system

Abstract

The system according to this embodiment aims to support conversations during high-quality video calls and generate a virtual environment. [Solution] The system according to the embodiment comprises a call unit, a conversation support unit, and an environment generation unit. The call unit provides high-quality video calls. The conversation support unit supports the conversation during the video call provided by the call unit. The environment generation unit generates a virtual environment based on the conversation supported by the conversation support unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, supporting conversations during high-quality video calls and generating a virtual environment have not been sufficiently carried out, and there is room for improvement.

[0005] The system according to the embodiment aims to support conversations during high-quality video calls and generate a virtual environment.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a call unit, a conversation support unit, and an environment generation unit. The call unit provides high-quality video calls. The conversation support unit supports the conversation during the video call provided by the call unit. The environment generation unit generates a virtual environment based on the conversation supported by the conversation support unit. [Effects of the Invention]

[0007] The system according to this embodiment can support conversations during high-quality video calls and generate a virtual environment. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the numbered communication I / F (Interface) 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 such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The virtual visit application according to an embodiment of the present invention is a system that enables virtual visits to one's in-laws' home. This system includes a high-quality video call function, an AI-powered conversation support function, a virtual environment generation function, a memory sharing system, and a smart scheduling function. This allows for the deepening of family bonds while reducing the burden of actual visits. For example, the virtual visit application provides a high-quality video call function. This function allows users to communicate with their in-laws with clear video and audio. For example, the spread of 5G enables rich content delivery and low-latency video calls. Next, the virtual visit application includes an AI-powered conversation support function. This function allows users to smoothly conduct conversations. For example, the AI ​​suggests conversation topics and supports appropriate responses, allowing conversations to continue without interruption. Furthermore, the virtual visit application uses a virtual environment generation function to provide users with an experience as if they were actually visiting their in-laws' home. This function allows users to virtually recreate rooms, gardens, etc., in their in-laws' home, giving them the feeling of being there. For example, 3D modeling technology can be used to generate a detailed virtual environment of the in-laws' home. Furthermore, the virtual visit app features a memory-sharing system, making it easy to share family photos and videos. This feature allows users to deepen their family bonds while reminiscing about past memories. For example, family photos and videos can be securely stored and shared using cloud storage. Finally, the virtual visit app includes a smart scheduling function, making it easier to coordinate the schedules of all family members. This feature allows users to centrally manage everyone's schedules and conduct virtual visits at the optimal time. For example, AI can analyze each member's schedule and suggest the best time to visit. In this way, the AI-powered virtual visit app for in-laws can deepen family bonds while reducing the burden of actual visits. This addresses the increased demand for remote communication due to the pandemic and the importance of family communication in an aging society.Furthermore, it can address social needs such as work-style reform and childcare support. This means that virtual visit apps can reduce the burden of actual visits while strengthening family bonds.

[0029] The virtual visit application according to this embodiment comprises a call unit, a conversation support unit, and an environment generation unit. The call unit provides high-quality video calls. The call unit enables rich content delivery, for example, through the spread of 5G. The call unit allows for communication with in-laws with clear video and audio. The call unit can also, for example, achieve low-latency video calls. The conversation support unit supports the conversation during the video call provided by the call unit. The conversation support unit can, for example, suggest conversation topics. The conversation support unit can also, for example, support appropriate responses. The conversation support unit can also, for example, ensure that the conversation continues without interruption. The environment generation unit generates a virtual environment based on the conversation supported by the conversation support unit. The environment generation unit generates the virtual environment using, for example, 3D modeling technology. The environment generation unit can also, for example, virtually recreate rooms and gardens in the in-laws' home. The environment generation unit can also, for example, provide a virtual environment that gives the user the feeling of being there. This enables the virtual visit application according to the embodiment to provide high-quality video calls, conversation support, and virtual environment generation.

[0030] The calling function provides high-quality video calls. Specifically, it leverages 5G high-speed communication technology to deliver rich content. This allows users to communicate with their in-laws with clear video and audio. For example, the calling function uses high-resolution cameras and high-performance microphones to maximize video and audio quality. In addition, the calling function employs low-latency codecs and network optimization technologies to achieve low-latency video calls. This enables natural conversations in real time, making you feel closer to family members who are far away. Furthermore, the calling function uses noise cancellation technology to remove background noise and provide clear audio. This allows for comfortable conversations even in noisy environments. The calling function also supports seamless connectivity between multiple devices and is accessible from various devices such as smartphones, tablets, and PCs. This allows users to choose the optimal device according to their preferences and circumstances and enjoy making calls. Moreover, the calling function has robust security measures in place, providing a safe calling environment by protecting privacy through end-to-end encryption. This allows users to make calls with peace of mind.

[0031] The Conversation Support Unit provides support during video calls provided by the Call Unit. Specifically, the Conversation Support Unit uses AI to suggest conversation topics. For example, if a user struggles to find a topic of conversation while talking with their in-laws, the AI ​​analyzes past conversation history and the user's interests to suggest an appropriate topic. The Conversation Support Unit can also support appropriate responses. For example, the AI ​​analyzes the content of the conversation in real time and provides the user with example responses. This allows the user to continue the conversation smoothly. Furthermore, to ensure that the conversation continues without interruption, the Conversation Support Unit monitors the flow of the conversation and suggests new topics at the appropriate time. This allows the conversation to progress naturally, and the user can enjoy communicating without stress. The Conversation Support Unit uses speech recognition technology to analyze the user's statements in real time and provide appropriate support. For example, when a user utters a specific keyword, it immediately presents related information and topics. The Conversation Support Unit can also analyze the user's emotions and provide appropriate support. For example, if the user is feeling nervous, it suggests topics that will help them relax. In this way, the Conversation Support Unit can support the user in continuing the conversation comfortably and improve the quality of communication.

[0032] The environment generation unit generates a virtual environment based on conversations supported by the conversation support unit. Specifically, the environment generation unit generates the virtual environment using 3D modeling technology. For example, it can virtually recreate rooms and gardens in a user's in-laws' house, giving the user the feeling of actually being there. The environment generation unit provides a customizable virtual environment according to the user's requests. For example, if a user wants to add specific furniture or decorations, they can be added to the virtual environment using 3D modeling technology. The environment generation unit can also update the environment in real time. For example, if a user suggests a new topic during a conversation, the virtual environment can be changed accordingly. This allows the user to always enjoy a fresh and interesting virtual environment. Furthermore, the environment generation unit can track the user's movements and gaze, and dynamically adjust the virtual environment accordingly. For example, if a user faces a specific direction, it provides a viewpoint corresponding to that direction, providing a more realistic experience. The environment generation unit also enhances the realism of the virtual environment using sound effects. For example, it can recreate sounds from the in-laws' garden or rooms, reinforcing the user's feeling of being there. This allows the environment generation unit to provide users with a high-quality, immersive virtual environment, enriching communication with family members who are in remote locations.

