System

Abstract

An object of a system according to an embodiment is to reproduce a tactile sense in a virtual world to improve a sense of immersion.SOLUTION: A system according to an embodiment includes a collection unit, a generation unit, and a provision unit. The collection unit collects the tactile data. The generation unit analyzes the tactile data collected by the collection unit and generates tactile data to be applied to the virtual object. The provision unit provides the virtual world with the tactile data generated by the generation 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] While existing technology has made progress in reproducing sight and sound in virtual worlds, it has not adequately reproduced the sense of touch, leaving room for improvement in improving the sense of immersion.

[0005] The system according to the embodiment aims to reproduce the sense of touch in a virtual world and improve the sense of immersion. [Means for solving the problem]

[0006] A system according to an embodiment includes a collection unit, a generation unit, and a provision unit. The collection unit collects haptic data. The generation unit analyzes the haptic data collected by the collection unit and generates haptic data to be applied to a virtual object. The provision unit provides the haptic data generated by the generation unit to the virtual world. [Effects of the Invention]

[0007] The system according to the embodiment can reproduce the sense of touch in a virtual world, improving the sense of immersion. [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) A system according to an embodiment of the present invention generates haptic data using a generative AI. This system enhances the sense of immersion in a virtual world by collecting, generating, and providing haptic data. For example, a tactile sensor is used to collect haptic data from real-world objects, and a generative AI analyzes the data to generate haptic data to be applied to a virtual object. Furthermore, by providing the generated haptic data to the virtual world, users can experience a realistic haptic experience, not only visual and auditory, but also haptic sensations. This allows for realistic haptic sensations to be provided in various scenarios, such as open-world games, character interactions, and musical instrument playing in the virtual world. This allows the system to enhance the sense of immersion in the virtual world. For example, users can realistically reproduce the tactile sensation of objects they touch in the virtual world, providing a more realistic experience.

[0029] A tactile data generation system according to an embodiment includes a collection unit, a generation unit, and a provision unit. The collection unit collects tactile data. The collection unit collects tactile data of a real-world object using, for example, a tactile sensor. The collection unit can collect, for example, pressure data, vibration data, temperature data, and the like. The collection unit brings the tactile sensor into contact with the object and collects the tactile sensation as digital data. The collection unit collects pressure data of the object using, for example, a pressure sensor. The collection unit can also collect vibration data of the object using a vibration sensor. The collection unit can also collect temperature data of the object using a temperature sensor. The generation unit analyzes the tactile data collected by the collection unit and generates tactile data to be applied to a virtual object. The generation unit analyzes the collected tactile data using, for example, a generation AI. The generation unit inputs the collected tactile data into the generation AI and generates tactile data to be applied to the virtual object. The generation unit analyzes the tactile data using, for example, data filtering, feature extraction, pattern recognition, or other methods. The generation unit generates haptic data to be applied to the virtual object based on the data analyzed by the generation AI. The provision unit provides the haptic data generated by the generation unit to the virtual world. The provision unit provides the haptic data using methods such as real-time provision, batch provision, and provision in response to user operations. The provision unit applies the generated haptic data to the virtual world to provide the user with a realistic haptic sensation. This allows the haptic data generation system according to the embodiment to improve the sense of immersion in the virtual world. For example, the user can realistically reproduce the tactile sensation of an object they touch in the virtual world, providing a more realistic experience.

[0030] The collection unit can collect tactile data of real-world objects using a tactile sensor. For example, the collection unit collects tactile data of real-world objects using a tactile sensor. The collection unit can collect pressure data of objects using a pressure sensor. For example, the collection unit contacts a pressure sensor with an object and collects the pressure data as digital data. The collection unit can also collect vibration data of an object using a vibration sensor. For example, the collection unit contacts a vibration sensor with an object and collects the vibration data as digital data. The collection unit can also collect temperature data of an object using a temperature sensor. For example, the collection unit contacts a temperature sensor with an object and collects the temperature data as digital data. In this way, by collecting tactile data of real-world objects, a realistic tactile sensation can be provided in the virtual world. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data collected by the tactile sensor into a generation AI and have the generation AI analyze the data.

[0031] The generation unit can analyze the collected tactile data and generate tactile data to be applied to the virtual object. For example, the generation unit analyzes the collected tactile data and generates tactile data to be applied to the virtual object. The generation unit analyzes the collected tactile data using a generation AI. The generation unit inputs the collected tactile data into the generation AI and generates tactile data to be applied to the virtual object. The generation unit analyzes the tactile data using methods such as data filtering, feature extraction, and pattern recognition. The generation unit generates tactile data to be applied to the virtual object based on the data analyzed by the generation AI. For example, the generation unit filters the collected pressure data and removes noise. The generation unit can also extract features from collected vibration data and generate tactile data to be applied to the virtual object. The generation unit can also perform pattern recognition on collected temperature data and generate tactile data to be applied to the virtual object. In this way, by analyzing the collected tactile data and generating tactile data to be applied to the virtual object, a realistic tactile sensation can be provided in the virtual world. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit may input collected haptic data to a generation AI and cause the generation AI to generate haptic data to be applied to a virtual object.

[0032] The providing unit can provide the generated haptic data to the virtual world. For example, the providing unit provides the generated haptic data to the virtual world. The providing unit provides the haptic data using a method such as real-time provision, batch provision, or provision in response to a user's operation. The providing unit applies the generated haptic data to the virtual world to provide a realistic haptic sensation to the user. For example, the providing unit can provide the generated haptic data to the virtual world in real time to instantly reproduce the tactile sensation of an object touched by the user. The providing unit can also provide the generated haptic data to the virtual world using batch processing to simultaneously reproduce the tactile sensations of multiple objects. The providing unit can also provide the generated haptic data in response to a user's operation to dynamically change the tactile sensation of an object touched by the user. In this way, by providing the generated haptic data to the virtual world, a realistic tactile sensation can be provided to the user. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the generated haptic data to a generating AI and cause the generating AI to optimize the haptic data to be provided to the virtual world.

[0033] The generation unit can generate the tactile sensation of an object that a user touches in a virtual world in an open-world game. For example, in an open-world game, the generation unit generates the tactile sensation of an object that a user touches in a virtual world. The generation unit uses a generation AI to generate the tactile sensation of an object in the open-world game. The generation unit inputs collected tactile data into the generation AI and generates tactile data to be applied to an object in the open-world game. The generation unit generates the tactile sensation of furniture in the game. For example, the generation unit generates the tactile sensation of a chair in the game, providing a realistic tactile sensation when a user touches the chair. The generation unit can also generate the tactile sensation of natural objects in the game. For example, the generation unit generates the tactile sensation of wood in the game, providing a realistic tactile sensation when a user touches the wood. The generation unit can also generate the tactile sensation of tools in the game. For example, the generation unit generates the tactile sensation of a hammer in the game, providing a realistic tactile sensation when a user holds the hammer. This allows the immersion of the game to be improved by generating the tactile sensation of objects that a user touches in the virtual world in an open-world game. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit may input collected haptic data to a generation AI and cause the generation AI to generate haptic data to be applied to objects in an open-world game.

[0034] The generation unit can generate the tactile sensation of an instrument when playing the instrument in a virtual space. For example, the generation unit generates the tactile sensation of an instrument when playing the instrument in a virtual space. The generation unit uses a generation AI to generate the tactile sensation of the instrument in the virtual space. The generation unit inputs collected tactile data into the generation AI and generates tactile data to be applied to the instrument in the virtual space. The generation unit generates, for example, the tactile sensation of a piano. For example, the generation unit generates the tactile sensation of a piano key, providing a realistic tactile sensation when a user presses a key. The generation unit can also generate the tactile sensation of a guitar. For example, the generation unit generates the tactile sensation of guitar strings, providing a realistic tactile sensation when a user plucks the strings. The generation unit can also generate the tactile sensation of a drum. For example, the generation unit generates the tactile sensation of a drumstick, providing a realistic tactile sensation when a user holds the stick. In this way, by generating the tactile sensation of an instrument when playing the instrument in a virtual space, a more realistic playing experience can be provided. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit may input collected haptic data to a generation AI and cause the generation AI to generate haptic data to be applied to a musical instrument in a virtual space.

[0035] The collection unit can adjust the sensitivity of the tactile sensor to collect detailed tactile sensations of different objects. For example, the collection unit adjusts the sensitivity of the tactile sensor to collect detailed tactile sensations of different objects. The collection unit can increase the sensitivity of the tactile sensor to collect the fine tactile sensations of fabric fibers. For example, the collection unit increases the sensitivity of the tactile sensor to collect the fine tactile sensations of fabric fibers as digital data. The collection unit can also adjust the sensitivity of the tactile sensor to collect the fine irregularities on a metal surface. For example, the collection unit adjusts the sensitivity of the tactile sensor to collect the fine irregularities on a metal surface as digital data. The collection unit can also change the sensitivity of the tactile sensor to collect the annual rings and texture of wood in detail. For example, the collection unit changes the sensitivity of the tactile sensor to collect the annual rings and texture of wood as digital data. In this way, by adjusting the sensitivity of the tactile sensor, the fine tactile sensations of different objects can be collected. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input data collected by a tactile sensor into the generation AI and have the generation AI adjust the sensitivity.

[0036] The collection unit can analyze the collected tactile data in real time and provide instant feedback. The collection unit, for example, analyzes the collected tactile data in real time and provides instant feedback. The collection unit can analyze data collected by a tactile sensor in real time and provide instant tactile feedback to the user. The collection unit can also analyze the collected tactile data in real time and instantly reflect the result on a virtual object. The collection unit can also analyze the collected tactile data in real time and instantly reflect the result on a virtual object. The collection unit can also analyze the tactile data in real time and instantly change the tactile sensation in response to a user's operation. The collection unit, for example, analyzes the tactile data in real time and instantly changes the tactile sensation in response to a user's operation. In this way, the collected tactile data can be analyzed in real time and instant feedback can be provided to the user, thereby instantly providing a tactile sensation to the user. Some or all of the above-described processing in the collection unit may be performed, for example, using AI or without AI. For example, the collection unit can input data collected by a tactile sensor into the generation AI, allowing the generation AI to perform real-time analysis and feedback.

