system

Abstract

The system according to this embodiment aims to provide an immersive and realistic experience in communication with family members who are in a remote location. [Solution] The system according to the embodiment comprises a reception unit, a conversation support unit, a VR unit, an AR unit, and an emotion analysis unit. The reception unit receives user input. The conversation support unit supports natural conversation based on the information received by the reception unit. The VR unit provides an immersive experience based on the conversation content generated by the conversation support unit. The AR unit creates the feeling that family members in a remote location are around the user based on the experience provided by the VR unit. The emotion analysis unit provides feedback based on the emotion data analyzed 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 as a 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 prior art, there is a problem that in communication with family members who are in a remote location, an immersive and realistic experience has not been sufficiently provided. [[ID=CO]]

[0005] The system according to the embodiment aims to provide an immersive and realistic experience in communication with family members who are in a remote location.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a conversation support unit, a VR unit, an AR unit, and an emotion analysis unit. The reception unit receives user input. The conversation support unit supports natural conversation based on the information received by the reception unit. The VR unit provides an immersive experience based on the conversation content generated by the conversation support unit. The AR unit creates the feeling that family members in remote locations are around the user based on the experience provided by the VR unit. The emotion analysis unit provides feedback based on the emotion data analyzed by the conversation support unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide an immersive and realistic experience when communicating with family members who are in a remote location. [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, and the like. The communication I / F controls communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), 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 communication system according to an embodiment of the present invention combines deep learning using a large-scale language model (LLM), virtual reality (VR) technology, and augmented reality (AR) technology to realistically recreate the time spent by a person working away from home and their family. This system uses LLM to support natural conversation so that the user can communicate with family members who are in a remote location. Next, VR technology is used to provide an immersive experience that makes it seem as if the user is in the same place, transcending physical distance. Furthermore, AR technology is used to create the feeling that family members in remote locations are around the user. By incorporating not only words but also visual elements and sharing them in real time, it becomes possible to experience the feeling of being together even when living far away. First, LLM is used to support natural conversation so that the user can communicate with family members in a remote location. For example, when the user speaks, the LLM analyzes the content and generates an appropriate response. This enables natural conversation that does not make the user feel the distance. Next, VR technology is used to provide an immersive experience that makes it seem as if the user is in the same place, transcending physical distance. For example, when the user wears a VR headset, they can spend time in the same virtual space as family members in remote locations. This enables communication that is no different from the real world. Furthermore, AR technology is used to create the feeling that family members in remote locations are nearby. For example, when a user uses an AR-enabled device, digital information is overlaid on the real world, allowing them to experience the sensation that family members in remote locations are right around them. This enhances a sense of unity and strengthens the feeling of being together even when far apart. In addition, through LLM's emotion analysis and feedback function, the system analyzes the user's emotions from their actions and expressions and provides feedback to improve the experience. For example, it analyzes the user's emotions from what they are saying and their facial expressions, and provides appropriate advice and feedback to promote richer communication. This system can provide effective communication and collaboration that transcends physical distance for companies with employees working away from home or in remote locations, aiming to improve their quality of life and productivity.This allows the communication system to analyze the user's emotions and provide appropriate feedback.

[0029] The communication system according to this embodiment comprises a reception unit, a conversation support unit, a VR unit, an AR unit, and an emotion analysis unit. The reception unit receives user input. The reception unit supports various input methods, such as voice input, text input, and gesture input. For example, the reception unit can convert the user's voice into text using speech recognition technology. The reception unit can also provide an interface for receiving text input. Furthermore, the reception unit can recognize the user's gestures using gesture recognition technology and process them as input. The conversation support unit supports natural conversation based on the information received by the reception unit. The conversation support unit analyzes the user's input using, for example, LLM and generates an appropriate response. For example, when the user speaks, the conversation support unit analyzes the content and generates an appropriate response. The conversation support unit can also control the flow of conversation based on user input. For example, the conversation support unit provides appropriate answers to the user's questions and facilitates the smooth progress of the conversation. The VR unit provides an immersive experience based on the conversation content generated by the conversation support unit. The VR unit provides a virtual space to the user using, for example, a VR headset. For example, the VR unit provides users with the experience of spending time in the same virtual space as their family members who are in a remote location when they wear a VR headset. The VR unit can also support interaction within the virtual space. For example, the VR unit allows users to manipulate objects and communicate with other users within the virtual space. The AR unit creates the feeling that family members in a remote location are around the user based on the experience provided by the VR unit. The AR unit, for example, overlays digital information onto the real world using AR-enabled devices. For example, the AR unit provides users with the experience of having family members in a remote location around them when they use an AR-enabled device. The AR unit can also seamlessly integrate the real world and digital information. For example, the AR unit places digital objects in the real world and allows users to interact with them. The emotion analysis unit provides feedback based on emotion data analyzed by the conversation support unit.The emotion analysis unit analyzes emotions from, for example, the user's facial expressions and voice. For instance, it uses facial recognition technology to analyze the user's facial expressions and estimate their emotions. It can also use voice analysis technology to estimate emotions from the user's voice. Furthermore, the emotion analysis unit provides appropriate feedback based on the user's emotions. For example, if the user is feeling stressed, it provides advice on how to relax. As a result, the communication system according to this embodiment can accept user input, support natural conversation, provide an immersive experience, create the feeling that family members in distant locations are nearby, and provide feedback based on emotional data.

[0030] The reception unit receives user input. The reception unit supports various input methods, such as voice input, text input, and gesture input. Specifically, it can convert user speech into text using speech recognition technology. This speech recognition technology utilizes deep learning-based speech models to accurately transcribe user speech. This allows users to interact with the system in a natural conversational manner. The reception unit can also provide an interface for receiving text input. For example, it can provide input interfaces using keyboards or touchscreens, allowing users to easily input text. Furthermore, the reception unit can recognize user gestures using gesture recognition technology and process them as input. Gesture recognition technology uses cameras and sensors to detect the user's hand and body movements and interprets them as specific commands. This allows users to control the system with intuitive operations such as waving or pointing. The reception unit integrates these diverse input methods to provide an advanced interface that accurately understands user intent.

[0031] The conversation support unit facilitates natural conversation based on information received by the reception unit. For example, the conversation support unit uses an LLM (Large-Scale Language Model) to analyze user input and generate appropriate responses. The LLM has learned from vast amounts of text data, understands context, and is capable of generating natural language. For example, if a user asks, "What's the weather like today?", the conversation support unit analyzes the content and generates an appropriate response such as, "It's sunny today. The temperature is 25 degrees." The conversation support unit can also control the flow of the conversation based on user input. For example, if the user continues to ask questions, the conversation support unit will provide answers to those questions sequentially, keeping the conversation flowing smoothly. Furthermore, the conversation support unit can understand the user's intentions and emotions and respond with appropriate tone and expression. This allows the user to feel more natural in their interaction with the system.

[0032] The VR unit provides an immersive experience based on conversation content generated by the conversation support unit. For example, the VR unit provides users with a virtual space using a VR headset. The VR headset features a high-resolution display and 3D audio, allowing users to experience a realistic virtual environment. For instance, when a user puts on a VR headset, they can experience spending time in the same virtual space as family members who are in a remote location. Within this virtual space, users can enjoy conversations with family in a virtual living room or play games together. The VR unit can also support interaction within the virtual space. For example, users can manipulate objects within the virtual space or communicate with other users. This allows users to share realistic experiences across physical distances. Furthermore, the VR unit can track user movements and reflect actions within the virtual space in real time. This allows users to enjoy more natural interactions.

[0033] The AR (Augmented Reality) unit creates the feeling that family members in remote locations are around the user, based on the experience provided by the VR (Virtual Reality) unit. The AR unit overlays digital information onto the real world using AR-enabled devices, for example. AR-enabled devices include smartphones, tablets, and AR glasses. For example, when a user uses an AR-enabled device, they can experience the feeling that family members in remote locations are around them. Specifically, the AR unit displays avatars of family members in the real world, allowing the user to interact with these avatars. For example, a user can see family avatars sitting in their living room through AR glasses. The AR unit can also seamlessly integrate the real world with digital information. For example, the AR unit can place digital objects in the real world, allowing the user to interact with them. This allows the user to enjoy a new experience where the real environment and digital information are integrated. Furthermore, the AR unit can track the user's gaze and gestures to make the interaction more natural. This allows the user to enjoy the AR experience more intuitively.