[0033] The virtual visit app features a memory sharing system. This system makes it easy to share family photos and videos. For example, it can save family photos and videos using cloud storage. The system also allows for secure sharing of family photos and videos. For example, it allows you to deepen family bonds by reminiscing about past memories. This makes sharing family photos and videos easy.

[0034] The virtual visit app features smart scheduling capabilities. These smart scheduling features make it easier to coordinate the schedules of the entire family. For example, the AI ​​analyzes each member's schedule. It can also suggest the optimal visit time. Furthermore, it allows for centralized management of the entire family's schedule, making it easier to coordinate everyone's plans.

[0035] The call unit will enable rich content delivery with the spread of 5G. For example, the call unit can deliver high-resolution video. The call unit can also deliver interactive content. For example, the call unit can achieve low-latency delivery. This will enable rich content delivery with the spread of 5G.

[0036] The conversation support unit suggests conversation topics and provides support for appropriate responses. For example, the conversation support unit suggests conversation topics using natural language processing techniques. It can also suggest topics based on user interests, for example. Furthermore, it can support appropriate responses using methods that generate responses, for example. This allows for smoother conversation flow.

[0037] The environment generation unit generates a virtual environment using 3D modeling technology. For example, the environment generation unit performs 3D modeling using CAD software. The environment generation unit can also generate a virtual environment using, for example, real-time rendering technology. Furthermore, the environment generation unit can generate a virtual environment using, for example, physical simulation technology. This allows for the creation of detailed virtual environments using 3D modeling technology.

[0038] The memory sharing system uses cloud storage to save and share family photos and videos. For example, the memory sharing system uses a cloud storage service to save photos and videos. The memory sharing system can also share photos and videos using a cloud storage service. For example, the memory sharing system can use cloud storage with security measures in place. This allows for the safe saving and sharing of photos and videos through cloud storage.

[0039] The smart scheduling function uses AI to analyze each member's schedule and suggest the optimal visit time. The smart scheduling function can analyze schedules using, for example, machine learning algorithms. It can also analyze schedules using, for example, data analysis methods. Furthermore, the smart scheduling function can suggest the optimal visit time based on, for example, suggestion criteria. This allows the AI ​​to analyze schedules and suggest the optimal visit time.

[0040] The call unit removes background noise in real time during a call, providing clear audio. For example, the call unit's AI detects ambient noise during a call and performs noise cancellation in real time. The call unit's AI can also analyze the audio during a call and emphasize only the important parts. The call unit's AI can also filter out ambient noise during a call, providing clear audio. This allows for clear audio by removing background noise in real time.

[0041] The call function analyzes the user's facial expressions during a call and automatically applies appropriate filters and effects. For example, when the user is smiling, the AI ​​automatically applies a brightening filter. For example, when the user has a serious expression, the AI ​​can automatically apply a calming effect. For example, when the user has a surprised expression, the AI ​​can automatically apply a cheerful effect. This allows for more enjoyable calls by automatically applying filters and effects according to the user's facial expressions.

[0042] The call unit monitors the user's internet connection status during a call and automatically adjusts to the optimal bitrate. For example, if the user's internet connection is unstable, the AI ​​will lower the bitrate to maintain the call. For example, if the user's internet connection is good, the AI ​​can increase the bitrate to provide a high-quality call. For example, if the user's internet connection fluctuates, the AI ​​can adjust the bitrate in real time. This ensures stable calls by automatically adjusting the bitrate according to the internet connection status.

[0043] The call unit monitors the user's device battery status during a call and provides appropriate notifications. For example, if the user's device battery is low, the AI ​​will notify the user during the call. The call unit can also suggest ending the call if the user's device battery is insufficient. For example, if the user's device battery is rapidly depleting, the AI ​​can suggest a power-saving mode. This prevents the battery from running out during a call by providing appropriate notifications based on the device's battery status.

[0044] The conversation support unit analyzes the user's statements during a conversation and generates appropriate responses in real time. For example, if the user asks a question, the AI ​​in the conversation support unit will generate an appropriate response in real time. For example, if the user expresses gratitude, the AI ​​in the conversation support unit can also generate an appropriate response in real time. For example, if the user expresses an opinion, the AI ​​in the conversation support unit can also generate an appropriate response in real time. This allows the conversation to continue without interruption by generating appropriate responses in real time according to the user's statements.

[0045] The conversation support unit refers to the user's past conversation history during a conversation and suggests relevant topics. For example, the AI ​​suggests relevant topics based on topics the user has previously discussed. The AI ​​can also suggest relevant topics based on topics the user has previously shown interest in. The AI ​​can also suggest relevant topics based on topics the user has previously avoided. In this way, relevant topics can be suggested by referring to past conversation history.

[0046] The conversation support unit automatically detects the user's language settings during a conversation and provides support in the appropriate language. For example, the conversation support unit uses AI to provide support in the appropriate language based on the language settings of the user's device. The conversation support unit can also provide a language switching function if the user uses multiple languages. For example, if the user selects a specific language, the conversation support unit can provide support in that language using AI. This enables smoother conversations by providing support in the appropriate language according to the user's language settings.

[0047] The conversation support unit considers the user's cultural background during a conversation and suggests appropriate topics. For example, the AI ​​suggests appropriate topics based on the user's cultural background. The AI ​​can also suggest appropriate topics based on the user's nationality. The AI ​​can also suggest appropriate topics based on the user's religion. This allows for smoother conversations by suggesting appropriate topics according to the user's cultural background.

[0048] The environment generation unit recreates the optimal environment by referring to the user's past visit history when generating a virtual environment. For example, the environment generation unit can use AI to recreate the optimal virtual environment based on places the user has visited in the past. The environment generation unit can also use AI to recreate the optimal virtual environment based on the user's past visit history. For example, the environment generation unit can use AI to recreate the optimal virtual environment based on details of places the user has visited in the past. In this way, by referring to past visit history, the optimal virtual environment for the user can be recreated.

