system

Abstract

The system according to this embodiment aims to enable quick message confirmation and response even when away from the PC. [Solution] The system according to the embodiment comprises a receiving unit, a text conversion unit, and an integration unit. The receiving unit reads messages from a chat tool aloud using Bluetooth earphones. The text conversion unit responds verbally based on the messages received by the receiving unit, and a generating AI converts the content into text and immediately posts it to the chat tool. The integration unit integrates multiple digital communication tools using existing communication tools and APIs.

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 a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is difficult to check and respond to messages when away from a PC, and since multiple communication means are not integrated, there is a risk of response delay.

[0005] The system according to the embodiment aims to quickly check and respond to messages even when away from a PC.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a receiving unit, a text conversion unit, and an integration unit. The receiving unit reads messages from a chat tool aloud using Bluetooth® earphones. The text conversion unit responds verbally based on the messages received by the receiving unit, and a generating AI converts the content into text and immediately posts it to the chat tool. The integration unit integrates multiple digital communication tools using existing communication tools and APIs. [Effects of the Invention]

[0007] The system according to this embodiment allows for quick message confirmation and response even when away from the PC. [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 labeled communication I / F (Interface) is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applicable 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 message management system according to an embodiment of the present invention is a system that allows users to check notifications when they leave their PC in a secure environment or a different operating environment. This message management system uses Bluetooth earphones and an internal telephone to receive messages from a chat tool by voice. Next, the user replies by voice, and a generating AI converts the content into text and immediately posts it to the chat tool. Furthermore, by using existing communication tools and APIs to integrate multiple digital communication tools and coordinating with the internal telephone system, a secure means of communication is ensured. With this system, even when working away from a PC, it is possible to check and respond to messages via Bluetooth earphones and an internal telephone, allowing for immediate confirmation and reply to important messages no matter where you are. In addition, in environments where security is required, secure message communication can be achieved using the internal telephone. For example, messages from a chat tool are received by voice using Bluetooth earphones and an internal telephone. In this case, the specific protocol and communication means for the coordination between the Bluetooth earphones and the internal telephone will be described. Next, the user replies by voice, and a generating AI converts the content into text and immediately posts it to the chat tool. The process by which the generating AI converts voice to text will also be described in detail. Furthermore, by integrating multiple digital communication tools using existing communication tools and APIs, and linking with the internal telephone system, a secure means of communication is ensured. This system allows users to check and respond to messages via Bluetooth earphones and internal telephones even when working away from their PCs, enabling immediate confirmation and replies to important messages from anywhere. It also enables secure message communication using internal telephones in environments where security is paramount. As a result, the message management system allows users to check and respond to messages via Bluetooth earphones and internal telephones even when working away from their PCs.

[0029] The message management system according to this embodiment comprises a receiving unit, a text conversion unit, and an integration unit. The receiving unit reads messages from a chat tool aloud using Bluetooth earphones. The receiving unit can, for example, read messages from a chat tool aloud using Bluetooth earphones. The receiving unit can also receive messages via an internal telephone. For example, the receiving unit can receive messages via an internal telephone and read them aloud using Bluetooth earphones. Furthermore, the receiving unit may also include a coordination unit that specifically describes how the Bluetooth earphones and the internal telephones work together. For example, the receiving unit may include a coordination unit that describes the pairing procedure and communication protocol between the Bluetooth earphones and the internal telephones. The text conversion unit responds aloud based on the messages received by the receiving unit, and a generating AI converts the content into text and immediately posts it to the chat tool. The text conversion unit can, for example, respond aloud, and a generating AI converts the content into text and immediately posts it to the chat tool. Furthermore, the text conversion unit may also include a process unit that describes the process by which the generating AI converts speech into text. For example, the text conversion unit includes a process unit that describes the specific process and procedures by which the generation AI converts speech into text. The integration unit integrates multiple digital communication tools using existing communication tools and APIs. The integration unit can, for example, integrate multiple digital communication tools using existing communication tools and APIs. The integration unit can also cooperate with an internal telephone system. For example, the integration unit can cooperate with an internal telephone system to ensure secure communication. As a result, the message management system according to the embodiment allows users to check and respond to messages via Bluetooth earphones and an internal telephone, even when working away from a PC.

[0030] The receiving unit reads messages from the chat tool aloud via Bluetooth earphones. Specifically, the receiving unit receives messages from the chat tool in real time and notifies the user aloud via Bluetooth earphones. This allows the user to instantly check important messages even when away from their PC. The receiving unit also includes a linking unit that enables the Bluetooth earphones to work with the internal telephone system, allowing messages received via the internal telephone to also be read aloud via Bluetooth earphones. The linking unit provides detailed instructions on the pairing procedure and communication protocol between the Bluetooth earphones and the internal telephone, allowing the user to easily connect the devices. For example, the pairing procedure for Bluetooth earphones and the internal telephone involves first setting the Bluetooth earphones to pairing mode, and then searching for and connecting to the Bluetooth device from the internal telephone's settings menu. Bluetooth Low Energy (BLE) is used as the communication protocol, achieving stable communication with low power consumption. This allows the receiving unit to efficiently receive messages from the chat tool and internal telephone and notify the user instantly. Furthermore, the receiving unit can also change the notification method according to the message priority. For example, important messages can be notified instantly by voice, while general messages can be notified by vibration, allowing for flexible responses tailored to user needs. This enables the receiving unit to communicate efficiently without users missing important messages.