[0034] The emotion analysis unit provides feedback based on emotional data analyzed by the conversation support unit. For example, the emotion analysis unit analyzes emotions from the user's facial expressions and voice. Specifically, it uses facial recognition technology to analyze the user's facial expressions and estimate their emotions. Facial recognition technology uses a camera to capture the features of the user's face and can detect subtle changes in facial expression. The emotion analysis unit can also estimate emotions from the user's voice using voice analysis technology. Voice analysis technology analyzes the tone, pitch, and speed of the user's voice to detect changes in emotion. Furthermore, the emotion analysis unit provides appropriate feedback based on the user's emotions. For example, if the user is stressed, the emotion analysis unit provides advice to help them relax. If the user is happy, it shares that emotion and provides positive feedback. This allows the emotion analysis unit to understand the user's emotional state in real time and respond appropriately. Additionally, the emotion analysis unit can accumulate long-term emotional data and analyze trends in the user's emotions. This allows for continuous monitoring of changes in the user's emotions and the provision of more personalized feedback.

[0035] The emotion analysis unit includes a feedback unit that analyzes the user's emotions and provides feedback. The emotion analysis unit analyzes emotions from, for example, the user's facial expressions and voice. For example, the emotion analysis unit uses facial recognition technology to analyze the user's facial expressions and estimate their emotions. The emotion analysis unit can also estimate emotions from the user's voice using voice analysis technology. Furthermore, the emotion analysis unit provides appropriate feedback based on the user's emotions. For example, if the emotion analysis unit is feeling stressed, it provides advice on how to relax. This allows the unit to analyze the user's emotions and provide appropriate feedback. Some or all of the above processing in the emotion analysis unit may be performed using, for example, AI, or without AI. For example, the emotion analysis unit can input the user's facial expression data into a generating AI and have the generating AI perform emotion estimation.

[0036] The VR unit provides users with the experience of spending time in the same virtual space as their family members who are in a remote location, by having them wear a VR headset. The VR unit provides users with a virtual space using a VR headset, for example. For example, the VR unit provides users with the experience of spending time in the same virtual space as their family members who are in a remote location, by having them wear a VR headset. The VR unit can also support interaction within the virtual space. For example, the VR unit allows users to manipulate objects in the virtual space and communicate with other users. This allows users to spend time in the same virtual space as their family members who are in a remote location, by having them wear a VR headset. Some or all of the above processing in the VR unit may be performed using AI, for example, or not using AI. For example, the VR unit can have a generation AI perform the generation of the virtual space.

[0037] The AR unit overlays digital information onto the real world when a user uses an AR-enabled device, providing an experience as if family members in a remote location were around them. For example, the AR unit overlays digital information onto the real world using an AR-enabled device. The AR unit can also seamlessly integrate the real world and digital information. For example, the AR unit can place digital objects in the real world, allowing users to interact with them. This allows the AR unit to overlay digital information onto the real world when a user uses an AR-enabled device, providing an experience as if family members in a remote location were around them. Some or all of the above processing in the AR unit may be performed using AI, for example, or without AI. For example, the AR unit can have a generative AI perform the generation of digital information.

[0038] The conversation support unit analyzes what the user says and generates an appropriate response. For example, the conversation support unit can analyze user input using an LLM (Liquid Language Machine) and generate an appropriate response. The conversation support unit can also control the flow of the conversation based on user input. For example, it can provide appropriate answers to user questions and keep the conversation running smoothly. This allows the unit to analyze what the user says and generate an appropriate response. Some or all of the above-described processes in the conversation support unit may be performed using AI, or not. For example, the conversation support unit can input user input data into a generating AI and have the generating AI generate the response.

[0039] The feedback unit analyzes the user's emotions and provides appropriate advice and feedback. For example, the feedback unit analyzes emotions from the user's facial expressions and voice. For example, the feedback unit uses facial recognition technology to analyze the user's facial expressions and estimate their emotions. The feedback unit can also estimate emotions from the user's voice using voice analysis technology. Furthermore, the feedback unit provides appropriate advice and feedback based on the user's emotions. For example, if the user is feeling stressed, the feedback unit provides advice on how to relax. This allows the feedback unit to analyze the user's emotions and provide appropriate advice and feedback. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the user's facial expression data into a generating AI and have the generating AI perform emotion estimation.

[0040] The reception desk analyzes the user's past input history and selects the optimal input method. For example, the reception desk prioritizes suggesting input methods (voice, text, etc.) that the user has frequently used in the past. For example, the reception desk predicts and suggests input methods to be used during specific time periods based on the user's past input history. The reception desk can also suggest relevant input methods based on content the user has previously entered. This allows the reception desk to analyze the user's past input history and select the optimal input method. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's input history data into a generating AI and have the generating AI select the optimal input method.

[0041] The reception unit filters input based on the user's current situation and areas of interest. For example, the reception unit suggests an appropriate input method based on the user's current situation (e.g., working, on break). For example, the reception unit filters relevant input content based on the user's areas of interest (e.g., hobbies, interests). The reception unit can also simplify input content and provide appropriate information depending on the user's current situation. This allows filtering based on the user's current situation and areas of interest at the time of input reception. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input user situation data into a generating AI and have the generating AI perform the filtering.

[0042] The reception unit, upon receiving input, prioritizes accepting inputs that are highly relevant, taking into account the user's geographical location. For example, if the user is in a specific region, the reception unit prioritizes accepting inputs related to that region. For example, if the user is traveling, the reception unit prioritizes accepting inputs related to the travel destination. The reception unit can also prioritize accepting inputs related to the user's home if the user is at home. This allows the reception unit to prioritize accepting inputs that are highly relevant, taking into account the user's geographical location. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's location data into a generating AI and have the generating AI select highly relevant inputs.

[0043] The reception unit analyzes the user's social media activity and accepts relevant inputs when receiving input. For example, the reception unit accepts relevant inputs based on information the user has shared on social media. For example, the reception unit analyzes the user's interests from their social media activity and accepts relevant inputs. The reception unit can also accept relevant inputs based on accounts the user follows on social media. This allows the reception unit to analyze the user's social media activity and accept relevant inputs when receiving input. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's social media data into a generating AI and have the generating AI select relevant inputs.

[0044] The conversation support unit adjusts the level of detail in a conversation based on its importance. For example, in the case of an important conversation, the conversation support unit provides detailed information. For example, in the case of a general conversation, the conversation support unit provides concise information. The conversation support unit can also provide concise information if the user is in a hurry. This allows the conversation support unit to adjust the level of detail in a conversation based on its importance. Some or all of the above processing in the conversation support unit may be performed using AI, for example, or without AI. For example, the conversation support unit can input user input data into a generating AI and have the generating AI perform the adjustment of the level of detail in the conversation.

[0045] The conversation support unit applies different conversation algorithms depending on the category of the conversation during conversation support. For example, the conversation support unit applies a formal conversation algorithm for business conversations. For example, the conversation support unit applies a relaxed conversation algorithm for casual conversations. The conversation support unit can also apply a conversation algorithm that includes technical terms for technical conversations. This allows the conversation support unit to apply different conversation algorithms depending on the category of the conversation during conversation support. Some or all of the above processing in the conversation support unit may be performed using AI, for example, or without AI. For example, the conversation support unit can input user input data into a generating AI and have the generating AI perform the application of conversation algorithms.

[0046] The conversation support unit determines the priority of conversations based on when they are submitted. For example, the conversation support unit will prioritize urgent conversations. For example, the conversation support unit will handle regular conversations with normal priority. The conversation support unit can also prioritize conversations submitted by users within a specific time frame. This allows the conversation support unit to determine the priority of conversations based on when they are submitted. Some or all of the above processing in the conversation support unit may be performed using AI, for example, or not using AI. For example, the conversation support unit can input user input data into a generating AI and have the generating AI perform the task of determining the priority of conversations.

[0047] The conversation support unit adjusts the order of conversations based on their relevance during conversation support. For example, the conversation support unit prioritizes highly relevant conversations. For example, it postpones less relevant conversations. The conversation support unit can also prioritize conversations related to a specific topic if the user has shown interest in that topic. This allows the conversation support unit to adjust the order of conversations based on their relevance during conversation support. Some or all of the above processing in the conversation support unit may be performed using AI, for example, or without AI. For example, the conversation support unit can input user input data into a generating AI and have the generating AI perform the adjustment of the conversation order.

[0048] The VR unit provides the optimal experience during a VR experience by referring to the user's past VR experience history. For example, the VR unit may provide experiences that the user has enjoyed in the past again. For example, the VR unit may suggest new experiences that are relevant to the user based on the user's past experience history. The VR unit can also exclude experiences that the user has avoided in the past when making suggestions. This allows the VR unit to provide the optimal experience by referring to the user's past VR experience history. Some or all of the above processing in the VR unit may be performed using AI, for example, or without AI. For example, the VR unit may input the user's experience history data into a generating AI and have the generating AI select the optimal experience.

[0049] The VR unit customizes the VR experience based on the user's current life circumstances. For example, if the user is at work, the VR unit provides a short, refreshing experience. For example, if the user is on vacation, the VR unit provides an experience that can be enjoyed for a longer period. The VR unit can also provide a relaxing experience if the user is feeling stressed. This allows the experience to be customized based on the user's current life circumstances. Some or all of the above processing in the VR unit may be performed using AI, for example, or without AI. For example, the VR unit can input the user's life circumstances data into a generating AI and have the generating AI perform the customization of the experience.

