system

Abstract

The system according to this embodiment aims to efficiently manage multiple conversations and facilitate the understanding of information. [Solution] The system according to the embodiment comprises a skin interface unit, a conversation summary unit, a dialogue experience unit, and an update feedback unit. The skin interface unit attaches a dedicated skin to an existing chat tool and customizes the display. The conversation summary unit analyzes multiple posts and conversations to create a summary. The dialogue experience unit supports information exchange in an intuitive one-to-one format for the user. The update feedback unit updates the summary in accordance with the progress of the conversation and provides feedback.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to manage multiple conversations and information is scattered and difficult to grasp.

[0005] The system according to the embodiment aims to efficiently manage multiple conversations and facilitate the grasping of information.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a skin interface unit, a conversation summary unit, a dialogue experience unit, and an update feedback unit. The skin interface unit attaches a dedicated skin to an existing chat tool and customizes the display. The conversation summary unit analyzes multiple posts and conversations to create a summary. The dialogue experience unit supports information exchange in an intuitive one-to-one format for the user. The update feedback unit updates the summary in accordance with the progress of the conversation and provides feedback. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently manage multiple conversations and facilitate the understanding of information. [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 manages communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

[0019] The smart device 14 comprises a computer 36, a reception 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 reception 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 conversation management system according to an embodiment of the present invention is a system that makes it easier to manage multiple conversations, prevents information scattering, and reduces the time spent tracking individual posts. This conversation management system has a skin interface that allows users to customize the display by attaching a dedicated skin to an existing chat tool. This allows users to manage conversations with a visually organized interface. The conversation management system also has a conversation summary function in which AI analyzes multiple posts and conversations and creates a summary. This allows users to quickly grasp important information. Furthermore, the conversation management system has a dialogue experience function that supports information exchange in an intuitive one-to-one format for users. This allows users to track information without confusion even when multiple conversations are progressing simultaneously. In addition, the conversation management system has a real-time update and feedback function that updates the summary and provides feedback as the conversation progresses. This allows users to always stay up-to-date with the latest information. For example, when a user is managing multiple conversations simultaneously, the conversation management system automatically displays the important points of each conversation as a summary. The conversation management system also provides a function to temporarily hide other conversations when the user wants to focus on a specific conversation. This allows the user to concentrate on important conversations. Furthermore, the conversation management system includes features to organize and store conversation history, allowing users to quickly search past conversations. This enables users to quickly find the information they need. As a result, the conversation management system makes it easier to manage multiple conversations, prevents information from becoming scattered, and reduces the time it takes to track individual posts.

[0029] The conversation management system according to this embodiment comprises a skin interface unit, a conversation summary unit, a dialogue experience unit, and an update feedback unit. The skin interface unit applies a dedicated skin to an existing chat tool and customizes its display. For example, the skin interface unit can apply a specific theme or design to the chat tool used by the user. For example, the skin interface unit can reflect the colors and layout selected by the user. The skin interface unit can also change the font size and icon placement according to the user's preferences. For example, the skin interface unit provides customization options to create a visually comfortable interface for the user. The conversation summary unit uses AI to analyze multiple posts and conversations and create a summary. For example, the conversation summary unit can use natural language processing technology to extract important information and generate a summary. For example, the conversation summary unit creates a summary based on keywords and phrases that frequently appear in the conversation. The conversation summary unit can also provide a summary so that the user can quickly grasp information about a specific topic. For example, the conversation summary unit analyzes the content of the conversation and concisely summarizes the important points. The Dialogue Experience section supports information exchange in an intuitive one-on-one format for the user. For example, when a user manages multiple conversations simultaneously, the Dialogue Experience section can display each conversation in a separate window. For example, the Dialogue Experience section provides a function to temporarily hide other conversations so that the user can concentrate on a specific conversation. The Dialogue Experience section also has a function to organize and save conversation history so that the user can quickly search past conversations. For example, the Dialogue Experience section provides a search function so that the user can quickly find the information they need. The Update Feedback section updates the summary and provides feedback as the conversation progresses. For example, the Update Feedback section can automatically update the summary when the content of the conversation changes. For example, the Update Feedback section updates the summary in real time so that the user is always aware of the latest information.Furthermore, the update feedback unit ensures that users receive appropriate feedback according to the progress of the conversation. For example, the update feedback unit provides a notification function to ensure that users do not miss important points in the conversation. This makes it easier to manage multiple conversations, prevents information from becoming scattered, and reduces the time required to track individual posts.

[0030] The Skin Interface section customizes the display of existing chat tools by applying dedicated skins. For example, the Skin Interface section can apply specific themes and designs to the chat tool a user uses. Specifically, it can reflect the user's chosen colors and layouts. For instance, users can personalize the appearance of their chat tool by setting their preferred color scheme and background image. The Skin Interface section can also change font size and icon placement according to user preferences. For example, to create a visually pleasing interface, the font size can be increased or icons can be moved to an intuitively understandable position. Furthermore, the Skin Interface section provides a function that allows users to switch between multiple skins depending on the situation or purpose. For example, different skins can be set for work and personal use, and easily switched between as needed. In this way, the Skin Interface section supports users in using chat tools comfortably and efficiently. Additionally, the Skin Interface section learns the user's operation history and usage patterns to suggest optimal customization options. For example, it can analyze frequently used functions and operations and suggest the optimal layout and design based on that analysis. This allows the skin interface to be flexibly customized to meet user needs, providing a more comfortable chat experience.

[0031] The Conversation Summary Unit uses AI to analyze multiple posts and conversations and create summaries. For example, it can use natural language processing techniques to extract important information and generate summaries. Specifically, it creates summaries based on keywords and phrases that frequently appear in conversations. For instance, it can extract important information about a specific topic from a conversation and summarize it concisely. The Conversation Summary Unit can also provide summaries to help users quickly grasp information about specific topics. For example, by analyzing the content of a conversation and concisely summarizing the key points, users can obtain the necessary information in a short amount of time. Furthermore, the Conversation Summary Unit has a function to update summaries as the conversation progresses. For example, if the content of the conversation changes, it can automatically update the summary to provide the latest information. This allows users to always stay informed and manage conversations efficiently. Additionally, the Conversation Summary Unit has a function to improve the accuracy of summaries based on user feedback. For example, when users provide feedback on the content of a summary, the AI ​​learns from that feedback and incorporates it into future summary generation. This allows the conversation summary unit to provide highly accurate summaries tailored to the user's needs.

[0032] The Dialogue Experience section supports information exchange in an intuitive one-on-one format for users. For example, when a user manages multiple conversations simultaneously, the Dialogue Experience section can display each conversation in a separate window. Specifically, the Dialogue Experience section provides a function to temporarily hide other conversations so that the user can concentrate on a particular conversation. For example, other conversations can be minimized or notifications can be temporarily turned off to concentrate on an important conversation. The Dialogue Experience section also has a function to organize and save conversation history so that users can quickly search past conversations. For example, past conversations can be searched based on specific keywords or dates, and necessary information can be quickly found. Furthermore, the Dialogue Experience section also provides a function to highlight important points in conversations so that users can easily grasp the content of the conversation visually. For example, by highlighting important statements or questions in a conversation, users can prevent missing important information. In this way, the Dialogue Experience section supports users in efficiently exchanging information and not missing important conversations. The Dialogue Experience section also has a function to learn the user's operation history and usage patterns and suggest the optimal conversation management method. For example, by analyzing the operations and functions that users frequently perform, the system can suggest optimal window layouts and notification settings based on that analysis. This allows the conversational experience unit to achieve flexible conversation management tailored to user needs and provide a more comfortable conversational experience.