[0031] The text conversion unit responds verbally based on the message received by the receiving unit, and the generating AI converts the content into text and immediately posts it to the chat tool. Specifically, when a user responds verbally via Bluetooth earphones, the voice data is sent to the text conversion unit. The text conversion unit uses the generating AI to convert the voice data into text and posts that text to the chat tool. The generating AI analyzes the voice data using speech recognition technology to generate accurate text. For example, if a user verbally instructs, "Please change the meeting time," the generating AI analyzes the voice, generates the text "Please change the meeting time," and posts it to the chat tool. The text conversion unit includes a process unit that describes the process by which the generating AI converts voice to text, detailing the specific processes and procedures. For example, the process unit describes a series of steps such as preprocessing of voice data, application of the speech recognition model, text generation, and text proofreading. Preprocessing of voice data includes noise reduction and volume adjustment, and the application of the speech recognition model uses deep learning-based speech recognition technology. In text generation, text is generated based on the recognized voice data, and finally, the text is proofread. This allows the text conversion unit to accurately transcribe user voice instructions into text and quickly post them to the chat tool. Furthermore, the text conversion unit can periodically update the training data of the generation AI to improve the accuracy of voice recognition. As a result, the text conversion unit can consistently achieve highly accurate voice recognition and text generation, supporting smooth user communication.

[0032] The Integration Department integrates multiple digital communication tools using existing communication tools and APIs. Specifically, the Integration Department centrally manages existing communication tools such as chat tools, email, and internal telephone systems, enabling users to efficiently utilize multiple tools. The Integration Department uses the APIs of each tool to link data and provide an integrated interface. For example, the Integration Department uses the chat tool's API to retrieve messages, the email API to send emails, and the internal telephone system's API to manage calls. This allows users to seamlessly use multiple communication tools through an integrated interface. Furthermore, the Integration Department also works with the internal telephone system to ensure secure communication. For example, the Integration Department uses the internal telephone system's encryption protocol to ensure the security of communication data. The Integration Department also centrally manages user authentication information and controls access to each tool. This allows the Integration Department to provide an environment where users can communicate safely and efficiently. In addition, the Integration Department can monitor user usage and analyze the usage and performance of each tool. This allows the Integration Department to provide an optimal communication environment tailored to user needs and improve the overall system performance. For example, the integration department proposes the optimal combination and settings of tools based on user usage patterns, thereby improving users' work efficiency. Furthermore, the integration department ensures system stability and reliability by detecting and responding quickly to system failures and anomalies. In this way, the integration department provides an environment where users can communicate with confidence, improving the overall reliability and performance of the system.

[0033] The receiving unit includes a linking unit that specifically describes how to link Bluetooth earphones and internal telephones. The receiving unit can include a linking unit that describes, for example, the pairing procedure and communication protocol between Bluetooth earphones and internal telephones. For example, the receiving unit describes the pairing procedure between Bluetooth earphones and internal telephones. The receiving unit can also describe the communication protocol between Bluetooth earphones and internal telephones. This allows for smooth system integration by specifically describing how to link Bluetooth earphones and internal telephones.

[0034] The text conversion unit includes a process unit that describes the process by which the generating AI converts speech into text. The text conversion unit may include a process unit that describes, for example, the specific process or procedure by which the generating AI converts speech into text. For example, the text conversion unit describes the process by which the generating AI converts speech into text. The text conversion unit can also describe the procedure by which the generating AI converts speech into text. By describing the process by which the generating AI converts speech into text, the accuracy of the text conversion is improved.

[0035] The receiving unit can automatically change the reading order of messages based on their importance. For example, it can prioritize urgent messages. It can also prioritize notifications for important meetings. Furthermore, it can postpone reading general messages. This allows important messages to be reviewed preferentially by changing the reading order based on their importance. Some or all of the above processing in the receiving unit may be performed using AI, for example, or not. For example, the receiving unit can input the importance of messages into the AI ​​and have the AI ​​change the reading order.

[0036] The receiving unit can read aloud messages using different tones and accents depending on the content. For example, it can read important messages in a calm tone, casual messages in a bright tone, and urgent messages in an emphasized tone. By changing the tone and accent according to the content of the message, it becomes easier to convey the importance and urgency of the message. Some or all of the above processing in the receiving unit may be performed using AI, for example, or not. For example, the receiving unit can input the content of the message into the AI ​​and have the AI ​​perform the changes in tone and accent.

[0037] The receiving unit can adjust the timing of message reading, taking into account the user's current activity status. For example, if the user is in a meeting, the receiving unit may postpone reading the message. It can also prioritize reading the message if the user is on the move. Furthermore, if the user is on a break, the receiving unit can read the message as usual. This allows the user to check messages at the appropriate time by adjusting the reading timing according to their activity status. Some or all of the above processing in the receiving unit may be performed using AI, or not. For example, the receiving unit can input user activity data into a generating AI and have the generating AI adjust the reading timing.

[0038] The receiving unit can, when reading messages aloud, refer to the user's past message history to prioritize reading relevant messages. For example, the receiving unit can prioritize reading messages that the user previously considered important. It can also postpone messages that the user previously ignored. Furthermore, the receiving unit can prioritize reading messages from people the user has frequently communicated with in the past. This allows the user to prioritize reviewing relevant messages by referring to their past message history. Some or all of the above processing in the receiving unit may be performed using AI, for example, or without AI. For example, the receiving unit can input past message history data into a generating AI and have the generating AI prioritize relevant messages.

[0039] The text conversion unit can automatically remove background noise from audio, thereby improving the accuracy of the transcription. For example, the text conversion unit can filter out ambient noise during voice input. It can also remove wind noise during voice input. Furthermore, the text conversion unit can remove other people's voices during voice input. This improves the accuracy of the transcription by removing background noise from the audio. Some or all of the above processing in the text conversion unit may be performed using AI, for example, or without AI. For example, the text conversion unit can input audio data into a generating AI and have the generating AI perform background noise removal.

[0040] The text conversion unit can add appropriate emphasis to the text by considering the intonation and emphasis of the speech. For example, the text conversion unit can display emphasized parts in bold. It can also display parts with intonation in italics. Furthermore, it can display important parts in color. In this way, appropriate emphasis can be added to the text by considering the intonation and emphasis of the speech. Some or all of the above processing in the text conversion unit may be performed using AI, for example, or without AI. For example, the text conversion unit can input speech data into a generating AI and have the generating AI perform analysis of intonation and emphasis.