[0049] The environment generation unit provides customizable options based on user preferences when generating a virtual environment. For example, the environment generation unit can provide an AI-customizable virtual environment based on user preferences. The environment generation unit can also provide an AI-customizable option based on user preferences. This enables a more personalized experience by providing a virtual environment that can be customized according to user preferences.

[0050] The environment generation unit provides relevant environments while considering the user's geographical location information during virtual environment generation. For example, the environment generation unit can provide relevant virtual environments based on the user's geographical location information using AI. The environment generation unit can also provide relevant virtual environments based on the user's geographical location information using AI. By providing relevant virtual environments based on the user's geographical location information, a more realistic experience becomes possible.

[0051] The environment generation unit provides the optimal environment when generating a virtual environment, taking into account the user's device performance. For example, the environment generation unit can use AI to provide the optimal virtual environment based on the user's device performance. This allows for a smoother user experience by providing the optimal virtual environment according to the user's device performance.

[0052] The memory sharing system suggests the most suitable content when users share memories, referencing their past sharing history. For example, the system uses AI to suggest the most suitable content based on photos and videos previously shared by the user. It can also suggest the most suitable content based on the user's past sharing history. Furthermore, it can suggest the most suitable content based on details of content previously shared by the user. This allows the system to suggest content that is optimal for the user by referencing their past sharing history.

[0053] The memory sharing system suggests the optimal storage method when sharing memories, taking into account the user's device storage status. For example, if the user's device storage is insufficient, the AI ​​will suggest cloud storage. The system can also suggest storage optimization if the user's device storage is inadequate. For example, if the user's device storage is rapidly decreasing, the AI ​​can suggest deleting unnecessary data. This enables efficient use of storage by suggesting the optimal storage method according to the device's storage status.

[0054] The memory sharing system suggests the optimal sharing method based on the user's past sharing history when sharing memories. For example, the AI ​​suggests the optimal sharing method based on the user's past sharing history. The AI ​​can also suggest the optimal sharing method based on the user's past sharing history. This allows for smoother sharing by suggesting the optimal sharing method based on past sharing history.

[0055] The smart scheduling function makes optimal suggestions by referring to the user's past schedule history during scheduling. For example, the smart scheduling function uses AI to make optimal suggestions based on the user's past schedule history. The smart scheduling function can also use AI to make optimal suggestions based on the user's past schedule history. For example, the smart scheduling function can also use AI to make optimal suggestions based on the details of the user's past schedule history. This allows the system to suggest the most suitable schedule for the user by referring to their past schedule history.

[0056] The smart scheduling function integrates the user's device calendar information to provide optimal suggestions during scheduling. For example, the smart scheduling function uses AI to provide optimal suggestions based on the user's device calendar information. This allows for more appropriate schedule suggestions by integrating the device calendar information.

[0057] The smart scheduling function provides optimal suggestions based on the user's past scheduling history. For example, the smart scheduling function uses AI to provide optimal suggestions based on the user's past schedules. The smart scheduling function can also use AI to provide optimal suggestions based on the user's past scheduling history. The smart scheduling function can also use AI to provide optimal suggestions based on the details of the user's past schedules. This allows for more appropriate scheduling by providing optimal suggestions based on past scheduling history.

[0058] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0059] Virtual visit apps can also include a feature that references the user's past conversation history and suggests relevant topics. For example, the AI ​​can suggest relevant topics based on topics the user has previously discussed. It can also suggest relevant topics based on topics the user has shown interest in in the past, or topics the user has avoided in the past. This allows for the suggestion of relevant topics by referencing past conversation history.

[0060] Virtual visit apps can also have a feature that integrates the user's device calendar information and suggests the optimal schedule. For example, the AI ​​can make optimal suggestions based on the user's device calendar information. This allows for more appropriate schedule suggestions by integrating the calendar information from multiple devices.

[0061] Virtual visit applications can also include features that allow them to recreate the optimal virtual environment by referencing the user's past visit history. For example, AI can recreate the optimal virtual environment based on places the user has visited in the past. AI can also recreate the optimal virtual environment based on the user's past visit history. This allows for the recreation of the optimal virtual environment for the user by referencing past visit history.

[0062] Virtual visit applications can also include features that consider the user's device storage status and suggest the optimal storage method. For example, if the user's device storage is insufficient, the AI ​​can suggest cloud storage. If the user's device storage is inadequate, the AI ​​can also suggest storage optimization. If the user's device storage is rapidly decreasing, the AI ​​can suggest deleting unnecessary data. This enables efficient storage utilization by suggesting the optimal storage method according to the device's storage status.

[0063] Virtual visit apps can also include features that consider the user's cultural background and suggest appropriate topics. For example, the AI ​​can suggest appropriate topics based on the user's cultural background. It can also suggest appropriate topics based on the user's nationality or religion. This allows for smoother conversations by suggesting topics appropriate to the user's cultural background.

[0064] The following briefly describes the processing flow for example form 1.

[0065] Step 1: The call section provides high-quality video calls. For example, the spread of 5G enables rich content delivery, allowing for communication with clear video and audio. It also enables video calls with low latency. Step 2: The conversation support team provides support for the conversation during the video call provided by the call team. For example, they suggest conversation topics, support appropriate responses, and ensure the conversation continues without interruption. Step 3: The environment generation unit generates a virtual environment based on the conversation supported by the conversation support unit. For example, it can generate a virtual environment using 3D modeling technology to virtually recreate rooms and gardens in the in-laws' house. This provides a virtual environment that gives the user the feeling of actually being there.

[0066] (Example of form 2) The virtual visit application according to an embodiment of the present invention is a system that enables virtual visits to one's in-laws' home. This system includes a high-quality video call function, an AI-powered conversation support function, a virtual environment generation function, a memory sharing system, and a smart scheduling function. This allows for the deepening of family bonds while reducing the burden of actual visits. For example, the virtual visit application provides a high-quality video call function. This function allows users to communicate with their in-laws with clear video and audio. For example, the spread of 5G enables rich content delivery and low-latency video calls. Next, the virtual visit application includes an AI-powered conversation support function. This function allows users to smoothly conduct conversations. For example, the AI ​​suggests conversation topics and supports appropriate responses, allowing conversations to continue without interruption. Furthermore, the virtual visit application uses a virtual environment generation function to provide users with an experience as if they were actually visiting their in-laws' home. This function allows users to virtually recreate rooms, gardens, etc., in their in-laws' home, giving them the feeling of being there. For example, 3D modeling technology can be used to generate a detailed virtual environment of the in-laws' home. Furthermore, the virtual visit app features a memory-sharing system, making it easy to share family photos and videos. This feature allows users to deepen their family bonds while reminiscing about past memories. For example, family photos and videos can be securely stored and shared using cloud storage. Finally, the virtual visit app includes a smart scheduling function, making it easier to coordinate the schedules of all family members. This feature allows users to centrally manage everyone's schedules and conduct virtual visits at the optimal time. For example, AI can analyze each member's schedule and suggest the best time to visit. In this way, the AI-powered virtual visit app for in-laws can deepen family bonds while reducing the burden of actual visits. This addresses the increased demand for remote communication due to the pandemic and the importance of family communication in an aging society.Furthermore, it can address social needs such as work-style reform and childcare support. This means that virtual visit apps can reduce the burden of actual visits while strengthening family bonds.