[0050] The VR unit provides an optimal experience during a VR experience, taking into account the user's geographical location. For example, if the user is in an urban area, the VR unit provides a natural landscape. For example, if the user is in a rural area, the VR unit provides an urban landscape. Furthermore, if the user is traveling, the VR unit can provide an experience related to their travel destination. This allows the system to provide an optimal experience by considering the user's geographical location. Some or all of the above processing in the VR unit may be performed using AI, for example, or without AI. For example, the VR unit can input the user's location data into a generating AI and have the generating AI select the optimal experience.

[0051] The VR department analyzes the user's social media activity during a VR experience and suggests relevant experiences. For example, the VR department suggests relevant experiences based on information the user has shared on social media. For example, the VR department analyzes the user's interests from their social media activity and suggests relevant experiences. The VR department can also suggest relevant experiences based on the accounts the user follows on social media. This allows the VR department to analyze the user's social media activity and suggest relevant experiences. Some or all of the above processing in the VR department may be performed using AI, for example, or without AI. For example, the VR department can input the user's social media data into a generating AI and have the generating AI suggest experiences.

[0052] The AR unit provides the optimal experience during an AR experience by referring to the user's past AR experience history. For example, the AR unit may re-provide AR experiences that the user has enjoyed in the past. For example, the AR unit may suggest new AR experiences that are relevant to the user based on their past experience history. The AR unit can also exclude and suggest AR experiences that the user has avoided in the past. This allows the AR unit to provide the optimal experience by referring to the user's past AR experience history. Some or all of the above processing in the AR unit may be performed using AI, for example, or without AI. For example, the AR unit may input the user's experience history data into a generating AI and have the generating AI select the optimal experience.

[0053] The AR unit customizes the AR experience based on the user's current lifestyle. For example, if the user is at work, the AR unit provides a short, refreshing AR experience. For example, if the user is on vacation, the AR unit provides a longer, more enjoyable AR experience. The AR unit can also provide a relaxing AR experience if the user is feeling stressed. This allows the experience to be customized based on the user's current lifestyle. Some or all of the above processing in the AR unit may be performed using AI, for example, or without AI. For example, the AR unit can input the user's lifestyle data into a generating AI and have the generating AI perform the customization of the experience.

[0054] The AR unit provides an optimal experience during an AR experience, taking into account the user's geographical location. For example, if the user is in an urban area, the AR unit provides a natural landscape. For example, if the user is in a rural area, the AR unit provides an urban landscape. Furthermore, if the user is traveling, the AR unit can provide an AR experience related to their travel destination. This allows the system to provide an optimal experience by taking into account the user's geographical location. Some or all of the above processing in the AR unit may be performed using AI, for example, or without AI. For example, the AR unit can input the user's location data into a generating AI and have the generating AI select the optimal experience.

[0055] The AR unit analyzes the user's social media activity during an AR experience and suggests relevant experiences. For example, the AR unit suggests relevant AR experiences based on information the user has shared on social media. For example, the AR unit analyzes the user's interests from their social media activity and suggests relevant AR experiences. The AR unit can also suggest relevant AR experiences based on the accounts the user follows on social media. This allows the AR unit to analyze the user's social media activity and suggest relevant experiences. Some or all of the above processing in the AR unit may be performed using AI, for example, or without AI. For example, the AR unit can input the user's social media data into a generating AI and have the generating AI suggest experiences.

[0056] The sentiment analysis unit optimizes its analysis algorithm by referring to the user's past sentiment data when analyzing sentiment data. For example, the sentiment analysis unit selects the optimal analysis algorithm based on the user's past sentiment data. For example, the sentiment analysis unit extracts specific patterns from the user's past sentiment data and adjusts the analysis algorithm. The sentiment analysis unit can also improve the accuracy of the analysis results by referring to the user's past sentiment data. This allows the analysis algorithm to be optimized by referring to the user's past sentiment data. Some or all of the above processes in the sentiment analysis unit may be performed using AI, for example, or without AI. For example, the sentiment analysis unit can input the user's past sentiment data into a generating AI and have the generating AI perform the optimization of the analysis algorithm.

[0057] The emotion analysis unit customizes the analysis content based on the user's current living situation when analyzing emotional data. For example, if the user is at work, the emotion analysis unit analyzes work-related emotional data. For example, if the user is on vacation, the emotion analysis unit analyzes emotional data based on a relaxed state. The emotion analysis unit can also analyze stress-related emotional data if the user is feeling stressed. This allows the analysis content to be customized based on the user's current living situation. Some or all of the above processing in the emotion analysis unit may be performed using AI, for example, or without AI. For example, the emotion analysis unit can input the user's living situation data into a generating AI and have the generating AI perform the customization of the analysis content.

[0058] The sentiment analysis unit selects the optimal analysis method when analyzing sentiment data, taking into account the user's geographical location. For example, if the user is in an urban area, the sentiment analysis unit analyzes sentiment data related to urban areas. For example, if the user is in a rural area, the sentiment analysis unit analyzes sentiment data related to rural areas. Furthermore, if the user is traveling, the sentiment analysis unit can also analyze sentiment data related to the travel destination. This allows the system to select the optimal analysis method while considering the user's geographical location. Some or all of the above processing in the sentiment analysis unit may be performed using AI, for example, or without AI. For example, the sentiment analysis unit can input the user's location data into a generating AI and have the generating AI select the optimal analysis method.

[0059] The sentiment analysis unit analyzes the user's social media activity and proposes analysis content when analyzing sentiment data. For example, the sentiment analysis unit analyzes relevant sentiment data based on information shared by the user on social media. For example, the sentiment analysis unit analyzes the user's interests from their social media activity and analyzes relevant sentiment data. The sentiment analysis unit can also analyze relevant sentiment data based on the accounts the user follows on social media. This allows the unit to analyze the user's social media activity and propose analysis content. Some or all of the above processing in the sentiment analysis unit may be performed using AI, for example, or without AI. For example, the sentiment analysis unit can input the user's social media data into a generating AI and have the generating AI execute the proposal of analysis content.

[0060] The feedback unit provides optimal feedback by referring to the user's past feedback history when providing feedback. For example, the feedback unit provides relevant feedback based on feedback the user has received in the past. For example, the feedback unit extracts specific patterns from the user's past feedback history and provides optimal feedback. The feedback unit can also customize the feedback content by referring to the user's past feedback history. This allows the feedback unit to provide optimal feedback by referring to the user's past feedback history. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the user's feedback history data into a generating AI and have the generating AI select the optimal feedback.

[0061] The feedback unit provides optimal feedback by considering the user's geographical location when providing feedback. For example, if the user is in an urban area, the feedback unit provides feedback related to urban areas. For example, if the user is in a rural area, the feedback unit provides feedback related to rural areas. Furthermore, if the user is traveling, the feedback unit can also provide feedback related to the travel destination. This allows the feedback unit to provide optimal feedback by considering the user's geographical location. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the user's location data into a generating AI and have the generating AI select the optimal feedback.

[0062] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0063] The communication system can also include a health management unit that monitors the user's health status. This unit can, for example, monitor vital signs such as the user's heart rate, blood pressure, and body temperature in real time. For instance, if the user is stressed, it can detect fluctuations in heart rate and blood pressure and provide advice for relaxation. The health management unit can also record the user's health data over the long term and analyze changes in their health status. For example, if the user exercises regularly, it can monitor the effects and provide appropriate feedback. This allows for real-time monitoring of the user's health status and the provision of appropriate advice.

[0064] The communication system may also include a content delivery unit that provides customized content based on the user's hobbies and interests. For example, the content delivery unit collects information on the user's favorite music, movies, books, etc., and suggests related content. If the user prefers a particular genre of movies, it would provide information on new releases in that genre. Furthermore, the content delivery unit can suggest personalized content based on the user's past viewing history and ratings. For example, it could suggest works similar to movies the user has given high ratings to. This allows for the provision of customized content based on the user's hobbies and interests.

[0065] The communication system can also include a schedule management unit to manage the user's schedule. This unit can, for example, automatically organize the user's appointments and provide reminders. For instance, it can notify the user to ensure they don't forget important meetings or events. Furthermore, the schedule management unit can send notifications at the most opportune time based on the user's schedule, avoiding busy periods. This helps users efficiently manage their schedules and prevents them from forgetting important appointments.

[0066] The communication system can also include a learning support unit to further assist user learning. This unit, for example, provides learning materials and resources related to the area the user wants to study. For instance, if a user wants to acquire a specific skill, it might suggest online courses and materials related to that skill. The learning support unit can also monitor the user's learning progress and provide appropriate feedback. For example, it might suggest the next steps based on the user's learning progress. This effectively supports the user's learning.

