prioritize introducing related artworks. Visit history and interests are obtained from user behavior data and survey results, and the generation AI analyzes this data to personalize the explanations. This allows the system to provide users with highly relevant information.

[0044] The voice guidance unit is equipped with an interactive question-and-answer function that answers users' questions in real time. For example, when a user asks a question about a work of art, the AI ​​responds in real time. If the user asks, "Who is the artist of this work?", the AI ​​will respond, "This work was painted by Picasso." The question-and-answer function uses natural language processing technology to analyze the user's question and generate an appropriate answer. This allows the user's question to be answered in real time.

[0045] The voice guidance unit has a multilingual support function that can accommodate users who speak different languages. For example, AI provides voice guidance based on the language selected by the user. It can support multiple languages, including English, French, and Chinese. The multilingual support function uses translation technology and voice synthesis technology to provide guidance in different languages. This allows it to accommodate users who speak different languages.

[0046] The audio guidance unit has the function of providing audio guidance in combination with visual supplementary information. For example, when a user stands in front of a work of art, AI provides a visual guide using AR technology along with audio guidance. Detailed information about the work and related images can be displayed via a smartphone or tablet. Visual supplementary information includes images, videos, text information, etc. and is provided to deepen the user's understanding. This allows it to be provided in combination with visual supplementary information.

[0047] The haptic feedback unit has the function of providing detailed haptic information based on the historical background and production process of a work. For example, when a user touches a painting, the AI ​​provides haptic feedback that reproduces the brush strokes, allowing the user to feel the brush strokes and the thickness of the paint. The haptic information based on the historical background and production process reproduces the texture and shape of the work in detail and is provided to the user. This makes it possible to provide haptic information based on the historical background and production process of a work.

[0048] The haptic feedback unit has the ability to analyze the user's hand movement and pressure in real time, providing a more precise haptic experience. For example, when a user touches a work of art, AI analyzes the hand movement and pressure in real time and adjusts the haptic feedback. The strength and pattern of the haptic sensation can be changed according to the hand movement. Hand movement and pressure are analyzed in real time using sensor technology, and haptic feedback is provided based on the results. This allows for a precise haptic experience that corresponds to the user's hand movement and pressure.

[0049] The haptic feedback unit has the function of providing haptic feedback in combination with other sensory feedback such as temperature and vibration. For example, when a user touches a work of art, haptic feedback can be combined with temperature feedback. Touching a metal work of art can provide a cool feeling, while touching a wooden work of art can provide a warm feeling. Sensory feedback such as temperature and vibration can be provided in combination with haptic feedback to enrich the user's experience. This allows it to be provided in combination with other sensory feedback such as temperature and vibration.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: The audio guidance unit provides audio guidance. For example, when a user stands in front of a particular artwork, the AI ​​assistant "Gemini" will explain, "This work was painted by a famous 19th-century painter and is characterized by its use of color." The generation AI generates an appropriate explanation based on the user's location information and the selected artwork, and provides it via audio. The input to the generation AI is the user's location information and prompts regarding the artwork selection, and the generation AI generates audio guidance based on those prompts. Step 2: The haptic feedback unit provides haptic feedback. For example, when a user touches a particular sculpture, they can feel the unevenness and texture of the work's surface through the haptic device. The generation AI analyzes the work's data and provides appropriate feedback to the haptic device. The input to the generation AI is the work's data and the user's operation information on the haptic device, and the generation AI generates haptic feedback based on that information. Step 3: The gesture operation unit enables gesture operation. For example, when a user moves their hand and points in a specific direction, information about the artwork in that direction is displayed. The generation AI analyzes the user's gesture and performs appropriate operations based on that gesture. The input to the generation AI is the user's gesture information, and the generation AI performs operations based on that information.

[0052] (Example 2) The 3D museum service according to an embodiment of the present invention is a system that allows everyone, including the visually impaired, to enjoy artworks with senses beyond the visual. This system uses the AI ​​assistant "Gemini" to guide visitors through artworks through voice guidance, haptic feedback, and gesture control. This allows the 3D museum service to allow everyone, including the visually impaired, to enjoy artworks with senses beyond the visual.