[0067] The virtual visit application according to this embodiment comprises a call unit, a conversation support unit, and an environment generation unit. The call unit provides high-quality video calls. The call unit enables rich content delivery, for example, through the spread of 5G. The call unit allows for communication with in-laws with clear video and audio. The call unit can also, for example, achieve low-latency video calls. The conversation support unit supports the conversation during the video call provided by the call unit. The conversation support unit can, for example, suggest conversation topics. The conversation support unit can also, for example, support appropriate responses. The conversation support unit can also, for example, ensure that the conversation continues without interruption. The environment generation unit generates a virtual environment based on the conversation supported by the conversation support unit. The environment generation unit generates the virtual environment using, for example, 3D modeling technology. The environment generation unit can also, for example, virtually recreate rooms and gardens in the in-laws' home. The environment generation unit can also, for example, provide a virtual environment that gives the user the feeling of being there. This enables the virtual visit application according to the embodiment to provide high-quality video calls, conversation support, and virtual environment generation.

[0068] The calling function provides high-quality video calls. Specifically, it leverages 5G high-speed communication technology to deliver rich content. This allows users to communicate with their in-laws with clear video and audio. For example, the calling function uses high-resolution cameras and high-performance microphones to maximize video and audio quality. In addition, the calling function employs low-latency codecs and network optimization technologies to achieve low-latency video calls. This enables natural conversations in real time, making you feel closer to family members who are far away. Furthermore, the calling function uses noise cancellation technology to remove background noise and provide clear audio. This allows for comfortable conversations even in noisy environments. The calling function also supports seamless connectivity between multiple devices and is accessible from various devices such as smartphones, tablets, and PCs. This allows users to choose the optimal device according to their preferences and circumstances and enjoy making calls. Moreover, the calling function has robust security measures in place, providing a safe calling environment by protecting privacy through end-to-end encryption. This allows users to make calls with peace of mind.

[0069] The Conversation Support Unit provides support during video calls provided by the Call Unit. Specifically, the Conversation Support Unit uses AI to suggest conversation topics. For example, if a user struggles to find a topic of conversation while talking with their in-laws, the AI ​​analyzes past conversation history and the user's interests to suggest an appropriate topic. The Conversation Support Unit can also support appropriate responses. For example, the AI ​​analyzes the content of the conversation in real time and provides the user with example responses. This allows the user to continue the conversation smoothly. Furthermore, to ensure that the conversation continues without interruption, the Conversation Support Unit monitors the flow of the conversation and suggests new topics at the appropriate time. This allows the conversation to progress naturally, and the user can enjoy communicating without stress. The Conversation Support Unit uses speech recognition technology to analyze the user's statements in real time and provide appropriate support. For example, when a user utters a specific keyword, it immediately presents related information and topics. The Conversation Support Unit can also analyze the user's emotions and provide appropriate support. For example, if the user is feeling nervous, it suggests topics that will help them relax. In this way, the Conversation Support Unit can support the user in continuing the conversation comfortably and improve the quality of communication.

[0070] The environment generation unit generates a virtual environment based on conversations supported by the conversation support unit. Specifically, the environment generation unit generates the virtual environment using 3D modeling technology. For example, it can virtually recreate rooms and gardens in a user's in-laws' house, giving the user the feeling of actually being there. The environment generation unit provides a customizable virtual environment according to the user's requests. For example, if a user wants to add specific furniture or decorations, they can be added to the virtual environment using 3D modeling technology. The environment generation unit can also update the environment in real time. For example, if a user suggests a new topic during a conversation, the virtual environment can be changed accordingly. This allows the user to always enjoy a fresh and interesting virtual environment. Furthermore, the environment generation unit can track the user's movements and gaze, and dynamically adjust the virtual environment accordingly. For example, if a user faces a specific direction, it provides a viewpoint corresponding to that direction, providing a more realistic experience. The environment generation unit also enhances the realism of the virtual environment using sound effects. For example, it can recreate sounds from the in-laws' garden or rooms, reinforcing the user's feeling of being there. This allows the environment generation unit to provide users with a high-quality, immersive virtual environment, enriching communication with family members who are in remote locations.

[0071] The virtual visit app features a memory sharing system. This system makes it easy to share family photos and videos. For example, it can save family photos and videos using cloud storage. The system also allows for secure sharing of family photos and videos. For example, it allows you to deepen family bonds by reminiscing about past memories. This makes sharing family photos and videos easy.

[0072] The virtual visit app features smart scheduling capabilities. These smart scheduling features make it easier to coordinate the schedules of the entire family. For example, the AI ​​analyzes each member's schedule. It can also suggest the optimal visit time. Furthermore, it allows for centralized management of the entire family's schedule, making it easier to coordinate everyone's plans.

[0073] The call unit will enable rich content delivery with the spread of 5G. For example, the call unit can deliver high-resolution video. The call unit can also deliver interactive content. For example, the call unit can achieve low-latency delivery. This will enable rich content delivery with the spread of 5G.

[0074] The conversation support unit suggests conversation topics and provides support for appropriate responses. For example, the conversation support unit suggests conversation topics using natural language processing techniques. It can also suggest topics based on user interests, for example. Furthermore, it can support appropriate responses using methods that generate responses, for example. This allows for smoother conversation flow.

[0075] The environment generation unit generates a virtual environment using 3D modeling technology. For example, the environment generation unit performs 3D modeling using CAD software. The environment generation unit can also generate a virtual environment using, for example, real-time rendering technology. Furthermore, the environment generation unit can generate a virtual environment using, for example, physical simulation technology. This allows for the creation of detailed virtual environments using 3D modeling technology.

[0076] The memory sharing system uses cloud storage to save and share family photos and videos. For example, the memory sharing system uses a cloud storage service to save photos and videos. The memory sharing system can also share photos and videos using a cloud storage service. For example, the memory sharing system can use cloud storage with security measures in place. This allows for the safe saving and sharing of photos and videos through cloud storage.