[0037] The collection unit can collect three-dimensional tactile data by bringing the tactile sensor into contact with an object from multiple angles. The collection unit, for example, brings the tactile sensor into contact with an object from multiple angles to collect three-dimensional tactile data. The collection unit, for example, brings the tactile sensor into contact with the surface of an object from multiple angles to collect three-dimensional tactile data. The collection unit, for example, brings the tactile sensor into contact with the surface of an object from multiple angles to collect three-dimensional tactile data as digital data. The collection unit can also collect omnidirectional tactile data by bringing the tactile sensor into contact with an object while rotating it. The collection unit, for example, brings the tactile sensor into contact with an object while rotating it to collect omnidirectional tactile data as digital data. The collection unit can also collect detailed three-dimensional tactile data by bringing the tactile sensor into contact with an object from different heights or positions. The collection unit, for example, brings the tactile sensor into contact with an object from different heights or positions to collect detailed three-dimensional tactile data as digital data. This allows the tactile sensor to contact an object from multiple angles, thereby collecting three-dimensional tactile data. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input data collected by the tactile sensor into a generation AI, causing the generation AI to collect three-dimensional tactile data.

[0038] The collection unit can simultaneously collect environmental data such as the temperature and humidity of an object when collecting tactile data. For example, the collection unit simultaneously collects environmental data such as the temperature and humidity of an object when collecting tactile data. When the tactile sensor contacts an object, the collection unit simultaneously collects the temperature of the object using a temperature sensor. For example, when the tactile sensor contacts an object, the collection unit simultaneously collects the temperature of the object using a temperature sensor as digital data. The collection unit can also simultaneously collect the humidity of the object using a humidity sensor when collecting tactile data. For example, when collecting tactile data, the collection unit collects the humidity of the object using a humidity sensor as digital data. The collection unit can also simultaneously collect the ambient temperature and humidity using an environmental sensor when collecting tactile data. For example, when collecting tactile data, the collection unit collects the ambient temperature and humidity as digital data using an environmental sensor. By simultaneously collecting environmental data when collecting tactile data, a more detailed data set can be created. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data collected by a tactile sensor into the generation AI and cause the generation AI to collect environmental data.

[0039] The collection unit can simultaneously collect color and shape data of an object when collecting tactile data, thereby creating a comprehensive dataset. For example, the collection unit simultaneously collects color and shape data of an object when collecting tactile data, thereby creating a comprehensive dataset. The collection unit simultaneously collects the color of the object using a camera when the tactile sensor contacts the object. For example, the collection unit simultaneously collects the color of the object using a camera when the tactile sensor contacts the object as digital data. The collection unit can also simultaneously collect shape data of the object using a 3D scanner when collecting tactile data. For example, the collection unit collects shape data of the object as digital data when collecting tactile data. The collection unit can also integrate the color and shape data of the object when collecting tactile data, thereby creating a comprehensive dataset. For example, the collection unit integrates the color and shape data of the object when collecting tactile data, thereby creating a comprehensive dataset as digital data. In this way, a comprehensive dataset can be created by simultaneously collecting color and shape data of an object when collecting tactile data. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input data collected by a tactile sensor into the generation AI and cause the generation AI to collect color and shape data.

[0040] The collection unit can customize the collection method by referring to the user's past tactile data collection history when collecting tactile data. For example, the collection unit customizes the collection method by referring to the user's past tactile data collection history when collecting tactile data. The collection unit analyzes the user's past tactile data collection history and proposes an optimal collection method. For example, the collection unit analyzes the user's past tactile data collection history and proposes an optimal collection method. The collection unit can also customize the collection method based on tactile data collected by the user in the past. The collection unit customizes the collection method based on tactile data collected by the user in the past. The collection unit can also adjust the collection timing and sensitivity by referring to the user's past tactile data collection history. For example, the collection unit adjusts the collection timing and sensitivity by referring to the user's past tactile data collection history. In this way, by customizing the collection method by referring to the user's past tactile data collection history, a more appropriate collection method can be provided. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past tactile data collection history into the generation AI and cause the generation AI to customize the collection method.

[0041] The generation unit can adjust the generated haptic data for application to different virtual objects. For example, the generation unit adjusts the generated haptic data for application to different virtual objects. The generation unit uses a generation AI to optimize the generated haptic data for application to different virtual objects. The generation unit inputs collected haptic data into the generation AI and optimizes it for application to different virtual objects. For example, the generation unit optimizes the generated haptic data for application to furniture in a virtual space. For example, the generation unit optimizes the generated haptic data for application to furniture in a virtual space. The generation unit can also optimize the generated haptic data for application to a character in a virtual space. For example, the generation unit optimizes the generated haptic data for application to a character in a virtual space. The generation unit can also optimize the generated haptic data for application to natural objects (trees, stones, etc.) in the virtual space. For example, the generation unit optimizes the generated haptic data for application to natural objects (trees, stones, etc.) in the virtual space. In this way, by optimizing the generated haptic data for application to different virtual objects, a more realistic tactile sensation can be provided. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit may input collected tactile data to a generation AI and cause the generation AI to perform optimization for application to different virtual objects.

[0042] The generation unit can improve accuracy by comparing the generated tactile data with the user's past tactile experiences. For example, the generation unit compares the generated tactile data with the user's past tactile experiences to improve accuracy. The generation unit uses a generation AI to compare the generated tactile data with the user's past tactile experiences. The generation unit inputs collected tactile data into the generation AI and compares it with the user's past tactile experiences to improve accuracy. For example, the generation unit compares the generated tactile data with the user's past tactile experiences to improve accuracy. For example, the generation unit compares the generated tactile data with the user's past tactile experiences to improve accuracy. The generation unit can also fine-tune the generated tactile data based on the user's past tactile experiences. For example, the generation unit fine-tunes the generated tactile data based on the user's past tactile experiences. For example, the generation unit can optimize the generated tactile data by comparing it with the user's past tactile experiences. For example, the generation unit optimizes the generated tactile data by comparing it with the user's past tactile experiences. This allows for improved accuracy by comparing the generated haptic data with the user's past haptic experiences. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit may input collected haptic data into the generation AI and cause the generation AI to perform processing to improve accuracy by comparing the data with the user's past haptic experiences.

[0043] The generation unit can customize the generated haptic data according to different scenarios. For example, the generation unit customizes the generated haptic data according to different scenarios. The generation unit customizes the generated haptic data according to different scenarios using a generation AI. The generation unit inputs collected haptic data into the generation AI and customizes it according to different scenarios. For example, the generation unit customizes the generated haptic data to apply to a battle scene in a game. For example, the generation unit customizes the generated haptic data to apply to a battle scene in a game. The generation unit can also customize the generated haptic data to apply to an educational virtual experiment. For example, the generation unit customizes the generated haptic data to apply to an educational virtual experiment. The generation unit can also customize the generated haptic data to apply to a medical simulation. For example, the generation unit customizes the generated haptic data to apply to a medical simulation. By customizing the generated haptic data according to different scenarios, a more appropriate haptic sensation can be provided. Some or all of the above-described processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input the collected tactile data into the generation AI and have the generation AI perform customization according to different scenarios.

[0044] The generation unit can adapt the generated tactile data to different environmental conditions in the virtual space. For example, the generation unit adapts the generated tactile data to different environmental conditions in the virtual space. The generation unit uses a generation AI to adapt the generated tactile data to different environmental conditions in the virtual space. The generation unit inputs collected tactile data into the generation AI and adapts it to different environmental conditions in the virtual space. For example, the generation unit adapts the generated tactile data to a cold region in the virtual space. For example, the generation unit adapts the generated tactile data to a cold region in the virtual space. The generation unit can also adapt the generated tactile data to a humid environment in the virtual space. For example, the generation unit adapts the generated tactile data to a humid environment in the virtual space. The generation unit can also adapt the generated tactile data to a high temperature environment in the virtual space. For example, the generation unit adapts the generated tactile data to a high temperature environment in the virtual space. In this way, by adapting the generated tactile data to different environmental conditions in the virtual space, a more realistic tactile sensation can be provided. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit may input the collected tactile data to the generation AI and cause the generation AI to perform processing to adapt to different environmental conditions in the virtual space.

[0045] The generation unit can customize the generated tactile data based on the user's physical characteristics. For example, the generation unit customizes the generated tactile data based on the user's physical characteristics. The generation unit customizes the generated tactile data based on the user's physical characteristics using a generation AI. The generation unit inputs collected tactile data into the generation AI and customizes it based on the user's physical characteristics. For example, the generation unit customizes the generated tactile data to match the size of the user's hand. For example, the generation unit customizes the generated tactile data to match the size of the user's hand. The generation unit can also adjust the generated tactile data based on the user's tactile sensitivity. For example, the generation unit adjusts the generated tactile data based on the user's tactile sensitivity. The generation unit can also optimize the generated tactile data based on the user's physical characteristics. For example, the generation unit optimizes the generated tactile data based on the user's physical characteristics. By customizing the generated tactile data based on the user's physical characteristics, a more appropriate tactile sensation can be provided. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the collected tactile data into the generation AI and cause the generation AI to perform customization based on the user's physical characteristics.

[0046] The generation unit can integrate the generated tactile data with other sensory data to provide a comprehensive experience. For example, the generation unit can integrate the generated tactile data with other sensory data to provide a comprehensive experience. The generation unit can integrate the generated tactile data with other sensory data using a generation AI. The generation unit inputs collected tactile data into the generation AI and integrates it with other sensory data. For example, the generation unit can integrate the generated tactile data with visual data to provide a realistic experience. For example, the generation unit can integrate the generated tactile data with visual data to provide a realistic experience. The generation unit can also integrate the generated tactile data with auditory data to enhance a sense of realism. For example, the generation unit can integrate the generated tactile data with auditory data to enhance a sense of realism. For example, the generation unit can integrate the generated tactile data with visual and auditory data to provide a comprehensive experience. For example, the generation unit can integrate the generated tactile data with visual and auditory data to provide a comprehensive experience. In this way, by integrating the generated tactile data with other sensory data, a more realistic experience can be provided. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit may input collected tactile data to the generation AI and have the generation AI integrate it with other sensory data.

[0047] The providing unit can dynamically adjust the provided haptic data based on real-time feedback from the user. For example, the providing unit dynamically adjusts the provided haptic data based on real-time feedback from the user. When the user provides real-time feedback on a tactile sensation, the providing unit adjusts the tactile sensation based on the feedback. For example, when the user provides real-time feedback on a tactile sensation, the providing unit adjusts the tactile sensation based on the feedback. Furthermore, the providing unit can change the tactile sensation in real time if the user is dissatisfied with the tactile sensation. For example, the providing unit changes the tactile sensation in real time if the user is dissatisfied with the tactile sensation. Furthermore, the providing unit can maintain the tactile sensation if the user is satisfied with the tactile sensation. For example, the providing unit maintains the tactile sensation if the user is satisfied with the tactile sensation. In this way, by dynamically adjusting the provided haptic data based on real-time feedback from the user, a more appropriate tactile sensation can be provided. Some or all of the above-described processing by the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's real-time feedback to the generating AI and cause the generating AI to dynamically adjust the tactile data.

