System

Abstract

An object of a system according to an embodiment is to improve work efficiency on an information transmitting side and an information receiving side.SOLUTION: A system includes a text input unit, an analysis unit, and an output unit. The text input part inputs a text from an information transmitting side. The analysis unit analyzes the text input by the text input unit. The output unit outputs the information analyzed by the analysis unit in an optimal format for a receiver.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the problem that document creation and storage is cumbersome on both the sender and receiver sides of information, resulting in reduced business efficiency.

[0005] The system according to the embodiment aims to improve the business efficiency of both the sender and receiver of information. [Means for solving the problem]

[0006] The system according to the embodiment includes a text input unit, an analysis unit, and an output unit. The text input unit inputs text from an information sender. The analysis unit analyzes the text input by the text input unit. The output unit outputs the information analyzed by the analysis unit in a format optimal for the recipient. [Effects of the Invention]

[0007] The system according to the embodiment can improve the business efficiency of both the sender and receiver of information. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The communication tool according to an embodiment of the present invention is a system in which the sender of information inputs information in text format and the generation AI outputs it in any format. This reduces the effort required for creating and storing documents, and allows the recipient to receive information in a way that is easy to understand.

[0029] A communication tool according to an embodiment includes a text input unit, an analysis unit, and an output unit. The text input unit inputs text from a sender of information. For example, it provides an interface for a user to input text. The text input unit can also support voice input and handwriting input. The analysis unit analyzes the text input by the text input unit. For example, the generation AI understands the context of the text and automatically completes or corrects it as necessary. The generation AI can also automatically suggest related additional information based on the sender's input. Furthermore, the generation AI can analyze the sender's emotions and convert them into an appropriate tone or expression. The output unit outputs the information analyzed by the analysis unit in a format optimal for the recipient. For example, the generation AI provides information in the form of a chat, image, reminder, or other format depending on the recipient's needs. The generation AI can also output information in an optimal format depending on the recipient's device or application environment. This allows the communication tool according to an embodiment to reduce the effort required for document creation and storage and provide information in a manner that is easy for recipients to understand. For example, when a school teacher wants to inform a class schedule, they only need to input the text, and the generation AI will automatically analyze the information and output it in the optimal format for the recipient, significantly reducing the effort required for document creation, printing, and distribution. Recipients also no longer need to store documents, making information management easier.

[0030] The analysis unit understands the context of the text entered by the text input unit and can automatically complete or correct it as needed. For example, if the sender enters "Tomorrow's meeting is in the afternoon," the generation AI will understand the context and automatically complete it as "Tomorrow's meeting starts at 2:00 p.m." The analysis unit can also perform context analysis and extract related topics to understand the context. Furthermore, the analysis unit can perform dictionary-based completion and correction using machine learning models. This allows the context of the sender's text input to be understood and automatically completed or corrected as needed, maintaining the accuracy and consistency of the information.

[0031] The analysis unit can automatically suggest related additional information based on the text entered by the text input unit. For example, if the sender enters "Regarding the start of a new project," the generation AI automatically suggests communication content related to past project starts. The analysis unit can also retrieve related additional information from a database. Furthermore, the analysis unit can also retrieve and suggest data from an external API. This makes it possible to automatically suggest related additional information based on the sender's input, thereby improving the richness and convenience of the information.

[0032] The text input unit supports voice input and handwritten input, which the generation AI can analyze and convert into text. For example, if the caller voice-inputs, "Tomorrow's meeting starts at 2:00 PM," the generation AI analyzes the voice and converts it into text. The text input unit can also input handwritten characters with a digital pen, which the generation AI analyzes and converts into text. Furthermore, the text input unit can analyze input using voice recognition technology and handwritten character recognition technology. This allows for voice input and handwritten input, which the generation AI can analyze and convert into text, providing a variety of input methods and improving convenience.

[0033] The analysis unit translates the input content of the sender in real time and can output it in an appropriate format for recipients who speak different languages. For example, if the sender inputs "Tomorrow's meeting will start at 2 PM" in Japanese, the generation AI will translate it into English in real time and output it as "The meeting will start at 2 PM tomorrow." The analysis unit can also perform translation in real time using a translation algorithm. Furthermore, the analysis unit can also set different supported language types. This allows the sender to translate the input content in real time and output it in an appropriate format for recipients who speak different languages, making international communication smoother.