[0077] The smart scheduling function uses AI to analyze each member's schedule and suggest the optimal visit time. The smart scheduling function can analyze schedules using, for example, machine learning algorithms. It can also analyze schedules using, for example, data analysis methods. Furthermore, the smart scheduling function can suggest the optimal visit time based on, for example, suggestion criteria. This allows the AI ​​to analyze schedules and suggest the optimal visit time.

[0078] The call unit estimates the user's emotions and adjusts the video and audio quality based on the estimated emotions. For example, if the user is nervous, the AI ​​will automatically adjust the video quality to provide a more relaxing image. For example, if the user is tired, the AI ​​can adjust the audio quality to provide a more comfortable sound. For example, if the user is excited, the AI ​​can enhance the video quality to provide a clearer image. This allows for more comfortable conversations by adjusting the video and audio quality according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0079] The call unit removes background noise in real time during a call, providing clear audio. For example, the call unit's AI detects ambient noise during a call and performs noise cancellation in real time. The call unit's AI can also analyze the audio during a call and emphasize only the important parts. The call unit's AI can also filter out ambient noise during a call, providing clear audio. This allows for clear audio by removing background noise in real time.

[0080] The call function analyzes the user's facial expressions during a call and automatically applies appropriate filters and effects. For example, when the user is smiling, the AI ​​automatically applies a brightening filter. For example, when the user has a serious expression, the AI ​​can automatically apply a calming effect. For example, when the user has a surprised expression, the AI ​​can automatically apply a cheerful effect. This allows for more enjoyable calls by automatically applying filters and effects according to the user's facial expressions.

[0081] The call unit estimates the user's emotions and adjusts the timing of the call's start based on the estimated emotions. For example, if the user is relaxed, the AI ​​may delay the start of the call. If the user is in a hurry, the AI ​​may also start the call earlier. If the user is nervous, the AI ​​may adjust the start of the call to choose a time when the user can relax. This allows for a more appropriate start to the call by adjusting the timing of the call's start according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0082] The call unit monitors the user's internet connection status during a call and automatically adjusts to the optimal bitrate. For example, if the user's internet connection is unstable, the AI ​​will lower the bitrate to maintain the call. For example, if the user's internet connection is good, the AI ​​can increase the bitrate to provide a high-quality call. For example, if the user's internet connection fluctuates, the AI ​​can adjust the bitrate in real time. This ensures stable calls by automatically adjusting the bitrate according to the internet connection status.

[0083] The call unit monitors the user's device battery status during a call and provides appropriate notifications. For example, if the user's device battery is low, the AI ​​will notify the user during the call. The call unit can also suggest ending the call if the user's device battery is insufficient. For example, if the user's device battery is rapidly depleting, the AI ​​can suggest a power-saving mode. This prevents the battery from running out during a call by providing appropriate notifications based on the device's battery status.

[0084] The conversation support unit estimates the user's emotions and suggests conversation topics based on those emotions. For example, if the user is relaxed, the AI ​​will suggest relaxing topics. If the user is excited, the AI ​​can also suggest exciting topics. If the user is tired, the AI ​​can also suggest relaxing topics. This allows for smoother conversations by suggesting topics according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0085] The conversation support unit analyzes the user's statements during a conversation and generates appropriate responses in real time. For example, if the user asks a question, the AI ​​in the conversation support unit will generate an appropriate response in real time. For example, if the user expresses gratitude, the AI ​​in the conversation support unit can also generate an appropriate response in real time. For example, if the user expresses an opinion, the AI ​​in the conversation support unit can also generate an appropriate response in real time. This allows the conversation to continue without interruption by generating appropriate responses in real time according to the user's statements.

[0086] The conversation support unit refers to the user's past conversation history during a conversation and suggests relevant topics. For example, the AI ​​suggests relevant topics based on topics the user has previously discussed. The AI ​​can also suggest relevant topics based on topics the user has previously shown interest in. The AI ​​can also suggest relevant topics based on topics the user has previously avoided. In this way, relevant topics can be suggested by referring to past conversation history.

[0087] The conversation support unit estimates the user's emotions and adjusts the conversation speed based on the estimated emotions. For example, if the user is relaxed, the AI ​​will slow down the conversation speed. If the user is in a hurry, the AI ​​can speed up the conversation speed. If the user is nervous, the AI ​​can adjust the conversation speed to a more relaxing pace. This allows the conversation to proceed at a more appropriate speed by adjusting the conversation speed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0088] The conversation support unit automatically detects the user's language settings during a conversation and provides support in the appropriate language. For example, the conversation support unit uses AI to provide support in the appropriate language based on the language settings of the user's device. The conversation support unit can also provide a language switching function if the user uses multiple languages. For example, if the user selects a specific language, the conversation support unit can provide support in that language using AI. This enables smoother conversations by providing support in the appropriate language according to the user's language settings.

[0089] The conversation support unit considers the user's cultural background during a conversation and suggests appropriate topics. For example, the AI ​​suggests appropriate topics based on the user's cultural background. The AI ​​can also suggest appropriate topics based on the user's nationality. The AI ​​can also suggest appropriate topics based on the user's religion. This allows for smoother conversations by suggesting appropriate topics according to the user's cultural background.

[0090] The environment generation unit estimates the user's emotions and adjusts the atmosphere of the virtual environment based on the estimated emotions. For example, if the user is relaxed, the environment generation unit will generate a virtual environment with a relaxing atmosphere. For example, if the user is excited, the environment generation unit can also generate a virtual environment with an exciting atmosphere. For example, if the user is tired, the environment generation unit can also generate a virtual environment with a relaxing atmosphere. By adjusting the atmosphere of the virtual environment according to the user's emotions, a more comfortable virtual experience becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0091] The environment generation unit recreates the optimal environment by referring to the user's past visit history when generating a virtual environment. For example, the environment generation unit can use AI to recreate the optimal virtual environment based on places the user has visited in the past. The environment generation unit can also use AI to recreate the optimal virtual environment based on the user's past visit history. For example, the environment generation unit can use AI to recreate the optimal virtual environment based on details of places the user has visited in the past. In this way, by referring to past visit history, the optimal virtual environment for the user can be recreated.

[0092] The environment generation unit provides customizable options based on user preferences when generating a virtual environment. For example, the environment generation unit can provide an AI-customizable virtual environment based on user preferences. The environment generation unit can also provide an AI-customizable option based on user preferences. This enables a more personalized experience by providing a virtual environment that can be customized according to user preferences.