[0041] The text conversion unit can use consistent expressions by referring to the user's past speech history when converting speech to text. For example, the text conversion unit can prioritize expressions the user has used in the past. It can also consistently use specific phrases from the user's past speech history. Furthermore, the text conversion unit can select appropriate expressions based on the user's past speech history. This makes consistent expressions possible by referring to past speech history. Some or all of the above processing in the text conversion unit may be performed using AI, for example, or without AI. For example, the text conversion unit can input past speech history data into a generating AI and have the generating AI perform the selection of consistent expressions.

[0042] The text conversion unit can automatically recognize and appropriately convert the user's technical terms and specific phrases when converting speech to text. For example, the text conversion unit can automatically recognize and appropriately convert the technical terms used by the user. Furthermore, if the user uses specific phrases, the text conversion unit can appropriately convert those as well. In addition, the text conversion unit can automatically recognize and appropriately convert industry-specific terminology used by the user. This improves the accuracy of the text conversion by appropriately converting technical terms and specific phrases. Some or all of the above-described processes in the text conversion unit may be performed using AI, for example, or without AI. For example, the text conversion unit can input technical terminology and specific phrase data into a generating AI and have the generating AI perform appropriate conversions.

[0043] The integration unit can synchronize data between the tools being integrated in real time, ensuring information consistency. For example, the integration unit can synchronize data between a chat tool and an email tool in real time. It can also synchronize data between an internal telephone system and a chat tool in real time. Furthermore, the integration unit can synchronize data between multiple digital communication tools in real time. This ensures information consistency by synchronizing data between tools in real time. Some or all of the above processing in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input data between tools into a generating AI and have the generating AI perform real-time synchronization.

[0044] The integration unit can automatically select the optimal integration method based on the frequency of use of the tools to be integrated. For example, the integration unit can prioritize the integration of frequently used tools. It can also postpone the integration of less frequently used tools. Furthermore, the integration unit can select the optimal integration method based on the user's usage patterns. This enables efficient tool integration by selecting the optimal integration method based on usage frequency. Some or all of the above processing in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input tool usage frequency data into a generating AI and have the generating AI select the optimal integration method.

[0045] The integration department can analyze data from the tools being integrated and make suggestions to improve the user's work efficiency. For example, the integration department can analyze the user's work patterns and suggest efficient ways to use the tools. It can also suggest combinations of tools to improve the user's work efficiency. Furthermore, the integration department can suggest the introduction of new tools to improve the user's work efficiency. This makes it possible to make suggestions to improve work efficiency by analyzing tool data. Some or all of the above processes in the integration department may be performed using AI, for example, or not. For example, the integration department can input tool data into a generating AI and have the generating AI execute suggestions for improving work efficiency.

[0046] The integration unit can automatically evaluate the security level of the tools to be integrated and apply the optimal security settings. For example, the integration unit can evaluate the security level of the tools and apply the optimal security settings. The integration unit can also evaluate the security risks of the tools and take appropriate measures. Furthermore, the integration unit can periodically review the security settings of the tools and apply the latest security measures. This improves security by evaluating the security level of the tools and applying the optimal security settings. Some or all of the above processes in the integration unit may be performed using AI, for example, or not using AI. For example, the integration unit can input the security data of the tools into a generating AI and have the generating AI perform the application of security settings.

[0047] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0048] The receiving unit can automatically change the reading order of messages based on their importance. For example, it can prioritize reading urgent messages. It can also prioritize reading notifications for important meetings. Furthermore, it can postpone reading general messages. This allows important messages to be reviewed preferentially by changing the reading order based on their importance. Some or all of the above processing in the receiving unit may be performed using AI, for example, or not. For example, the receiving unit can input the importance of messages into the AI ​​and have the AI ​​change the reading order.

[0049] The receiving unit can read aloud messages using different tones and accents depending on the content. For example, important messages can be read in a calm tone, casual messages in a bright tone, and urgent messages in an emphasized tone. This makes it easier to convey the importance and urgency of a message by changing the tone and accent according to its content. Some or all of the above processing in the receiving unit may be performed using AI, for example, or not. For example, the receiving unit can input the content of the message into the AI ​​and have the AI ​​perform the changes in tone and accent.

[0050] The receiving unit can adjust the timing of message reading, taking into account the user's current activity status. For example, if the user is in a meeting, the reading of the message can be postponed. Conversely, if the user is on the move, the reading of the message can be prioritized. Furthermore, if the user is on a break, the message can be read as usual. This allows the user to check messages at the appropriate time by adjusting the reading timing according to their activity status. Some or all of the above processing in the receiving unit may be performed using AI, for example, or without AI. For example, the receiving unit can input user activity data into a generating AI and have the generating AI adjust the reading timing.

[0051] The receiving unit can, when reading messages aloud, refer to the user's past message history to prioritize reading relevant messages. For example, it can prioritize reading messages that the user previously considered important. It can also postpone reading messages that the user previously ignored. Furthermore, it can prioritize reading messages from people the user has frequently communicated with in the past. This allows the user to prioritize reviewing relevant messages by referring to their past message history. Some or all of the above processing in the receiving unit may be performed using AI, for example, or without AI. For example, the receiving unit can input past message history data into a generating AI and have the generating AI prioritize relevant messages.

[0052] The text conversion unit can automatically remove background noise from audio, thereby improving the accuracy of the text conversion. For example, it can filter out ambient noise during voice input. It can also remove wind noise during voice input. Furthermore, it can remove other people's voices during voice input. By removing background noise from the audio, the accuracy of the text conversion is improved. Some or all of the above processing in the text conversion unit may be performed using AI, for example, or without AI. For example, the text conversion unit can input audio data into a generating AI and have the generating AI perform background noise removal.

[0053] The text conversion unit can add appropriate emphasis to the text by considering the intonation and emphasis of the speech. For example, it can display emphasized parts in bold. It can also display parts with intonation in italics. Furthermore, it can display important parts in color. In this way, appropriate emphasis can be added to the text by considering the intonation and emphasis of the speech. Some or all of the above processing in the text conversion unit may be performed using AI, for example, or without AI. For example, the text conversion unit can input speech data into a generating AI and have the generating AI perform analysis of intonation and emphasis.

