System

Abstract

An object of a system according to an embodiment is to provide means for a user to relax and enjoy a conversation.SOLUTION: A system according to an embodiment includes a 3D reproduction unit, a sound reproduction unit, and a dialogue providing unit. The 3D reproduction part three dimensionally reproduces the figure and face of the loved person in 3D. The voice reproduction unit reproduces the voice of the loved person. The dialogue provider provides information and topics according to the user's preference.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately provide means for users to relax and enjoy conversation in care settings, and there is room for improvement.

[0005] The system according to the embodiment aims to provide a means for users to relax and enjoy conversation. [Means for solving the problem]

[0006] The system according to the embodiment includes a 3D reproduction unit, a voice reproduction unit, and a dialogue provision unit. The 3D reproduction unit reproduces the appearance and face of a loved one in 3D. The voice reproduction unit reproduces the voice of the loved one. The dialogue provision unit provides information and topics according to the user's preferences. [Effects of the Invention]

[0007] The system according to the embodiment can provide a means for users to relax and enjoy conversation. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The interactive experience system according to an embodiment of the present invention provides users with an immersive interactive experience, reducing the burden on caregivers. This system uses AR technology to recreate the appearance and face of a loved one in 3D, and a conversation is held in the voice of the loved one, learned by a generation AI. This allows the interactive experience system to relax users, encourage lively conversation, and reduce the burden on caregivers.

[0029] The dialogue experience system according to the embodiment includes a 3D reproduction unit, a voice reproduction unit, and a dialogue providing unit. The 3D reproduction unit reproduces the appearance and face of a loved one in 3D. For example, the 3D reproduction unit scans a family photo owned by the user and generates a 3D model based on the data. The 3D reproduction unit can also generate a 3D model based on data such as photographs and videos. The voice reproduction unit reproduces the voice of the loved one. For example, the voice reproduction unit uses a generation AI to analyze past phone recordings and video messages and learn the characteristics of the voice. The voice reproduction unit can also reproduce the voice using voice synthesis technology. The dialogue providing unit provides information and topics based on the user's preferences. For example, the dialogue providing unit uses the generation AI to provide topics based on the user's favorite hobbies and past memories. The dialogue providing unit can also use the generation AI to provide topics based on the user's past conversation history. As a result, the dialogue experience system according to the embodiment can provide an immersive dialogue experience to the user and reduce the burden on caregivers. For example, if a user is feeling stressed, the generative AI can provide relaxing topics to soothe the user. Furthermore, as users enjoy conversation, their mental health improves. This reduces the amount of time caregivers spend interacting with users, allowing them to focus on other tasks. Furthermore, as users enjoy conversations with the AR robot, the burden on caregivers is reduced.

[0030] The 3D reproduction unit can scan family photos and generate a 3D model based on that data. For example, the 3D reproduction unit can scan a user's family photos and generate a 3D model based on that data. The 3D reproduction unit can also use image processing technology to improve the resolution and quality of the photos. For example, the image data read by the scanner can be converted to high resolution to improve the accuracy of the 3D model. The 3D reproduction unit can also integrate data from multiple photos to generate a more detailed 3D model. This allows the generation of a 3D model that is familiar to the user.

[0031] The voice reproduction unit can analyze telephone recordings and video messages and learn the characteristics of the voice. For example, the voice reproduction unit analyzes past telephone recordings and video messages and learns the characteristics of the voice. For example, the generation AI analyzes voice data and extracts characteristics such as voice tone, pitch, and speed. The generation AI can also remove noise from the voice data and generate clearer voices. Furthermore, the generation AI can integrate multiple voice data to reproduce more natural voices. This allows the reproduction of a voice that is familiar to the user.

[0032] The dialogue providing unit can provide topics based on the user's favorite hobbies and past memories. The dialogue providing unit can provide topics based on the user's favorite hobbies and past memories, for example. For example, the generation AI can collect information about the user's hobbies and provide topics based on that information. The generation AI can also analyze data about the user's past memories and provide topics based on that data. Furthermore, the generation AI can provide topics that the user is likely to be interested in based on the user's past conversation history. This makes it possible to provide topics that are familiar to the user.

[0033] The dialogue providing unit can provide topics that will help the user relax when they are feeling stressed. For example, the dialogue providing unit can provide topics that will help the user relax when they are feeling stressed. For example, the generation AI can analyze the user's emotional data and provide topics that will help the user relax. The generation AI can also monitor the user's stress level in real time and provide topics based on that data. Furthermore, the generation AI can provide topics that will help the user relax according to their preferences. This helps reduce stress and relax the user.

[0034] The dialogue providing unit can reduce the time that nursing staff spends interacting with the user. For example, the generation AI can automate dialogue with the user, reducing the burden on nursing staff. The generation AI can also provide topics that match the user's preferences, allowing the user to enjoy the dialogue on their own. Furthermore, the generation AI can also provide appropriate topics based on the user's emotional data, allowing the user to relax. This can reduce the burden on nursing staff.