[0033] The update feedback unit updates the summary and provides feedback as the conversation progresses. For example, it can automatically update the summary if the content of the conversation changes. Specifically, the update feedback unit updates the summary in real time so that users can always stay informed of the latest information. For example, it can instantly update the summary and notify the user if new information is added or the direction of the conversation changes. Furthermore, the update feedback unit ensures that users receive appropriate feedback according to the progress of the conversation. For example, it provides a notification function to prevent users from missing important points in the conversation. This allows users to efficiently manage the conversation without missing important information. In addition, the update feedback unit has a function to improve the accuracy of the summary based on user feedback. For example, by providing feedback on the content of the summary, the AI ​​can learn from that feedback and reflect it in future summary generation. This allows the update feedback unit to provide highly accurate summaries tailored to user needs. The update feedback unit also has a function to suggest appropriate actions to the user as the conversation progresses. For example, by suggesting the next action to take or providing helpful information during a conversation, the system supports users in efficiently progressing through the discussion. This allows the update feedback section to help users maintain a smooth conversation and avoid missing important information.

[0034] The skin interface unit can analyze the user's past operation history and propose the optimal skin layout. For example, the skin interface unit can propose the optimal layout based on the skin patterns the user has previously selected. For example, the skin interface unit can also propose a skin that prioritizes the functions the user frequently uses. For example, the skin interface unit can propose the most user-friendly layout based on the user's operation history. This improves usability by proposing the optimal skin layout based on the user's operation history. Some or all of the above processing in the skin interface unit may be performed using AI, for example, or without AI. For example, the skin interface unit can input user operation history data into a generating AI and have the generating AI propose the optimal skin layout.

[0035] The skin interface unit can select the optimal color scheme when customizing the skin, taking into account the user's visual fatigue level. For example, if the user is using it for a long time, the skin interface unit can select a color scheme that is easy on the eyes. For example, if the user is using it for a short time, the skin interface unit can also select a vibrant color scheme. For example, the skin interface unit can monitor the user's visual fatigue level in real time and select the optimal color scheme. By selecting a color scheme that takes the user's visual fatigue level into account, a comfortable visual experience can be provided even during prolonged use. Some or all of the above processing in the skin interface unit may be performed using AI, for example, or without AI. For example, the skin interface unit can input the user's visual fatigue level data into a generating AI and have the generating AI select the optimal color scheme.

[0036] The skin interface unit can select the optimal display method when customizing a skin, taking into account the user's device information. For example, if the user is using a smartphone, the skin interface unit provides a display method that matches the screen size. For example, if the user is using a tablet, the skin interface unit can also provide a display method optimized for a larger screen. For example, if the user is using a desktop, the skin interface unit can also provide a display method optimized for a wider screen. In this way, by selecting the optimal display method based on the user's device information, it is possible to provide the optimal display according to the device. Some or all of the above processing in the skin interface unit may be performed using AI, for example, or without AI. For example, the skin interface unit can input the user's device information into a generating AI and have the generating AI select the optimal display method.

[0037] The skin interface unit can analyze the user's social media activity and suggest relevant themes when customizing skins. For example, the skin interface unit can suggest skins that match the social media themes the user frequently uses. The skin interface unit can also suggest relevant themes based on the content of the user's social media posts. The skin interface unit can also suggest skins that match the time of day the user is active on social media. In this way, by suggesting relevant themes based on the user's social media activity, it is possible to provide skins that match the user's preferences. Some or all of the above processing in the skin interface unit may be performed using AI, for example, or without AI. For example, the skin interface unit can input the user's social media activity data into a generating AI and have the generating AI suggest relevant themes.

[0038] The conversation summary unit can adjust the level of detail in the summary based on the importance of the conversation when generating the summary. For example, the conversation summary unit provides a detailed summary for important conversations. For example, the conversation summary unit can also provide a concise summary for less important conversations. The conversation summary unit can also evaluate the importance of conversations in real time and provide a summary with the optimal level of detail. This allows for quick identification of important information by adjusting the level of detail in the summary based on the importance of the conversation. Some or all of the above processing in the conversation summary unit may be performed using AI, for example, or without AI. For example, the conversation summary unit can input conversation importance data into a generating AI and have the generating AI adjust the level of detail in the summary.

[0039] The conversation summary unit can apply different summarization algorithms depending on the category of the conversation when generating a summary. For example, in the case of a business conversation, the conversation summary unit can provide a summary that captures the main points. For example, in the case of a casual conversation, the conversation summary unit can also provide a summary using relaxed language. For example, in the case of a technical conversation, the conversation summary unit can also provide a detailed summary that includes technical terms. This allows for the provision of more appropriate summaries by applying different summarization algorithms depending on the category of the conversation. Some or all of the above processing in the conversation summary unit may be performed using AI, for example, or without AI. For example, the conversation summary unit can input conversation category data into a generating AI and have the generating AI execute the application of different summary algorithms.

[0040] The conversation summary unit can determine the priority of summaries based on when the conversations occurred during summary generation. For example, the conversation summary unit can prioritize summarizing recent conversations. It can also prioritize summarizing important past conversations. The conversation summary unit can also evaluate the timing of conversations in real time and provide summaries with the optimal priority. This allows for quick access to the latest information by determining the priority of summaries based on the timing of conversations. Some or all of the above processing in the conversation summary unit may be performed using AI, for example, or without AI. For example, the conversation summary unit can input conversation timing data into a generating AI and have the generating AI determine the priority of the summaries.

[0041] The conversation summary unit can adjust the order of summaries based on the relevance of the conversations when generating summaries. For example, the conversation summary unit can prioritize summarizing highly relevant conversations. For example, the conversation summary unit can also postpone summarizing less relevant conversations. For example, the conversation summary unit can evaluate the relevance of conversations in real time and provide summaries in the optimal order. This allows for quick identification of important information by adjusting the order of summaries based on the relevance of conversations. Some or all of the above processing in the conversation summary unit may be performed using AI, for example, or without AI. For example, the conversation summary unit can input conversation relevance data into a generating AI and have the generating AI perform the adjustment of the summary order.

[0042] The dialogue experience unit can analyze the user's past dialogue history during a dialogue experience and propose the optimal dialogue method. For example, the dialogue experience unit can propose the optimal dialogue method based on the dialogue method the user has preferred in the past. For example, the dialogue experience unit can propose the most effective dialogue method based on the user's past dialogue history. For example, the dialogue experience unit can analyze the user's dialogue history in real time and propose the optimal dialogue method. This improves usability by proposing the optimal dialogue method based on the user's past dialogue history. Some or all of the above processing in the dialogue experience unit may be performed using AI, for example, or without AI. For example, the dialogue experience unit can input the user's dialogue history data into a generating AI and have the generating AI propose the optimal dialogue method.