[0054] The following briefly describes the processing flow for example form 1.

[0055] Step 1: The receiving unit reads messages from the chat tool aloud via Bluetooth earphones. For example, the receiving unit can also receive messages via an internal telephone and read them aloud via Bluetooth earphones. The receiving unit may also include a linking unit that describes the pairing procedure and communication protocol between the Bluetooth earphones and the internal telephone. Step 2: The text conversion unit responds in voice based on the message received by the receiving unit, and the generating AI converts the content into text and immediately posts it to the chat tool. For example, the text conversion unit may also include a process unit that describes the specific process and steps by which the generating AI converts voice into text. Step 3: The integration department integrates multiple digital communication tools using existing communication tools and APIs. For example, the integration department can work with the internal telephone system to ensure secure communication.

[0056] (Example of form 2) The message management system according to an embodiment of the present invention is a system that allows users to check notifications when they leave their PC in a secure environment or a different operating environment. This message management system uses Bluetooth earphones and an internal telephone to receive messages from a chat tool by voice. Next, the user replies by voice, and a generating AI converts the content into text and immediately posts it to the chat tool. Furthermore, by using existing communication tools and APIs to integrate multiple digital communication tools and coordinating with the internal telephone system, a secure means of communication is ensured. With this system, even when working away from a PC, it is possible to check and respond to messages via Bluetooth earphones and an internal telephone, allowing for immediate confirmation and reply to important messages no matter where you are. In addition, in environments where security is required, secure message communication can be achieved using the internal telephone. For example, messages from a chat tool are received by voice using Bluetooth earphones and an internal telephone. In this case, the specific protocol and communication means for the coordination between the Bluetooth earphones and the internal telephone will be described. Next, the user replies by voice, and a generating AI converts the content into text and immediately posts it to the chat tool. The process by which the generating AI converts voice to text will also be described in detail. Furthermore, by integrating multiple digital communication tools using existing communication tools and APIs, and linking with the internal telephone system, a secure means of communication is ensured. This system allows users to check and respond to messages via Bluetooth earphones and internal telephones even when working away from their PCs, enabling immediate confirmation and replies to important messages from anywhere. It also enables secure message communication using internal telephones in environments where security is paramount. As a result, the message management system allows users to check and respond to messages via Bluetooth earphones and internal telephones even when working away from their PCs.

[0057] The message management system according to this embodiment comprises a receiving unit, a text conversion unit, and an integration unit. The receiving unit reads messages from a chat tool aloud using Bluetooth earphones. The receiving unit can, for example, read messages from a chat tool aloud using Bluetooth earphones. The receiving unit can also receive messages via an internal telephone. For example, the receiving unit can receive messages via an internal telephone and read them aloud using Bluetooth earphones. Furthermore, the receiving unit may also include a coordination unit that specifically describes how the Bluetooth earphones and the internal telephones work together. For example, the receiving unit may include a coordination unit that describes the pairing procedure and communication protocol between the Bluetooth earphones and the internal telephones. The text conversion unit responds aloud based on the messages received by the receiving unit, and a generating AI converts the content into text and immediately posts it to the chat tool. The text conversion unit can, for example, respond aloud, and a generating AI converts the content into text and immediately posts it to the chat tool. Furthermore, the text conversion unit may also include a process unit that describes the process by which the generating AI converts speech into text. For example, the text conversion unit includes a process unit that describes the specific process and procedures by which the generation AI converts speech into text. The integration unit integrates multiple digital communication tools using existing communication tools and APIs. The integration unit can, for example, integrate multiple digital communication tools using existing communication tools and APIs. The integration unit can also cooperate with an internal telephone system. For example, the integration unit can cooperate with an internal telephone system to ensure secure communication. As a result, the message management system according to the embodiment allows users to check and respond to messages via Bluetooth earphones and an internal telephone, even when working away from a PC.

[0058] The receiving unit reads messages from the chat tool aloud via Bluetooth earphones. Specifically, the receiving unit receives messages from the chat tool in real time and notifies the user aloud via Bluetooth earphones. This allows the user to instantly check important messages even when away from their PC. The receiving unit also includes a linking unit that enables the Bluetooth earphones to work with the internal telephone system, allowing messages received via the internal telephone to also be read aloud via Bluetooth earphones. The linking unit provides detailed instructions on the pairing procedure and communication protocol between the Bluetooth earphones and the internal telephone, allowing the user to easily connect the devices. For example, the pairing procedure for Bluetooth earphones and the internal telephone involves first setting the Bluetooth earphones to pairing mode, and then searching for and connecting to the Bluetooth device from the internal telephone's settings menu. Bluetooth Low Energy (BLE) is used as the communication protocol, achieving stable communication with low power consumption. This allows the receiving unit to efficiently receive messages from the chat tool and internal telephone and notify the user instantly. Furthermore, the receiving unit can also change the notification method according to the message priority. For example, important messages can be notified instantly by voice, while general messages can be notified by vibration, allowing for flexible responses tailored to user needs. This enables the receiving unit to communicate efficiently without users missing important messages.

[0059] The text conversion unit responds verbally based on the message received by the receiving unit, and the generating AI converts the content into text and immediately posts it to the chat tool. Specifically, when a user responds verbally via Bluetooth earphones, the voice data is sent to the text conversion unit. The text conversion unit uses the generating AI to convert the voice data into text and posts that text to the chat tool. The generating AI analyzes the voice data using speech recognition technology to generate accurate text. For example, if a user verbally instructs, "Please change the meeting time," the generating AI analyzes the voice, generates the text "Please change the meeting time," and posts it to the chat tool. The text conversion unit includes a process unit that describes the process by which the generating AI converts voice to text, detailing the specific processes and procedures. For example, the process unit describes a series of steps such as preprocessing of voice data, application of the speech recognition model, text generation, and text proofreading. Preprocessing of voice data includes noise reduction and volume adjustment, and the application of the speech recognition model uses deep learning-based speech recognition technology. In text generation, text is generated based on the recognized voice data, and finally, the text is proofread. This allows the text conversion unit to accurately transcribe user voice instructions into text and quickly post them to the chat tool. Furthermore, the text conversion unit can periodically update the training data of the generation AI to improve the accuracy of voice recognition. As a result, the text conversion unit can consistently achieve highly accurate voice recognition and text generation, supporting smooth user communication.