[0053] The 3D museum service according to the embodiment includes an audio guidance unit, a haptic feedback unit, and a gesture operation unit. The audio guidance unit provides audio guidance. For example, when a user stands in front of a particular artwork, the AI ​​assistant "Gemini" explains, "This artwork was painted by a famous 19th-century artist and features a distinctive use of color." The generation AI generates an appropriate explanation and provides it audibly based on the user's location information and the selected artwork. The generation AI receives inputs from the user's location information and prompts regarding artwork selection, and generates audio guidance based on the prompts. The haptic feedback unit provides haptic feedback. For example, when a user touches a particular sculpture, the user can feel the unevenness and texture of the artwork's surface through a haptic device. The generation AI analyzes artwork data and provides appropriate feedback to the haptic device. The generation AI receives inputs from artwork data and the user's haptic device operation information, and the generation AI generates haptic feedback based on that information. The gesture operation unit enables gesture operation. For example, when a user moves their hand to point in a specific direction, information about the artwork in that direction is displayed. The generation AI analyzes the user's gestures and performs appropriate operations based on those gestures. The input to the generation AI is the user's gesture information, and the generation AI performs operations based on that information. As a result, the 3D museum service according to the embodiment allows everyone, including the visually impaired, to enjoy artworks with senses beyond their sense of sight. For example, the visually impaired can understand the details of the artwork through audio guidance and feel the texture of the artwork through haptic feedback. They can also freely manipulate the artwork through gesture control, resulting in a deeper understanding and appreciation.

[0054] The voice guidance unit is equipped with an emotion estimation function that adjusts the tone and content of the explanation according to the user's emotional state. For example, when a user stands in front of a work of art, the voice guidance unit uses AI to analyze the user's facial expressions and tone of voice to estimate their emotional state. For example, if the user is excited, the tone of the explanation will be brighter and more detailed information will be provided. The emotion estimation function uses facial expression recognition and voice analysis technology to estimate the user's emotions and adjusts the content of the voice guidance based on the results. This makes it possible to provide explanations that correspond to the user's emotions.

[0055] The audio guidance unit has the ability to personalize explanations based on the user's past visit history and interests. For example, the audio guidance unit records the artworks the user has previously visited and themes that interest them, and customizes explanations for new artworks based on that information. For example, if the user is interested in Impressionist paintings, related artworks will be introduced first. Visit history and interests are obtained from the user's behavioral data and survey results, and the generation AI analyzes this data to personalize the explanation content. This allows the user to be provided with highly relevant information.

[0056] The voice guidance unit has an interactive question-answering function that answers user questions in real time. For example, when a user asks a question about a work of art, the AI ​​responds in real time. For example, if the user asks, "Who is the artist of this work?", the AI ​​will respond, "This work was painted by Picasso." The question-answering function uses natural language processing technology to analyze the user's question and generate an appropriate answer. This allows the user's question to be answered in real time.

[0057] The voice guidance unit has a multilingual support function that can accommodate users who speak different languages. The voice guidance unit uses AI to provide voice guidance based on the language selected by the user, for example. For example, it supports multiple languages, such as English, French, and Chinese. The multilingual support function uses translation technology and voice synthesis technology to provide guidance in different languages. This makes it possible to accommodate users who speak different languages.

[0058] The audio guidance unit has the function of providing information in combination with visual auxiliary information. For example, when a user stands in front of a work of art, the audio guidance unit uses AI to provide a visual guide using AR technology along with audio guidance. For example, detailed information about the work and related images can be displayed via a smartphone or tablet. Visual auxiliary information includes images, videos, text information, etc. and is provided to deepen the user's understanding. This allows it to be provided in combination with visual auxiliary information.

[0059] The audio guidance unit has a function of playing music and sound effects in the background according to the user's emotions. For example, the audio guidance unit analyzes the user's emotional state and plays music in the background according to that emotion. For example, if the user is relaxed, calm music is played. The emotion estimation function estimates the user's emotions using facial expression recognition and voice analysis technology and selects music and sound effects based on the results. This makes it possible to provide music and sound effects according to the user's emotions.

[0060] The haptic feedback unit is equipped with an emotion estimation function that adjusts the intensity and pattern of haptics according to the user's emotional state. For example, when a user touches a work of art, the haptic feedback unit uses AI to analyze the user's emotional state and adjust the intensity of haptic feedback. For example, if the user is excited, the intensity of haptic feedback will be increased. The emotion estimation function estimates the user's emotions using facial expression recognition and biometric data analysis technology, and adjusts the intensity and pattern of haptic feedback based on the results. This makes it possible to provide haptic feedback that corresponds to the user's emotions.