[0035] The 3D reproduction unit can capture the user's facial expressions and movements in real time and dynamically change the 3D model's expressions and movements accordingly. For example, the 3D reproduction unit captures the user's facial expressions and movements in real time and dynamically changes the 3D model's expressions and movements accordingly. For example, the 3D reproduction unit captures the user's facial expressions in real time with a camera and dynamically changes the 3D model's expressions based on that data. For example, if the user smiles, the 3D model smiles back. The 3D reproduction unit also detects the user's body movements with a sensor and adjusts the 3D model's movements in real time accordingly. For example, if the user waves their hand, the 3D model will do the same. Furthermore, the 3D reproduction unit analyzes the user's tone and speed of speech and dynamically changes the 3D model's mouth movements and facial expressions based on that data. For example, if the user speaks quickly, the 3D model's mouth movements will also speed up accordingly. This enables natural dialogue that responds to the user's real-time reactions.

[0036] The 3D reproduction unit adds haptic feedback to the 3D model, allowing the user to feel a sensation when touching it. The 3D reproduction unit adds haptic feedback to the 3D model, allowing the user to feel a sensation when touching it. For example, a haptic feedback device can be worn by the user, allowing the user to feel vibrations and pressure when touching the 3D model. For example, when a user grasps the hand of a 3D model, the device can reproduce the pressure. To provide haptic feedback, tactile sensors can be embedded in the surface of the 3D model, providing different sensations depending on the part the user touches. For example, touching the face can reproduce a soft sensation. To further enhance the haptic feedback, a device that reproduces temperature changes can be used, allowing the user to feel warmth or coldness when touching the 3D model. For example, grasping the hand can feel warmth. This provides the user with a more realistic experience.

[0037] The 3D reproduction unit can reflect ambient light and shadow information in real time to blend the 3D model into the user's surroundings. For example, the 3D reproduction unit detects the ambient light around the user with a sensor and adjusts the lighting of the 3D model in real time based on that information. For example, if the room is dark, the 3D model will appear dark. The unit also dynamically generates shadows for the 3D model in response to changes in ambient light, achieving a more realistic display. For example, if the user moves a light, the shadow of the 3D model will move accordingly. Furthermore, the unit detects the color temperature around the user and adjusts the color tone of the 3D model based on that information. For example, if there is warm lighting, the 3D model will also appear warm. This provides the user with a more realistic experience.

[0038] The 3D reproduction unit can enable customization according to the user's preferences by allowing the selection of costumes and backgrounds from different cultures and regions. The 3D reproduction unit, for example, allows the selection of costumes and backgrounds from different cultures and regions, allowing customization according to the user's preferences. For example, the 3D reproduction unit changes the costumes and backgrounds of the 3D model according to the culture or region selected by the user. For example, it allows the selection of traditional Japanese costumes and backgrounds. In addition, a function is provided to customize the costumes and backgrounds of the 3D model according to the user's preferences. For example, it allows the user to select their favorite colors and designs. Furthermore, a database of different cultures and regions is built and the user can select from among them. For example, it provides historical costumes and backgrounds from Europe. This makes it possible to provide the user with a more personalized experience.

[0039] The voice reproduction unit can dynamically adjust the tone and speed of the voice generated by the generation AI according to the tone and speed of the user's voice. The voice reproduction unit dynamically adjusts the tone and speed of the voice generated by the generation AI according to the tone and speed of the user's voice, for example. For example, it analyzes the tone of the user's voice in real time and adjusts the tone of the voice generated by the generation AI based on that data. For example, if the user speaks in a high voice, the generation AI also responds in a high voice. It also analyzes the user's speaking speed and adjusts the speed of the voice generated by the generation AI to match that speed. For example, if the user speaks slowly, the generation AI also responds slowly. It also analyzes the strength of the user's voice and adjusts the strength of the voice generated by the generation AI based on that data. For example, if the user speaks loudly, the generation AI also responds in a louder voice. This allows for more natural dialogue to be provided to the user.

[0040] The voice reproduction unit supports different languages ​​and dialects, and can converse in the user's native language or regional dialect. For example, the voice reproduction unit can support different languages ​​and dialects, and can converse in the user's native language or regional dialect. For example, multilingual speech synthesis technology can be incorporated into the generation AI to build a system that converses in the user's native language. For example, it can support English, French, Chinese, etc. In addition, a generation AI can be developed that learns the user's regional dialect and converses in that dialect. For example, it can support dialects such as Kansai dialect and Tohoku dialect. Furthermore, a system can be built in which the generation AI automatically converses in the language or dialect selected by the user. For example, the conversation can be based on the language set by the user. This makes it possible to provide more personalized dialogue to the user.

[0041] The voice reproduction unit can add background sounds and environmental sounds to the voice generated by the generation AI, providing a more realistic conversation environment. For example, the voice reproduction unit can add background sounds and environmental sounds to the voice generated by the generation AI, providing a more realistic conversation environment. For example, we will build a system that adds background sounds to the voice generated by the generation AI. For example, cafe noises and natural sounds can be added to the background. Furthermore, environmental sounds can be added to the voice generated by the generation AI depending on the user's environment. For example, if the user is in a park, birds chirping and the sound of the wind can be added. Furthermore, we will develop a system that adds environmental sounds in real time to the voice generated by the generation AI, providing a more natural conversation environment. For example, if the user is at home, household sounds can be added to the background. This can provide a more realistic conversation environment for the user.