[0048] The providing unit can provide the tactile data in conjunction with the user's past tactile experiences. For example, the providing unit provides the tactile data in conjunction with the user's past tactile experiences. The providing unit provides an optimal tactile sensation based on the user's past tactile experiences. For example, the providing unit provides an optimal tactile sensation based on the user's past tactile experiences. The providing unit can also reproduce and provide a tactile sensation that the user previously preferred. For example, the providing unit can reproduce and provide a tactile sensation that the user previously preferred. The providing unit can also customize a tactile sensation in conjunction with the user's past tactile experiences. For example, the providing unit customizes a tactile sensation in conjunction with the user's past tactile experiences. In this way, by linking the tactile data to the user's past tactile experiences, a more appropriate tactile sensation can be provided. Some or all of the above-described processing by the providing unit may be performed using, or without, AI. For example, the providing unit can input the user's past tactile experience data to the generation AI and cause the generation AI to link the tactile data.

[0049] The providing unit can adapt the provided haptic data to different devices. For example, the providing unit adapts the provided haptic data to different devices. The providing unit provides the provided haptic data in a manner compatible with VR gloves. For example, the providing unit provides the provided haptic data in a manner compatible with VR gloves. The providing unit can also provide the provided haptic data in a manner compatible with a haptic feedback device. For example, the providing unit provides the provided haptic data in a manner compatible with a haptic feedback device. The providing unit can also provide the provided haptic data optimized for different devices. For example, the providing unit provides the provided haptic data optimized for different devices. In this way, by adapting the provided haptic data to different devices, tactile sensations can be provided on a wider variety of devices. Some or all of the above-described processing by the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit may input the provided haptic data to a generation AI and cause the generation AI to adapt to different devices.

[0050] The providing unit can customize the provided tactile data based on the user's geographical location information. The providing unit customizes the provided tactile data based on the user's geographical location information, for example. The providing unit provides a warm tactile sensation when the user is in a cold region. The providing unit provides a warm tactile sensation when the user is in a cold region, for example. The providing unit can also provide a dry tactile sensation when the user is in a humid region. The providing unit can also provide a dry tactile sensation when the user is in a humid region, for example. The providing unit can also provide a cool tactile sensation when the user is in a hot region. The providing unit can also provide a cool tactile sensation when the user is in a hot region, for example. In this way, by customizing the provided tactile data based on the user's geographical location information, a more appropriate tactile sensation can be provided. Some or all of the above-described processing by the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's geographical location information to the generation AI and cause the generation AI to customize the tactile data.

[0051] The providing unit can provide the provided tactile data in conjunction with the user's social media activity. For example, the providing unit provides the provided tactile data in conjunction with the user's social media activity. The providing unit provides a tactile sensation related to a place where the user has checked in on social media. For example, the providing unit provides a tactile sensation related to a place where the user has checked in on social media. The providing unit can also analyze the content of the user's social media posts and provide a related tactile sensation. For example, the providing unit can analyze the content of the user's social media posts and provide a related tactile sensation. The providing unit can also provide a related tactile sensation by referring to the activities of the user's friends on social media. For example, the providing unit can provide a related tactile sensation by referring to the activities of the user's friends on social media. In this way, by linking the provided tactile data with the user's social media activity, more appropriate tactile sensations can be provided. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's social media activity data into a generation AI and cause the generation AI to link the tactile data.

[0052] The providing unit can customize the method of providing the provided tactile data by reflecting the user's past feedback. For example, the providing unit customizes the method of providing the provided tactile data by reflecting the user's past feedback. The providing unit adjusts the intensity of the tactile sensation based on the user's past feedback. For example, the providing unit adjusts the intensity of the tactile sensation based on the user's past feedback. The providing unit can also change the type of tactile sensation by referring to the user's past feedback. For example, the providing unit changes the type of tactile sensation by referring to the user's past feedback. The providing unit can also optimize the method of providing the tactile sensation by reflecting the user's past feedback. For example, the providing unit optimizes the method of providing the tactile sensation by reflecting the user's past feedback. In this way, a more appropriate tactile sensation can be provided by customizing the method of providing the provided tactile data by reflecting the user's past feedback. Some or all of the above-described processing by the providing unit may be performed using, or without, AI. For example, the providing unit can input the user's past feedback data to the generation AI and cause the generation AI to customize the method of providing the tactile data.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] In addition to collecting tactile data, the collection unit can also simultaneously collect acoustic data of an object. For example, the collection unit combines a tactile sensor and a microphone to collect acoustic data when an object is touched. This allows for a more realistic experience by reproducing both the tactile feel and the sound of the object. The collection unit can also analyze the acoustic data to estimate the material and condition of the object. For example, the collection unit can collect tactile feel and acoustic data of wood and estimate the type and condition of the wood based on that data. Furthermore, the collection unit can also estimate the internal structure of an object using the acoustic data. For example, the collection unit can collect tactile feel and acoustic data of metal and estimate the internal structure of the metal based on that data.

[0055] When analyzing the collected tactile data, the generation unit can refer to the user's past tactile data history. For example, the generation unit analyzes the current tactile data based on tactile data previously collected by the user. This allows tactile data to be generated based on the user's preferences and past experiences. The generation unit can also improve the accuracy of the tactile data analysis using the user's past tactile data history. For example, the generation unit can remove noise from the current tactile data based on tactile data previously collected by the user. Furthermore, the generation unit can customize the method of generating tactile data using the user's past tactile data history. For example, the generation unit can extract features of tactile data that the user previously preferred and generate current tactile data based on the features.

[0056] When providing the generated haptic data, the providing unit can collect real-time feedback from the user and dynamically adjust the haptic data based on the feedback. For example, when the user provides real-time feedback on a tactile sensation, the providing unit adjusts the tactile sensation based on the feedback. The providing unit can also change the tactile sensation in real time if the user is dissatisfied with the tactile sensation. For example, the providing unit changes the tactile sensation in real time if the user is dissatisfied with the tactile sensation. The providing unit can also maintain the tactile sensation if the user is satisfied with the tactile sensation. For example, the providing unit maintains the tactile sensation if the user is satisfied with the tactile sensation. In this way, by dynamically adjusting the tactile data to be provided based on the user's real-time feedback, a more appropriate tactile sensation can be provided.

[0057] The collection unit can simultaneously collect color and shape data of an object when collecting tactile data to create a comprehensive dataset. For example, when a tactile sensor contacts an object, the collection unit simultaneously collects the color of the object using a camera. This allows the tactile data and visual data to be integrated to create a more detailed dataset. The collection unit can also simultaneously collect shape data of an object using a 3D scanner when collecting tactile data. For example, the collection unit collects shape data of an object as digital data using a 3D scanner when collecting tactile data. The collection unit can also integrate color and shape data of an object when collecting tactile data to create a comprehensive dataset. For example, the collection unit integrates color and shape data of an object when collecting tactile data to create a comprehensive dataset as digital data.

[0058] The generation unit can adjust the generated haptic data for application to different virtual objects. For example, the generation unit uses a generation AI to optimize the generated haptic data for application to different virtual objects. This allows for a more realistic tactile sensation to be provided by applying the generated haptic data to different virtual objects. The generation unit can also optimize the generated haptic data for application to furniture in a virtual space. For example, the generation unit optimizes the generated haptic data for application to furniture in a virtual space. The generation unit can also optimize the generated haptic data for application to a character in a virtual space. For example, the generation unit optimizes the generated haptic data for application to a character in a virtual space. The generation unit can also optimize the generated haptic data for application to natural objects (trees, stones, etc.) in the virtual space. For example, the generation unit optimizes the generated haptic data for application to natural objects (trees, stones, etc.) in the virtual space.

[0059] The collection unit can simultaneously collect environmental data such as the temperature and humidity of an object when collecting tactile data. For example, when the tactile sensor contacts an object, the collection unit simultaneously collects the temperature of the object using a temperature sensor. This allows the tactile data and environmental data to be integrated to create a more detailed data set. The collection unit can also simultaneously collect the humidity of the object using a humidity sensor when collecting tactile data. For example, when collecting tactile data, the collection unit collects the humidity of the object as digital data using a humidity sensor. Furthermore, the collection unit can also simultaneously collect the ambient temperature and humidity using an environmental sensor when collecting tactile data. For example, when collecting tactile data, the collection unit collects the ambient temperature and humidity as digital data using an environmental sensor.

[0060] The collection unit can simultaneously collect color and shape data of an object when collecting tactile data to create a comprehensive dataset. For example, when a tactile sensor contacts an object, the collection unit simultaneously collects the color of the object using a camera. This allows the tactile data and visual data to be integrated to create a more detailed dataset. The collection unit can also simultaneously collect shape data of an object using a 3D scanner when collecting tactile data. For example, the collection unit collects shape data of an object as digital data using a 3D scanner when collecting tactile data. The collection unit can also integrate color and shape data of an object when collecting tactile data to create a comprehensive dataset. For example, the collection unit integrates color and shape data of an object when collecting tactile data to create a comprehensive dataset as digital data.

[0061] When collecting tactile data, the collection unit can customize the collection method by referring to the user's past tactile data collection history. For example, the collection unit analyzes the user's past tactile data collection history and proposes an optimal collection method. This makes it possible to provide a more appropriate collection method by customizing the collection method by referring to the user's past tactile data collection history. The collection unit can also customize the collection method based on tactile data collected by the user in the past. For example, the collection unit customizes the collection method based on tactile data collected by the user in the past. The collection unit can also adjust the collection timing and sensitivity by referring to the user's past tactile data collection history. For example, the collection unit adjusts the collection timing and sensitivity by referring to the user's past tactile data collection history.

[0062] The generation unit can adjust the generated haptic data for application to different virtual objects. For example, the generation unit uses a generation AI to optimize the generated haptic data for application to different virtual objects. This allows for a more realistic tactile sensation to be provided by applying the generated haptic data to different virtual objects. The generation unit can also optimize the generated haptic data for application to furniture in a virtual space. For example, the generation unit optimizes the generated haptic data for application to furniture in a virtual space. The generation unit can also optimize the generated haptic data for application to a character in a virtual space. For example, the generation unit optimizes the generated haptic data for application to a character in a virtual space. The generation unit can also optimize the generated haptic data for application to natural objects (trees, stones, etc.) in the virtual space. For example, the generation unit optimizes the generated haptic data for application to natural objects (trees, stones, etc.) in the virtual space.