[0093] The environment generation unit estimates the user's emotions and adjusts the level of detail of the virtual environment based on the estimated emotions. For example, if the user is relaxed, the AI ​​generates a detailed virtual environment. The environment generation unit can also generate a detailed virtual environment if the user is excited, for example. The environment generation unit can also generate a detailed virtual environment if the user is tired, for example. By adjusting the level of detail of the virtual environment according to the user's emotions, a more appropriate virtual experience becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0094] The environment generation unit provides relevant environments while considering the user's geographical location information during virtual environment generation. For example, the environment generation unit can provide relevant virtual environments based on the user's geographical location information using AI. The environment generation unit can also provide relevant virtual environments based on the user's geographical location information using AI. By providing relevant virtual environments based on the user's geographical location information, a more realistic experience becomes possible.

[0095] The environment generation unit provides the optimal environment when generating a virtual environment, taking into account the user's device performance. For example, the environment generation unit can use AI to provide the optimal virtual environment based on the user's device performance. This allows for a smoother user experience by providing the optimal virtual environment according to the user's device performance.

[0096] The memory-sharing system estimates the user's emotions and selects photos and videos to share based on those estimated emotions. For example, if the user is relaxed, the AI ​​will select photos and videos that promote relaxation. If the user is excited, the AI ​​can also select photos and videos that promote excitement. If the user is tired, the AI ​​can also select photos and videos that promote relaxation. This allows for more emotionally impactful sharing by selecting the most suitable photos and videos according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0097] The memory sharing system suggests the most suitable content when users share memories, referencing their past sharing history. For example, the system uses AI to suggest the most suitable content based on photos and videos previously shared by the user. It can also suggest the most suitable content based on the user's past sharing history. Furthermore, it can suggest the most suitable content based on details of content previously shared by the user. This allows the system to suggest content that is optimal for the user by referencing their past sharing history.

[0098] The memory-sharing system suggests the optimal sharing method based on the user's current emotions when sharing memories. For example, if the user is relaxed, the AI ​​suggests a relaxing sharing method. If the user is excited, the AI ​​can also suggest an exciting sharing method. If the user is tired, the AI ​​can also suggest a relaxing sharing method. This allows for more emotionally impactful sharing by suggesting the optimal sharing method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0099] The memory-sharing system estimates the user's emotions and adjusts the timing of sharing based on those emotions. For example, if the user is relaxed, the AI ​​may delay the sharing. If the user is excited, the AI ​​may also speed up the sharing. If the user is tired, the AI ​​may adjust the sharing timing to choose a time when the user can relax. This allows for sharing at a more appropriate time by adjusting the sharing timing according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0100] The memory sharing system suggests the optimal storage method when sharing memories, taking into account the user's device storage status. For example, if the user's device storage is insufficient, the AI ​​will suggest cloud storage. The system can also suggest storage optimization if the user's device storage is inadequate. For example, if the user's device storage is rapidly decreasing, the AI ​​can suggest deleting unnecessary data. This enables efficient use of storage by suggesting the optimal storage method according to the device's storage status.

[0101] The memory sharing system suggests the optimal sharing method based on the user's past sharing history when sharing memories. For example, the AI ​​suggests the optimal sharing method based on the user's past sharing history. The AI ​​can also suggest the optimal sharing method based on the user's past sharing history. This allows for smoother sharing by suggesting the optimal sharing method based on past sharing history.

[0102] The smart scheduling function estimates the user's emotions and suggests a schedule based on those emotions. For example, if the user is relaxed, the AI ​​will suggest a relaxing schedule. If the user is excited, the AI ​​can also suggest an exciting schedule. If the user is tired, the AI ​​can also suggest a relaxing schedule. This allows for a more appropriate schedule by suggesting schedules according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0103] The smart scheduling function makes optimal suggestions by referring to the user's past schedule history during scheduling. For example, the smart scheduling function uses AI to make optimal suggestions based on the user's past schedule history. The smart scheduling function can also use AI to make optimal suggestions based on the user's past schedule history. For example, the smart scheduling function can also use AI to make optimal suggestions based on the details of the user's past schedule history. This allows the system to suggest the most suitable schedule for the user by referring to their past schedule history.

[0104] The smart scheduling feature suggests the optimal schedule based on the user's current emotions during scheduling. For example, if the user is relaxed, the AI ​​will suggest a relaxing schedule. If the user is excited, the AI ​​can also suggest an exciting schedule. If the user is tired, the AI ​​can also suggest a relaxing schedule. This allows for more appropriate scheduling by suggesting the optimal schedule according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0105] The smart scheduling function estimates the user's emotions and prioritizes schedules based on those emotions. For example, if the user is relaxed, the AI ​​will prioritize relaxing schedules. If the user is excited, the AI ​​can also prioritize exciting schedules. If the user is tired, the AI ​​can also prioritize relaxing schedules. This allows for more appropriate scheduling by prioritizing schedules according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0106] The smart scheduling function integrates the user's device calendar information to provide optimal suggestions during scheduling. For example, the smart scheduling function uses AI to provide optimal suggestions based on the user's device calendar information. This allows for more appropriate schedule suggestions by integrating the device calendar information.

[0107] The smart scheduling function provides optimal suggestions based on the user's past scheduling history. For example, the smart scheduling function uses AI to provide optimal suggestions based on the user's past schedules. The smart scheduling function can also use AI to provide optimal suggestions based on the user's past scheduling history. The smart scheduling function can also use AI to provide optimal suggestions based on the details of the user's past schedules. This allows for more appropriate scheduling by providing optimal suggestions based on past scheduling history.

[0108] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0109] Virtual visit apps can also have a feature that estimates the user's emotions and suggests conversation topics based on those emotions. For example, if the user is relaxed, the AI ​​can suggest relaxing topics. If the user is excited, the AI ​​can suggest exciting topics. If the user is tired, the AI ​​can suggest relaxing topics. This allows for smoother conversations by suggesting topics according to the user's emotions.

[0110] Virtual visit apps can also have the ability to estimate the user's emotions and adjust the atmosphere of the virtual environment based on those emotions. For example, if the user is relaxed, the AI ​​can create a virtual environment with a relaxing atmosphere. If the user is excited, the AI ​​can create a virtual environment with an exciting atmosphere. If the user is tired, the AI ​​can create a virtual environment with a relaxing atmosphere. By adjusting the atmosphere of the virtual environment according to the user's emotions, a more comfortable virtual experience can be achieved.