[0061] The haptic feedback unit has the function of providing detailed haptic information based on the historical background and production process of a work. For example, when a user touches a painting, the haptic feedback unit provides haptic feedback in which the AI ​​reproduces the movement of the brush. For example, it allows the user to feel the brush strokes and the thickness of the paint. The haptic information based on the historical background and production process reproduces the texture and shape of the work in detail and is provided to the user. This makes it possible to provide haptic information based on the historical background and production process of a work.

[0062] The haptic feedback unit has the ability to analyze the user's hand movement and pressure in real time, providing a more precise haptic experience. For example, when a user touches a work of art, the haptic feedback unit uses AI to analyze the hand movement and pressure in real time and adjust the haptic feedback. For example, it changes the strength and pattern of the haptic sensation according to the hand movement. Hand movement and pressure are analyzed in real time using sensor technology, and haptic feedback is provided based on the results. This makes it possible to provide a precise haptic experience that corresponds to the user's hand movement and pressure.

[0063] The haptic feedback unit has a function to provide haptic feedback in combination with other sensory feedback such as temperature and vibration. For example, the haptic feedback unit combines temperature feedback with haptic feedback when a user touches a work of art. For example, touching a metal work of art provides a cool feeling, and touching a wooden work of art provides a warm feeling. Sensory feedback such as temperature and vibration is provided in combination with haptic feedback to enrich the user's experience. This allows it to be provided in combination with other sensory feedback such as temperature and vibration.

[0064] The haptic feedback unit has a function of providing haptic feedback using a wearable device that extends over the user's entire body. The haptic feedback unit, for example, builds a system in which the user wears the wearable device and feels haptic feedback throughout the entire body. For example, the user feels the texture and shape of the artwork with their entire body. The wearable device provides haptic feedback that extends over the user's entire body, realizing the experience of feeling the artwork with their entire body. This makes it possible to provide haptic feedback that extends over the user's entire body.

[0065] The haptic feedback unit has an emotion estimation function that generates a haptic pattern according to the user's emotion. The haptic feedback unit, for example, analyzes the user's emotional state and generates a haptic pattern according to that emotion. For example, if the user is excited, it provides strong vibrations or rhythmic haptics. The emotion estimation function estimates the user's emotion using facial expression recognition and biometric data analysis technology and generates a haptic pattern based on the results. This makes it possible to provide a haptic pattern according to the user's emotion.

[0066] The gesture operation unit is equipped with an emotion estimation function that adjusts operation response and interface according to the user's emotional state. For example, when a user performs a gesture operation on a work, the gesture operation unit uses AI to analyze the user's emotional state and adjust the operation response. For example, if the user is excited, the operation response will be faster. The emotion estimation function estimates the user's emotions using facial expression recognition and biometric data analysis technology, and adjusts the operation response and interface based on the results. This makes it possible to provide an operation response and interface that matches the user's emotions.

[0067] The gesture operation unit has a multi-user support function that allows multiple users to operate the unit simultaneously. The gesture operation unit, for example, can be used to build a system that allows multiple users to perform gesture operations simultaneously, providing the experience of operating a work collaboratively. For example, operating a work together with family and friends. The multi-user support function is designed to allow multiple users to operate the work simultaneously, and each user's operation is reflected in real time. This allows multiple users to operate the work simultaneously.

[0068] The gesture operation unit has a function of analyzing the user's body movements and posture in real time to provide a more natural operation feel. The gesture operation unit, for example, analyzes the user's body movements and posture in real time to improve the accuracy of gesture operations. For example, it analyzes hand movements and body tilt to adjust the operation response. Body movements and posture are analyzed in real time using sensor technology, and the operation feel is adjusted based on the results. This makes it possible to provide a natural operation feel that matches the user's body movements and posture.

[0069] The gesture operation unit has functions that are provided in combination with voice commands and gaze tracking. The gesture operation unit builds a system in which, for example, a user can operate artworks by combining gesture operations and voice commands. For example, a work can be selected with hand movements, and detailed information displayed by voice. Voice commands and gaze tracking are provided in combination with gesture operations to realize a composite operation method. This enables operations that are combined with voice commands and gaze tracking.