[0067] The communication system can also include a travel support section to further assist users with their travels. This section can, for example, provide information about the user's travel destination, such as tourist attractions and restaurants the user plans to visit. Furthermore, the travel support section can manage the user's travel schedule and suggest optimal routes, such as routes that allow the user to efficiently visit tourist spots. This effectively supports the user's travels.

[0068] The communication system can also include a shopping support unit to assist users with their shopping. This unit can, for example, provide information about products the user wants to purchase. For instance, it can provide information on the best place to buy the product the user is looking for and its price. Furthermore, the shopping support unit can suggest related products based on the user's past purchase history. For example, it can suggest new products related to items the user has previously purchased. This effectively supports the user's shopping process.

[0069] The following briefly describes the processing flow for example form 1.

[0070] Step 1: The reception desk receives user input. The reception desk supports various input methods such as voice input, text input, and gesture input. For example, it uses speech recognition technology to convert the user's voice into text and provides an interface for receiving text input, and gesture recognition technology to recognize the user's gestures. Step 2: The conversation support unit supports natural conversation based on the information received by the reception unit. The conversation support unit analyzes user input using LLM and generates appropriate responses. It also controls the flow of the conversation and provides appropriate answers to user questions. Step 3: The VR unit provides an immersive experience based on the conversation content generated by the conversation support unit. The VR unit provides the user with a virtual space using a VR headset, allowing them to experience spending time in the same virtual space as family members who are in a remote location. It also supports interaction within the virtual space. Step 4: The AR team creates the feeling that family members in remote locations are around you, based on the experience provided by the VR team. The AR team uses AR-enabled devices to overlay digital information onto the real world, providing an experience that makes it seem as if family members in remote locations are around you. Step 5: The emotion analysis unit provides feedback based on the emotion data analyzed by the conversation support unit. The emotion analysis unit analyzes the user's emotions using facial recognition technology and voice analysis technology and provides appropriate feedback.

[0071] (Example of form 2) The communication system according to an embodiment of the present invention combines deep learning using a large-scale language model (LLM), virtual reality (VR) technology, and augmented reality (AR) technology to realistically recreate the time spent by a person working away from home and their family. This system uses LLM to support natural conversation so that the user can communicate with family members who are in a remote location. Next, VR technology is used to provide an immersive experience that makes it seem as if the user is in the same place, transcending physical distance. Furthermore, AR technology is used to create the feeling that family members in remote locations are around the user. By incorporating not only words but also visual elements and sharing them in real time, it becomes possible to experience the feeling of being together even when living far away. First, LLM is used to support natural conversation so that the user can communicate with family members in a remote location. For example, when the user speaks, the LLM analyzes the content and generates an appropriate response. This enables natural conversation that does not make the user feel the distance. Next, VR technology is used to provide an immersive experience that makes it seem as if the user is in the same place, transcending physical distance. For example, when the user wears a VR headset, they can spend time in the same virtual space as family members in remote locations. This enables communication that is no different from the real world. Furthermore, AR technology is used to create the feeling that family members in remote locations are nearby. For example, when a user uses an AR-enabled device, digital information is overlaid on the real world, allowing them to experience the sensation that family members in remote locations are right around them. This enhances a sense of unity and strengthens the feeling of being together even when far apart. In addition, through LLM's emotion analysis and feedback function, the system analyzes the user's emotions from their actions and expressions and provides feedback to improve the experience. For example, it analyzes the user's emotions from what they are saying and their facial expressions, and provides appropriate advice and feedback to promote richer communication. This system can provide effective communication and collaboration that transcends physical distance for companies with employees working away from home or in remote locations, aiming to improve their quality of life and productivity.This allows the communication system to analyze the user's emotions and provide appropriate feedback.

[0072] The communication system according to this embodiment comprises a reception unit, a conversation support unit, a VR unit, an AR unit, and an emotion analysis unit. The reception unit receives user input. The reception unit supports various input methods, such as voice input, text input, and gesture input. For example, the reception unit can convert the user's voice into text using speech recognition technology. The reception unit can also provide an interface for receiving text input. Furthermore, the reception unit can recognize the user's gestures using gesture recognition technology and process them as input. The conversation support unit supports natural conversation based on the information received by the reception unit. The conversation support unit analyzes the user's input using, for example, LLM and generates an appropriate response. For example, when the user speaks, the conversation support unit analyzes the content and generates an appropriate response. The conversation support unit can also control the flow of conversation based on user input. For example, the conversation support unit provides appropriate answers to the user's questions and facilitates the smooth progress of the conversation. The VR unit provides an immersive experience based on the conversation content generated by the conversation support unit. The VR unit provides a virtual space to the user using, for example, a VR headset. For example, the VR unit provides users with the experience of spending time in the same virtual space as their family members who are in a remote location when they wear a VR headset. The VR unit can also support interaction within the virtual space. For example, the VR unit allows users to manipulate objects and communicate with other users within the virtual space. The AR unit creates the feeling that family members in a remote location are around the user based on the experience provided by the VR unit. The AR unit, for example, overlays digital information onto the real world using AR-enabled devices. For example, the AR unit provides users with the experience of having family members in a remote location around them when they use an AR-enabled device. The AR unit can also seamlessly integrate the real world and digital information. For example, the AR unit places digital objects in the real world and allows users to interact with them. The emotion analysis unit provides feedback based on emotion data analyzed by the conversation support unit.The emotion analysis unit analyzes emotions from, for example, the user's facial expressions and voice. For instance, it uses facial recognition technology to analyze the user's facial expressions and estimate their emotions. It can also use voice analysis technology to estimate emotions from the user's voice. Furthermore, the emotion analysis unit provides appropriate feedback based on the user's emotions. For example, if the user is feeling stressed, it provides advice on how to relax. As a result, the communication system according to this embodiment can accept user input, support natural conversation, provide an immersive experience, create the feeling that family members in distant locations are nearby, and provide feedback based on emotional data.

[0073] The reception unit receives user input. The reception unit supports various input methods, such as voice input, text input, and gesture input. Specifically, it can convert user speech into text using speech recognition technology. This speech recognition technology utilizes deep learning-based speech models to accurately transcribe user speech. This allows users to interact with the system in a natural conversational manner. The reception unit can also provide an interface for receiving text input. For example, it can provide input interfaces using keyboards or touchscreens, allowing users to easily input text. Furthermore, the reception unit can recognize user gestures using gesture recognition technology and process them as input. Gesture recognition technology uses cameras and sensors to detect the user's hand and body movements and interprets them as specific commands. This allows users to control the system with intuitive operations such as waving or pointing. The reception unit integrates these diverse input methods to provide an advanced interface that accurately understands user intent.

[0074] The conversation support unit facilitates natural conversation based on information received by the reception unit. For example, the conversation support unit uses an LLM (Large-Scale Language Model) to analyze user input and generate appropriate responses. The LLM has learned from vast amounts of text data, understands context, and is capable of generating natural language. For example, if a user asks, "What's the weather like today?", the conversation support unit analyzes the content and generates an appropriate response such as, "It's sunny today. The temperature is 25 degrees." The conversation support unit can also control the flow of the conversation based on user input. For example, if the user continues to ask questions, the conversation support unit will provide answers to those questions sequentially, keeping the conversation flowing smoothly. Furthermore, the conversation support unit can understand the user's intentions and emotions and respond with appropriate tone and expression. This allows the user to feel more natural in their interaction with the system.

[0075] The VR unit provides an immersive experience based on conversation content generated by the conversation support unit. For example, the VR unit provides users with a virtual space using a VR headset. The VR headset features a high-resolution display and 3D audio, allowing users to experience a realistic virtual environment. For instance, when a user puts on a VR headset, they can experience spending time in the same virtual space as family members who are in a remote location. Within this virtual space, users can enjoy conversations with family in a virtual living room or play games together. The VR unit can also support interaction within the virtual space. For example, users can manipulate objects within the virtual space or communicate with other users. This allows users to share realistic experiences across physical distances. Furthermore, the VR unit can track user movements and reflect actions within the virtual space in real time. This allows users to enjoy more natural interactions.

[0076] The AR (Augmented Reality) unit creates the feeling that family members in remote locations are around the user, based on the experience provided by the VR (Virtual Reality) unit. The AR unit overlays digital information onto the real world using AR-enabled devices, for example. AR-enabled devices include smartphones, tablets, and AR glasses. For example, when a user uses an AR-enabled device, they can experience the feeling that family members in remote locations are around them. Specifically, the AR unit displays avatars of family members in the real world, allowing the user to interact with these avatars. For example, a user can see family avatars sitting in their living room through AR glasses. The AR unit can also seamlessly integrate the real world with digital information. For example, the AR unit can place digital objects in the real world, allowing the user to interact with them. This allows the user to enjoy a new experience where the real environment and digital information are integrated. Furthermore, the AR unit can track the user's gaze and gestures to make the interaction more natural. This allows the user to enjoy the AR experience more intuitively.