[0063] The generation unit can improve accuracy by comparing the generated tactile data with the user's past tactile experiences. For example, the generation unit uses a generation AI to compare the generated tactile data with the user's past tactile experiences. This allows accuracy to be improved by comparing the generated tactile data with the user's past tactile experiences. The generation unit can also fine-tune the generated tactile data based on the user's past tactile experiences. For example, the generation unit fine-tunes the generated tactile data based on the user's past tactile experiences. The generation unit can also optimize the generated tactile data by comparing it with the user's past tactile experiences. For example, the generation unit optimizes the generated tactile data by comparing it with the user's past tactile experiences.

[0064] The generation unit can customize the generated haptic data according to different scenarios. For example, the generation unit uses a generation AI to customize the generated haptic data according to different scenarios. In this way, by customizing the generated haptic data according to different scenarios, a more appropriate haptic sensation can be provided. The generation unit can also customize the generated haptic data for application to a battle scene in a game. For example, the generation unit customizes the generated haptic data for application to a battle scene in a game. The generation unit can also customize the generated haptic data for application to a virtual experiment for education. For example, the generation unit customizes the generated haptic data for application to a virtual experiment for education. The generation unit can also customize the generated haptic data for application to a medical simulation. For example, the generation unit customizes the generated haptic data for application to a medical simulation.

[0065] The generation unit can customize the generated tactile data based on the user's physical characteristics. For example, the generation unit uses a generation AI to customize the generated tactile data based on the user's physical characteristics. By customizing the generated tactile data based on the user's physical characteristics, a more appropriate tactile sensation can be provided. The generation unit can also customize the generated tactile data to match the size of the user's hand. For example, the generation unit customizes the generated tactile data to match the size of the user's hand. The generation unit can also adjust the generated tactile data based on the user's tactile sensitivity. For example, the generation unit adjusts the generated tactile data based on the user's tactile sensitivity. The generation unit can also optimize the generated tactile data based on the user's physical characteristics. For example, the generation unit optimizes the generated tactile data based on the user's physical characteristics.

[0066] The generation unit can integrate the generated tactile data with other sensory data to provide a comprehensive experience. For example, the generation unit can integrate the generated tactile data with other sensory data using a generation AI. In this way, by integrating the generated tactile data with other sensory data, a more realistic experience can be provided. The generation unit can also integrate the generated tactile data with visual data to provide a realistic experience. For example, the generation unit can integrate the generated tactile data with visual data to provide a realistic experience. The generation unit can also integrate the generated tactile data with auditory data to enhance the sense of realism. For example, the generation unit can integrate the generated tactile data with auditory data to enhance the sense of realism. The generation unit can also integrate the generated tactile data with visual and auditory data to provide a comprehensive experience. For example, the generation unit can integrate the generated tactile data with visual and auditory data to provide a comprehensive experience.

[0067] The processing flow of the first embodiment will be briefly explained below.

[0068] Step 1: The collection unit collects tactile data. For example, the collection unit uses a tactile sensor to collect tactile data of a real-world object. The collection unit can collect pressure data, vibration data, temperature data, etc. The collection unit brings the tactile sensor into contact with the object and collects the tactile sensation as digital data. For example, the collection unit uses a pressure sensor to collect pressure data of the object, a vibration sensor to collect vibration data of the object, and a temperature sensor to collect temperature data of the object. Step 2: The generation unit analyzes the tactile data collected by the collection unit and generates tactile data to be applied to the virtual object. The generation unit analyzes the collected tactile data using a generation AI and analyzes the tactile data using methods such as data filtering, feature extraction, and pattern recognition. The generation unit generates tactile data to be applied to the virtual object based on the data analyzed by the generation AI. Step 3: The providing unit provides the haptic data generated by the generating unit to the virtual world. The providing unit provides the haptic data using a method such as real-time provision, batch provision, or provision in response to a user's operation. The providing unit applies the generated haptic data to the virtual world to provide the user with a realistic tactile sensation.

[0069] (Example 2) A system according to an embodiment of the present invention generates haptic data using a generative AI. This system enhances the sense of immersion in a virtual world by collecting, generating, and providing haptic data. For example, a tactile sensor is used to collect haptic data from real-world objects, and a generative AI analyzes the data to generate haptic data to be applied to a virtual object. Furthermore, by providing the generated haptic data to the virtual world, users can experience a realistic haptic experience, not only visual and auditory, but also haptic sensations. This allows for realistic haptic sensations to be provided in various scenarios, such as open-world games, character interactions, and musical instrument playing in the virtual world. This allows the system to enhance the sense of immersion in the virtual world. For example, users can realistically reproduce the tactile sensation of objects they touch in the virtual world, providing a more realistic experience.

[0070] A tactile data generation system according to an embodiment includes a collection unit, a generation unit, and a provision unit. The collection unit collects tactile data. The collection unit collects tactile data of a real-world object using, for example, a tactile sensor. The collection unit can collect, for example, pressure data, vibration data, temperature data, and the like. The collection unit brings the tactile sensor into contact with the object and collects the tactile sensation as digital data. The collection unit collects pressure data of the object using, for example, a pressure sensor. The collection unit can also collect vibration data of the object using a vibration sensor. The collection unit can also collect temperature data of the object using a temperature sensor. The generation unit analyzes the tactile data collected by the collection unit and generates tactile data to be applied to a virtual object. The generation unit analyzes the collected tactile data using, for example, a generation AI. The generation unit inputs the collected tactile data into the generation AI and generates tactile data to be applied to the virtual object. The generation unit analyzes the tactile data using, for example, data filtering, feature extraction, pattern recognition, or other methods. The generation unit generates haptic data to be applied to the virtual object based on the data analyzed by the generation AI. The provision unit provides the haptic data generated by the generation unit to the virtual world. The provision unit provides the haptic data using methods such as real-time provision, batch provision, and provision in response to user operations. The provision unit applies the generated haptic data to the virtual world to provide the user with a realistic haptic sensation. This allows the haptic data generation system according to the embodiment to improve the sense of immersion in the virtual world. For example, the user can realistically reproduce the tactile sensation of an object they touch in the virtual world, providing a more realistic experience.

[0071] The collection unit can collect tactile data of real-world objects using a tactile sensor. For example, the collection unit collects tactile data of real-world objects using a tactile sensor. The collection unit can collect pressure data of objects using a pressure sensor. For example, the collection unit contacts a pressure sensor with an object and collects the pressure data as digital data. The collection unit can also collect vibration data of an object using a vibration sensor. For example, the collection unit contacts a vibration sensor with an object and collects the vibration data as digital data. The collection unit can also collect temperature data of an object using a temperature sensor. For example, the collection unit contacts a temperature sensor with an object and collects the temperature data as digital data. In this way, by collecting tactile data of real-world objects, a realistic tactile sensation can be provided in the virtual world. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data collected by the tactile sensor into a generation AI and have the generation AI analyze the data.

[0072] The generation unit can analyze the collected tactile data and generate tactile data to be applied to the virtual object. For example, the generation unit analyzes the collected tactile data and generates tactile data to be applied to the virtual object. The generation unit analyzes the collected tactile data using a generation AI. The generation unit inputs the collected tactile data into the generation AI and generates tactile data to be applied to the virtual object. The generation unit analyzes the tactile data using methods such as data filtering, feature extraction, and pattern recognition. The generation unit generates tactile data to be applied to the virtual object based on the data analyzed by the generation AI. For example, the generation unit filters the collected pressure data and removes noise. The generation unit can also extract features from collected vibration data and generate tactile data to be applied to the virtual object. The generation unit can also perform pattern recognition on collected temperature data and generate tactile data to be applied to the virtual object. In this way, by analyzing the collected tactile data and generating tactile data to be applied to the virtual object, a realistic tactile sensation can be provided in the virtual world. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit may input collected haptic data to a generation AI and cause the generation AI to generate haptic data to be applied to a virtual object.

[0073] The providing unit can provide the generated haptic data to the virtual world. For example, the providing unit provides the generated haptic data to the virtual world. The providing unit provides the haptic data using a method such as real-time provision, batch provision, or provision in response to a user's operation. The providing unit applies the generated haptic data to the virtual world to provide a realistic haptic sensation to the user. For example, the providing unit can provide the generated haptic data to the virtual world in real time to instantly reproduce the tactile sensation of an object touched by the user. The providing unit can also provide the generated haptic data to the virtual world using batch processing to simultaneously reproduce the tactile sensations of multiple objects. The providing unit can also provide the generated haptic data in response to a user's operation to dynamically change the tactile sensation of an object touched by the user. In this way, by providing the generated haptic data to the virtual world, a realistic tactile sensation can be provided to the user. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the generated haptic data to a generating AI and cause the generating AI to optimize the haptic data to be provided to the virtual world.

[0074] The generation unit can generate the tactile sensation of an object that a user touches in a virtual world in an open-world game. For example, in an open-world game, the generation unit generates the tactile sensation of an object that a user touches in a virtual world. The generation unit uses a generation AI to generate the tactile sensation of an object in the open-world game. The generation unit inputs collected tactile data into the generation AI and generates tactile data to be applied to an object in the open-world game. The generation unit generates the tactile sensation of furniture in the game. For example, the generation unit generates the tactile sensation of a chair in the game, providing a realistic tactile sensation when a user touches the chair. The generation unit can also generate the tactile sensation of natural objects in the game. For example, the generation unit generates the tactile sensation of wood in the game, providing a realistic tactile sensation when a user touches the wood. The generation unit can also generate the tactile sensation of tools in the game. For example, the generation unit generates the tactile sensation of a hammer in the game, providing a realistic tactile sensation when a user holds the hammer. This allows the immersion of the game to be improved by generating the tactile sensation of objects that a user touches in the virtual world in an open-world game. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit may input collected haptic data to a generation AI and cause the generation AI to generate haptic data to be applied to objects in an open-world game.