[0042] The dialogue providing unit can provide health advice and reminders based on the user's health condition and daily activity data. The dialogue providing unit provides health advice and reminders based on the user's health condition and daily activity data, for example. For example, the dialogue providing unit collects the user's health condition data, and the generation AI provides health advice based on that data. For example, health management advice is given based on the user's blood pressure and heart rate. In addition, a system is constructed in which the generation AI analyzes the user's daily activity data and provides reminders. For example, if the user is not getting enough exercise, a reminder is sent to encourage exercise. Furthermore, the generation AI provides personalized health advice based on the user's health condition and activity data. For example, advice on nutritional balance is given based on the user's dietary data. This can support the user's health management.

[0043] The dialogue providing unit can provide moving stories and episodes based on the user's past memories with family and friends. The dialogue providing unit provides moving stories and episodes based on, for example, the user's past memories with family and friends. For example, data on past memories with the user's family and friends is collected, and the generation AI provides moving stories based on that data. For example, memories of a family trip are recreated. In addition, a system is constructed in which the generation AI analyzes the user's past photos and videos and generates episodes based on that data. For example, episodes of specific events or anniversaries are provided. Furthermore, the generation AI learns the user's past conversation history with family and friends, and provides moving episodes based on that data. For example, special moments or moving events are recreated. This makes it possible to provide the user with a moving experience.

[0044] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0045] The dialogue provider can provide health advice and reminders based on the user's health condition and daily activity data. For example, it collects the user's health condition data and the generation AI provides health advice based on that data. For example, it provides health management advice based on the user's blood pressure and heart rate. It also builds a system that analyzes the user's daily activity data and the generation AI provides reminders. For example, if the user is not getting enough exercise, it sends a reminder to encourage them to exercise. Furthermore, the generation AI provides personalized health advice based on the user's health condition and activity data. For example, it provides advice on nutritional balance based on the user's dietary data. This can support the user's health management.

[0046] The dialogue provider can provide moving stories and episodes based on the user's past memories with family and friends. For example, data on past memories with the user's family and friends is collected, and the generation AI provides moving stories based on that data. For example, recreating memories of a family trip. A system can also be built that analyzes the user's past photos and videos, and the generation AI generates episodes based on that data. For example, it can provide episodes about specific events or anniversaries. Furthermore, the generation AI can learn the user's past conversation history with family and friends, and provide moving episodes based on that data. For example, it can recreate special moments or moving events. This can provide a moving experience for the user.

[0047] The 3D reproduction unit can capture the user's facial expressions and movements in real time and dynamically change the 3D model's expressions and movements accordingly. For example, a camera can capture the user's facial expressions in real time and dynamically change the 3D model's expressions based on that data. For example, if the user smiles, the 3D model will smile back. It also uses sensors to detect the user's body movements and adjust the 3D model's movements in real time accordingly. For example, if the user waves their hand, the 3D model will do the same. It also analyzes the user's tone and speed of speech and dynamically changes the 3D model's mouth movements and facial expressions based on that data. For example, if the user speaks quickly, the 3D model's mouth movements will also speed up accordingly. This enables natural dialogue based on the user's real-time reactions.

[0048] The 3D reproduction unit adds haptic feedback to the 3D model, allowing the user to feel a sensation when touching it. For example, a haptic feedback device can be worn by the user, allowing them to feel vibrations and pressure when touching the 3D model. For example, if the user grasps the hand of a 3D model, the device will reproduce the pressure. To provide haptic feedback, tactile sensors can be embedded in the surface of the 3D model, providing different sensations depending on the part the user touches. For example, touching the face will reproduce a soft feeling. Furthermore, to enhance the haptic feedback, a device that reproduces temperature changes can be used, allowing the user to feel warmth or coldness when touching the 3D model. For example, grasping the hand will feel warmth. This provides a more realistic experience for the user.

[0049] The 3D reproduction unit can reflect ambient light and shadow information in real time to blend the 3D model into the user's surroundings. For example, a sensor detects the ambient light around the user and adjusts the lighting of the 3D model in real time based on that information. For example, if the room is dark, the 3D model will appear dark. Shadows of the 3D model can also be dynamically generated according to changes in ambient light, achieving a more realistic display. For example, if the user moves a light, the shadow of the 3D model will move accordingly. Furthermore, the unit detects the color temperature around the user and adjusts the color tone of the 3D model based on that information. For example, if there is warm lighting, the 3D model will also appear in warm colors. This provides the user with a more realistic experience.

[0050] The 3D reproduction unit can enable customization according to the user's preferences by allowing the selection of costumes and backgrounds from different cultures and regions. For example, the 3D model's costume and background can be changed according to the culture or region selected by the user. For example, traditional Japanese costumes and backgrounds can be selected. The unit also provides a function to customize the 3D model's costume and background according to the user's preferences. For example, the unit allows the user to select their favorite colors and designs. Furthermore, the unit can build a database of different cultures and regions and allow the user to select from them. For example, historical European costumes and backgrounds can be provided. This allows the user to have a more personalized experience.