[0043] The dialogue experience unit can customize the content of the dialogue based on the user's current situation during the dialogue experience. For example, if the user is busy, the dialogue experience unit can provide concise dialogue. For example, if the user is relaxed, the dialogue experience unit can also provide detailed dialogue. For example, the dialogue experience unit can evaluate the user's current situation in real time and provide the most appropriate dialogue. This allows for more appropriate dialogue by customizing the content of the dialogue based on the user's current situation. Some or all of the above processing in the dialogue experience unit may be performed using AI, for example, or without AI. For example, the dialogue experience unit can input the user's current situation data into a generating AI and have the generating AI perform the customization of the dialogue content.

[0044] The dialogue experience unit can select the optimal dialogue method during a dialogue experience, taking into account the user's geographical location information. For example, if the user is outdoors, the dialogue experience unit can provide a concise dialogue method. For example, if the user is indoors, the dialogue experience unit can also provide a detailed dialogue method. The dialogue experience unit can also evaluate the user's geographical location information in real time and provide the optimal dialogue method. This improves usability by selecting the optimal dialogue method based on the user's geographical location information. Some or all of the above processing in the dialogue experience unit may be performed using AI, for example, or without AI. For example, the dialogue experience unit can input the user's geographical location information data into a generating AI and have the generating AI select the optimal dialogue method.

[0045] The dialogue experience unit can analyze the user's social media activity during a dialogue experience and suggest dialogue content. For example, the dialogue experience unit can suggest dialogue content based on the content of social media that the user frequently uses. For example, the dialogue experience unit can also suggest relevant dialogue content based on the content of the user's social media posts. For example, the dialogue experience unit can also suggest dialogue content that matches the time of day when the user is active on social media. In this way, by suggesting dialogue content based on the user's social media activity, it is possible to provide dialogue that suits the user's preferences. Some or all of the above processing in the dialogue experience unit may be performed using AI, for example, or without AI. For example, the dialogue experience unit can input the user's social media activity data into a generating AI and have the generating AI perform dialogue content suggestions.

[0046] The update feedback unit can adjust the content of the feedback based on the progress of the conversation when providing feedback. For example, the update feedback unit provides concise feedback when the conversation is in progress. For example, the update feedback unit can also provide detailed feedback when the conversation has ended. For example, the update feedback unit can evaluate the progress of the conversation in real time and provide optimal feedback. This allows for the provision of more appropriate feedback by adjusting the content of the feedback based on the progress of the conversation. Some or all of the above processing in the update feedback unit may be performed using AI, for example, or without AI. For example, the update feedback unit can input conversation progress data into a generating AI and have the generating AI perform the adjustment of the feedback content.

[0047] The update feedback unit can provide optimal feedback by referring to the user's past feedback history when providing feedback. For example, the update feedback unit can provide optimal feedback based on feedback the user has received in the past. For example, the update feedback unit can also provide the most effective feedback from the user's past feedback history. For example, the update feedback unit can analyze the user's feedback history in real time and provide optimal feedback. This improves usability by providing optimal feedback based on the user's past feedback history. Some or all of the above processing in the update feedback unit may be performed using AI, for example, or without AI. For example, the update feedback unit can input the user's feedback history data into a generating AI and have the generating AI perform the task of providing optimal feedback.

[0048] The update feedback unit can select the optimal feedback method when providing feedback, taking into account the user's device information. For example, if the user is using a smartphone, the update feedback unit can provide a feedback method that matches the screen size. For example, if the user is using a tablet, the update feedback unit can also provide a feedback method optimized for a larger screen. For example, if the user is using a desktop, the update feedback unit can also provide a feedback method optimized for a larger screen. This improves usability by selecting the optimal feedback method based on the user's device information. Some or all of the above processing in the update feedback unit may be performed using AI, for example, or without AI. For example, the update feedback unit can input the user's device information into a generating AI and have the generating AI select the optimal feedback method.

[0049] The update feedback unit can analyze the user's social media activity and suggest feedback content when providing feedback. For example, the update feedback unit can suggest feedback content based on the content of social media that the user frequently uses. For example, the update feedback unit can also suggest relevant feedback content based on the content of the user's social media posts. For example, the update feedback unit can also suggest feedback content that matches the time of day when the user is active on social media. This allows the system to provide feedback that is tailored to the user's preferences by suggesting feedback content based on the user's social media activity. Some or all of the above processing in the update feedback unit may be performed using AI, for example, or without AI. For example, the update feedback unit can input the user's social media activity data into a generating AI and have the generating AI suggest feedback content.

[0050] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0051] The conversation management system can also be equipped with a function to analyze the user's past conversation history and suggest the optimal way to proceed with the conversation. For example, it can suggest the optimal way to proceed based on the conversation methods the user has preferred in the past. It can also suggest the most effective way to proceed based on the user's past conversation history. Furthermore, it can analyze the user's conversation history in real time and suggest the optimal way to proceed. This improves usability by suggesting the optimal way to proceed with the conversation based on the user's past conversation history.

[0052] The conversation management system can also include features to customize the conversation content based on the user's current situation. For example, it can provide concise conversation content when the user is busy, and more detailed conversation content when the user is relaxed. Furthermore, it can assess the user's current situation in real time and provide the most appropriate conversation content. This allows for more appropriate conversations by customizing the content based on the user's current situation.

[0053] The conversation management system can also include a function to select the optimal conversation method by considering the user's geographical location. For example, it can provide a concise conversation method if the user is outdoors, and a more detailed conversation method if the user is indoors. Furthermore, it can evaluate the user's geographical location in real time and provide the optimal conversation method. This improves usability by selecting the optimal conversation method based on the user's geographical location.

[0054] The conversation management system can also include a function to analyze the user's social media activity and suggest conversation topics. For example, it can suggest conversation topics based on the social media content the user frequently uses. It can also suggest relevant conversation topics based on the user's social media posts. Furthermore, it can suggest conversation topics tailored to the user's social media activity times. This allows the system to provide conversations that match the user's preferences by suggesting conversation topics based on the user's social media activity.

[0055] The conversation management system can also include a function to select the optimal conversation method by considering the user's device information. For example, if the user is using a smartphone, it can provide a conversation method adapted to the screen size. If the user is using a tablet, it can provide a conversation method optimized for a larger screen. Furthermore, if the user is using a desktop, it can provide a conversation method optimized for a larger screen. This improves usability by selecting the optimal conversation method based on the user's device information.

[0056] The following briefly describes the processing flow for example form 1.