[0060] The Integration Department integrates multiple digital communication tools using existing communication tools and APIs. Specifically, the Integration Department centrally manages existing communication tools such as chat tools, email, and internal telephone systems, enabling users to efficiently utilize multiple tools. The Integration Department uses the APIs of each tool to link data and provide an integrated interface. For example, the Integration Department uses the chat tool's API to retrieve messages, the email API to send emails, and the internal telephone system's API to manage calls. This allows users to seamlessly use multiple communication tools through an integrated interface. Furthermore, the Integration Department also works with the internal telephone system to ensure secure communication. For example, the Integration Department uses the internal telephone system's encryption protocol to ensure the security of communication data. The Integration Department also centrally manages user authentication information and controls access to each tool. This allows the Integration Department to provide an environment where users can communicate safely and efficiently. In addition, the Integration Department can monitor user usage and analyze the usage and performance of each tool. This allows the Integration Department to provide an optimal communication environment tailored to user needs and improve the overall system performance. For example, the integration department proposes the optimal combination and settings of tools based on user usage patterns, thereby improving users' work efficiency. Furthermore, the integration department ensures system stability and reliability by detecting and responding quickly to system failures and anomalies. In this way, the integration department provides an environment where users can communicate with confidence, improving the overall reliability and performance of the system.

[0061] The receiving unit includes a linking unit that specifically describes how to link Bluetooth earphones and internal telephones. The receiving unit can include a linking unit that describes, for example, the pairing procedure and communication protocol between Bluetooth earphones and internal telephones. For example, the receiving unit describes the pairing procedure between Bluetooth earphones and internal telephones. The receiving unit can also describe the communication protocol between Bluetooth earphones and internal telephones. This allows for smooth system integration by specifically describing how to link Bluetooth earphones and internal telephones.

[0062] The text conversion unit includes a process unit that describes the process by which the generating AI converts speech into text. The text conversion unit may include a process unit that describes, for example, the specific process or procedure by which the generating AI converts speech into text. For example, the text conversion unit describes the process by which the generating AI converts speech into text. The text conversion unit can also describe the procedure by which the generating AI converts speech into text. By describing the process by which the generating AI converts speech into text, the accuracy of the text conversion is improved.

[0063] The receiving unit can estimate the user's emotions and adjust the message reading speed based on the estimated emotions. For example, if the user is stressed, the receiving unit can slow down the message reading speed to make it easier to understand. The receiving unit can also read the message at a normal speed if the user is relaxed. Furthermore, if the user is in a hurry, the receiving unit can speed up the message reading speed to convey information quickly. This adjusts the message reading speed according to the user's emotions, making it easier to understand. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the receiving unit may be performed using AI, or not. For example, the receiving unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0064] The receiving unit can automatically change the reading order of messages based on their importance. For example, it can prioritize urgent messages. It can also prioritize notifications for important meetings. Furthermore, it can postpone reading general messages. This allows important messages to be reviewed preferentially by changing the reading order based on their importance. Some or all of the above processing in the receiving unit may be performed using AI, for example, or not. For example, the receiving unit can input the importance of messages into the AI ​​and have the AI ​​change the reading order.

[0065] The receiving unit can read aloud messages using different tones and accents depending on the content. For example, it can read important messages in a calm tone, casual messages in a bright tone, and urgent messages in an emphasized tone. By changing the tone and accent according to the content of the message, it becomes easier to convey the importance and urgency of the message. Some or all of the above processing in the receiving unit may be performed using AI, for example, or not. For example, the receiving unit can input the content of the message into the AI ​​and have the AI ​​perform the changes in tone and accent.

[0066] The receiving unit can estimate the user's emotions and pause reading the message based on the estimated emotions. For example, the receiving unit may pause reading the message if the user is concentrating. It may also continue reading the message if the user is relaxed. Furthermore, the receiving unit may pause reading the message if the user is stressed. This ensures that the reading of the message is paused according to the user's emotions, without disrupting the user's concentration. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the receiving unit may be performed using AI or not using AI. For example, the receiving unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0067] The receiving unit can adjust the timing of message reading, taking into account the user's current activity status. For example, if the user is in a meeting, the receiving unit may postpone reading the message. It can also prioritize reading the message if the user is on the move. Furthermore, if the user is on a break, the receiving unit can read the message as usual. This allows the user to check messages at the appropriate time by adjusting the reading timing according to their activity status. Some or all of the above processing in the receiving unit may be performed using AI, or not. For example, the receiving unit can input user activity data into a generating AI and have the generating AI adjust the reading timing.

[0068] The receiving unit can, when reading messages aloud, refer to the user's past message history to prioritize reading relevant messages. For example, the receiving unit can prioritize reading messages that the user previously considered important. It can also postpone messages that the user previously ignored. Furthermore, the receiving unit can prioritize reading messages from people the user has frequently communicated with in the past. This allows the user to prioritize reviewing relevant messages by referring to their past message history. Some or all of the above processing in the receiving unit may be performed using AI, for example, or without AI. For example, the receiving unit can input past message history data into a generating AI and have the generating AI prioritize relevant messages.

[0069] The text generation unit can estimate the user's emotions and adjust the textual expression based on the estimated emotions. For example, if the user is relaxed, the text generation unit will use polite language. If the user is in a hurry, the text generation unit can use concise language. Furthermore, if the user is stressed, the text generation unit can use gentle language. By adjusting the textual expression according to the user's emotions, more appropriate expression becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generative AI, but is not limited to these examples. Some or all of the above processing in the text generation unit may be performed using AI, or not using AI. For example, the text generation unit can input user emotion data into the generative AI and have the generative AI adjust the textual expression.