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

[0052] Step 1: The 3D reconstruction unit recreates the appearance and face of your loved one in 3D. For example, the 3D reconstruction unit can scan a family photo that the user owns and generate a 3D model based on that data. It can also generate a 3D model based on data such as photos and videos. Step 2: The voice reproduction unit recreates the voice of your loved one. For example, the generation AI can analyze past phone recordings and video messages to learn the characteristics of that person's voice. Alternatively, the generation AI can use voice synthesis technology to recreate the voice. Step 3: The dialogue provider provides information and topics based on the user's preferences. For example, the generation AI can provide topics based on the user's favorite hobbies or past memories. The generation AI can also provide topics based on the user's past conversation history. This provides the user with an immersive dialogue experience and reduces the burden on caregivers.

[0053] (Example 2) The interactive experience system according to an embodiment of the present invention provides users with an immersive interactive experience, reducing the burden on caregivers. This system uses AR technology to recreate the appearance and face of a loved one in 3D, and a conversation is held in the voice of the loved one, learned by a generation AI. This allows the interactive experience system to relax users, encourage lively conversation, and reduce the burden on caregivers.

[0054] The dialogue experience system according to the embodiment includes a 3D reproduction unit, a voice reproduction unit, and a dialogue providing unit. The 3D reproduction unit reproduces the appearance and face of a loved one in 3D. For example, the 3D reproduction unit scans a family photo owned by the user and generates a 3D model based on the data. The 3D reproduction unit can also generate a 3D model based on data such as photographs and videos. The voice reproduction unit reproduces the voice of the loved one. For example, the voice reproduction unit uses a generation AI to analyze past phone recordings and video messages and learn the characteristics of the voice. The voice reproduction unit can also reproduce the voice using voice synthesis technology. The dialogue providing unit provides information and topics based on the user's preferences. For example, the dialogue providing unit uses the generation AI to provide topics based on the user's favorite hobbies and past memories. The dialogue providing unit can also use the generation AI to provide topics based on the user's past conversation history. As a result, the dialogue experience system according to the embodiment can provide an immersive dialogue experience to the user and reduce the burden on caregivers. For example, if a user is feeling stressed, the generative AI can provide relaxing topics to soothe the user. Furthermore, as users enjoy conversation, their mental health improves. This reduces the amount of time caregivers spend interacting with users, allowing them to focus on other tasks. Furthermore, as users enjoy conversations with the AR robot, the burden on caregivers is reduced.

[0055] The 3D reproduction unit can scan family photos and generate a 3D model based on that data. For example, the 3D reproduction unit can scan a user's family photos and generate a 3D model based on that data. The 3D reproduction unit can also use image processing technology to improve the resolution and quality of the photos. For example, the image data read by the scanner can be converted to high resolution to improve the accuracy of the 3D model. The 3D reproduction unit can also integrate data from multiple photos to generate a more detailed 3D model. This allows the generation of a 3D model that is familiar to the user.

[0056] The voice reproduction unit can analyze telephone recordings and video messages and learn the characteristics of the voice. For example, the voice reproduction unit analyzes past telephone recordings and video messages and learns the characteristics of the voice. For example, the generation AI analyzes voice data and extracts characteristics such as voice tone, pitch, and speed. The generation AI can also remove noise from the voice data and generate clearer voices. Furthermore, the generation AI can integrate multiple voice data to reproduce more natural voices. This allows the reproduction of a voice that is familiar to the user.

[0057] The dialogue providing unit can provide topics based on the user's favorite hobbies and past memories. The dialogue providing unit can provide topics based on the user's favorite hobbies and past memories, for example. For example, the generation AI can collect information about the user's hobbies and provide topics based on that information. The generation AI can also analyze data about the user's past memories and provide topics based on that data. Furthermore, the generation AI can provide topics that the user is likely to be interested in based on the user's past conversation history. This makes it possible to provide topics that are familiar to the user.

[0058] The dialogue providing unit can provide topics that will help the user relax when they are feeling stressed. For example, the dialogue providing unit can provide topics that will help the user relax when they are feeling stressed. For example, the generation AI can analyze the user's emotional data and provide topics that will help the user relax. The generation AI can also monitor the user's stress level in real time and provide topics based on that data. Furthermore, the generation AI can provide topics that will help the user relax according to their preferences. This helps reduce stress and relax the user.

[0059] The dialogue providing unit can reduce the time that nursing staff spends interacting with the user. For example, the generation AI can automate dialogue with the user, reducing the burden on nursing staff. The generation AI can also provide topics that match the user's preferences, allowing the user to enjoy the dialogue on their own. Furthermore, the generation AI can also provide appropriate topics based on the user's emotional data, allowing the user to relax. This can reduce the burden on nursing staff.

[0060] The 3D reproduction unit can capture the user's facial expressions and movements in real time and dynamically change the 3D model's expressions and movements accordingly. For example, the 3D reproduction unit captures the user's facial expressions and movements in real time and dynamically changes the 3D model's expressions and movements accordingly. For example, the 3D reproduction unit captures the user's facial expressions in real time with a camera and dynamically changes the 3D model's expressions based on that data. For example, if the user smiles, the 3D model smiles back. The 3D reproduction unit also detects the user's body movements with a sensor and adjusts the 3D model's movements in real time accordingly. For example, if the user waves their hand, the 3D model will do the same. Furthermore, the 3D reproduction unit analyzes the user's tone and speed of speech and dynamically changes the 3D model's mouth movements and facial expressions based on that data. For example, if the user speaks quickly, the 3D model's mouth movements will also speed up accordingly. This enables natural dialogue that responds to the user's real-time reactions.