[0075] The generation unit can generate the tactile sensation of an instrument when playing the instrument in a virtual space. For example, the generation unit generates the tactile sensation of an instrument when playing the instrument in a virtual space. The generation unit uses a generation AI to generate the tactile sensation of the instrument in the virtual space. The generation unit inputs collected tactile data into the generation AI and generates tactile data to be applied to the instrument in the virtual space. The generation unit generates, for example, the tactile sensation of a piano. For example, the generation unit generates the tactile sensation of a piano key, providing a realistic tactile sensation when a user presses a key. The generation unit can also generate the tactile sensation of a guitar. For example, the generation unit generates the tactile sensation of guitar strings, providing a realistic tactile sensation when a user plucks the strings. The generation unit can also generate the tactile sensation of a drum. For example, the generation unit generates the tactile sensation of a drumstick, providing a realistic tactile sensation when a user holds the stick. In this way, by generating the tactile sensation of an instrument when playing the instrument in a virtual space, a more realistic playing experience can be provided. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit may input collected haptic data to a generation AI and cause the generation AI to generate haptic data to be applied to a musical instrument in a virtual space.

[0076] The collection unit can estimate the user's emotion and adjust the timing of collecting tactile data based on the estimated user's emotion. The collection unit, for example, estimates the user's emotion and adjusts the timing of collecting tactile data based on the estimated user's emotion. The collection unit estimates the user's emotion using an emotion estimation algorithm. The collection unit, for example, analyzes the user's facial expression using facial expression recognition technology to estimate the emotion. The collection unit can use an emotion estimation algorithm that receives the user's facial expression data as input and outputs an emotion score. The collection unit can also analyze the user's voice using voice analysis technology to estimate the emotion. The collection unit can use a voice analysis algorithm that receives the user's voice data as input and outputs an emotion score. The collection unit can also estimate the user's emotion using biometric data (such as heart rate or electrodermal activity). The collection unit can use an algorithm that receives the user's biometric data as input and outputs an emotion score. In this way, by adjusting the timing of collecting tactile data based on the user's emotion, tactile data can be collected at a more appropriate timing. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit may input the user's emotion data into the generation AI and cause the generation AI to adjust the collection timing.

[0077] The collection unit can adjust the sensitivity of the tactile sensor to collect detailed tactile sensations of different objects. For example, the collection unit adjusts the sensitivity of the tactile sensor to collect detailed tactile sensations of different objects. The collection unit can increase the sensitivity of the tactile sensor to collect the fine tactile sensations of fabric fibers. For example, the collection unit increases the sensitivity of the tactile sensor to collect the fine tactile sensations of fabric fibers as digital data. The collection unit can also adjust the sensitivity of the tactile sensor to collect the fine irregularities on a metal surface. For example, the collection unit adjusts the sensitivity of the tactile sensor to collect the fine irregularities on a metal surface as digital data. The collection unit can also change the sensitivity of the tactile sensor to collect the annual rings and texture of wood in detail. For example, the collection unit changes the sensitivity of the tactile sensor to collect the annual rings and texture of wood as digital data. In this way, by adjusting the sensitivity of the tactile sensor, the fine tactile sensations of different objects can be collected. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input data collected by a tactile sensor into the generation AI and have the generation AI adjust the sensitivity.

[0078] The collection unit can analyze the collected tactile data in real time and provide instant feedback. The collection unit, for example, analyzes the collected tactile data in real time and provides instant feedback. The collection unit can analyze data collected by a tactile sensor in real time and provide instant tactile feedback to the user. The collection unit can also analyze the collected tactile data in real time and instantly reflect the result on a virtual object. The collection unit can also analyze the collected tactile data in real time and instantly reflect the result on a virtual object. The collection unit can also analyze the tactile data in real time and instantly change the tactile sensation in response to a user's operation. The collection unit, for example, analyzes the tactile data in real time and instantly changes the tactile sensation in response to a user's operation. In this way, the collected tactile data can be analyzed in real time and instant feedback can be provided to the user, thereby instantly providing a tactile sensation to the user. Some or all of the above-described processing in the collection unit may be performed, for example, using AI or without AI. For example, the collection unit can input data collected by a tactile sensor into the generation AI, allowing the generation AI to perform real-time analysis and feedback.

[0079] The collection unit can collect three-dimensional tactile data by bringing the tactile sensor into contact with an object from multiple angles. The collection unit, for example, brings the tactile sensor into contact with an object from multiple angles to collect three-dimensional tactile data. The collection unit, for example, brings the tactile sensor into contact with the surface of an object from multiple angles to collect three-dimensional tactile data. The collection unit, for example, brings the tactile sensor into contact with the surface of an object from multiple angles to collect three-dimensional tactile data as digital data. The collection unit can also collect omnidirectional tactile data by bringing the tactile sensor into contact with an object while rotating it. The collection unit, for example, brings the tactile sensor into contact with an object while rotating it to collect omnidirectional tactile data as digital data. The collection unit can also collect detailed three-dimensional tactile data by bringing the tactile sensor into contact with an object from different heights or positions. The collection unit, for example, brings the tactile sensor into contact with an object from different heights or positions to collect detailed three-dimensional tactile data as digital data. This allows the tactile sensor to contact an object from multiple angles, thereby collecting three-dimensional tactile data. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input data collected by the tactile sensor into a generation AI, causing the generation AI to collect three-dimensional tactile data.

[0080] The collection unit can estimate a user's emotion and determine the priority of haptic data to be collected based on the estimated user's emotion. The collection unit, for example, estimates a user's emotion and determines the priority of haptic data to be collected based on the estimated user's emotion. The collection unit estimates the user's emotion using an emotion estimation algorithm. The collection unit, for example, analyzes the user's facial expression using facial expression recognition technology to estimate the emotion. The collection unit can use an emotion estimation algorithm that takes the user's facial expression data as input and outputs an emotion score. The collection unit can also analyze the user's voice using voice analysis technology to estimate the emotion. The collection unit can use a voice analysis algorithm that takes the user's voice data as input and outputs an emotion score. The collection unit can also estimate the user's emotion using biometric data (such as heart rate or electrodermal activity). The collection unit can use an algorithm that takes the user's biometric data as input and outputs an emotion score. In this way, by determining the priority of haptic data to be collected based on the user's emotion, more important haptic data can be collected preferentially. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit may input the user's emotion data into the generation AI and have the generation AI determine the priority of the haptic data to be collected.

[0081] The collection unit can simultaneously collect environmental data such as the temperature and humidity of an object when collecting tactile data. For example, the collection unit simultaneously collects environmental data such as the temperature and humidity of an object when collecting tactile data. When the tactile sensor contacts an object, the collection unit simultaneously collects the temperature of the object using a temperature sensor. For example, when the tactile sensor contacts an object, the collection unit simultaneously collects the temperature of the object using a temperature sensor as digital data. The collection unit can also simultaneously collect the humidity of the object using a humidity sensor when collecting tactile data. For example, when collecting tactile data, the collection unit collects the humidity of the object using a humidity sensor as digital data. The collection unit can also simultaneously collect the ambient temperature and humidity using an environmental sensor when collecting tactile data. For example, when collecting tactile data, the collection unit collects the ambient temperature and humidity as digital data using an environmental sensor. By simultaneously collecting environmental data when collecting tactile data, a more detailed data set can be created. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data collected by a tactile sensor into the generation AI and cause the generation AI to collect environmental data.

[0082] The collection unit can simultaneously collect color and shape data of an object when collecting tactile data, thereby creating a comprehensive dataset. For example, the collection unit simultaneously collects color and shape data of an object when collecting tactile data, thereby creating a comprehensive dataset. The collection unit simultaneously collects the color of the object using a camera when the tactile sensor contacts the object. For example, the collection unit simultaneously collects the color of the object using a camera when the tactile sensor contacts the object as digital data. The collection unit can also simultaneously collect shape data of the object using a 3D scanner when collecting tactile data. For example, the collection unit collects shape data of the object as digital data when collecting tactile data. The collection unit can also integrate the color and shape data of the object when collecting tactile data, thereby creating a comprehensive dataset. For example, the collection unit integrates the color and shape data of the object when collecting tactile data, thereby creating a comprehensive dataset as digital data. In this way, a comprehensive dataset can be created by simultaneously collecting color and shape data of an object when collecting tactile data. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input data collected by a tactile sensor into the generation AI and cause the generation AI to collect color and shape data.

[0083] The collection unit can customize the collection method by referring to the user's past tactile data collection history when collecting tactile data. For example, the collection unit customizes the collection method by referring to the user's past tactile data collection history when collecting tactile data. The collection unit analyzes the user's past tactile data collection history and proposes an optimal collection method. For example, the collection unit analyzes the user's past tactile data collection history and proposes an optimal collection method. The collection unit can also customize the collection method based on tactile data collected by the user in the past. The collection unit customizes the collection method based on tactile data collected by the user in the past. The collection unit can also adjust the collection timing and sensitivity by referring to the user's past tactile data collection history. For example, the collection unit adjusts the collection timing and sensitivity by referring to the user's past tactile data collection history. In this way, by customizing the collection method by referring to the user's past tactile data collection history, a more appropriate collection method can be provided. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past tactile data collection history into the generation AI and cause the generation AI to customize the collection method.

[0084] The generation unit can estimate the user's emotion and adjust the representation method of the generated haptic data based on the estimated user's emotion. The generation unit, for example, estimates the user's emotion and adjusts the representation method of the generated haptic data based on the estimated user's emotion. The generation unit estimates the user's emotion using an emotion estimation algorithm. The generation unit, for example, analyzes the user's facial expression using facial expression recognition technology to estimate the emotion. The generation unit can use an emotion estimation algorithm that receives the user's facial expression data as input and outputs an emotion score. The generation unit can also analyze the user's voice using voice analysis technology to estimate the emotion. The generation unit can use a voice analysis algorithm that receives the user's voice data as input and outputs an emotion score. The generation unit can also estimate the user's emotion using biometric data (such as heart rate or electrodermal activity). The generation unit can use an algorithm that receives the user's biometric data as input and outputs an emotion score. In this way, by adjusting the representation method of the generated haptic data based on the user's emotion, a more appropriate tactile sensation can be provided. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit may input user emotion data into the generation AI and cause the generation AI to adjust the method of expressing haptic data.

[0085] The generation unit can adjust the generated haptic data for application to different virtual objects. For example, the generation unit adjusts the generated haptic data for application to different virtual objects. The generation unit uses a generation AI to optimize the generated haptic data for application to different virtual objects. The generation unit inputs collected haptic data into the generation AI and optimizes it for application to different virtual objects. For example, the generation unit optimizes the generated haptic data for application to furniture in a virtual space. For example, the generation unit optimizes the generated haptic data for application to furniture in a virtual space. The generation unit can also optimize the generated haptic data for application to a character in a virtual space. For example, the generation unit optimizes the generated haptic data for application to a character in a virtual space. The generation unit can also optimize the generated haptic data for application to natural objects (trees, stones, etc.) in the virtual space. For example, the generation unit optimizes the generated haptic data for application to natural objects (trees, stones, etc.) in the virtual space. In this way, by optimizing the generated haptic data for application to different virtual objects, a more realistic tactile sensation can be provided. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit may input collected tactile data to a generation AI and cause the generation AI to perform optimization for application to different virtual objects.