[0077] The emotion analysis unit provides feedback based on emotional data analyzed by the conversation support unit. For example, the emotion analysis unit analyzes emotions from the user's facial expressions and voice. Specifically, it uses facial recognition technology to analyze the user's facial expressions and estimate their emotions. Facial recognition technology uses a camera to capture the features of the user's face and can detect subtle changes in facial expression. The emotion analysis unit can also estimate emotions from the user's voice using voice analysis technology. Voice analysis technology analyzes the tone, pitch, and speed of the user's voice to detect changes in emotion. Furthermore, the emotion analysis unit provides appropriate feedback based on the user's emotions. For example, if the user is stressed, the emotion analysis unit provides advice to help them relax. If the user is happy, it shares that emotion and provides positive feedback. This allows the emotion analysis unit to understand the user's emotional state in real time and respond appropriately. Additionally, the emotion analysis unit can accumulate long-term emotional data and analyze trends in the user's emotions. This allows for continuous monitoring of changes in the user's emotions and the provision of more personalized feedback.

[0078] The emotion analysis unit includes a feedback unit that analyzes the user's emotions and provides feedback. The emotion analysis unit analyzes emotions from, for example, the user's facial expressions and voice. For example, the emotion analysis unit uses facial recognition technology to analyze the user's facial expressions and estimate their emotions. The emotion analysis unit can also estimate emotions from the user's voice using voice analysis technology. Furthermore, the emotion analysis unit provides appropriate feedback based on the user's emotions. For example, if the emotion analysis unit is feeling stressed, it provides advice on how to relax. This allows the unit to analyze the user's emotions and provide appropriate feedback. Some or all of the above processing in the emotion analysis unit may be performed using, for example, AI, or without AI. For example, the emotion analysis unit can input the user's facial expression data into a generating AI and have the generating AI perform emotion estimation.

[0079] The VR unit provides users with the experience of spending time in the same virtual space as their family members who are in a remote location, by having them wear a VR headset. The VR unit provides users with a virtual space using a VR headset, for example. For example, the VR unit provides users with the experience of spending time in the same virtual space as their family members who are in a remote location, by having them wear a VR headset. The VR unit can also support interaction within the virtual space. For example, the VR unit allows users to manipulate objects in the virtual space and communicate with other users. This allows users to spend time in the same virtual space as their family members who are in a remote location, by having them wear a VR headset. Some or all of the above processing in the VR unit may be performed using AI, for example, or not using AI. For example, the VR unit can have a generation AI perform the generation of the virtual space.

[0080] The AR unit overlays digital information onto the real world when a user uses an AR-enabled device, providing an experience as if family members in a remote location were around them. For example, the AR unit overlays digital information onto the real world using an AR-enabled device. The AR unit can also seamlessly integrate the real world and digital information. For example, the AR unit can place digital objects in the real world, allowing users to interact with them. This allows the AR unit to overlay digital information onto the real world when a user uses an AR-enabled device, providing an experience as if family members in a remote location were around them. Some or all of the above processing in the AR unit may be performed using AI, for example, or without AI. For example, the AR unit can have a generative AI perform the generation of digital information.

[0081] The conversation support unit analyzes what the user says and generates an appropriate response. For example, the conversation support unit can analyze user input using an LLM (Liquid Language Machine) and generate an appropriate response. The conversation support unit can also control the flow of the conversation based on user input. For example, it can provide appropriate answers to user questions and keep the conversation running smoothly. This allows the unit to analyze what the user says and generate an appropriate response. Some or all of the above-described processes in the conversation support unit may be performed using AI, or not. For example, the conversation support unit can input user input data into a generating AI and have the generating AI generate the response.

[0082] The feedback unit analyzes the user's emotions and provides appropriate advice and feedback. For example, the feedback unit analyzes emotions from the user's facial expressions and voice. For example, the feedback unit uses facial recognition technology to analyze the user's facial expressions and estimate their emotions. The feedback unit can also estimate emotions from the user's voice using voice analysis technology. Furthermore, the feedback unit provides appropriate advice and feedback based on the user's emotions. For example, if the user is feeling stressed, the feedback unit provides advice on how to relax. This allows the feedback unit to analyze the user's emotions and provide appropriate advice and feedback. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the user's facial expression data into a generating AI and have the generating AI perform emotion estimation.

[0083] The reception desk estimates the user's emotions and adjusts the timing of input acceptance based on the estimated emotions. For example, if the user is stressed, the reception desk may delay the timing of input acceptance to give the user time to relax. For example, if the user is excited, the reception desk may accept input quickly and respond immediately. The reception desk may also adjust the timing of input acceptance and suggest a simpler input method if the user is tired. This allows the system to estimate the user's emotions and adjust the timing of input acceptance based on the estimated emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not using AI. For example, the reception desk may input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0084] The reception desk analyzes the user's past input history and selects the optimal input method. For example, the reception desk prioritizes suggesting input methods (voice, text, etc.) that the user has frequently used in the past. For example, the reception desk predicts and suggests input methods to be used during specific time periods based on the user's past input history. The reception desk can also suggest relevant input methods based on content the user has previously entered. This allows the reception desk to analyze the user's past input history and select the optimal input method. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's input history data into a generating AI and have the generating AI select the optimal input method.

[0085] The reception unit filters input based on the user's current situation and areas of interest. For example, the reception unit suggests an appropriate input method based on the user's current situation (e.g., working, on break). For example, the reception unit filters relevant input content based on the user's areas of interest (e.g., hobbies, interests). The reception unit can also simplify input content and provide appropriate information depending on the user's current situation. This allows filtering based on the user's current situation and areas of interest at the time of input reception. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input user situation data into a generating AI and have the generating AI perform the filtering.

[0086] The reception desk estimates the user's emotions and determines the priority of inputs to be received based on the estimated emotions. For example, if the user is nervous, the reception desk will prioritize important inputs. For example, if the user is relaxed, the reception desk will prioritize detailed inputs. Also, if the user is in a hurry, the reception desk can prioritize inputs that require a quick response. This allows the reception desk to estimate the user's emotions and determine the priority of inputs to be received based on the estimated emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI, or not using AI. For example, the reception desk can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0087] The reception unit, upon receiving input, prioritizes accepting inputs that are highly relevant, taking into account the user's geographical location. For example, if the user is in a specific region, the reception unit prioritizes accepting inputs related to that region. For example, if the user is traveling, the reception unit prioritizes accepting inputs related to the travel destination. The reception unit can also prioritize accepting inputs related to the user's home if the user is at home. This allows the reception unit to prioritize accepting inputs that are highly relevant, taking into account the user's geographical location. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's location data into a generating AI and have the generating AI select highly relevant inputs.

[0088] The reception unit analyzes the user's social media activity and accepts relevant inputs when receiving input. For example, the reception unit accepts relevant inputs based on information the user has shared on social media. For example, the reception unit analyzes the user's interests from their social media activity and accepts relevant inputs. The reception unit can also accept relevant inputs based on accounts the user follows on social media. This allows the reception unit to analyze the user's social media activity and accept relevant inputs when receiving input. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's social media data into a generating AI and have the generating AI select relevant inputs.

[0089] The conversation support unit estimates the user's emotions and adjusts the way the conversation is expressed based on the estimated emotions. For example, if the user is nervous, the conversation support unit will proceed with the conversation in a calm tone. For example, if the user is relaxed, the conversation support unit will proceed with the conversation in a cheerful tone. The conversation support unit can also proceed with the conversation in an energetic tone if the user is excited. In this way, the conversation support unit can estimate the user's emotions and adjust the way the conversation is expressed based on the estimated emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the conversation support unit may be performed using AI, for example, or without AI. For example, the conversation support unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0090] The conversation support unit adjusts the level of detail in a conversation based on its importance. For example, in the case of an important conversation, the conversation support unit provides detailed information. For example, in the case of a general conversation, the conversation support unit provides concise information. The conversation support unit can also provide concise information if the user is in a hurry. This allows the conversation support unit to adjust the level of detail in a conversation based on its importance. Some or all of the above processing in the conversation support unit may be performed using AI, for example, or without AI. For example, the conversation support unit can input user input data into a generating AI and have the generating AI perform the adjustment of the level of detail in the conversation.

[0091] The conversation support unit applies different conversation algorithms depending on the category of the conversation during conversation support. For example, the conversation support unit applies a formal conversation algorithm for business conversations. For example, the conversation support unit applies a relaxed conversation algorithm for casual conversations. The conversation support unit can also apply a conversation algorithm that includes technical terms for technical conversations. This allows the conversation support unit to apply different conversation algorithms depending on the category of the conversation during conversation support. Some or all of the above processing in the conversation support unit may be performed using AI, for example, or without AI. For example, the conversation support unit can input user input data into a generating AI and have the generating AI perform the application of conversation algorithms.