[0061] The 3D reproduction unit adds haptic feedback to the 3D model, allowing the user to feel a sensation when touching it. The 3D reproduction unit adds haptic feedback to the 3D model, allowing the user to feel a sensation when touching it. For example, a haptic feedback device can be worn by the user, allowing the user to feel vibrations and pressure when touching the 3D model. For example, when a user grasps the hand of a 3D model, the device can reproduce the pressure. To provide haptic feedback, tactile sensors can be embedded in the surface of the 3D model, providing different sensations depending on the part the user touches. For example, touching the face can reproduce a soft sensation. To further enhance the haptic feedback, a device that reproduces temperature changes can be used, allowing the user to feel warmth or coldness when touching the 3D model. For example, grasping the hand can feel warmth. This provides the user with a more realistic experience.

[0062] The 3D reproduction unit can use the emotion estimation function to change the facial expressions and movements of the 3D model according to the user's emotions. For example, the 3D reproduction unit uses the emotion estimation function to change the facial expressions and movements of the 3D model according to the user's emotions. For example, the unit analyzes the user's emotions in real time and dynamically changes the facial expressions of the 3D model based on the results. For example, if the user is sad, the 3D model will also have a sad expression. The emotion estimation function also adds movements to the 3D model according to the user's emotions. For example, if the user is excited, the 3D model will also move more actively. Furthermore, the tone and speed of the 3D model's voice can be adjusted based on the user's emotion data to achieve more natural dialogue. For example, if the user is relaxed, the 3D model will speak in a calm voice. This allows for natural dialogue according to the user's emotions.

[0063] The 3D reproduction unit can reflect ambient light and shadow information in real time to blend the 3D model into the user's surroundings. For example, the 3D reproduction unit detects the ambient light around the user with a sensor and adjusts the lighting of the 3D model in real time based on that information. For example, if the room is dark, the 3D model will appear dark. The unit also dynamically generates shadows for the 3D model in response to changes in ambient light, achieving a more realistic display. For example, if the user moves a light, the shadow of the 3D model will move accordingly. Furthermore, the unit detects the color temperature around the user and adjusts the color tone of the 3D model based on that information. For example, if there is warm lighting, the 3D model will also appear warm. This provides the user with a more realistic experience.

[0064] The 3D reproduction unit can enable customization according to the user's preferences by allowing the selection of costumes and backgrounds from different cultures and regions. The 3D reproduction unit, for example, allows the selection of costumes and backgrounds from different cultures and regions, allowing customization according to the user's preferences. For example, the 3D reproduction unit changes the costumes and backgrounds of the 3D model according to the culture or region selected by the user. For example, it allows the selection of traditional Japanese costumes and backgrounds. In addition, a function is provided to customize the costumes and backgrounds of the 3D model according to the user's preferences. For example, it allows the user to select their favorite colors and designs. Furthermore, a database of different cultures and regions is built and the user can select from among them. For example, it provides historical costumes and backgrounds from Europe. This makes it possible to provide the user with a more personalized experience.

[0065] The 3D reproduction unit can use the emotion estimation function to automatically select and display an environment or background that is most relaxing for the user. For example, the 3D reproduction unit uses the emotion estimation function to automatically select and display an environment or background that is most relaxing for the user. For example, a system is constructed that analyzes the user's emotion data and automatically selects a relaxing environment or background. For example, a background of a natural landscape or a quiet room is displayed. Furthermore, the emotion estimation function is used to add music or sounds that the user can use to relax to the background. For example, calm music or sounds of nature are played. Furthermore, a system is developed that dynamically changes the background or environment in response to changes in the user's emotion. For example, if the user is feeling stressed, the background is switched to one that is relaxing. This can provide the user with a more relaxing experience.

[0066] The voice reproduction unit can dynamically adjust the tone and speed of the voice generated by the generation AI according to the tone and speed of the user's voice. The voice reproduction unit dynamically adjusts the tone and speed of the voice generated by the generation AI according to the tone and speed of the user's voice, for example. For example, it analyzes the tone of the user's voice in real time and adjusts the tone of the voice generated by the generation AI based on that data. For example, if the user speaks in a high voice, the generation AI also responds in a high voice. It also analyzes the user's speaking speed and adjusts the speed of the voice generated by the generation AI to match that speed. For example, if the user speaks slowly, the generation AI also responds slowly. It also analyzes the strength of the user's voice and adjusts the strength of the voice generated by the generation AI based on that data. For example, if the user speaks loudly, the generation AI also responds in a louder voice. This allows for more natural dialogue to be provided to the user.

[0067] The voice reproduction unit can add emotional nuances to the voice generated by the generation AI, enabling more natural conversations. For example, the voice reproduction unit can add emotional nuances to the voice generated by the generation AI, enabling more natural conversations. For example, speech synthesis technology can be used to add emotional nuances to the voice generated by the generation AI. For example, emotions such as joy and sadness can be reflected in the voice. In addition, based on the user's emotional data, the tone and speed of the voice generated by the generation AI can be adjusted to add emotional nuances. For example, if the user is excited, the generation AI will also respond in an excited voice. Furthermore, using an emotion estimation function, a system can be built that adds emotional nuances to the voice generated by the generation AI in real time. For example, if the user is relaxed, the generation AI will also respond in a calm voice. This allows for more natural conversations to be provided to the user.