[0070] The text conversion unit can automatically remove background noise from audio, thereby improving the accuracy of the transcription. For example, the text conversion unit can filter out ambient noise during voice input. It can also remove wind noise during voice input. Furthermore, the text conversion unit can remove other people's voices during voice input. This improves the accuracy of the transcription by removing background noise from the audio. Some or all of the above processing in the text conversion unit may be performed using AI, for example, or without AI. For example, the text conversion unit can input audio data into a generating AI and have the generating AI perform background noise removal.

[0071] The text conversion unit can add appropriate emphasis to the text by considering the intonation and emphasis of the speech. For example, the text conversion unit can display emphasized parts in bold. It can also display parts with intonation in italics. Furthermore, it can display important parts in color. In this way, appropriate emphasis can be added to the text by considering the intonation and emphasis of the speech. Some or all of the above processing in the text conversion unit may be performed using AI, for example, or without AI. For example, the text conversion unit can input speech data into a generating AI and have the generating AI perform analysis of intonation and emphasis.

[0072] The text conversion unit can estimate the user's emotions and adjust the conversion speed based on the estimated emotions. For example, if the user is in a hurry, the text conversion unit will speed up the conversion. If the user is relaxed, the text conversion unit can also convert at a normal speed. Furthermore, if the user is stressed, the text conversion unit can slow down the conversion speed. By adjusting the conversion speed according to the user's emotions, it becomes possible to convert at a more appropriate speed. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the text conversion unit may be performed using AI, for example, or without AI. For example, the text conversion unit can input user emotion data into the generative AI and have the generative AI adjust the conversion speed.

[0073] The text conversion unit can use consistent expressions by referring to the user's past speech history when converting speech to text. For example, the text conversion unit can prioritize expressions the user has used in the past. It can also consistently use specific phrases from the user's past speech history. Furthermore, the text conversion unit can select appropriate expressions based on the user's past speech history. This makes consistent expressions possible by referring to past speech history. Some or all of the above processing in the text conversion unit may be performed using AI, for example, or without AI. For example, the text conversion unit can input past speech history data into a generating AI and have the generating AI perform the selection of consistent expressions.

[0074] The text conversion unit can automatically recognize and appropriately convert the user's technical terms and specific phrases when converting speech to text. For example, the text conversion unit can automatically recognize and appropriately convert the technical terms used by the user. Furthermore, if the user uses specific phrases, the text conversion unit can appropriately convert those as well. In addition, the text conversion unit can automatically recognize and appropriately convert industry-specific terminology used by the user. This improves the accuracy of the text conversion by appropriately converting technical terms and specific phrases. Some or all of the above-described processes in the text conversion unit may be performed using AI, for example, or without AI. For example, the text conversion unit can input technical terminology and specific phrase data into a generating AI and have the generating AI perform appropriate conversions.

[0075] The integration unit can estimate the user's emotions and determine the priority of tools to integrate based on the estimated emotions. For example, if the user is stressed, the integration unit will prioritize integrating important tools. If the user is relaxed, the integration unit can also integrate tools in the usual priority order. Furthermore, if the user is in a hurry, the integration unit can prioritize integrating tools that can be accessed quickly. This allows for the priority integration of important tools by determining tool priorities 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 integration unit may be performed using AI or not. For example, the integration unit can input user emotion data into a generative AI and have the generative AI perform the task of determining tool priorities.

[0076] The integration unit can synchronize data between the tools being integrated in real time, ensuring information consistency. For example, the integration unit can synchronize data between a chat tool and an email tool in real time. It can also synchronize data between an internal telephone system and a chat tool in real time. Furthermore, the integration unit can synchronize data between multiple digital communication tools in real time. This ensures information consistency by synchronizing data between tools in real time. Some or all of the above processing in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input data between tools into a generating AI and have the generating AI perform real-time synchronization.

[0077] The integration unit can automatically select the optimal integration method based on the frequency of use of the tools to be integrated. For example, the integration unit can prioritize the integration of frequently used tools. It can also postpone the integration of less frequently used tools. Furthermore, the integration unit can select the optimal integration method based on the user's usage patterns. This enables efficient tool integration by selecting the optimal integration method based on usage frequency. Some or all of the above processing in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input tool usage frequency data into a generating AI and have the generating AI select the optimal integration method.

[0078] The integration unit can estimate the user's emotions and adjust the display method of the integrated tool based on the estimated user emotions. For example, if the user is stressed, the integration unit can provide a simple and highly visible display method. It can also provide a display method that includes detailed information if the user is relaxed. Furthermore, if the user is in a hurry, the integration unit can provide a concise display method. This improves visibility by adjusting the tool's display method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the integration unit may be performed using AI, or not. For example, the integration unit can input user emotion data into a generative AI and have the generative AI adjust the display method.

[0079] The integration department can analyze data from the tools being integrated and make suggestions to improve the user's work efficiency. For example, the integration department can analyze the user's work patterns and suggest efficient ways to use the tools. It can also suggest combinations of tools to improve the user's work efficiency. Furthermore, the integration department can suggest the introduction of new tools to improve the user's work efficiency. This makes it possible to make suggestions to improve work efficiency by analyzing tool data. Some or all of the above processes in the integration department may be performed using AI, for example, or not. For example, the integration department can input tool data into a generating AI and have the generating AI execute suggestions for improving work efficiency.

[0080] The integration unit can automatically evaluate the security level of the tools to be integrated and apply the optimal security settings. For example, the integration unit can evaluate the security level of the tools and apply the optimal security settings. The integration unit can also evaluate the security risks of the tools and take appropriate measures. Furthermore, the integration unit can periodically review the security settings of the tools and apply the latest security measures. This improves security by evaluating the security level of the tools and applying the optimal security settings. Some or all of the above processes in the integration unit may be performed using AI, for example, or not using AI. For example, the integration unit can input the security data of the tools into a generating AI and have the generating AI perform the application of security settings.