[0057] Step 1: The Skin Interface section applies a dedicated skin to an existing chat tool to customize its display. For example, a specific theme or design can be applied to the chat tool a user uses. The system can reflect the colors and layout selected by the user, and it can also change the font size and icon placement. This provides users with customization options to create a visually comfortable interface. Step 2: The conversation summary section uses AI to analyze multiple posts and conversations and create summaries. For example, it can use natural language processing technology to extract important information and generate summaries. It creates summaries based on keywords and phrases that frequently appear in conversations, allowing users to quickly grasp information about specific topics. Step 3: The conversational experience section supports information exchange in an intuitive one-on-one format for the user. For example, when a user is managing multiple conversations simultaneously, each conversation can be displayed in a separate window. It includes features to temporarily hide other conversations so that the user can focus on a specific conversation, and features to organize and save conversation history so that past conversations can be quickly searched. Step 4: The Update Feedback section updates the summary and provides feedback as the conversation progresses. For example, it automatically updates the summary when the content of the conversation changes, updating it in real time so that the user is always up-to-date. It also ensures that the user receives appropriate feedback according to the progress of the conversation.

[0058] (Example of form 2) The conversation management system according to an embodiment of the present invention is a system that makes it easier to manage multiple conversations, prevents information scattering, and reduces the time spent tracking individual posts. This conversation management system has a skin interface that allows users to customize the display by attaching a dedicated skin to an existing chat tool. This allows users to manage conversations with a visually organized interface. The conversation management system also has a conversation summary function in which AI analyzes multiple posts and conversations and creates a summary. This allows users to quickly grasp important information. Furthermore, the conversation management system has a dialogue experience function that supports information exchange in an intuitive one-to-one format for users. This allows users to track information without confusion even when multiple conversations are progressing simultaneously. In addition, the conversation management system has a real-time update and feedback function that updates the summary and provides feedback as the conversation progresses. This allows users to always stay up-to-date with the latest information. For example, when a user is managing multiple conversations simultaneously, the conversation management system automatically displays the important points of each conversation as a summary. The conversation management system also provides a function to temporarily hide other conversations when the user wants to focus on a specific conversation. This allows the user to concentrate on important conversations. Furthermore, the conversation management system includes features to organize and store conversation history, allowing users to quickly search past conversations. This enables users to quickly find the information they need. As a result, the conversation management system makes it easier to manage multiple conversations, prevents information from becoming scattered, and reduces the time it takes to track individual posts.

[0059] The conversation management system according to this embodiment comprises a skin interface unit, a conversation summary unit, a dialogue experience unit, and an update feedback unit. The skin interface unit applies a dedicated skin to an existing chat tool and customizes its display. For example, the skin interface unit can apply a specific theme or design to the chat tool used by the user. For example, the skin interface unit can reflect the colors and layout selected by the user. The skin interface unit can also change the font size and icon placement according to the user's preferences. For example, the skin interface unit provides customization options to create a visually comfortable interface for the user. The conversation summary unit uses AI to analyze multiple posts and conversations and create a summary. For example, the conversation summary unit can use natural language processing technology to extract important information and generate a summary. For example, the conversation summary unit creates a summary based on keywords and phrases that frequently appear in the conversation. The conversation summary unit can also provide a summary so that the user can quickly grasp information about a specific topic. For example, the conversation summary unit analyzes the content of the conversation and concisely summarizes the important points. The Dialogue Experience section supports information exchange in an intuitive one-on-one format for the user. For example, when a user manages multiple conversations simultaneously, the Dialogue Experience section can display each conversation in a separate window. For example, the Dialogue Experience section provides a function to temporarily hide other conversations so that the user can concentrate on a specific conversation. The Dialogue Experience section also has a function to organize and save conversation history so that the user can quickly search past conversations. For example, the Dialogue Experience section provides a search function so that the user can quickly find the information they need. The Update Feedback section updates the summary and provides feedback as the conversation progresses. For example, the Update Feedback section can automatically update the summary when the content of the conversation changes. For example, the Update Feedback section updates the summary in real time so that the user is always aware of the latest information.Furthermore, the update feedback unit ensures that users receive appropriate feedback according to the progress of the conversation. For example, the update feedback unit provides a notification function to ensure that users do not miss important points in the conversation. This makes it easier to manage multiple conversations, prevents information from becoming scattered, and reduces the time required to track individual posts.

[0060] The Skin Interface section customizes the display of existing chat tools by applying dedicated skins. For example, the Skin Interface section can apply specific themes and designs to the chat tool a user uses. Specifically, it can reflect the user's chosen colors and layouts. For instance, users can personalize the appearance of their chat tool by setting their preferred color scheme and background image. The Skin Interface section can also change font size and icon placement according to user preferences. For example, to create a visually pleasing interface, the font size can be increased or icons can be moved to an intuitively understandable position. Furthermore, the Skin Interface section provides a function that allows users to switch between multiple skins depending on the situation or purpose. For example, different skins can be set for work and personal use, and easily switched between as needed. In this way, the Skin Interface section supports users in using chat tools comfortably and efficiently. Additionally, the Skin Interface section learns the user's operation history and usage patterns to suggest optimal customization options. For example, it can analyze frequently used functions and operations and suggest the optimal layout and design based on that analysis. This allows the skin interface to be flexibly customized to meet user needs, providing a more comfortable chat experience.

[0061] The Conversation Summary Unit uses AI to analyze multiple posts and conversations and create summaries. For example, it can use natural language processing techniques to extract important information and generate summaries. Specifically, it creates summaries based on keywords and phrases that frequently appear in conversations. For instance, it can extract important information about a specific topic from a conversation and summarize it concisely. The Conversation Summary Unit can also provide summaries to help users quickly grasp information about specific topics. For example, by analyzing the content of a conversation and concisely summarizing the key points, users can obtain the necessary information in a short amount of time. Furthermore, the Conversation Summary Unit has a function to update summaries as the conversation progresses. For example, if the content of the conversation changes, it can automatically update the summary to provide the latest information. This allows users to always stay informed and manage conversations efficiently. Additionally, the Conversation Summary Unit has a function to improve the accuracy of summaries based on user feedback. For example, when users provide feedback on the content of a summary, the AI ​​learns from that feedback and incorporates it into future summary generation. This allows the conversation summary unit to provide highly accurate summaries tailored to the user's needs.

[0062] The Dialogue Experience section supports information exchange in an intuitive one-on-one format for users. For example, when a user manages multiple conversations simultaneously, the Dialogue Experience section can display each conversation in a separate window. Specifically, the Dialogue Experience section provides a function to temporarily hide other conversations so that the user can concentrate on a particular conversation. For example, other conversations can be minimized or notifications can be temporarily turned off to concentrate on an important conversation. The Dialogue Experience section also has a function to organize and save conversation history so that users can quickly search past conversations. For example, past conversations can be searched based on specific keywords or dates, and necessary information can be quickly found. Furthermore, the Dialogue Experience section also provides a function to highlight important points in conversations so that users can easily grasp the content of the conversation visually. For example, by highlighting important statements or questions in a conversation, users can prevent missing important information. In this way, the Dialogue Experience section supports users in efficiently exchanging information and not missing important conversations. The Dialogue Experience section also has a function to learn the user's operation history and usage patterns and suggest the optimal conversation management method. For example, by analyzing the operations and functions that users frequently perform, the system can suggest optimal window layouts and notification settings based on that analysis. This allows the conversational experience unit to achieve flexible conversation management tailored to user needs and provide a more comfortable conversational experience.