[0086] The generation unit can improve accuracy by comparing the generated tactile data with the user's past tactile experiences. For example, the generation unit compares the generated tactile data with the user's past tactile experiences to improve accuracy. The generation unit uses a generation AI to compare the generated tactile data with the user's past tactile experiences. The generation unit inputs collected tactile data into the generation AI and compares it with the user's past tactile experiences to improve accuracy. For example, the generation unit compares the generated tactile data with the user's past tactile experiences to improve accuracy. For example, the generation unit compares the generated tactile data with the user's past tactile experiences to improve accuracy. The generation unit can also fine-tune the generated tactile data based on the user's past tactile experiences. For example, the generation unit fine-tunes the generated tactile data based on the user's past tactile experiences. For example, the generation unit can optimize the generated tactile data by comparing it with the user's past tactile experiences. For example, the generation unit optimizes the generated tactile data by comparing it with the user's past tactile experiences. This allows for improved accuracy by comparing the generated haptic data with the user's past haptic experiences. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit may input collected haptic data into the generation AI and cause the generation AI to perform processing to improve accuracy by comparing the data with the user's past haptic experiences.

[0087] The generation unit can customize the generated haptic data according to different scenarios. For example, the generation unit customizes the generated haptic data according to different scenarios. The generation unit customizes the generated haptic data according to different scenarios using a generation AI. The generation unit inputs collected haptic data into the generation AI and customizes it according to different scenarios. For example, the generation unit customizes the generated haptic data to apply to a battle scene in a game. For example, the generation unit customizes the generated haptic data to apply to a battle scene in a game. The generation unit can also customize the generated haptic data to apply to an educational virtual experiment. For example, the generation unit customizes the generated haptic data to apply to an educational virtual experiment. The generation unit can also customize the generated haptic data to apply to a medical simulation. For example, the generation unit customizes the generated haptic data to apply to a medical simulation. By customizing the generated haptic data according to different scenarios, a more appropriate haptic sensation can be provided. Some or all of the above-described processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input the collected tactile data into the generation AI and have the generation AI perform customization according to different scenarios.

[0088] The generation unit can estimate the user's emotion and adjust the level of detail of the generated haptic data based on the estimated user's emotion. The generation unit, for example, estimates the user's emotion and adjusts the level of detail of the generated haptic data based on the estimated user's emotion. The generation unit estimates the user's emotion using an emotion estimation algorithm. The generation unit, for example, analyzes the user's facial expression using facial expression recognition technology to estimate the emotion. The generation unit can use an emotion estimation algorithm that receives the user's facial expression data as input and outputs an emotion score. The generation unit can also analyze the user's voice using voice analysis technology to estimate the emotion. The generation unit can use a voice analysis algorithm that receives the user's voice data as input and outputs an emotion score. The generation unit can also estimate the user's emotion using biometric data (such as heart rate or electrodermal activity). The generation unit can use an algorithm that receives the user's biometric data as input and outputs an emotion score. In this way, by adjusting the level of detail of the generated haptic data based on the user's emotion, a more appropriate tactile sensation can be provided. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit may input the user's emotion data into the generation AI and cause the generation AI to adjust the level of detail of the haptic data.

[0089] The generation unit can adapt the generated tactile data to different environmental conditions in the virtual space. For example, the generation unit adapts the generated tactile data to different environmental conditions in the virtual space. The generation unit uses a generation AI to adapt the generated tactile data to different environmental conditions in the virtual space. The generation unit inputs collected tactile data into the generation AI and adapts it to different environmental conditions in the virtual space. For example, the generation unit adapts the generated tactile data to a cold region in the virtual space. For example, the generation unit adapts the generated tactile data to a cold region in the virtual space. The generation unit can also adapt the generated tactile data to a humid environment in the virtual space. For example, the generation unit adapts the generated tactile data to a humid environment in the virtual space. The generation unit can also adapt the generated tactile data to a high temperature environment in the virtual space. For example, the generation unit adapts the generated tactile data to a high temperature environment in the virtual space. In this way, by adapting the generated tactile data to different environmental conditions in the virtual space, a more realistic tactile sensation can be provided. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit may input the collected tactile data to the generation AI and cause the generation AI to perform processing to adapt to different environmental conditions in the virtual space.

[0090] The generation unit can customize the generated tactile data based on the user's physical characteristics. For example, the generation unit customizes the generated tactile data based on the user's physical characteristics. The generation unit customizes the generated tactile data based on the user's physical characteristics using a generation AI. The generation unit inputs collected tactile data into the generation AI and customizes it based on the user's physical characteristics. For example, the generation unit customizes the generated tactile data to match the size of the user's hand. For example, the generation unit customizes the generated tactile data to match the size of the user's hand. The generation unit can also adjust the generated tactile data based on the user's tactile sensitivity. For example, the generation unit adjusts the generated tactile data based on the user's tactile sensitivity. The generation unit can also optimize the generated tactile data based on the user's physical characteristics. For example, the generation unit optimizes the generated tactile data based on the user's physical characteristics. By customizing the generated tactile data based on the user's physical characteristics, a more appropriate tactile sensation can be provided. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the collected tactile data into the generation AI and cause the generation AI to perform customization based on the user's physical characteristics.

[0091] The generation unit can integrate the generated tactile data with other sensory data to provide a comprehensive experience. For example, the generation unit can integrate the generated tactile data with other sensory data to provide a comprehensive experience. The generation unit can integrate the generated tactile data with other sensory data using a generation AI. The generation unit inputs collected tactile data into the generation AI and integrates it with other sensory data. For example, the generation unit can integrate the generated tactile data with visual data to provide a realistic experience. For example, the generation unit can integrate the generated tactile data with visual data to provide a realistic experience. The generation unit can also integrate the generated tactile data with auditory data to enhance a sense of realism. For example, the generation unit can integrate the generated tactile data with auditory data to enhance a sense of realism. For example, the generation unit can integrate the generated tactile data with visual and auditory data to provide a comprehensive experience. For example, the generation unit can integrate the generated tactile data with visual and auditory data to provide a comprehensive experience. In this way, by integrating the generated tactile data with other sensory data, a more realistic experience can be provided. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit may input collected tactile data to the generation AI and have the generation AI integrate it with other sensory data.

[0092] The providing unit can estimate the user's emotion and adjust the method of providing haptic data based on the estimated user's emotion. The providing unit, for example, estimates the user's emotion and adjusts the method of providing haptic data based on the estimated user's emotion. The providing unit estimates the user's emotion using an emotion estimation algorithm. The providing unit, for example, analyzes the user's facial expression using facial expression recognition technology to estimate the emotion. The providing unit can use an emotion estimation algorithm that takes the user's facial expression data as input and outputs an emotion score. The providing unit can also analyze the user's voice using voice analysis technology to estimate the emotion. The providing unit can use a voice analysis algorithm that takes the user's voice data as input and outputs an emotion score. The providing unit can also estimate the user's emotion using biometric data (such as heart rate or electrodermal activity). The providing unit can use an algorithm that takes the user's biometric data as input and outputs an emotion score. In this way, a more appropriate tactile sensation can be provided by adjusting the method of providing haptic data based on the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit may input the user's emotion data into the generation AI and cause the generation AI to adjust the method of providing haptic data.

[0093] The providing unit can dynamically adjust the provided haptic data based on real-time feedback from the user. For example, the providing unit dynamically adjusts the provided haptic data based on real-time feedback from the user. When the user provides real-time feedback on a tactile sensation, the providing unit adjusts the tactile sensation based on the feedback. For example, when the user provides real-time feedback on a tactile sensation, the providing unit adjusts the tactile sensation based on the feedback. Furthermore, the providing unit can change the tactile sensation in real time if the user is dissatisfied with the tactile sensation. For example, the providing unit changes the tactile sensation in real time if the user is dissatisfied with the tactile sensation. Furthermore, the providing unit can maintain the tactile sensation if the user is satisfied with the tactile sensation. For example, the providing unit maintains the tactile sensation if the user is satisfied with the tactile sensation. In this way, by dynamically adjusting the provided haptic data based on real-time feedback from the user, a more appropriate tactile sensation can be provided. Some or all of the above-described processing by the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's real-time feedback to the generating AI and cause the generating AI to dynamically adjust the tactile data.

[0094] The providing unit can provide the tactile data in conjunction with the user's past tactile experiences. For example, the providing unit provides the tactile data in conjunction with the user's past tactile experiences. The providing unit provides an optimal tactile sensation based on the user's past tactile experiences. For example, the providing unit provides an optimal tactile sensation based on the user's past tactile experiences. The providing unit can also reproduce and provide a tactile sensation that the user previously preferred. For example, the providing unit can reproduce and provide a tactile sensation that the user previously preferred. The providing unit can also customize a tactile sensation in conjunction with the user's past tactile experiences. For example, the providing unit customizes a tactile sensation in conjunction with the user's past tactile experiences. In this way, by linking the tactile data to the user's past tactile experiences, a more appropriate tactile sensation can be provided. Some or all of the above-described processing by the providing unit may be performed using, or without, AI. For example, the providing unit can input the user's past tactile experience data to the generation AI and cause the generation AI to link the tactile data.

[0095] The providing unit can adapt the provided haptic data to different devices. For example, the providing unit adapts the provided haptic data to different devices. The providing unit provides the provided haptic data in a manner compatible with VR gloves. For example, the providing unit provides the provided haptic data in a manner compatible with VR gloves. The providing unit can also provide the provided haptic data in a manner compatible with a haptic feedback device. For example, the providing unit provides the provided haptic data in a manner compatible with a haptic feedback device. The providing unit can also provide the provided haptic data optimized for different devices. For example, the providing unit provides the provided haptic data optimized for different devices. In this way, by adapting the provided haptic data to different devices, tactile sensations can be provided on a wider variety of devices. Some or all of the above-described processing by the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit may input the provided haptic data to a generation AI and cause the generation AI to adapt to different devices.