[0111] Virtual visit apps can also have features that estimate the user's emotions and adjust the video and audio quality based on those emotions. For example, if the user is nervous, the AI ​​can automatically adjust the video quality to provide a more relaxing image. If the user is tired, the AI ​​can adjust the audio quality to provide a more pleasant sound. If the user is excited, the AI ​​can enhance the video quality to provide a clearer image. This allows for more comfortable conversations by adjusting the video and audio quality according to the user's emotions.

[0112] Virtual visit apps can also have features that estimate the user's emotions and adjust the timing of the call based on those emotions. For example, if the user is relaxed, the AI ​​can delay the start of the call. If the user is in a hurry, the AI ​​can start the call earlier. If the user is nervous, the AI ​​can adjust the start of the call to choose a time when they can relax. This allows for a more appropriate start to the call by adjusting the timing according to the user's emotions.

[0113] Virtual visit apps can also have features that estimate the user's emotions and select photos and videos to share based on those estimated emotions. For example, if the user is relaxed, the AI ​​can select relaxing photos and videos. If the user is excited, the AI ​​can select exciting photos and videos. If the user is tired, the AI ​​can select relaxing photos and videos. This allows for more emotionally impactful sharing by selecting the most suitable photos and videos according to the user's emotions.

[0114] Virtual visit apps can also include a feature that references the user's past conversation history and suggests relevant topics. For example, the AI ​​can suggest relevant topics based on topics the user has previously discussed. It can also suggest relevant topics based on topics the user has shown interest in in the past, or topics the user has avoided in the past. This allows for the suggestion of relevant topics by referencing past conversation history.

[0115] Virtual visit apps can also have a feature that integrates the user's device calendar information and suggests the optimal schedule. For example, the AI ​​can make optimal suggestions based on the user's device calendar information. This allows for more appropriate schedule suggestions by integrating the calendar information from multiple devices.

[0116] Virtual visit applications can also include features that allow them to recreate the optimal virtual environment by referencing the user's past visit history. For example, AI can recreate the optimal virtual environment based on places the user has visited in the past. AI can also recreate the optimal virtual environment based on the user's past visit history. This allows for the recreation of the optimal virtual environment for the user by referencing past visit history.

[0117] Virtual visit applications can also include features that consider the user's device storage status and suggest the optimal storage method. For example, if the user's device storage is insufficient, the AI ​​can suggest cloud storage. If the user's device storage is inadequate, the AI ​​can also suggest storage optimization. If the user's device storage is rapidly decreasing, the AI ​​can suggest deleting unnecessary data. This enables efficient storage utilization by suggesting the optimal storage method according to the device's storage status.

[0118] Virtual visit apps can also include features that consider the user's cultural background and suggest appropriate topics. For example, the AI ​​can suggest appropriate topics based on the user's cultural background. It can also suggest appropriate topics based on the user's nationality or religion. This allows for smoother conversations by suggesting topics appropriate to the user's cultural background.

[0119] The following briefly describes the processing flow for example form 2.

[0120] Step 1: The call function provides high-quality video calls. For example, the spread of 5G enables rich content delivery, allowing for communication with clear video and audio. It also enables video calls with low latency. Step 2: The conversation support team assists with the conversation during the video call provided by the call team. For example, they suggest conversation topics, support appropriate responses, and ensure the conversation continues without interruption. Step 3: The environment generation unit generates a virtual environment based on the conversation supported by the conversation support unit. For example, it can generate a virtual environment using 3D modeling technology to virtually recreate rooms and gardens in the in-laws' house. This provides a virtual environment that gives the user the feeling of actually being there.

[0121] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0122] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0123] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0124] Each of the multiple elements described above, including the call unit, conversation support unit, environment generation unit, memory sharing system, and smart scheduling function, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the call unit is implemented by the processor 46 of the smart device 14, enabling rich content delivery with the spread of 5G. The conversation support unit is implemented by the specific processing unit 290 of the data processing unit 12, for example, to suggest conversation topics and support appropriate responses. The environment generation unit is implemented by the control unit 46A of the smart device 14, for example, to generate a detailed virtual environment of the in-laws' home using 3D modeling technology. The memory sharing system uses the database 24 of the data processing unit 12 to store and share family photos and videos. The smart scheduling function is implemented by the specific processing unit 290 of the data processing unit 12, for example, where AI analyzes each member's schedule and suggests the optimal visit time. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0125] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0126] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0127] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0128] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0129] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0130] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0131] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0132] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0133] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0134] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0135] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0136] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0137] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0138] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0139] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0140] Each of the multiple elements described above, including the call unit, conversation support unit, environment generation unit, memory sharing system, and smart scheduling function, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the call unit is implemented by the processor 46 of the smart glasses 214, enabling rich content delivery with the spread of 5G. The conversation support unit is implemented by the specific processing unit 290 of the data processing unit 12, for example, to suggest conversation topics and support appropriate responses. The environment generation unit is implemented by the control unit 46A of the smart glasses 214, for example, to generate a detailed virtual environment of the in-laws' home using 3D modeling technology. The memory sharing system uses the database 24 of the data processing unit 12 to store and share family photos and videos. The smart scheduling function is implemented by the specific processing unit 290 of the data processing unit 12, for example, where AI analyzes each member's schedule and suggests the optimal visit time. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0141] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0142] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0143] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0144] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0145] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0146] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0147] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0148] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0149] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0150] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0151] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0152] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0153] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0154] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0155] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0156] Each of the multiple elements described above, including the call unit, conversation support unit, environment generation unit, memory sharing system, and smart scheduling function, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the call unit is implemented by the processor 46 of the headset terminal 314, enabling rich content delivery with the spread of 5G. The conversation support unit is implemented by the specific processing unit 290 of the data processing unit 12, for example, to suggest conversation topics and support appropriate responses. The environment generation unit is implemented by the control unit 46A of the headset terminal 314, for example, to generate a detailed virtual environment of the in-laws' home using 3D modeling technology. The memory sharing system uses the database 24 of the data processing unit 12 to store and share family photos and videos. The smart scheduling function is implemented by the specific processing unit 290 of the data processing unit 12, for example, where AI analyzes each member's schedule and suggests the optimal visit time. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0157] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0158] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0159] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0160] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0161] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0162] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0163] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0164] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0165] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0166] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0167] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0168] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0169] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0170] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0171] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0172] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0173] Each of the multiple elements described above, including the call unit, conversation support unit, environment generation unit, memory sharing system, and smart scheduling function, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the call unit is implemented by the processor 46 of the robot 414, enabling rich content delivery with the spread of 5G. The conversation support unit is implemented by the specific processing unit 290 of the data processing unit 12, for example, to suggest conversation topics and support appropriate responses. The environment generation unit is implemented by the control unit 46A of the robot 414, for example, to generate a detailed virtual environment of the in-laws' home using 3D modeling technology. The memory sharing system uses the database 24 of the data processing unit 12 to store and share family photos and videos. The smart scheduling function is implemented by the specific processing unit 290 of the data processing unit 12, for example, where AI analyzes each member's schedule and suggests the optimal visit time. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0174] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0175] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0176] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0177] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0178] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0179] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0180] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0181] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0182] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0183] 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.