[0063] The update feedback unit updates the summary and provides feedback as the conversation progresses. For example, it can automatically update the summary if the content of the conversation changes. Specifically, the update feedback unit updates the summary in real time so that users can always stay informed of the latest information. For example, it can instantly update the summary and notify the user if new information is added or the direction of the conversation changes. Furthermore, the update feedback unit ensures that users receive appropriate feedback according to the progress of the conversation. For example, it provides a notification function to prevent users from missing important points in the conversation. This allows users to efficiently manage the conversation without missing important information. In addition, the update feedback unit has a function to improve the accuracy of the summary based on user feedback. For example, by providing feedback on the content of the summary, the AI ​​can learn from that feedback and reflect it in future summary generation. This allows the update feedback unit to provide highly accurate summaries tailored to user needs. The update feedback unit also has a function to suggest appropriate actions to the user as the conversation progresses. For example, by suggesting the next action to take or providing helpful information during a conversation, the system supports users in efficiently progressing through the discussion. This allows the update feedback section to help users maintain a smooth conversation and avoid missing important information.

[0064] The skin interface unit can estimate the user's emotions and dynamically change the skin's color and design based on the estimated emotions. For example, if the user is stressed, the skin interface unit can change to a calmer color scheme. For example, if the user is relaxed, the skin interface unit can also change to a brighter color scheme. For example, if the user is excited, the skin interface unit can change to a visually stimulating design. This allows for a more comfortable user experience by changing the skin's color and design according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the skin interface unit may be performed using AI, or not using AI. For example, the skin interface unit can input user facial expression data into the generative AI and have the generative AI perform the estimation of the user's emotions.

[0065] The skin interface unit can analyze the user's past operation history and propose the optimal skin layout. For example, the skin interface unit can propose the optimal layout based on the skin patterns the user has previously selected. For example, the skin interface unit can also propose a skin that prioritizes the functions the user frequently uses. For example, the skin interface unit can propose the most user-friendly layout based on the user's operation history. This improves usability by proposing the optimal skin layout based on the user's operation history. Some or all of the above processing in the skin interface unit may be performed using AI, for example, or without AI. For example, the skin interface unit can input user operation history data into a generating AI and have the generating AI propose the optimal skin layout.

[0066] The skin interface unit can select the optimal color scheme when customizing the skin, taking into account the user's visual fatigue level. For example, if the user is using it for a long time, the skin interface unit can select a color scheme that is easy on the eyes. For example, if the user is using it for a short time, the skin interface unit can also select a vibrant color scheme. For example, the skin interface unit can monitor the user's visual fatigue level in real time and select the optimal color scheme. By selecting a color scheme that takes the user's visual fatigue level into account, a comfortable visual experience can be provided even during prolonged use. Some or all of the above processing in the skin interface unit may be performed using AI, for example, or without AI. For example, the skin interface unit can input the user's visual fatigue level data into a generating AI and have the generating AI select the optimal color scheme.

[0067] The skin interface unit can estimate the user's emotions and change the skin theme based on the estimated emotions. For example, if the user is sad, the skin interface unit can change to a calm theme. For example, if the user is happy, the skin interface unit can also change to a cheerful theme. For example, if the user is angry, the skin interface unit can also change to a calming theme. This allows for a more comfortable user experience by changing the skin theme according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI 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 skin interface unit may be performed using AI, or not using AI. For example, the skin interface unit can input user facial expression data into the generative AI and have the generative AI perform the estimation of the user's emotions.

[0068] The skin interface unit can select the optimal display method when customizing a skin, taking into account the user's device information. For example, if the user is using a smartphone, the skin interface unit provides a display method that matches the screen size. For example, if the user is using a tablet, the skin interface unit can also provide a display method optimized for a larger screen. For example, if the user is using a desktop, the skin interface unit can also provide a display method optimized for a wider screen. In this way, by selecting the optimal display method based on the user's device information, it is possible to provide the optimal display according to the device. Some or all of the above processing in the skin interface unit may be performed using AI, for example, or without AI. For example, the skin interface unit can input the user's device information into a generating AI and have the generating AI select the optimal display method.

[0069] The skin interface unit can analyze the user's social media activity and suggest relevant themes when customizing skins. For example, the skin interface unit can suggest skins that match the social media themes the user frequently uses. The skin interface unit can also suggest relevant themes based on the content of the user's social media posts. The skin interface unit can also suggest skins that match the time of day the user is active on social media. In this way, by suggesting relevant themes based on the user's social media activity, it is possible to provide skins that match the user's preferences. Some or all of the above processing in the skin interface unit may be performed using AI, for example, or without AI. For example, the skin interface unit can input the user's social media activity data into a generating AI and have the generating AI suggest relevant themes.

[0070] The conversation summary unit can estimate the user's emotions and adjust the way the summary is presented based on the estimated emotions. For example, if the user is stressed, the conversation summary unit can provide a concise and to-the-point summary. If the user is relaxed, the conversation summary unit can also provide a detailed summary. If the user is excited, the conversation summary unit can also provide a visually appealing summary. By adjusting the way the summary is presented according to the user's emotions, a more appropriate summary can be provided. 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 summary unit may be performed using AI or not. For example, the conversation summary unit can input user facial expression data into the generative AI and have the generative AI perform the estimation of the user's emotions.

[0071] The conversation summary unit can adjust the level of detail in the summary based on the importance of the conversation when generating the summary. For example, the conversation summary unit provides a detailed summary for important conversations. For example, the conversation summary unit can also provide a concise summary for less important conversations. The conversation summary unit can also evaluate the importance of conversations in real time and provide a summary with the optimal level of detail. This allows for quick identification of important information by adjusting the level of detail in the summary based on the importance of the conversation. Some or all of the above processing in the conversation summary unit may be performed using AI, for example, or without AI. For example, the conversation summary unit can input conversation importance data into a generating AI and have the generating AI adjust the level of detail in the summary.

[0072] The conversation summary unit can apply different summarization algorithms depending on the category of the conversation when generating a summary. For example, in the case of a business conversation, the conversation summary unit can provide a summary that captures the main points. For example, in the case of a casual conversation, the conversation summary unit can also provide a summary using relaxed language. For example, in the case of a technical conversation, the conversation summary unit can also provide a detailed summary that includes technical terms. This allows for the provision of more appropriate summaries by applying different summarization algorithms depending on the category of the conversation. Some or all of the above processing in the conversation summary unit may be performed using AI, for example, or without AI. For example, the conversation summary unit can input conversation category data into a generating AI and have the generating AI execute the application of different summary algorithms.