[0096] The providing unit can estimate the user's emotion and adjust the frequency of providing haptic data based on the estimated user's emotion. The providing unit, for example, estimates the user's emotion and adjusts the frequency of providing haptic data based on the estimated user's emotion. The providing unit estimates the user's emotion using an emotion estimation algorithm. The providing unit, for example, analyzes the user's facial expression using facial expression recognition technology to estimate the emotion. The providing unit can use an emotion estimation algorithm that receives the user's facial expression data as input and outputs an emotion score. The providing unit can also analyze the user's voice using voice analysis technology to estimate the emotion. The providing unit can use a voice analysis algorithm that receives the user's voice data as input and outputs an emotion score. The providing unit can also estimate the user's emotion using biometric data (such as heart rate or electrodermal activity). The providing unit can use an algorithm that receives the user's biometric data as input and outputs an emotion score. In this way, by adjusting the frequency of providing haptic data based on the user's emotion, it is possible to provide tactile sensations at a more appropriate frequency. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may input the user's emotion data into the generation AI and cause the generation AI to adjust the frequency of providing haptic data.

[0097] The providing unit can customize the provided tactile data based on the user's geographical location information. The providing unit customizes the provided tactile data based on the user's geographical location information, for example. The providing unit provides a warm tactile sensation when the user is in a cold region. The providing unit provides a warm tactile sensation when the user is in a cold region, for example. The providing unit can also provide a dry tactile sensation when the user is in a humid region. The providing unit can also provide a dry tactile sensation when the user is in a humid region, for example. The providing unit can also provide a cool tactile sensation when the user is in a hot region. The providing unit can also provide a cool tactile sensation when the user is in a hot region, for example. In this way, by customizing the provided tactile data based on the user's geographical location information, a more appropriate tactile sensation can be provided. Some or all of the above-described processing by the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's geographical location information to the generation AI and cause the generation AI to customize the tactile data.

[0098] The providing unit can provide the provided tactile data in conjunction with the user's social media activity. For example, the providing unit provides the provided tactile data in conjunction with the user's social media activity. The providing unit provides a tactile sensation related to a place where the user has checked in on social media. For example, the providing unit provides a tactile sensation related to a place where the user has checked in on social media. The providing unit can also analyze the content of the user's social media posts and provide a related tactile sensation. For example, the providing unit can analyze the content of the user's social media posts and provide a related tactile sensation. The providing unit can also provide a related tactile sensation by referring to the activities of the user's friends on social media. For example, the providing unit can provide a related tactile sensation by referring to the activities of the user's friends on social media. In this way, by linking the provided tactile data with the user's social media activity, more appropriate tactile sensations can be provided. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's social media activity data into a generation AI and cause the generation AI to link the tactile data.

[0099] The providing unit can customize the method of providing the provided tactile data by reflecting the user's past feedback. For example, the providing unit customizes the method of providing the provided tactile data by reflecting the user's past feedback. The providing unit adjusts the intensity of the tactile sensation based on the user's past feedback. For example, the providing unit adjusts the intensity of the tactile sensation based on the user's past feedback. The providing unit can also change the type of tactile sensation by referring to the user's past feedback. For example, the providing unit changes the type of tactile sensation by referring to the user's past feedback. The providing unit can also optimize the method of providing the tactile sensation by reflecting the user's past feedback. For example, the providing unit optimizes the method of providing the tactile sensation by reflecting the user's past feedback. In this way, a more appropriate tactile sensation can be provided by customizing the method of providing the provided tactile data by reflecting the user's past feedback. Some or all of the above-described processing by the providing unit may be performed using, or without, AI. For example, the providing unit can input the user's past feedback data to the generation AI and cause the generation AI to customize the method of providing the tactile data. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, generation unit, and provision unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects tactile data of real-world objects using a tactile sensor of the smart device 14. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected tactile data, and generates tactile data to be applied to the virtual object. The provision unit is realized, for example, by the control unit 46A of the smart device 14, and provides the generated tactile data to the virtual world. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned collection unit, generation unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects tactile data of real-world objects using a tactile sensor of the smart glasses 214. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected tactile data and generates tactile data to be applied to the virtual object. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214, and provides the generated tactile data to the virtual world. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned collection unit, generation unit, and provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit collects tactile data of objects in the real world using a tactile sensor of the headset type terminal 314. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected tactile data and generates tactile data to be applied to the virtual object. The provision unit is realized, for example, by the control unit 46A of the headset type terminal 314, and provides the generated tactile data to the virtual world. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned collection unit, generation unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects tactile data of objects in the real world using a tactile sensor of the robot 414. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected tactile data and generates tactile data to be applied to the virtual object. The provision unit is realized, for example, by the control unit 46A of the robot 414, and provides the generated tactile data to the virtual world.

[0100] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0101] In addition to collecting tactile data, the collection unit can also simultaneously collect acoustic data of an object. For example, the collection unit combines a tactile sensor and a microphone to collect acoustic data when an object is touched. This allows for a more realistic experience by reproducing both the tactile feel and the sound of the object. The collection unit can also analyze the acoustic data to estimate the material and condition of the object. For example, the collection unit can collect tactile feel and acoustic data of wood and estimate the type and condition of the wood based on that data. Furthermore, the collection unit can also estimate the internal structure of an object using the acoustic data. For example, the collection unit can collect tactile feel and acoustic data of metal and estimate the internal structure of the metal based on that data.

[0102] When analyzing the collected tactile data, the generation unit can refer to the user's past tactile data history. For example, the generation unit analyzes the current tactile data based on tactile data previously collected by the user. This allows tactile data to be generated based on the user's preferences and past experiences. The generation unit can also improve the accuracy of the tactile data analysis using the user's past tactile data history. For example, the generation unit can remove noise from the current tactile data based on tactile data previously collected by the user. Furthermore, the generation unit can customize the method of generating tactile data using the user's past tactile data history. For example, the generation unit can extract features of tactile data that the user previously preferred and generate current tactile data based on the features.

[0103] When providing the generated haptic data, the providing unit can collect real-time feedback from the user and dynamically adjust the haptic data based on the feedback. For example, when the user provides real-time feedback on a tactile sensation, the providing unit adjusts the tactile sensation based on the feedback. The providing unit can also change the tactile sensation in real time if the user is dissatisfied with the tactile sensation. For example, the providing unit changes the tactile sensation in real time if the user is dissatisfied with the tactile sensation. The providing unit can also maintain the tactile sensation if the user is satisfied with the tactile sensation. For example, the providing unit maintains the tactile sensation if the user is satisfied with the tactile sensation. In this way, by dynamically adjusting the tactile data to be provided based on the user's real-time feedback, a more appropriate tactile sensation can be provided.

[0104] The collection unit can simultaneously collect color and shape data of an object when collecting tactile data to create a comprehensive dataset. For example, when a tactile sensor contacts an object, the collection unit simultaneously collects the color of the object using a camera. This allows the tactile data and visual data to be integrated to create a more detailed dataset. The collection unit can also simultaneously collect shape data of an object using a 3D scanner when collecting tactile data. For example, the collection unit collects shape data of an object as digital data using a 3D scanner when collecting tactile data. The collection unit can also integrate color and shape data of an object when collecting tactile data to create a comprehensive dataset. For example, the collection unit integrates color and shape data of an object when collecting tactile data to create a comprehensive dataset as digital data.

[0105] The generation unit can adjust the generated haptic data for application to different virtual objects. For example, the generation unit uses a generation AI to optimize the generated haptic data for application to different virtual objects. This allows for a more realistic tactile sensation to be provided by applying the generated haptic data to different virtual objects. The generation unit can also optimize the generated haptic data for application to furniture in a virtual space. For example, the generation unit optimizes the generated haptic data for application to furniture in a virtual space. The generation unit can also optimize the generated haptic data for application to a character in a virtual space. For example, the generation unit optimizes the generated haptic data for application to a character in a virtual space. The generation unit can also optimize the generated haptic data for application to natural objects (trees, stones, etc.) in the virtual space. For example, the generation unit optimizes the generated haptic data for application to natural objects (trees, stones, etc.) in the virtual space.

[0106] The collection unit can estimate the user's emotion and adjust the timing of collecting tactile data based on the estimated user's emotion. For example, the collection unit estimates the user's emotion using an emotion estimation algorithm. By adjusting the timing of collecting tactile data based on the user's emotion, the tactile data can be collected at a more appropriate timing. The collection unit can also analyze the user's facial expression using facial expression recognition technology to estimate the emotion. For example, the collection unit can use an emotion estimation algorithm that receives the user's facial expression data as input and outputs an emotion score. The collection unit can also analyze the user's voice using voice analysis technology to estimate the emotion. For example, the collection unit can use a voice analysis algorithm that receives the user's voice data as input and outputs an emotion score. The collection unit can also estimate the user's emotion using biometric data (such as heart rate or electrodermal activity). For example, the collection unit can use an algorithm that receives the user's biometric data as input and outputs an emotion score.

[0107] The generation unit can estimate the user's emotion and adjust the representation method of the generated haptic data based on the estimated user's emotion. For example, the generation unit estimates the user's emotion using an emotion estimation algorithm. By adjusting the representation method of the generated haptic data based on the user's emotion, a more appropriate tactile sensation can be provided. The generation unit can also analyze the user's facial expression using facial expression recognition technology to estimate the emotion. For example, the generation unit can use an emotion estimation algorithm that receives the user's facial expression data as input and outputs an emotion score. The generation unit can also analyze the user's voice using voice analysis technology to estimate the emotion. For example, the generation unit can use a voice analysis algorithm that receives the user's voice data as input and outputs an emotion score. The generation unit can also estimate the user's emotion using biometric data (such as heart rate or electrodermal activity). For example, the generation unit can use an algorithm that receives the user's biometric data as input and outputs an emotion score.

[0108] The providing unit can estimate the user's emotion and adjust the method of providing haptic data based on the estimated user's emotion. For example, the providing unit can estimate the user's emotion using an emotion estimation algorithm. By adjusting the method of providing haptic data based on the user's emotion, a more appropriate tactile sensation can be provided. The providing unit can also analyze the user's facial expression using facial expression recognition technology to estimate the emotion. For example, the providing unit can use an emotion estimation algorithm that receives the user's facial expression data as input and outputs an emotion score. The providing unit can also analyze the user's voice using voice analysis technology to estimate the emotion. For example, the providing unit can use a voice analysis algorithm that receives the user's voice data as input and outputs an emotion score. The providing unit can also estimate the user's emotion using biometric data (such as heart rate or electrodermal activity). For example, the providing unit can use an algorithm that receives the user's biometric data as input and outputs an emotion score.