[0068] The voice reproduction unit uses the emotion estimation function to generate a tone and content of voice that corresponds to the user's emotions, thereby providing a conversation that is considerate to the user's feelings. The voice reproduction unit, for example, uses the emotion estimation function to generate a tone and content of voice that corresponds to the user's emotions, thereby providing a conversation that is considerate to the user's feelings. For example, the voice reproduction unit analyzes the user's emotions in real time and adjusts the tone and content of voice generated by the generation AI based on that data. For example, if the user is sad, the generation AI responds in a comforting voice. In addition, the emotion estimation function is used to build a system that generates topics and content that correspond to the user's emotions. For example, if the user is relaxed, a relaxing topic is provided. Furthermore, the tone and content of voice generated by the generation AI is dynamically adjusted based on the user's emotion data, realizing a conversation that is considerate to the user's feelings. For example, if the user is excited, the AI ​​responds with content that shares the user's excitement. This makes it possible to provide a dialogue that is considerate to the user's emotions.

[0069] The voice reproduction unit supports different languages ​​and dialects, and can converse in the user's native language or regional dialect. For example, the voice reproduction unit can support different languages ​​and dialects, and can converse in the user's native language or regional dialect. For example, multilingual speech synthesis technology can be incorporated into the generation AI to build a system that converses in the user's native language. For example, it can support English, French, Chinese, etc. In addition, a generation AI can be developed that learns the user's regional dialect and converses in that dialect. For example, it can support dialects such as Kansai dialect and Tohoku dialect. Furthermore, a system can be built in which the generation AI automatically converses in the language or dialect selected by the user. For example, the conversation can be based on the language set by the user. This makes it possible to provide more personalized dialogue to the user.

[0070] The voice reproduction unit can add background sounds and environmental sounds to the voice generated by the generation AI, providing a more realistic conversation environment. For example, the voice reproduction unit can add background sounds and environmental sounds to the voice generated by the generation AI, providing a more realistic conversation environment. For example, we will build a system that adds background sounds to the voice generated by the generation AI. For example, cafe noises and natural sounds can be added to the background. Furthermore, environmental sounds can be added to the voice generated by the generation AI depending on the user's environment. For example, if the user is in a park, birds chirping and the sound of the wind can be added. Furthermore, we will develop a system that adds environmental sounds in real time to the voice generated by the generation AI, providing a more natural conversation environment. For example, if the user is at home, household sounds can be added to the background. This can provide a more realistic conversation environment for the user.

[0071] The voice reproduction unit can use the emotion estimation function to automatically select and generate the voice tone and speaking style that the user finds most comfortable. For example, the voice reproduction unit uses the emotion estimation function to automatically select and generate the voice tone and speaking style that the user finds most comfortable. For example, a system is constructed that analyzes the user's emotional data and automatically selects the voice tone and speaking style that the user finds most comfortable. For example, a gentle tone and a slow speaking style are selected. Furthermore, the emotion estimation function is used to generate a voice tone and speaking style that relaxes the user in real time. For example, if the user is feeling stressed, the system responds in a relaxing voice. Furthermore, a system is developed that dynamically adjusts the voice tone and speaking style generated by the generation AI in response to changes in the user's emotions. For example, if the user is excited, the system responds in a calm voice. This allows for a more comfortable dialogue for the user.

[0072] The dialogue providing unit can analyze the user's real-time reactions and dynamically adjust the content and tone of the conversation. The dialogue providing unit, for example, analyzes the user's real-time reactions and dynamically adjusts the content and tone of the conversation. For example, the dialogue providing unit analyzes the user's real-time reactions and dynamically adjusts the content and tone of the conversation. For example, the dialogue providing unit analyzes the user's facial expressions and voice tone in real time, and the generation AI dynamically adjusts the content and tone of the conversation based on that data. For example, if the user smiles, the generation AI also responds in a brighter tone. In addition, a system is built that analyzes the user's real-time reactions and adjusts the progress of the conversation. For example, it digs deeper into topics that the user is interested in. Furthermore, the generation AI dynamically changes the tone and content of the conversation based on the user's real-time reaction data. For example, if the user is tired, it switches to a topic that will help them relax. This makes it possible to provide the user with a more natural dialogue.

[0073] The dialogue providing unit can use the emotion estimation function to provide topics and stories that correspond to the user's emotions, thereby realizing a dialogue that is sensitive to the user's feelings. The dialogue providing unit can, for example, use the emotion estimation function to provide topics and stories that correspond to the user's emotions, thereby realizing a dialogue that is sensitive to the user's feelings. For example, the dialogue providing unit can analyze the user's emotions in real time, and based on that data, the generation AI can provide topics and stories that correspond to the emotions. For example, if the user is sad, a comforting topic can be provided. Furthermore, the emotion estimation function can be used to build a system that generates stories that correspond to the user's emotions. For example, if the user is relaxed, a relaxing story can be provided. Furthermore, based on the user's emotion data, the generation AI can dynamically adjust topics and stories to realize a dialogue that is sensitive to the user's emotions. For example, if the user is excited, a topic that shares the user's excitement can be provided. This makes it possible to provide a dialogue that is sensitive to the user's emotions.