[0073] The conversation summary unit can estimate the user's emotions and adjust the length of the summary based on the estimated emotions. For example, if the user is in a hurry, the conversation summary unit can provide a short, concise summary. If the user is relaxed, the conversation summary unit can also provide a detailed summary. If the user is excited, the conversation summary unit can also provide a visually appealing summary. By adjusting the length of the summary according to the user's emotions, a more appropriate summary can be provided. 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 summary unit may be performed using AI or not using AI. For example, the conversation summary unit can input user facial expression data into the generative AI and have the generative AI perform the estimation of the user's emotions.

[0074] The conversation summary unit can determine the priority of summaries based on when the conversations occurred during summary generation. For example, the conversation summary unit can prioritize summarizing recent conversations. It can also prioritize summarizing important past conversations. The conversation summary unit can also evaluate the timing of conversations in real time and provide summaries with the optimal priority. This allows for quick access to the latest information by determining the priority of summaries based on the timing of conversations. Some or all of the above processing in the conversation summary unit may be performed using AI, for example, or without AI. For example, the conversation summary unit can input conversation timing data into a generating AI and have the generating AI determine the priority of the summaries.

[0075] The conversation summary unit can adjust the order of summaries based on the relevance of the conversations when generating summaries. For example, the conversation summary unit can prioritize summarizing highly relevant conversations. For example, the conversation summary unit can also postpone summarizing less relevant conversations. For example, the conversation summary unit can evaluate the relevance of conversations in real time and provide summaries in the optimal order. This allows for quick identification of important information by adjusting the order of summaries based on the relevance of conversations. Some or all of the above processing in the conversation summary unit may be performed using AI, for example, or without AI. For example, the conversation summary unit can input conversation relevance data into a generating AI and have the generating AI perform the adjustment of the summary order.

[0076] The dialogue experience unit can estimate the user's emotions and adjust the way the dialogue is expressed based on the estimated emotions. For example, if the user is nervous, the dialogue experience unit can provide a calm expression. For example, if the user is relaxed, the dialogue experience unit can also provide a cheerful expression. For example, if the user is excited, the dialogue experience unit can also provide a visually appealing expression. By adjusting the way the dialogue is expressed according to the user's emotions, a more appropriate dialogue can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The 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 dialogue experience unit may be performed using AI, for example, or without AI. For example, the dialogue experience unit can input user facial expression data into the generative AI and have the generative AI perform the estimation of the user's emotions.

[0077] The dialogue experience unit can analyze the user's past dialogue history during a dialogue experience and propose the optimal dialogue method. For example, the dialogue experience unit can propose the optimal dialogue method based on the dialogue method the user has preferred in the past. For example, the dialogue experience unit can propose the most effective dialogue method based on the user's past dialogue history. For example, the dialogue experience unit can analyze the user's dialogue history in real time and propose the optimal dialogue method. This improves usability by proposing the optimal dialogue method based on the user's past dialogue history. Some or all of the above processing in the dialogue experience unit may be performed using AI, for example, or without AI. For example, the dialogue experience unit can input the user's dialogue history data into a generating AI and have the generating AI propose the optimal dialogue method.

[0078] The dialogue experience unit can customize the content of the dialogue based on the user's current situation during the dialogue experience. For example, if the user is busy, the dialogue experience unit can provide concise dialogue. For example, if the user is relaxed, the dialogue experience unit can also provide detailed dialogue. For example, the dialogue experience unit can evaluate the user's current situation in real time and provide the most appropriate dialogue. This allows for more appropriate dialogue by customizing the content of the dialogue based on the user's current situation. Some or all of the above processing in the dialogue experience unit may be performed using AI, for example, or without AI. For example, the dialogue experience unit can input the user's current situation data into a generating AI and have the generating AI perform the customization of the dialogue content.

[0079] The dialogue experience unit can estimate the user's emotions and determine the priority of conversations based on the estimated emotions. For example, the dialogue experience unit will prioritize conversations if the user needs an urgent conversation. For example, the dialogue experience unit can also provide normal conversations if the user is relaxed. For example, the dialogue experience unit can evaluate the user's emotions in real time and provide conversations with the optimal priority. This allows for the provision of more appropriate conversations by determining the priority of conversations according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI 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 dialogue experience unit may be performed using AI or not using AI. For example, the dialogue experience unit can input user facial expression data into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0080] The dialogue experience unit can select the optimal dialogue method during a dialogue experience, taking into account the user's geographical location information. For example, if the user is outdoors, the dialogue experience unit can provide a concise dialogue method. For example, if the user is indoors, the dialogue experience unit can also provide a detailed dialogue method. The dialogue experience unit can also evaluate the user's geographical location information in real time and provide the optimal dialogue method. This improves usability by selecting the optimal dialogue method based on the user's geographical location information. Some or all of the above processing in the dialogue experience unit may be performed using AI, for example, or without AI. For example, the dialogue experience unit can input the user's geographical location information data into a generating AI and have the generating AI select the optimal dialogue method.

[0081] The dialogue experience unit can analyze the user's social media activity during a dialogue experience and suggest dialogue content. For example, the dialogue experience unit can suggest dialogue content based on the content of social media that the user frequently uses. For example, the dialogue experience unit can also suggest relevant dialogue content based on the content of the user's social media posts. For example, the dialogue experience unit can also suggest dialogue content that matches the time of day when the user is active on social media. In this way, by suggesting dialogue content based on the user's social media activity, it is possible to provide dialogue that suits the user's preferences. Some or all of the above processing in the dialogue experience unit may be performed using AI, for example, or without AI. For example, the dialogue experience unit can input the user's social media activity data into a generating AI and have the generating AI perform dialogue content suggestions.

[0082] The update feedback unit can estimate the user's emotions and adjust the way feedback is presented based on the estimated emotions. For example, if the user is stressed, the update feedback unit can provide concise and to-the-point feedback. For example, if the user is relaxed, the update feedback unit can also provide detailed feedback. For example, if the user is excited, the update feedback unit can also provide visually appealing feedback. This allows for more appropriate feedback to be provided by adjusting the way feedback is presented according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI 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 update feedback unit may be performed using AI, for example, or without AI. For example, the update feedback unit can input user facial expression data into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0083] The update feedback unit can adjust the content of the feedback based on the progress of the conversation when providing feedback. For example, the update feedback unit provides concise feedback when the conversation is in progress. For example, the update feedback unit can also provide detailed feedback when the conversation has ended. For example, the update feedback unit can evaluate the progress of the conversation in real time and provide optimal feedback. This allows for the provision of more appropriate feedback by adjusting the content of the feedback based on the progress of the conversation. Some or all of the above processing in the update feedback unit may be performed using AI, for example, or without AI. For example, the update feedback unit can input conversation progress data into a generating AI and have the generating AI perform the adjustment of the feedback content.

[0084] The update feedback unit can provide optimal feedback by referring to the user's past feedback history when providing feedback. For example, the update feedback unit can provide optimal feedback based on feedback the user has received in the past. For example, the update feedback unit can also provide the most effective feedback from the user's past feedback history. For example, the update feedback unit can analyze the user's feedback history in real time and provide optimal feedback. This improves usability by providing optimal feedback based on the user's past feedback history. Some or all of the above processing in the update feedback unit may be performed using AI, for example, or without AI. For example, the update feedback unit can input the user's feedback history data into a generating AI and have the generating AI perform the task of providing optimal feedback.