[0070] The gesture operation unit is equipped with a remote interaction function that allows users in remote locations to operate the device collaboratively. The gesture operation unit, for example, builds a system that allows users in remote locations to perform gesture operations collaboratively. For example, operating a work of art together with friends or family in different locations. The remote interaction function synchronizes the operations of users in remote locations via the Internet, providing a collaborative experience. This allows users in remote locations to operate the work collaboratively.

[0071] The gesture operation unit has an emotion estimation function that provides operation feedback according to the user's emotion. The gesture operation unit, for example, analyzes the user's emotional state and provides operation feedback according to that emotion. For example, if the user is excited, it speeds up the operation response. The emotion estimation function estimates the user's emotion using facial expression recognition and biometric data analysis technology and adjusts the operation feedback based on the results. This makes it possible to provide operation feedback according to the user's emotion.

[0072] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0073] The voice guidance unit is equipped with an emotion estimation function that adjusts the tone and content of the explanation according to the user's emotional state. For example, when a user stands in front of a work of art, the AI ​​analyzes the user's facial expressions and tone of voice to estimate their emotional state. If the user is excited, the tone of the explanation will be brighter and more detailed information will be provided. Conversely, if the user is relaxed, the explanation can be given in a calmer tone and more concisely. The emotion estimation function uses facial expression recognition and voice analysis technology to estimate the user's emotions and adjusts the content of the voice guidance based on the results. This makes it possible to provide explanations that correspond to the user's emotions.

[0074] The audio guidance unit has the ability to personalize explanations based on the user's past visit history and interests. For example, it records the artworks the user has previously visited and themes that interest them, and customizes explanations for new artworks based on that information. If a user is interested in Impressionist paintings, it can prioritize introducing related artworks. Visit history and interests are obtained from user behavior data and survey results, and the generation AI analyzes this data to personalize the explanations. This allows the system to provide users with highly relevant information.

[0075] The voice guidance unit is equipped with an interactive question-and-answer function that answers users' questions in real time. For example, when a user asks a question about a work of art, the AI ​​responds in real time. If the user asks, "Who is the artist of this work?", the AI ​​will respond, "This work was painted by Picasso." The question-and-answer function uses natural language processing technology to analyze the user's question and generate an appropriate answer. This allows the user's question to be answered in real time.

[0076] The voice guidance unit has a multilingual support function that can accommodate users who speak different languages. For example, AI provides voice guidance based on the language selected by the user. It can support multiple languages, including English, French, and Chinese. The multilingual support function uses translation technology and voice synthesis technology to provide guidance in different languages. This allows it to accommodate users who speak different languages.

[0077] The audio guidance unit has the function of providing audio guidance in combination with visual supplementary information. For example, when a user stands in front of a work of art, AI provides a visual guide using AR technology along with audio guidance. Detailed information about the work and related images can be displayed via a smartphone or tablet. Visual supplementary information includes images, videos, text information, etc. and is provided to deepen the user's understanding. This allows it to be provided in combination with visual supplementary information.

[0078] The audio guidance unit has a function to play music and sound effects in the background according to the user's emotions. For example, it analyzes the user's emotional state and plays music in the background according to that emotion. If the user is relaxed, it can play calm music. The emotion estimation function estimates the user's emotions using facial expression recognition and voice analysis technology and selects music and sound effects based on the results. This makes it possible to provide music and sound effects according to the user's emotions.

[0079] The haptic feedback unit is equipped with an emotion estimation function that adjusts the intensity and pattern of haptics according to the user's emotional state. For example, when a user touches a work of art, AI analyzes the user's emotional state and adjusts the intensity of haptic feedback. If the user is excited, the intensity of haptic feedback can be increased. The emotion estimation function estimates the user's emotions using facial expression recognition and biometric data analysis technology, and adjusts the intensity and pattern of haptic feedback based on the results. This allows haptic feedback to be provided according to the user's emotions.

[0080] The haptic feedback unit has the function of providing detailed haptic information based on the historical background and production process of a work. For example, when a user touches a painting, the AI ​​provides haptic feedback that reproduces the brush strokes, allowing the user to feel the brush strokes and the thickness of the paint. The haptic information based on the historical background and production process reproduces the texture and shape of the work in detail and is provided to the user. This makes it possible to provide haptic information based on the historical background and production process of a work.