[0074] The dialogue providing unit can provide health advice and reminders based on the user's health condition and daily activity data. The dialogue providing unit provides health advice and reminders based on the user's health condition and daily activity data, for example. For example, the dialogue providing unit collects the user's health condition data, and the generation AI provides health advice based on that data. For example, health management advice is given based on the user's blood pressure and heart rate. In addition, a system is constructed in which the generation AI analyzes the user's daily activity data and provides reminders. For example, if the user is not getting enough exercise, a reminder is sent to encourage exercise. Furthermore, the generation AI provides personalized health advice based on the user's health condition and activity data. For example, advice on nutritional balance is given based on the user's dietary data. This can support the user's health management.

[0075] The dialogue providing unit can provide moving stories and episodes based on the user's past memories with family and friends. The dialogue providing unit provides moving stories and episodes based on, for example, the user's past memories with family and friends. For example, data on past memories with the user's family and friends is collected, and the generation AI provides moving stories based on that data. For example, memories of a family trip are recreated. In addition, a system is constructed in which the generation AI analyzes the user's past photos and videos and generates episodes based on that data. For example, episodes of specific events or anniversaries are provided. Furthermore, the generation AI learns the user's past conversation history with family and friends, and provides moving episodes based on that data. For example, special moments or moving events are recreated. This makes it possible to provide the user with a moving experience.

[0076] The dialogue provision unit can use the emotion estimation function to automatically select and provide topics and stories that are most relaxing to the user. The dialogue provision unit can, for example, use the emotion estimation function to automatically select and provide topics and stories that are most relaxing to the user. For example, a system can be built that analyzes a user's emotion data and automatically selects the most relaxing topics and stories. For example, topics related to natural scenery or calming music can be provided. Furthermore, the emotion estimation function can be used to generate stories that are relaxing to the user in real time. For example, if the user is feeling stressed, a relaxing story can be provided. Furthermore, a system can be developed in which the generation AI dynamically adjusts relaxing topics and stories according to changes in the user's emotions. For example, if the user is tired, the topic can be switched to a relaxing one. This makes it possible to provide the user with a more relaxing dialogue.

[0077] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0078] The dialogue provider can provide health advice and reminders based on the user's health condition and daily activity data. For example, it collects the user's health condition data and the generation AI provides health advice based on that data. For example, it provides health management advice based on the user's blood pressure and heart rate. It also builds a system that analyzes the user's daily activity data and the generation AI provides reminders. For example, if the user is not getting enough exercise, it sends a reminder to encourage them to exercise. Furthermore, the generation AI provides personalized health advice based on the user's health condition and activity data. For example, it provides advice on nutritional balance based on the user's dietary data. This can support the user's health management.

[0079] The dialogue provider can provide moving stories and episodes based on the user's past memories with family and friends. For example, data on past memories with the user's family and friends is collected, and the generation AI provides moving stories based on that data. For example, recreating memories of a family trip. A system can also be built that analyzes the user's past photos and videos, and the generation AI generates episodes based on that data. For example, it can provide episodes about specific events or anniversaries. Furthermore, the generation AI can learn the user's past conversation history with family and friends, and provide moving episodes based on that data. For example, it can recreate special moments or moving events. This can provide a moving experience for the user.

[0080] The dialogue provider can analyze the user's real-time reactions and dynamically adjust the content and tone of the conversation. For example, it analyzes the user's facial expressions and voice tone in real time, and the generation AI dynamically adjusts the content and tone of the conversation based on that data. For example, if the user smiles, the generation AI will respond in a brighter tone. We will also build a system that analyzes the user's real-time reactions and adjusts the progress of the conversation. For example, it will dig deeper into topics that the user is interested in. Furthermore, based on the user's real-time reaction data, the generation AI will dynamically change the tone and content of the conversation. For example, if the user is tired, it will switch to a topic that will help them relax. This makes it possible to provide the user with a more natural dialogue.

[0081] The dialogue provision unit uses the emotion estimation function to provide topics and stories that correspond to the user's emotions, enabling dialogue that is sensitive to the user's feelings. For example, the user's emotions are analyzed in real time, and the generation AI provides topics and stories that correspond to the emotions based on that data. For example, if the user is sad, a comforting topic is provided. Furthermore, a system that generates stories that correspond to the user's emotions is built using the emotion estimation function. For example, if the user is relaxed, a relaxing story is provided. Furthermore, based on the user's emotion data, the generation AI dynamically adjusts the topics and stories to enable dialogue that is sensitive to the user's emotions. For example, if the user is excited, a topic that shares the user's excitement is provided. This makes it possible to provide dialogue that is sensitive to the user's emotions.

[0082] The dialogue provider can provide relaxing topics when the user is feeling stressed. For example, the generation AI can analyze the user's emotional data and provide relaxing topics. The generation AI can also monitor the user's stress level in real time and provide topics based on that data. Furthermore, the generation AI can provide relaxing topics according to the user's preferences. This helps reduce the user's stress and relax them.