[0085] The update feedback unit can estimate the user's emotions and determine the priority of feedback based on the estimated user emotions. For example, the update feedback unit will provide priority feedback if the user needs urgent feedback. For example, the update feedback unit can also provide normal feedback if the user is relaxed. For example, the update feedback unit can evaluate the user's emotions in real time and provide feedback with the optimal priority. This allows for more appropriate feedback to be provided by determining the priority of feedback according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI 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 update feedback unit may be performed using AI or not using AI. For example, the update feedback unit can input user facial expression data into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0086] The update feedback unit can select the optimal feedback method when providing feedback, taking into account the user's device information. For example, if the user is using a smartphone, the update feedback unit can provide a feedback method that matches the screen size. For example, if the user is using a tablet, the update feedback unit can also provide a feedback method optimized for a larger screen. For example, if the user is using a desktop, the update feedback unit can also provide a feedback method optimized for a larger screen. This improves usability by selecting the optimal feedback method based on the user's device information. Some or all of the above processing in the update feedback unit may be performed using AI, for example, or without AI. For example, the update feedback unit can input the user's device information into a generating AI and have the generating AI select the optimal feedback method.

[0087] The update feedback unit can analyze the user's social media activity and suggest feedback content when providing feedback. For example, the update feedback unit can suggest feedback content based on the content of social media that the user frequently uses. For example, the update feedback unit can also suggest relevant feedback content based on the content of the user's social media posts. For example, the update feedback unit can also suggest feedback content that matches the time of day when the user is active on social media. This allows the system to provide feedback that is tailored to the user's preferences by suggesting feedback content based on the user's social media activity. Some or all of the above processing in the update feedback unit may be performed using AI, for example, or without AI. For example, the update feedback unit can input the user's social media activity data into a generating AI and have the generating AI suggest feedback content.

[0088] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0089] The conversation management system can also be equipped with the ability to estimate the user's emotions and dynamically change the priority of conversations based on those emotions. For example, if the user is stressed, important conversations can be displayed preferentially to allow the user to respond quickly. If the user is relaxed, less important conversations can be displayed preferentially to allow the user to enjoy the conversation in a relaxed state. Furthermore, if the user is excited, visually appealing conversations can be displayed preferentially to maintain the user's excitement. In this way, by dynamically changing the priority of conversations according to the user's emotions, more appropriate conversation management can be provided.

[0090] The conversation management system can also be equipped with a function to analyze the user's past conversation history and suggest the optimal way to proceed with the conversation. For example, it can suggest the optimal way to proceed based on the conversation methods the user has preferred in the past. It can also suggest the most effective way to proceed based on the user's past conversation history. Furthermore, it can analyze the user's conversation history in real time and suggest the optimal way to proceed. This improves usability by suggesting the optimal way to proceed with the conversation based on the user's past conversation history.

[0091] The conversation management system can also include features to customize the conversation content based on the user's current situation. For example, it can provide concise conversation content when the user is busy, and more detailed conversation content when the user is relaxed. Furthermore, it can assess the user's current situation in real time and provide the most appropriate conversation content. This allows for more appropriate conversations by customizing the content based on the user's current situation.

[0092] The conversation management system can also include a function to select the optimal conversation method by considering the user's geographical location. For example, it can provide a concise conversation method if the user is outdoors, and a more detailed conversation method if the user is indoors. Furthermore, it can evaluate the user's geographical location in real time and provide the optimal conversation method. This improves usability by selecting the optimal conversation method based on the user's geographical location.

[0093] The conversation management system can also include a function to analyze the user's social media activity and suggest conversation topics. For example, it can suggest conversation topics based on the social media content the user frequently uses. It can also suggest relevant conversation topics based on the user's social media posts. Furthermore, it can suggest conversation topics tailored to the user's social media activity times. This allows the system to provide conversations that match the user's preferences by suggesting conversation topics based on the user's social media activity.

[0094] The conversation management system can also be equipped with the ability to estimate the user's emotions and adjust the way the conversation is expressed based on those emotions. For example, if the user is nervous, it can provide a calm expression. If the user is relaxed, it can provide a cheerful expression. Furthermore, if the user is excited, it can provide a visually appealing expression. By adjusting the way the conversation is expressed according to the user's emotions, it can provide a more appropriate conversation.

[0095] The conversation management system can also be equipped with the ability to estimate the user's emotions and prioritize conversations based on those emotions. For example, if a user needs an urgent conversation, it can be prioritized. Conversely, if the user is relaxed, a normal conversation can be provided. Furthermore, it can evaluate the user's emotions in real time and provide conversations with the optimal priority. This allows for more appropriate conversations to be provided by prioritizing conversations according to the user's emotions.

[0096] The conversation management system can also be equipped with the ability to estimate the user's emotions and adjust the length of the conversation based on those emotions. For example, if the user is in a hurry, it can provide a short, to-the-point conversation. If the user is relaxed, it can provide a more detailed conversation. Furthermore, if the user is excited, it can provide a visually engaging conversation. By adjusting the length of the conversation according to the user's emotions, it is possible to provide a more appropriate conversation.

[0097] The conversation management system can also be equipped with the ability to estimate the user's emotions and adjust the way feedback is delivered based on those emotions. For example, if the user is stressed, it can provide concise and to-the-point feedback. If the user is relaxed, it can provide detailed feedback. Furthermore, if the user is excited, it can provide visually appealing feedback. This allows for more appropriate feedback to be provided by adjusting the way feedback is presented according to the user's emotions.

[0098] The conversation management system can also include a function to select the optimal conversation method by considering the user's device information. For example, if the user is using a smartphone, it can provide a conversation method adapted to the screen size. If the user is using a tablet, it can provide a conversation method optimized for a larger screen. Furthermore, if the user is using a desktop, it can provide a conversation method optimized for a larger screen. This improves usability by selecting the optimal conversation method based on the user's device information.

[0099] The following briefly describes the processing flow for example form 2.

[0100] Step 1: The Skin Interface section applies a dedicated skin to an existing chat tool to customize its display. For example, a specific theme or design can be applied to the chat tool a user uses. The system can reflect the colors and layout selected by the user, and it can also change the font size and icon placement. This provides users with customization options to create a visually comfortable interface. Step 2: The conversation summary section uses AI to analyze multiple posts and conversations and create summaries. For example, it can use natural language processing technology to extract important information and generate summaries. It creates summaries based on keywords and phrases that frequently appear in conversations, allowing users to quickly grasp information about specific topics. Step 3: The conversational experience section supports information exchange in an intuitive one-on-one format for the user. For example, when a user is managing multiple conversations simultaneously, each conversation can be displayed in a separate window. It includes features to temporarily hide other conversations so that the user can focus on a specific conversation, and features to organize and save conversation history so that past conversations can be quickly searched. Step 4: The Update Feedback section updates the summary and provides feedback as the conversation progresses. For example, it automatically updates the summary when the content of the conversation changes, updating it in real time so that the user is always up-to-date. It also ensures that the user receives appropriate feedback according to the progress of the conversation.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] Each of the multiple elements described above, including the skin interface unit, conversation summary unit, dialogue experience unit, and update feedback unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the skin interface unit is implemented by the control unit 46A of the smart device 14 and applies a specific theme or design to the chat tool used by the user. The conversation summary unit is implemented by the specific processing unit 290 of the data processing unit 12 and uses AI to analyze multiple posts and conversations and create a summary. The dialogue experience unit is implemented by the control unit 46A of the smart device 14 and supports information exchange in an intuitive one-to-one format for the user. The update feedback unit is implemented by the specific processing unit 290 of the data processing unit 12 and updates the summary in accordance with the progress of the conversation 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 changed in various ways.