[0184] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0185] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0186] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0187] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0188] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0189] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0190] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0191] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0192] (Note 1) The call department provides high-quality video calls, A conversation support unit that supports conversations during video calls provided by the aforementioned conversation unit, The system includes an environment generation unit that generates a virtual environment based on a conversation supported by the aforementioned conversation support unit. A system characterized by the following features. (Note 2) Equipped with a memory sharing system The system described in Appendix 1, characterized by the features described herein. (Note 3) Equipped with smart scheduling function The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned communication unit is, The spread of 5G will enable the delivery of rich content. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned conversation support unit, Suggest conversation topics and support appropriate responses. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned environment generation unit, 3D modeling technology is used to create virtual environments. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned memory sharing system is Use cloud storage to save and share family photos and videos. The system described in Appendix 2, characterized by the features described herein. (Note 8) The aforementioned smart scheduling function is The AI ​​analyzes each member's schedule and suggests the optimal time for visits. The system described in Appendix 3, characterized by the features described herein. (Note 9) The aforementioned communication unit is, It estimates the user's emotions and adjusts the video call quality and sound based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned communication unit is, It removes background noise in real time during calls, providing clear audio. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned communication unit is, The app analyzes the user's facial expressions during a call and automatically applies appropriate filters and effects. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned communication unit is, It estimates the user's emotions and adjusts the timing of the call's initiation based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned communication unit is, The system monitors the user's internet connection status during a call and automatically adjusts to the optimal bitrate. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned communication unit is, The system monitors the user's device battery status during a call and provides appropriate notifications. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned conversation support unit, It estimates the user's emotions and suggests conversation topics based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned conversation support unit, It analyzes user statements during a conversation and generates appropriate responses in real time. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned conversation support unit, During a conversation, the system references the user's past conversation history and suggests relevant topics. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned conversation support unit, It estimates the user's emotions and adjusts the conversation speed based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned conversation support unit, The system automatically detects the user's language settings during a conversation and provides support in the appropriate language. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned conversation support unit, During conversations, the system considers the user's cultural background and suggests appropriate topics. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned environment generation unit, It estimates the user's emotions and adjusts the atmosphere of the virtual environment based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned environment generation unit, When creating a virtual environment, the system recreates the optimal environment by referencing the user's past visit history. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned environment generation unit, When creating a virtual environment, provide customizable options based on user preferences. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned environment generation unit, It estimates the user's emotions and adjusts the level of detail in the virtual environment based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned environment generation unit, When creating a virtual environment, the system takes the user's geographical location into consideration and provides the relevant environment accordingly. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned environment generation unit, When creating a virtual environment, we provide the optimal environment considering the user's device performance. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned memory sharing system is It estimates the user's emotions and selects photos and videos to share based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 28) The aforementioned memory sharing system is When sharing memories, the system suggests the most suitable content by referring to the user's past sharing history. The system described in Appendix 2, characterized by the features described herein. (Note 29) The aforementioned memory sharing system is When sharing memories, the system suggests the optimal sharing method based on the user's current emotions. The system described in Appendix 2, characterized by the features described herein. (Note 30) The aforementioned memory sharing system is It estimates the user's emotions and adjusts the timing of sharing based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 31) The aforementioned memory sharing system is When sharing memories, we suggest the optimal storage method considering the user's device storage status. The system described in Appendix 2, characterized by the features described herein. (Note 32) The aforementioned memory sharing system is When sharing memories, the system suggests the optimal sharing method based on the user's past sharing history. The system described in Appendix 2, characterized by the features described herein. (Note 33) The aforementioned smart scheduling function is It estimates the user's emotions and suggests a schedule based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 34) The aforementioned smart scheduling function is During scheduling, the system makes optimal suggestions by referencing the user's past schedule history. The system described in Appendix 3, characterized by the features described herein. (Note 35) The aforementioned smart scheduling function is When scheduling, the system suggests the optimal schedule based on the user's current mood. The system described in Appendix 3, characterized by the features described herein. (Note 36) The aforementioned smart scheduling function is It estimates the user's emotions and determines schedule priorities based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 37) The aforementioned smart scheduling function is During scheduling, the system integrates calendar information from the user's device to provide optimal suggestions. The system described in Appendix 3, characterized by the features described herein. (Note 38) The aforementioned smart scheduling function is During scheduling, the system provides optimal suggestions based on the user's past schedule history. The system described in Appendix 3, characterized by the features described herein. [Explanation of symbols]

[0193] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The call department provides high-quality video calls, A conversation support unit that supports conversations during video calls provided by the aforementioned conversation unit, The system includes an environment generation unit that generates a virtual environment based on a conversation supported by the aforementioned conversation support unit. A system characterized by the following features.

2. Equipped with a memory sharing system The system according to feature 1.

3. Equipped with smart scheduling function The system according to feature 1.

4. The aforementioned communication unit is, The spread of 5G will enable the delivery of rich content. The system according to feature 1.

5. The aforementioned conversation support unit, Suggest conversation topics and support appropriate responses. The system according to feature 1.

6. The aforementioned environment generation unit, 3D modeling technology is used to create virtual environments. The system according to feature 1.

7. The aforementioned memory sharing system is Use cloud storage to save and share family photos and videos. The system according to feature 2.

8. The aforementioned smart scheduling function is The AI ​​analyzes each member's schedule and suggests the optimal time for visits. The system according to claim 3.

9. The aforementioned communication unit is, It estimates the user's emotions and adjusts the video call quality and sound based on those estimated emotions. The system according to feature 1.

10. The aforementioned communication unit is, It removes background noise in real time during calls, providing clear audio. The system according to feature 1.

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