[0109] The providing unit can estimate the user's emotion and adjust the frequency of providing haptic data based on the estimated user's emotion. For example, the providing unit estimates the user's emotion using an emotion estimation algorithm. By adjusting the frequency of providing haptic data based on the user's emotion, haptics can be provided at a more appropriate frequency. The providing unit can also analyze the user's facial expression using facial expression recognition technology to estimate the emotion. For example, the providing unit can use an emotion estimation algorithm that receives the user's facial expression data as input and outputs an emotion score. The providing unit can also analyze the user's voice using voice analysis technology to estimate the emotion. For example, the providing unit can use a voice analysis algorithm that receives the user's voice data as input and outputs an emotion score. Furthermore, the providing unit can also estimate the user's emotion using biometric data (such as heart rate or electrodermal activity). For example, the providing unit can use an algorithm that receives the user's biometric data as input and outputs an emotion score.

[0110] The collection unit can estimate the user's emotion and determine the priority of haptic data to be collected based on the estimated user's emotion. For example, the collection unit estimates the user's emotion using an emotion estimation algorithm. By determining the priority of haptic data to be collected based on the user's emotion, more important haptic data can be collected preferentially. The collection unit can also analyze the user's facial expression using facial expression recognition technology to estimate the emotion. For example, the collection unit can use an emotion estimation algorithm that receives the user's facial expression data as input and outputs an emotion score. The collection unit can also analyze the user's voice using voice analysis technology to estimate the emotion. For example, the collection unit can use a voice analysis algorithm that receives the user's voice data as input and outputs an emotion score. The collection unit can also estimate the user's emotion using biometric data (such as heart rate or electrodermal activity). For example, the collection unit can use an algorithm that receives the user's biometric data as input and outputs an emotion score.

[0111] The collection unit can simultaneously collect environmental data such as the temperature and humidity of an object when collecting tactile data. For example, when the tactile sensor contacts an object, the collection unit simultaneously collects the temperature of the object using a temperature sensor. This allows the tactile data and environmental data to be integrated to create a more detailed data set. The collection unit can also simultaneously collect the humidity of the object using a humidity sensor when collecting tactile data. For example, when collecting tactile data, the collection unit collects the humidity of the object as digital data using a humidity sensor. Furthermore, the collection unit can also simultaneously collect the ambient temperature and humidity using an environmental sensor when collecting tactile data. For example, when collecting tactile data, the collection unit collects the ambient temperature and humidity as digital data using an environmental sensor.

[0112] The collection unit can simultaneously collect color and shape data of an object when collecting tactile data to create a comprehensive dataset. For example, when a tactile sensor contacts an object, the collection unit simultaneously collects the color of the object using a camera. This allows the tactile data and visual data to be integrated to create a more detailed dataset. The collection unit can also simultaneously collect shape data of an object using a 3D scanner when collecting tactile data. For example, the collection unit collects shape data of an object as digital data using a 3D scanner when collecting tactile data. The collection unit can also integrate color and shape data of an object when collecting tactile data to create a comprehensive dataset. For example, the collection unit integrates color and shape data of an object when collecting tactile data to create a comprehensive dataset as digital data.

[0113] When collecting tactile data, the collection unit can customize the collection method by referring to the user's past tactile data collection history. For example, the collection unit analyzes the user's past tactile data collection history and proposes an optimal collection method. This makes it possible to provide a more appropriate collection method by customizing the collection method by referring to the user's past tactile data collection history. The collection unit can also customize the collection method based on tactile data collected by the user in the past. For example, the collection unit customizes the collection method based on tactile data collected by the user in the past. The collection unit can also adjust the collection timing and sensitivity by referring to the user's past tactile data collection history. For example, the collection unit adjusts the collection timing and sensitivity by referring to the user's past tactile data collection history.

[0114] The generation unit can estimate the user's emotion and adjust the representation method of the generated haptic data based on the estimated user's emotion. For example, the generation unit estimates the user's emotion using an emotion estimation algorithm. By adjusting the representation method of the generated haptic data based on the user's emotion, a more appropriate tactile sensation can be provided. The generation unit can also analyze the user's facial expression using facial expression recognition technology to estimate the emotion. For example, the generation unit can use an emotion estimation algorithm that receives the user's facial expression data as input and outputs an emotion score. The generation unit can also analyze the user's voice using voice analysis technology to estimate the emotion. For example, the generation unit can use a voice analysis algorithm that receives the user's voice data as input and outputs an emotion score. The generation unit can also estimate the user's emotion using biometric data (such as heart rate or electrodermal activity). For example, the generation unit can use an algorithm that receives the user's biometric data as input and outputs an emotion score.

[0115] The generation unit can adjust the generated haptic data for application to different virtual objects. For example, the generation unit uses a generation AI to optimize the generated haptic data for application to different virtual objects. This allows for a more realistic tactile sensation to be provided by applying the generated haptic data to different virtual objects. The generation unit can also optimize the generated haptic data for application to furniture in a virtual space. For example, the generation unit optimizes the generated haptic data for application to furniture in a virtual space. The generation unit can also optimize the generated haptic data for application to a character in a virtual space. For example, the generation unit optimizes the generated haptic data for application to a character in a virtual space. The generation unit can also optimize the generated haptic data for application to natural objects (trees, stones, etc.) in the virtual space. For example, the generation unit optimizes the generated haptic data for application to natural objects (trees, stones, etc.) in the virtual space.

[0116] The generation unit can improve accuracy by comparing the generated tactile data with the user's past tactile experiences. For example, the generation unit uses a generation AI to compare the generated tactile data with the user's past tactile experiences. This allows accuracy to be improved by comparing the generated tactile data with the user's past tactile experiences. The generation unit can also fine-tune the generated tactile data based on the user's past tactile experiences. For example, the generation unit fine-tunes the generated tactile data based on the user's past tactile experiences. The generation unit can also optimize the generated tactile data by comparing it with the user's past tactile experiences. For example, the generation unit optimizes the generated tactile data by comparing it with the user's past tactile experiences.

[0117] The generation unit can customize the generated haptic data according to different scenarios. For example, the generation unit uses a generation AI to customize the generated haptic data according to different scenarios. In this way, by customizing the generated haptic data according to different scenarios, a more appropriate haptic sensation can be provided. The generation unit can also customize the generated haptic data for application to a battle scene in a game. For example, the generation unit customizes the generated haptic data for application to a battle scene in a game. The generation unit can also customize the generated haptic data for application to a virtual experiment for education. For example, the generation unit customizes the generated haptic data for application to a virtual experiment for education. The generation unit can also customize the generated haptic data for application to a medical simulation. For example, the generation unit customizes the generated haptic data for application to a medical simulation.

[0118] The generation unit can customize the generated tactile data based on the user's physical characteristics. For example, the generation unit uses a generation AI to customize the generated tactile data based on the user's physical characteristics. By customizing the generated tactile data based on the user's physical characteristics, a more appropriate tactile sensation can be provided. The generation unit can also customize the generated tactile data to match the size of the user's hand. For example, the generation unit customizes the generated tactile data to match the size of the user's hand. The generation unit can also adjust the generated tactile data based on the user's tactile sensitivity. For example, the generation unit adjusts the generated tactile data based on the user's tactile sensitivity. The generation unit can also optimize the generated tactile data based on the user's physical characteristics. For example, the generation unit optimizes the generated tactile data based on the user's physical characteristics.

[0119] The generation unit can integrate the generated tactile data with other sensory data to provide a comprehensive experience. For example, the generation unit can integrate the generated tactile data with other sensory data using a generation AI. In this way, by integrating the generated tactile data with other sensory data, a more realistic experience can be provided. The generation unit can also integrate the generated tactile data with visual data to provide a realistic experience. For example, the generation unit can integrate the generated tactile data with visual data to provide a realistic experience. The generation unit can also integrate the generated tactile data with auditory data to enhance the sense of realism. For example, the generation unit can integrate the generated tactile data with auditory data to enhance the sense of realism. The generation unit can also integrate the generated tactile data with visual and auditory data to provide a comprehensive experience. For example, the generation unit can integrate the generated tactile data with visual and auditory data to provide a comprehensive experience.

[0120] The processing flow of the second embodiment will be briefly explained below.

[0121] Step 1: The collection unit collects tactile data. For example, the collection unit uses a tactile sensor to collect tactile data of a real-world object. The collection unit can collect pressure data, vibration data, temperature data, etc. The collection unit brings the tactile sensor into contact with the object and collects the tactile sensation as digital data. For example, the collection unit uses a pressure sensor to collect pressure data of the object, a vibration sensor to collect vibration data of the object, and a temperature sensor to collect temperature data of the object. Step 2: The generation unit analyzes the tactile data collected by the collection unit and generates tactile data to be applied to the virtual object. The generation unit analyzes the collected tactile data using a generation AI and analyzes the tactile data using methods such as data filtering, feature extraction, and pattern recognition. The generation unit generates tactile data to be applied to the virtual object based on the data analyzed by the generation AI. Step 3: The providing unit provides the haptic data generated by the generating unit to the virtual world. The providing unit provides the haptic data using a method such as real-time provision, batch provision, or provision in response to a user's operation. The providing unit applies the generated haptic data to the virtual world to provide the user with a realistic tactile sensation.

[0122] 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.

[0123] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<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.

[0124] 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.

[0125] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0126] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0142] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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 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 identification processing unit 290 using these models.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0158] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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).

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. 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 the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also 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 perform the same process as the identification processing unit 290 using these models.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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).

[0179] 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.

[0180] 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."

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] [Explanation of symbols]

[0194] 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 collection unit that collects tactile data; a generation unit that analyzes the haptic data collected by the collection unit and generates haptic data to be applied to a virtual object; a providing unit that provides the haptic data generated by the generating unit to a virtual world. A system characterized by:

2. The collecting unit Collecting tactile data from real-world objects using tactile sensors 2. The system of claim 1.

3. The generation unit Analyze the collected haptic data and generate haptic data to apply to virtual objects.

2. The system of claim 1.

4. The providing unit Providing the generated haptic data to the virtual world 2. The system of claim 1.

5. The generation unit In an open-world game, generating tactile sensations for objects that the user touches in the virtual world 2. The system of claim 1.

6. The generation unit Generating tactile sensations of musical instruments when playing them in virtual space 2. The system of claim 1.

7. The collecting unit The system estimates the user's emotions and adjusts the timing of tactile data collection based on the estimated user emotions.

2. The system of claim 1.

8. The collecting unit Adjust the sensitivity of the tactile sensor to capture detailed tactile sensations of different objects 2. The system of claim 1.

9. The collecting unit Analyzes collected tactile data in real time and provides immediate feedback 2. The system of claim 1.

Images