[0105] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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).

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.).

[0117] 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.

[0118] 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.

[0119] 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.

[0120] Each of the multiple elements described above, including the skin interface unit, conversation summary unit, dialogue experience unit, and update feedback unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the skin interface unit is implemented by the control unit 46A of the smart glasses 214 and applies a specific theme or design to the chat tool used by the user. The conversation summary unit is implemented by the specific processing unit 290 of the data processing unit 12 and uses AI to analyze multiple posts and conversations and create a summary. The dialogue experience unit is implemented by the control unit 46A of the smart glasses 214 and supports information exchange in an intuitive one-to-one format for the user. The update feedback unit is implemented by the specific processing unit 290 of the data processing unit 12 and updates the summary in accordance with the progress of the conversation 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 changed in various ways.

[0121] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] Each of the multiple elements described above, including the skin interface unit, conversation summary unit, dialogue experience unit, and update feedback unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the skin interface unit is implemented by the control unit 46A of the headset terminal 314 and applies a specific theme or design to the chat tool used by the user. The conversation summary unit is implemented by the specific processing unit 290 of the data processing unit 12 and uses AI to analyze multiple posts and conversations and create a summary. The dialogue experience unit is implemented by the control unit 46A of the headset terminal 314 and supports information exchange in an intuitive one-to-one format for the user. The update feedback unit is implemented by the specific processing unit 290 of the data processing unit 12 and updates the summary in accordance with the progress of the conversation 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 changed in various ways.

[0137] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.).

[0150] 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.

[0151] 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.

[0152] 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.

[0153] Each of the multiple elements described above, including the skin interface unit, conversation summary unit, dialogue experience unit, and update feedback unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the skin interface unit is implemented by the control unit 46A of the robot 414 and applies a specific theme or design to the chat tool used by the user. The conversation summary unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and uses AI to analyze multiple posts and conversations and create a summary. The dialogue experience unit is implemented by, for example, the control unit 46A of the robot 414 and supports information exchange in an intuitive one-to-one format for the user. The update feedback unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and updates the summary in accordance with the progress of the conversation 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 changed in various ways.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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."

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] (Note 1) A skin interface section that allows you to customize the display by applying a dedicated skin to an existing chat tool, The conversation summary section analyzes multiple posts and conversations to create summaries, The conversational experience section supports information exchange in an intuitive one-on-one format for the user, It includes an update feedback unit that updates the summary in accordance with the progress of the conversation and provides feedback. A system characterized by the following features. (Note 2) The aforementioned skin interface section is It estimates the user's emotions and dynamically changes the skin's color and design based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned skin interface section is It analyzes the user's past operation history and suggests the optimal skin layout. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned skin interface section is When customizing the skin, the optimal color scheme is selected considering the user's visual fatigue. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned skin interface section is It estimates the user's emotions and changes the skin theme based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned skin interface section is When customizing the skin, the optimal display method is selected considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned skin interface section is When customizing your skin, the system analyzes your social media activity and suggests relevant themes. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned conversation summary section is, It estimates the user's emotions and adjusts how the summary is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned conversation summary section is, When generating a summary, adjust the level of detail in the summary based on the importance of the conversation. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned conversation summary section is, When generating a summary, different summary algorithms are applied depending on the category of the conversation. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned conversation summary section is, It estimates the user's emotions and adjusts the length of the summary based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned conversation summary section is, When generating summaries, prioritize the summaries based on when the conversations occurred. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned conversation summary section is, When generating summaries, the order of the summaries is adjusted based on the relevance of the conversation. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned dialogue experience unit is It estimates the user's emotions and adjusts the way the dialogue is expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned dialogue experience unit is During the conversational experience, the system analyzes the user's past conversation history to suggest the optimal way to communicate. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned dialogue experience unit is During the conversational experience, the content of the conversation is customized based on the user's current situation. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned dialogue experience unit is It estimates the user's emotions and determines the priority of the conversation based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned dialogue experience unit is During the conversational experience, the system selects the optimal conversation method by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned dialogue experience unit is During the conversational experience, the system analyzes the user's social media activity and suggests conversational content. The system described in Appendix 1, characterized by the features described herein. (Note 20) The update feedback unit described above is: It estimates the user's emotions and adjusts how feedback is expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The update feedback unit described above is: When providing feedback, adjust the content of the feedback based on the progress of the conversation. The system described in Appendix 1, characterized by the features described herein. (Note 22) The update feedback unit described above is: When providing feedback, we refer to the user's past feedback history to provide the most appropriate feedback. The system described in Appendix 1, characterized by the features described herein. (Note 23) The update feedback unit described above is: It estimates the user's emotions and prioritizes feedback based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The update feedback unit described above is: When providing feedback, the optimal feedback method is selected considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 25) The update feedback unit described above is: When providing feedback, we analyze the user's social media activity and suggest content for the feedback. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0173] 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 skin interface section that allows you to customize the display by applying a dedicated skin to an existing chat tool, The conversation summary section analyzes multiple posts and conversations to create summaries, The conversational experience section supports information exchange in an intuitive one-on-one format for the user, It includes an update feedback unit that updates the summary in accordance with the progress of the conversation and provides feedback. A system characterized by the following features.

2. The aforementioned skin interface section is It estimates the user's emotions and dynamically changes the skin's color and design based on those estimated emotions. The system according to feature 1.

3. The aforementioned skin interface section is It analyzes the user's past operation history and suggests the optimal skin layout. The system according to feature 1.

4. The aforementioned skin interface section is When customizing the skin, the optimal color scheme is selected considering the user's visual fatigue. The system according to feature 1.

5. The aforementioned skin interface section is It estimates the user's emotions and changes the skin theme based on those emotions. The system according to feature 1.

6. The aforementioned skin interface section is When customizing the skin, the optimal display method is selected considering the user's device information. The system according to feature 1.

7. The aforementioned skin interface section is When customizing your skin, the system analyzes your social media activity and suggests relevant themes. The system according to feature 1.

8. The aforementioned conversation summary section is, It estimates the user's emotions and adjusts how the summary is presented based on those estimated emotions. The system according to feature 1.

9. The aforementioned conversation summary section is, When generating a summary, adjust the level of detail in the summary based on the importance of the conversation. The system according to feature 1.

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