[0081] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0082] The receiving unit can estimate the user's emotions and adjust the message reading speed based on the estimated emotions. For example, if the user is stressed, the message reading speed can be slowed down to make it easier to understand. If the user is relaxed, the message can be read at a normal speed. Furthermore, if the user is in a hurry, the message reading speed can be increased to convey information quickly. In this way, adjusting the message reading speed according to the user's emotions makes it easier to understand. 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 receiving unit may be performed using AI, or not using AI. For example, the receiving unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0083] The receiving unit can automatically change the reading order of messages based on their importance. For example, it can prioritize reading urgent messages. It can also prioritize reading notifications for important meetings. Furthermore, it can postpone reading general messages. This allows important messages to be reviewed preferentially by changing the reading order based on their importance. Some or all of the above processing in the receiving unit may be performed using AI, for example, or not. For example, the receiving unit can input the importance of messages into the AI ​​and have the AI ​​change the reading order.

[0084] The receiving unit can read aloud messages using different tones and accents depending on the content. For example, important messages can be read in a calm tone, casual messages in a bright tone, and urgent messages in an emphasized tone. This makes it easier to convey the importance and urgency of a message by changing the tone and accent according to its content. Some or all of the above processing in the receiving unit may be performed using AI, for example, or not. For example, the receiving unit can input the content of the message into the AI ​​and have the AI ​​perform the changes in tone and accent.

[0085] The receiving unit can estimate the user's emotions and pause reading the message based on the estimated emotions. For example, if the user is concentrating, the message reading can be paused. If the user is relaxed, the message reading can continue. Furthermore, if the user is stressed, the message reading can also be paused. This allows the reading of the message to be paused according to the user's emotions, without disrupting the user's concentration. 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 receiving unit may be performed using AI, or not using AI. For example, the receiving unit can input user emotion data into the generative AI and have the generative AI perform emotion estimation.

[0086] The receiving unit can adjust the timing of message reading, taking into account the user's current activity status. For example, if the user is in a meeting, the reading of the message can be postponed. Conversely, if the user is on the move, the reading of the message can be prioritized. Furthermore, if the user is on a break, the message can be read as usual. This allows the user to check messages at the appropriate time by adjusting the reading timing according to their activity status. Some or all of the above processing in the receiving unit may be performed using AI, for example, or without AI. For example, the receiving unit can input user activity data into a generating AI and have the generating AI adjust the reading timing.

[0087] The receiving unit can, when reading messages aloud, refer to the user's past message history to prioritize reading relevant messages. For example, it can prioritize reading messages that the user previously considered important. It can also postpone reading messages that the user previously ignored. Furthermore, it can prioritize reading messages from people the user has frequently communicated with in the past. This allows the user to prioritize reviewing relevant messages by referring to their past message history. Some or all of the above processing in the receiving unit may be performed using AI, for example, or without AI. For example, the receiving unit can input past message history data into a generating AI and have the generating AI prioritize relevant messages.

[0088] The text generation unit can estimate the user's emotions and adjust the textual expression based on the estimated emotions. For example, if the user is relaxed, the text will be written in a polite manner. If the user is in a hurry, the text can be written in a concise manner. Furthermore, if the user is stressed, the text can be written in a gentle manner. By adjusting the textual expression according to the user's emotions, more appropriate expression becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the text generation unit may be performed using AI, for example, or without AI. For example, the text generation unit can input user emotion data into the generative AI and have the generative AI adjust the textual expression.

[0089] The text conversion unit can automatically remove background noise from audio, thereby improving the accuracy of the text conversion. For example, it can filter out ambient noise during voice input. It can also remove wind noise during voice input. Furthermore, it can remove other people's voices during voice input. By removing background noise from the audio, the accuracy of the text conversion is improved. Some or all of the above processing in the text conversion unit may be performed using AI, for example, or without AI. For example, the text conversion unit can input audio data into a generating AI and have the generating AI perform background noise removal.

[0090] The text conversion unit can add appropriate emphasis to the text by considering the intonation and emphasis of the speech. For example, it can display emphasized parts in bold. It can also display parts with intonation in italics. Furthermore, it can display important parts in color. In this way, appropriate emphasis can be added to the text by considering the intonation and emphasis of the speech. Some or all of the above processing in the text conversion unit may be performed using AI, for example, or without AI. For example, the text conversion unit can input speech data into a generating AI and have the generating AI perform analysis of intonation and emphasis.

[0091] The text conversion unit can estimate the user's emotions and adjust the conversion speed based on the estimated emotions. For example, if the user is in a hurry, the conversion speed can be increased. If the user is relaxed, the conversion can be performed at a normal speed. Furthermore, if the user is stressed, the conversion speed can be slowed down. By adjusting the conversion speed according to the user's emotions, it becomes possible to convert text at a more appropriate speed. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the text conversion unit may be performed using AI, for example, or without AI. For example, the text conversion unit can input user emotion data into the generative AI and have the generative AI adjust the conversion speed.

[0092] The following briefly describes the processing flow for example form 2.

[0093] Step 1: The receiving unit reads messages from the chat tool aloud via Bluetooth earphones. For example, the receiving unit can also receive messages via an internal telephone and read them aloud via Bluetooth earphones. The receiving unit may also include a linking unit that describes the pairing procedure and communication protocol between the Bluetooth earphones and the internal telephone. Step 2: The text conversion unit responds in voice based on the message received by the receiving unit, and the generating AI converts the content into text and immediately posts it to the chat tool. For example, the text conversion unit may also include a process unit that describes the specific process and steps by which the generating AI converts voice into text. Step 3: The integration department integrates multiple digital communication tools using existing communication tools and APIs. For example, the integration department can work with the internal telephone system to ensure secure communication.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] Each of the multiple elements described above, including the receiving unit, text conversion unit, and integration unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the receiving unit is implemented by the processor 46 of the smart device 14, which receives messages via Bluetooth earphones and an internal telephone and reads them aloud. The text conversion unit is implemented by the specific processing unit 290 of the data processing unit 12, which uses a generating AI to convert speech into text and post it to a chat tool. The integration unit is implemented by the control unit 46A of the smart device 14, which integrates multiple digital communication tools using existing communication tools and APIs and also cooperates with the internal telephone system. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0098] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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).