[0083] The 3D reproduction unit can capture the user's facial expressions and movements in real time and dynamically change the 3D model's expressions and movements accordingly. For example, a camera can capture the user's facial expressions in real time and dynamically change the 3D model's expressions based on that data. For example, if the user smiles, the 3D model will smile back. It also uses sensors to detect the user's body movements and adjust the 3D model's movements in real time accordingly. For example, if the user waves their hand, the 3D model will do the same. It also analyzes the user's tone and speed of speech and dynamically changes the 3D model's mouth movements and facial expressions based on that data. For example, if the user speaks quickly, the 3D model's mouth movements will also speed up accordingly. This enables natural dialogue based on the user's real-time reactions.

[0084] The 3D reproduction unit adds haptic feedback to the 3D model, allowing the user to feel a sensation when touching it. For example, a haptic feedback device can be worn by the user, allowing them to feel vibrations and pressure when touching the 3D model. For example, if the user grasps the hand of a 3D model, the device will reproduce the pressure. To provide haptic feedback, tactile sensors can be embedded in the surface of the 3D model, providing different sensations depending on the part the user touches. For example, touching the face will reproduce a soft feeling. Furthermore, to enhance the haptic feedback, a device that reproduces temperature changes can be used, allowing the user to feel warmth or coldness when touching the 3D model. For example, grasping the hand will feel warmth. This provides a more realistic experience for the user.

[0085] The 3D reproduction unit can use the emotion estimation function to change the facial expressions and movements of the 3D model according to the user's emotions. For example, it can analyze the user's emotions in real time and dynamically change the 3D model's facial expression based on the results. For example, if the user is sad, the 3D model will also have a sad expression. The emotion estimation function can also be used to add movements to the 3D model according to the user's emotions. For example, if the user is excited, the 3D model will move more actively. Furthermore, based on the user's emotional data, the tone and speed of the 3D model's voice can be adjusted to achieve more natural dialogue. For example, if the user is relaxed, the 3D model will speak in a calm voice. This allows for natural dialogue that reflects the user's emotions.

[0086] The 3D reproduction unit can reflect ambient light and shadow information in real time to blend the 3D model into the user's surroundings. For example, a sensor detects the ambient light around the user and adjusts the lighting of the 3D model in real time based on that information. For example, if the room is dark, the 3D model will appear dark. Shadows of the 3D model can also be dynamically generated according to changes in ambient light, achieving a more realistic display. For example, if the user moves a light, the shadow of the 3D model will move accordingly. Furthermore, the unit detects the color temperature around the user and adjusts the color tone of the 3D model based on that information. For example, if there is warm lighting, the 3D model will also appear in warm colors. This provides the user with a more realistic experience.

[0087] The 3D reproduction unit can enable customization according to the user's preferences by allowing the selection of costumes and backgrounds from different cultures and regions. For example, the 3D model's costume and background can be changed according to the culture or region selected by the user. For example, traditional Japanese costumes and backgrounds can be selected. The unit also provides a function to customize the 3D model's costume and background according to the user's preferences. For example, the unit allows the user to select their favorite colors and designs. Furthermore, the unit can build a database of different cultures and regions and allow the user to select from them. For example, historical European costumes and backgrounds can be provided. This allows the user to have a more personalized experience.

[0088] The processing flow of the second embodiment will be briefly explained below.

[0089] Step 1: The 3D reconstruction unit recreates the appearance and face of your loved one in 3D. For example, the 3D reconstruction unit can scan a family photo that the user owns and generate a 3D model based on that data. It can also generate a 3D model based on data such as photos and videos. Step 2: The voice reproduction unit recreates the voice of your loved one. For example, the generation AI can analyze past phone recordings and video messages to learn the characteristics of that person's voice. Alternatively, the generation AI can use voice synthesis technology to recreate the voice. Step 3: The dialogue provider provides information and topics based on the user's preferences. For example, the generation AI can provide topics based on the user's favorite hobbies or past memories. The generation AI can also provide topics based on the user's past conversation history. This provides the user with an immersive dialogue experience and reduces the burden on caregivers.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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).

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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 AI 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.

[0107] 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.

[0108] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0109] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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).

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0119] 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.

[0120] 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.

[0121] 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 AI 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.

[0122] 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.

[0123] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0124] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 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 processing similar to that of the specific processing unit 290 using these models.

[0135] 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.

[0136] 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.

[0137] 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 AI 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0144] 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."

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0157] 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 3D reproduction section that reproduces the appearance and face of your beloved in 3D, A voice reproduction section that reproduces the voice of a loved one, A dialogue providing unit that provides information and topics according to the user's preferences. A system characterized by:

2. The 3D reproduction unit Scan the user's family photos and generate the 3D model based on that data 2. The system of claim 1.

3. The voice reproduction unit Analyzes past phone recordings and video messages to learn the characteristics of your voice 2. The system of claim 1.

4. The dialogue providing unit Providing topics based on the user's favorite hobbies and past memories 2. The system of claim 1.

5. The dialogue providing unit Provide a relaxing topic for users when they are feeling stressed 2. The system of claim 1.

6. The dialogue providing unit The time spent by care staff interacting with the user can be reduced.

2. The system of claim 1.

7. The 3D reproduction unit The user's facial expressions and movements are captured in real time, and the 3D model's facial expressions and movements are dynamically changed accordingly.

2. The system of claim 1.

8. The 3D reproduction unit Add haptic feedback to the 3D model so that the user can feel the sensation when touching it.

2. The system of claim 1.

Images