[0092] The conversation support unit estimates the user's emotions and adjusts the length of the conversation based on the estimated emotions. For example, if the user is in a hurry, the conversation support unit provides a short, to-the-point conversation. For example, if the user is relaxed, the conversation support unit provides a longer conversation with detailed explanations. The conversation support unit can also provide an energetic conversation if the user is excited. This allows the unit to estimate the user's emotions and adjust the length of the conversation based on the estimated emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the conversation support unit may be performed using AI or not using AI. For example, the conversation support unit can input user facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0093] The conversation support unit determines the priority of conversations based on when they are submitted. For example, the conversation support unit will prioritize urgent conversations. For example, the conversation support unit will handle regular conversations with normal priority. The conversation support unit can also prioritize conversations submitted by users within a specific time frame. This allows the conversation support unit to determine the priority of conversations based on when they are submitted. Some or all of the above processing in the conversation support unit may be performed using AI, for example, or not using AI. For example, the conversation support unit can input user input data into a generating AI and have the generating AI perform the task of determining the priority of conversations.

[0094] The conversation support unit adjusts the order of conversations based on their relevance during conversation support. For example, the conversation support unit prioritizes highly relevant conversations. For example, it postpones less relevant conversations. The conversation support unit can also prioritize conversations related to a specific topic if the user has shown interest in that topic. This allows the conversation support unit to adjust the order of conversations based on their relevance during conversation support. Some or all of the above processing in the conversation support unit may be performed using AI, for example, or without AI. For example, the conversation support unit can input user input data into a generating AI and have the generating AI perform the adjustment of the conversation order.

[0095] The VR unit estimates the user's emotions and adjusts the content of the VR experience based on the estimated emotions. For example, if the user is relaxed, the VR unit provides a calm landscape. For example, if the user is excited, the VR unit provides an active experience. The VR unit can also provide a relaxing experience if the user is tired. This allows the VR unit to estimate the user's emotions and adjust the content of the VR experience based on the estimated emotions. 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, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the VR unit may be performed using AI, for example, or without AI. For example, the VR unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0096] The VR unit provides the optimal experience during a VR experience by referring to the user's past VR experience history. For example, the VR unit may provide experiences that the user has enjoyed in the past again. For example, the VR unit may suggest new experiences that are relevant to the user based on the user's past experience history. The VR unit can also exclude experiences that the user has avoided in the past when making suggestions. This allows the VR unit to provide the optimal experience by referring to the user's past VR experience history. Some or all of the above processing in the VR unit may be performed using AI, for example, or without AI. For example, the VR unit may input the user's experience history data into a generating AI and have the generating AI select the optimal experience.

[0097] The VR unit customizes the VR experience based on the user's current life circumstances. For example, if the user is at work, the VR unit provides a short, refreshing experience. For example, if the user is on vacation, the VR unit provides an experience that can be enjoyed for a longer period. The VR unit can also provide a relaxing experience if the user is feeling stressed. This allows the experience to be customized based on the user's current life circumstances. Some or all of the above processing in the VR unit may be performed using AI, for example, or without AI. For example, the VR unit can input the user's life circumstances data into a generating AI and have the generating AI perform the customization of the experience.

[0098] The VR unit estimates the user's emotions and prioritizes VR experiences based on those emotions. For example, if the user is tense, the VR unit prioritizes relaxing experiences. For example, if the user is excited, the VR unit prioritizes active experiences. The VR unit can also prioritize refreshing experiences if the user is tired. This allows the VR unit to estimate the user's emotions and prioritize VR experiences based on those emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the VR unit may be performed using AI, or not using AI. For example, the VR unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0099] The VR unit provides an optimal experience during a VR experience, taking into account the user's geographical location. For example, if the user is in an urban area, the VR unit provides a natural landscape. For example, if the user is in a rural area, the VR unit provides an urban landscape. Furthermore, if the user is traveling, the VR unit can provide an experience related to their travel destination. This allows the system to provide an optimal experience by considering the user's geographical location. Some or all of the above processing in the VR unit may be performed using AI, for example, or without AI. For example, the VR unit can input the user's location data into a generating AI and have the generating AI select the optimal experience.

[0100] The VR department analyzes the user's social media activity during a VR experience and suggests relevant experiences. For example, the VR department suggests relevant experiences based on information the user has shared on social media. For example, the VR department analyzes the user's interests from their social media activity and suggests relevant experiences. The VR department can also suggest relevant experiences based on the accounts the user follows on social media. This allows the VR department to analyze the user's social media activity and suggest relevant experiences. Some or all of the above processing in the VR department may be performed using AI, for example, or without AI. For example, the VR department can input the user's social media data into a generating AI and have the generating AI suggest experiences.

[0101] The AR unit estimates the user's emotions and adjusts the content of the AR experience based on the estimated emotions. For example, if the user is relaxed, the AR unit provides a calm AR experience. For example, if the user is excited, the AR unit provides an active AR experience. The AR unit can also provide a relaxing AR experience if the user is tired. This allows the AR unit to estimate the user's emotions and adjust the content of the AR experience based on the estimated emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the AR unit may be performed using AI, for example, or without AI. For example, the AR unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0102] The AR unit provides the optimal experience during an AR experience by referring to the user's past AR experience history. For example, the AR unit may re-provide AR experiences that the user has enjoyed in the past. For example, the AR unit may suggest new AR experiences that are relevant to the user based on their past experience history. The AR unit can also exclude and suggest AR experiences that the user has avoided in the past. This allows the AR unit to provide the optimal experience by referring to the user's past AR experience history. Some or all of the above processing in the AR unit may be performed using AI, for example, or without AI. For example, the AR unit may input the user's experience history data into a generating AI and have the generating AI select the optimal experience.

[0103] The AR unit customizes the AR experience based on the user's current lifestyle. For example, if the user is at work, the AR unit provides a short, refreshing AR experience. For example, if the user is on vacation, the AR unit provides a longer, more enjoyable AR experience. The AR unit can also provide a relaxing AR experience if the user is feeling stressed. This allows the experience to be customized based on the user's current lifestyle. Some or all of the above processing in the AR unit may be performed using AI, for example, or without AI. For example, the AR unit can input the user's lifestyle data into a generating AI and have the generating AI perform the customization of the experience.

[0104] The AR unit estimates the user's emotions and prioritizes AR experiences based on the estimated emotions. For example, if the user is tense, the AR unit will prioritize providing relaxing AR experiences. For example, if the user is excited, the AR unit will prioritize providing active AR experiences. The AR unit can also prioritize providing refreshing AR experiences if the user is tired. This allows the AR unit to estimate the user's emotions and prioritize AR experiences based on the estimated emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the AR unit may be performed using AI, for example, or without AI. For example, the AR unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0105] The AR unit provides an optimal experience during an AR experience, taking into account the user's geographical location. For example, if the user is in an urban area, the AR unit provides a natural landscape. For example, if the user is in a rural area, the AR unit provides an urban landscape. Furthermore, if the user is traveling, the AR unit can provide an AR experience related to their travel destination. This allows the system to provide an optimal experience by taking into account the user's geographical location. Some or all of the above processing in the AR unit may be performed using AI, for example, or without AI. For example, the AR unit can input the user's location data into a generating AI and have the generating AI select the optimal experience.

[0106] The AR unit analyzes the user's social media activity during an AR experience and suggests relevant experiences. For example, the AR unit suggests relevant AR experiences based on information the user has shared on social media. For example, the AR unit analyzes the user's interests from their social media activity and suggests relevant AR experiences. The AR unit can also suggest relevant AR experiences based on the accounts the user follows on social media. This allows the AR unit to analyze the user's social media activity and suggest relevant experiences. Some or all of the above processing in the AR unit may be performed using AI, for example, or without AI. For example, the AR unit can input the user's social media data into a generating AI and have the generating AI suggest experiences.

[0107] The emotion analysis unit estimates the user's emotions and adjusts the method of analyzing the emotion data based on the estimated user emotions. For example, if the user is tense, the emotion analysis unit performs a careful analysis of the emotion data. For example, if the user is relaxed, the emotion analysis unit performs a detailed analysis of the emotion data. The emotion analysis unit can also perform a rapid analysis of the emotion data if the user is excited. This allows the system to estimate the user's emotions and adjust the method of analyzing the emotion data based on the estimated user emotions. 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, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the emotion analysis unit may be performed using AI, for example, or without AI. For example, the emotion analysis unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0108] The sentiment analysis unit optimizes its analysis algorithm by referring to the user's past sentiment data when analyzing sentiment data. For example, the sentiment analysis unit selects the optimal analysis algorithm based on the user's past sentiment data. For example, the sentiment analysis unit extracts specific patterns from the user's past sentiment data and adjusts the analysis algorithm. The sentiment analysis unit can also improve the accuracy of the analysis results by referring to the user's past sentiment data. This allows the analysis algorithm to be optimized by referring to the user's past sentiment data. Some or all of the above processes in the sentiment analysis unit may be performed using AI, for example, or without AI. For example, the sentiment analysis unit can input the user's past sentiment data into a generating AI and have the generating AI perform the optimization of the analysis algorithm.