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.).

[0110] 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.

[0111] 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.

[0112] 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.

[0113] Each of the multiple elements described above, including the receiving unit, text conversion unit, and integration unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the receiving unit is implemented by the processor 46 of the smart glasses 214, which receives messages via Bluetooth earphones and an internal telephone and reads them aloud. The text conversion unit is implemented by the specific processing unit 290 of the data processing unit 12, which uses a generating AI to convert speech into text and post it to a chat tool. The integration unit is implemented by the control unit 46A of the smart glasses 214, which integrates multiple digital communication tools using existing communication tools and APIs and also cooperates with the internal telephone system. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0114] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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).

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] Each of the multiple elements described above, including the receiving unit, text conversion unit, and integration unit, is implemented in at least one of the following: the headset terminal 314 and the data processing unit 12. For example, the receiving unit is implemented by the processor 46 of the headset terminal 314, which receives messages via Bluetooth earphones and an extension telephone and reads them aloud. The text conversion unit is implemented by the specific processing unit 290 of the data processing unit 12, which uses a generation AI to convert speech into text and post it to a chat tool. The integration unit is implemented by the control unit 46A of the headset terminal 314, which integrates multiple digital communication tools using existing communication tools and APIs and also cooperates with the extension telephone system. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0130] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] Each of the multiple elements described above, including the receiving unit, text conversion unit, and integration unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the receiving unit is implemented by the processor 46 of the robot 414, which receives messages via Bluetooth earphones and an internal telephone and reads them aloud. The text conversion unit is implemented by the specific processing unit 290 of the data processing unit 12, which uses a generating AI to convert speech into text and post it to a chat tool. The integration unit is implemented by the control unit 46A of the robot 414, which integrates multiple digital communication tools using existing communication tools and APIs and also cooperates with the internal telephone system. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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."

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] (Note 1) A receiver that reads messages from a chat tool aloud to Bluetooth earphones, A text conversion unit that responds with voice based on the message received by the receiving unit, and whose generating AI converts the content into text and immediately posts it to a chat tool, It includes an integration unit that integrates multiple digital communication tools using existing communication tools and APIs. A system characterized by the following features. (Note 2) The receiving unit is It includes a section that specifically describes how to connect Bluetooth earphones and internal telephones. The system described in Appendix 1, characterized by the features described herein. (Note 3) The text conversion unit, It includes a process section that describes the process by which the generation AI converts speech into text. The system described in Appendix 1, characterized by the features described herein. (Note 4) The receiving unit is It estimates the user's emotions and adjusts the message reading speed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 5) The receiving unit is The order in which messages are read aloud will be automatically changed based on their importance. The system described in Appendix 1, characterized by the features described herein. (Note 6) The receiving unit is Depending on the content of the message, the reader will use different tones and accents when reading it aloud. The system described in Appendix 1, characterized by the features described herein. (Note 7) The receiving unit is It estimates the user's emotions and pauses reading the message based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The receiving unit is When reading messages aloud, the timing of the reading is adjusted based on the user's current activity. The system described in Appendix 1, characterized by the features described herein. (Note 9) The receiving unit is When reading messages aloud, the system will prioritize reading relevant messages by referencing the user's past message history. The system described in Appendix 1, characterized by the features described herein. (Note 10) The text conversion unit, It estimates the user's emotions and adjusts the textual expression based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The text conversion unit, Automatically removes background noise from audio and improves the accuracy of text transcription. The system described in Appendix 1, characterized by the features described herein. (Note 12) The text conversion unit, Take intonation and emphasis into the audio and add appropriate emphasis to the text. The system described in Appendix 1, characterized by the features described herein. (Note 13) The text conversion unit, It estimates the user's emotions and adjusts the text conversion speed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The text conversion unit, When transcribing speech into text, we refer to the user's past speech history to use consistent wording. The system described in Appendix 1, characterized by the features described herein. (Note 15) The text conversion unit, When converting speech to text, the system automatically recognizes and appropriately converts the user's technical terms and specific phrases. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned integration unit is Prioritize tools that estimate user sentiment and integrate based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned integration unit is Real-time data synchronization between integrated tools ensures information consistency. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned integration unit is The system automatically selects the optimal integration method based on the frequency of use of the tools being integrated. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned integration unit is Adjust how the tool displays, which estimates user sentiment and integrates it based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned integration unit is We analyze data from integrated tools and make suggestions to improve users' work efficiency. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned integration unit is It automatically assesses the security level of the tools to be integrated and applies the optimal security settings. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0166] 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 receiving unit that reads messages from a chat tool aloud through earphones, A text conversion unit that responds with voice based on the message received by the receiving unit, and whose generating AI converts the content into text and immediately posts it to a chat tool, It includes an integration unit that integrates multiple digital communication tools using existing communication tools and APIs. A system characterized by the following features.

2. The receiving unit is It includes a section that specifically describes how to connect earphones and internal telephones. The system according to feature 1.

3. The text conversion unit, It includes a process section that describes the process by which the generation AI converts speech into text. The system according to feature 1.

4. The receiving unit is It estimates the user's emotions and adjusts the message reading speed based on the estimated emotions. The system according to feature 1.

5. The receiving unit is The order in which messages are read aloud will be automatically changed based on their importance. The system according to feature 1.

6. The receiving unit is Depending on the content of the message, the reader will use different tones and accents when reading it aloud. The system according to feature 1.

7. The receiving unit is It estimates the user's emotions and pauses reading the message based on the estimated emotions. The system according to feature 1.

8. The receiving unit is When reading messages aloud, the timing of the reading is adjusted based on the user's current activity. The system according to feature 1.

9. The receiving unit is When reading messages aloud, the system will prioritize reading relevant messages by referencing the user's past message history. The system according to feature 1.

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