[0081] The haptic feedback unit has the ability to analyze the user's hand movement and pressure in real time, providing a more precise haptic experience. For example, when a user touches a work of art, AI analyzes the hand movement and pressure in real time and adjusts the haptic feedback. The strength and pattern of the haptic sensation can be changed according to the hand movement. Hand movement and pressure are analyzed in real time using sensor technology, and haptic feedback is provided based on the results. This allows for a precise haptic experience that corresponds to the user's hand movement and pressure.

[0082] The haptic feedback unit has the function of providing haptic feedback in combination with other sensory feedback such as temperature and vibration. For example, when a user touches a work of art, haptic feedback can be combined with temperature feedback. Touching a metal work of art can provide a cool feeling, while touching a wooden work of art can provide a warm feeling. Sensory feedback such as temperature and vibration can be provided in combination with haptic feedback to enrich the user's experience. This allows it to be provided in combination with other sensory feedback such as temperature and vibration.

[0083] The haptic feedback unit has an emotion estimation function that generates a haptic pattern according to the user's emotion. For example, it analyzes the user's emotional state and generates a haptic pattern according to that emotion. If the user is excited, it can provide strong vibrations or rhythmic haptics. The emotion estimation function estimates the user's emotion using facial expression recognition and biometric data analysis technology and generates a haptic pattern based on the results. This makes it possible to provide a haptic pattern according to the user's emotion.

[0084] The gesture operation unit is equipped with an emotion estimation function that adjusts operation response and interface according to the user's emotional state. For example, when a user performs gesture operations on a work, AI analyzes the user's emotional state and adjusts the operation response. If the user is excited, the operation response can be made faster. The emotion estimation function estimates the user's emotions using facial expression recognition and biometric data analysis technology, and adjusts the operation response and interface based on the results. This makes it possible to provide operation response and interface that correspond to the user's emotions.

[0085] The processing flow of the second embodiment will be briefly explained below.

[0086] Step 1: The audio guidance unit provides audio guidance. For example, when a user stands in front of a particular artwork, the AI ​​assistant "Gemini" will explain, "This work was painted by a famous 19th-century painter and is characterized by its use of color." The generation AI generates an appropriate explanation based on the user's location information and the selected artwork, and provides it via audio. The input to the generation AI is the user's location information and prompts regarding the artwork selection, and the generation AI generates audio guidance based on those prompts. Step 2: The haptic feedback unit provides haptic feedback. For example, when a user touches a particular sculpture, they can feel the unevenness and texture of the work's surface through the haptic device. The generation AI analyzes the work's data and provides appropriate feedback to the haptic device. The input to the generation AI is the work's data and the user's operation information on the haptic device, and the generation AI generates haptic feedback based on that information. Step 3: The gesture operation unit enables gesture operation. For example, when a user moves their hand and points in a specific direction, information about the artwork in that direction is displayed. The generation AI analyzes the user's gesture and performs appropriate operations based on that gesture. The input to the generation AI is the user's gesture information, and the generation AI performs operations based on that information.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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).

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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).

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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."

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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]

[0154] 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 guidance unit that provides voice guidance; a haptic feedback unit for providing haptic feedback; and a gesture operation unit that enables gesture operation. A system characterized by:

2. The voice guidance unit Equipped with an emotion estimation function that adjusts the tone and content of explanations according to the user's emotional state 2. The system of claim 1.

3. The voice guidance unit Multilingual support for users who speak different languages 2. The system of claim 1.

4. The haptic feedback unit Equipped with an emotion estimation function that adjusts the intensity and pattern of haptics according to the user's emotional state 2. The system of claim 1.

5. The gesture operation unit Equipped with an emotion estimation function that adjusts operation response and interface according to the user's emotional state 2. The system of claim 1.

6. The voice guidance unit It has the ability to personalize explanations based on the user's past visit history and interests.

2. The system of claim 1.

7. The haptic feedback unit It has the ability to analyze the user's hand movements and pressure in real time to provide a more precise tactile experience.

2. The system of claim 1.

8. The gesture operation unit It has the functionality to provide combined with voice commands and eye tracking.

2. The system of claim 1.

Images