[0109] The emotion analysis unit customizes the analysis content based on the user's current living situation when analyzing emotional data. For example, if the user is at work, the emotion analysis unit analyzes work-related emotional data. For example, if the user is on vacation, the emotion analysis unit analyzes emotional data based on a relaxed state. The emotion analysis unit can also analyze stress-related emotional data if the user is feeling stressed. This allows the analysis content to be customized based on the user's current living situation. Some or all of the above processing in the emotion analysis unit may be performed using AI, for example, or without AI. For example, the emotion analysis unit can input the user's living situation data into a generating AI and have the generating AI perform the customization of the analysis content.

[0110] The emotion analysis unit estimates the user's emotions and prioritizes emotion data based on the estimated emotions. For example, if the user is tense, the emotion analysis unit prioritizes analyzing emotion data related to tension. For example, if the user is relaxed, the emotion analysis unit prioritizes analyzing emotion data related to relaxation. The emotion analysis unit can also prioritize analyzing emotion data related to excitement if the user is excited. This allows the system to estimate the user's emotions and prioritize emotion data based on the estimated emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the emotion analysis unit may be performed using AI, for example, or without AI. For example, the emotion analysis unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0111] The sentiment analysis unit selects the optimal analysis method when analyzing sentiment data, taking into account the user's geographical location. For example, if the user is in an urban area, the sentiment analysis unit analyzes sentiment data related to urban areas. For example, if the user is in a rural area, the sentiment analysis unit analyzes sentiment data related to rural areas. Furthermore, if the user is traveling, the sentiment analysis unit can also analyze sentiment data related to the travel destination. This allows the system to select the optimal analysis method while considering the user's geographical location. Some or all of the above processing in the sentiment analysis unit may be performed using AI, for example, or without AI. For example, the sentiment analysis unit can input the user's location data into a generating AI and have the generating AI select the optimal analysis method.

[0112] The sentiment analysis unit analyzes the user's social media activity and proposes analysis content when analyzing sentiment data. For example, the sentiment analysis unit analyzes relevant sentiment data based on information shared by the user on social media. For example, the sentiment analysis unit analyzes the user's interests from their social media activity and analyzes relevant sentiment data. The sentiment analysis unit can also analyze relevant sentiment data based on the accounts the user follows on social media. This allows the unit to analyze the user's social media activity and propose analysis content. Some or all of the above processing in the sentiment analysis unit may be performed using AI, for example, or without AI. For example, the sentiment analysis unit can input the user's social media data into a generating AI and have the generating AI execute the proposal of analysis content.

[0113] The feedback unit estimates the user's emotions and adjusts the content of the feedback based on the estimated emotions. For example, if the user is tense, the feedback unit provides relaxing feedback. For example, if the user is relaxed, the feedback unit provides detailed feedback. The feedback unit can also provide energetic feedback if the user is excited. This allows the feedback unit to estimate the user's emotions and adjust the content of the feedback based on the estimated emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the feedback unit may be performed using AI, for example, or not using AI. For example, the feedback unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0114] The feedback unit provides optimal feedback by referring to the user's past feedback history when providing feedback. For example, the feedback unit provides relevant feedback based on feedback the user has received in the past. For example, the feedback unit extracts specific patterns from the user's past feedback history and provides optimal feedback. The feedback unit can also customize the feedback content by referring to the user's past feedback history. This allows the feedback unit to provide optimal feedback by referring to the user's past feedback history. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the user's feedback history data into a generating AI and have the generating AI select the optimal feedback.

[0115] The feedback unit estimates the user's emotions and prioritizes feedback based on the estimated emotions. For example, if the user is tense, the feedback unit prioritizes providing relaxing feedback. For example, if the user is relaxed, the feedback unit prioritizes providing detailed feedback. The feedback unit can also prioritize providing energetic feedback if the user is excited. This allows the system to estimate the user's emotions and prioritize feedback based on the estimated emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the feedback unit may be performed using AI or not using AI. For example, the feedback unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0116] The feedback unit provides optimal feedback by considering the user's geographical location when providing feedback. For example, if the user is in an urban area, the feedback unit provides feedback related to urban areas. For example, if the user is in a rural area, the feedback unit provides feedback related to rural areas. Furthermore, if the user is traveling, the feedback unit can also provide feedback related to the travel destination. This allows the feedback unit to provide optimal feedback by considering the user's geographical location. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the user's location data into a generating AI and have the generating AI select the optimal feedback.

[0117] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0118] The communication system can also include a health management unit that monitors the user's health status. This unit can, for example, monitor vital signs such as the user's heart rate, blood pressure, and body temperature in real time. For instance, if the user is stressed, it can detect fluctuations in heart rate and blood pressure and provide advice for relaxation. The health management unit can also record the user's health data over the long term and analyze changes in their health status. For example, if the user exercises regularly, it can monitor the effects and provide appropriate feedback. This allows for real-time monitoring of the user's health status and the provision of appropriate advice.

[0119] The communication system may also include a content delivery unit that provides customized content based on the user's hobbies and interests. For example, the content delivery unit collects information on the user's favorite music, movies, books, etc., and suggests related content. If the user prefers a particular genre of movies, it would provide information on new releases in that genre. Furthermore, the content delivery unit can suggest personalized content based on the user's past viewing history and ratings. For example, it could suggest works similar to movies the user has given high ratings to. This allows for the provision of customized content based on the user's hobbies and interests.

[0120] The communication system may further include a music selection unit that estimates the user's emotions and selects music based on those emotions. For example, the music selection unit may provide calming music if the user is relaxed, or energetic music if the user is excited. It may also provide relaxing music if the user is stressed. This allows the system to estimate the user's emotions and select music based on those emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0121] The communication system can also include a schedule management unit to manage the user's schedule. This unit can, for example, automatically organize the user's appointments and provide reminders. For instance, it can notify the user to ensure they don't forget important meetings or events. Furthermore, the schedule management unit can send notifications at the most opportune time based on the user's schedule, avoiding busy periods. This helps users efficiently manage their schedules and prevents them from forgetting important appointments.

[0122] The communication system may further include a feedback adjustment unit that estimates the user's emotions and adjusts the content of the feedback based on the estimated emotions. For example, if the user is tense, the feedback adjustment unit will provide relaxing feedback. For example, if the user is relaxed, it will provide detailed feedback. The feedback adjustment unit can also provide energetic feedback if the user is excited. This allows the system to estimate the user's emotions and adjust the content of the feedback based on the estimated emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0123] The communication system can also include a learning support unit to further assist user learning. This unit, for example, provides learning materials and resources related to the area the user wants to study. For instance, if a user wants to acquire a specific skill, it might suggest online courses and materials related to that skill. The learning support unit can also monitor the user's learning progress and provide appropriate feedback. For example, it might suggest the next steps based on the user's learning progress. This effectively supports the user's learning.

[0124] The communication system may further include a notification adjustment unit that estimates the user's emotions and adjusts the timing of notifications based on the estimated emotions. For example, the notification adjustment unit might delay the notification if the user is stressed, or send an immediate notification if the user is relaxed. The notification adjustment unit can also refrain from sending notifications if the user is busy. This allows the system to estimate the user's emotions and adjust the timing of notifications based on the estimated emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0125] The communication system can also include a travel support section to further assist users with their travels. This section can, for example, provide information about the user's travel destination, such as tourist attractions and restaurants the user plans to visit. Furthermore, the travel support section can manage the user's travel schedule and suggest optimal routes, such as routes that allow the user to efficiently visit tourist spots. This effectively supports the user's travels.

[0126] The communication system may further include an exercise suggestion unit that estimates the user's emotions and suggests exercises based on those emotions. For example, if the user is relaxed, the exercise suggestion unit might suggest light stretching. If the user is excited, it might suggest energetic exercises. The exercise suggestion unit could also suggest relaxing yoga if the user is stressed. This allows the system to estimate the user's emotions and suggest exercises based on those emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0127] The communication system can also include a shopping support unit to assist users with their shopping. This unit can, for example, provide information about products the user wants to purchase. For instance, it can provide information on the best place to buy the product the user is looking for and its price. Furthermore, the shopping support unit can suggest related products based on the user's past purchase history. For example, it can suggest new products related to items the user has previously purchased. This effectively supports the user's shopping process.

[0128] The following briefly describes the processing flow for example form 2.

[0129] Step 1: The reception desk receives user input. The reception desk supports various input methods such as voice input, text input, and gesture input. For example, it uses speech recognition technology to convert the user's voice into text and provides an interface for receiving text input, and gesture recognition technology to recognize the user's gestures. Step 2: The conversation support unit supports natural conversation based on the information received by the reception unit. The conversation support unit analyzes user input using LLM and generates appropriate responses. It also controls the flow of the conversation and provides appropriate answers to user questions. Step 3: The VR unit provides an immersive experience based on the conversation content generated by the conversation support unit. The VR unit provides the user with a virtual space using a VR headset, allowing them to experience spending time in the same virtual space as family members who are in a remote location. It also supports interaction within the virtual space. Step 4: The AR team creates the feeling that family members in remote locations are around you, based on the experience provided by the VR team. The AR team uses AR-enabled devices to overlay digital information onto the real world, providing an experience that makes it seem as if family members in remote locations are around you. Step 5: The emotion analysis unit provides feedback based on the emotion data analyzed by the conversation support unit. The emotion analysis unit analyzes the user's emotions using facial recognition technology and voice analysis technology and provides appropriate feedback.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] Each of the multiple elements described above, including the reception unit, conversation support unit, VR unit, AR unit, and emotion analysis unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit receives user input using the microphone 38B or touch panel 38A of the smart device 14. The conversation support unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which analyzes user input using LLM and generates an appropriate response. The VR unit provides a virtual space using, for example, the display 40A of the smart device 14. The AR unit overlays digital information onto the real world using, for example, the camera 42 of the smart device 14. The emotion analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which analyzes emotions from the user's facial expressions and voice and provides feedback. 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.

[0134] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] Each of the multiple elements described above, including the reception unit, conversation support unit, VR unit, AR unit, and emotion analysis unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit receives the user's voice input using the microphone 238 of the smart glasses 214. The conversation support unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which analyzes the user's input using LLM and generates an appropriate response. The VR unit provides a virtual space using the display of the smart glasses 214. The AR unit overlays digital information onto the real world using the camera 42 of the smart glasses 214. The emotion analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which analyzes emotions from the user's facial expressions and voice and provides feedback. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0150] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.).

[0162] 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.

[0163] 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.

[0164] 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.

[0165] Each of the multiple elements described above, including the reception unit, conversation support unit, VR unit, AR unit, and emotion analysis unit, is implemented in at least one of the following: the headset terminal 314 and the data processing unit 12. For example, the reception unit receives the user's voice input using the microphone 238 of the headset terminal 314. The conversation support unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which analyzes the user's input using LLM and generates an appropriate response. The VR unit provides a virtual space using, for example, the display 343 of the headset terminal 314. The AR unit overlays digital information onto the real world using, for example, the camera 42 of the headset terminal 314. The emotion analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which analyzes emotions from the user's facial expressions and voice and provides feedback. 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.

[0166] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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).

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.).

[0179] 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.

[0180] 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.

[0181] 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.

[0182] Each of the multiple elements described above, including the reception unit, conversation support unit, VR unit, AR unit, and emotion analysis unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the reception unit receives the user's voice input using the microphone 238 of the robot 414. The conversation support unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which analyzes the user's input using LLM and generates an appropriate response. The VR unit provides a virtual space using the display of the robot 414. The AR unit overlays digital information onto the real world using the camera 42 of the robot 414. The emotion analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which analyzes emotions from the user's facial expressions and voice and provides feedback. 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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."

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] (Note 1) A reception area that receives user input, A conversation support unit that supports natural conversation based on the information received by the reception unit, A VR unit that provides an immersive experience based on the conversation content generated by the aforementioned conversation support unit, Based on the experience provided by the VR unit, the AR unit creates the feeling that family members in a remote location are around you, The system includes an emotion analysis unit that provides feedback based on emotion data analyzed by the conversation support unit. A system characterized by the following features. (Note 2) The aforementioned emotion analysis unit, It includes a feedback unit that analyzes user emotions and provides feedback. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned VR section is By wearing a VR headset, users can experience spending time in the same virtual space as their family members who are in a remote location. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned AR section is, By using an AR-enabled device, users can overlay digital information onto the real world, providing an experience that makes it feel as if family members in distant locations are right there with them. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned conversation support unit, When a user speaks to it, the system analyzes the content and generates an appropriate response. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned feedback unit is Analyze user emotions and provide appropriate advice and feedback. The system described in Appendix 2, characterized by the features described herein. (Note 7) The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of input acceptance based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is Analyze the user's past input history and select the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When receiving input, filtering is performed based on the user's current situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates the user's emotions and determines the priority of input to accept based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When receiving input, the system prioritizes accepting inputs that are highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When receiving input, the system analyzes the user's social media activity and accepts relevant input. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned conversation support unit, It estimates the user's emotions and adjusts the way the conversation is expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned conversation support unit, When providing conversation support, adjust the level of detail in the conversation based on its importance. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned conversation support unit, When providing conversation support, different conversation algorithms are applied depending on the category of the conversation. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned conversation support unit, It estimates the user's emotions and adjusts the length of the conversation based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned conversation support unit, When providing conversation support, we prioritize conversations based on when they were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned conversation support unit, When providing conversation support, the order of conversations is adjusted based on their relevance. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned VR section is It estimates the user's emotions and adjusts the VR experience based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned VR section is During a VR experience, the system provides the optimal experience by referencing the user's past VR experience history. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned VR section is During a VR experience, the content of the experience is customized based on the user's current lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned VR section is It estimates the user's emotions and prioritizes the VR experience based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned VR section is When providing a VR experience, the system takes the user's geographical location into consideration to deliver the optimal experience. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned VR section is During VR experiences, the system analyzes the user's social media activity to suggest content tailored to their experience. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned AR section is, It estimates the user's emotions and adjusts the AR experience based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned AR section is, During an AR experience, the system provides the optimal experience by referencing the user's past AR experience history. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned AR section is, During an AR experience, the content of the experience is customized based on the user's current lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned AR section is, It estimates the user's emotions and prioritizes the AR experience based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned AR section is, When providing an AR experience, we take the user's geographical location into consideration to deliver the optimal experience. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned AR section is, During the AR experience, the system analyzes the user's social media activity to suggest content for the experience. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned emotion analysis unit, We estimate the user's emotions and adjust the method of analyzing the emotion data based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned emotion analysis unit, When analyzing emotional data, the analysis algorithm is optimized by referring to the user's past emotional data. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned emotion analysis unit, When analyzing emotional data, the analysis is customized based on the user's current life situation. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned emotion analysis unit, It estimates the user's emotions and prioritizes emotion data based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned emotion analysis unit, When analyzing emotional data, the optimal analysis method is selected by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned emotion analysis unit, When analyzing emotional data, we analyze users' social media activity and propose analysis findings. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned feedback unit is It estimates the user's emotions and adjusts the content of the feedback based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 38) The aforementioned feedback unit is When providing feedback, we refer to the user's past feedback history to provide the most appropriate feedback. The system described in Appendix 2, characterized by the features described herein. (Note 39) The aforementioned feedback unit is It estimates the user's emotions and prioritizes feedback based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 40) The aforementioned feedback unit is When providing feedback, we take the user's geographical location into consideration to provide the most appropriate feedback. The system described in Appendix 2, characterized by the features described herein. [Explanation of Symbols]

[0202] 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. A reception area that receives user input, A conversation support unit that supports natural conversation based on the information received by the reception unit, A VR unit that provides an immersive experience based on the conversation content generated by the aforementioned conversation support unit, Based on the experience provided by the VR unit, the AR unit creates the feeling that family members in a remote location are around you, The system includes an emotion analysis unit that provides feedback based on emotion data analyzed by the conversation support unit. A system characterized by the following features.

2. The aforementioned emotion analysis unit, It includes a feedback unit that analyzes user emotions and provides feedback. The system according to feature 1.

3. The aforementioned VR section is By wearing a VR headset, users can experience spending time in the same virtual space as their family members who are in a remote location. The system according to feature 1.

4. The aforementioned AR section is, By using an AR-enabled device, users can overlay digital information onto the real world, providing an experience that makes it feel as if family members in distant locations are right there with them. The system according to feature 1.

5. The aforementioned conversation support unit, When a user speaks to it, the system analyzes the content and generates an appropriate response. The system according to feature 1.

6. The aforementioned feedback unit is Analyze user emotions and provide appropriate advice and feedback. The system according to feature 2.

7. The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of input acceptance based on the estimated emotions. The system according to feature 1.

8. The aforementioned reception unit is Analyze the user's past input history and select the optimal input method. The system according to feature 1.

9. The aforementioned reception unit is When receiving input, filtering is performed based on the user's current situation and areas of interest. The system according to feature 1.

10. The aforementioned reception unit is It estimates the user's emotions and determines the priority of input to accept based on the estimated user emotions. The system according to feature 1.

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