[0034] The output unit can automatically select the optimal output format based on the recipient's past behavioral history and preferences. For example, if the recipient has often received information in chat format in the past, the generation AI will automatically output the information in chat format. The output unit can also select the optimal output format based on the recipient's browsing history and purchase history. Furthermore, the output unit can also understand the recipient's preferences based on survey results and select the optimal output format. This makes it possible to provide the recipient with the most appropriate information by automatically selecting the optimal output format based on the recipient's past behavioral history and preferences.

[0035] The output unit can output information in the optimal format depending on the device and application environment of the recipient. For example, if the recipient is using a smartphone, the generation AI can output information in a format optimal for the smartphone. Also, if the recipient is using a tablet, the output unit can output information in a format optimal for the tablet. Furthermore, the output unit can also output information in the optimal format depending on a specific application environment. This makes it possible to improve the ease of receiving information by outputting information in the optimal format depending on the device and application environment of the recipient.

[0036] The output unit can add interactive elements to deepen the recipient's understanding when receiving information. For example, the output unit can provide information in the form of a quiz to check the recipient's level of understanding when receiving information. The output unit can also provide information using interactive content. Furthermore, the output unit can provide information in the form of a game to attract the recipient's interest. This can improve the recipient's ease of receiving and understanding of information by adding interactive elements to deepen their understanding when receiving information.

[0037] The output unit can use AR or VR to make the information visually easier to understand when the recipient receives it. For example, the output unit can use AR to visually display the information when the recipient receives it, deepening their understanding. The output unit can also provide the information using VR. Furthermore, the output unit can also use an AR kit or a VR headset to make the information visually easier to understand. This makes it possible to improve the recipient's understanding of the information by making the information visually easier to understand when the recipient receives it using AR or VR.

[0038] The analysis unit can automatically format the text input from the sender and generate professional documents. For example, if the sender inputs "meeting minutes," the generation AI automatically formats the text and generates professional minutes. The analysis unit can also set standards and formats for professional documents such as business letters and reports. Furthermore, the analysis unit can select and automatically format the appropriate format depending on the content of the document. This automatically formats the text input from the sender and generates professional documents, improving the efficiency of document creation.

[0039] The analysis unit automatically selects a template based on the input from the sender, enabling efficient document creation. For example, if the sender inputs "meeting minutes," the generation AI automatically selects an appropriate template and efficiently creates minutes. The analysis unit can also set types such as business document templates and presentation templates. Furthermore, the analysis unit can also select the optimal template based on the input and automatically create documents. This allows for automatic template selection based on the sender's input and efficient document creation, improving the efficiency of document creation.

[0040] The analysis unit can automatically generate video content based on the sender's text input and convey information visually. For example, if the sender inputs "introduction to a new product," the analysis unit's generation AI automatically creates a video that visually displays the product's features and advantages. The analysis unit can also set the type of video, such as animated video or live-action video. Furthermore, the analysis unit can generate optimal video content based on the input content and convey information visually. This allows the sender to automatically generate video content based on the sender's text input and convey information visually, improving the ease of information reception and comprehension.

[0041] The analysis unit can automatically add a digital signature to the text input from the sender and authenticate it as an official document. For example, if the sender inputs "minutes of the meeting," the generation AI automatically adds a digital signature and authenticates it as official minutes. The analysis unit can also use digital signature technologies such as public key cryptography and digital certificates. Furthermore, the analysis unit can also add an appropriate digital signature depending on the content of the document and authenticate it as an official document. This allows the reliability and officialness of the document to be improved by automatically adding a digital signature to the text input from the sender and authenticating it as an official document.

[0042] The analysis unit can automatically save the input content of the sender in cloud storage, making it accessible to the recipient at any time. For example, if the sender inputs "meeting minutes," the generation AI automatically saves it in cloud storage, making it accessible to the recipient at any time. The analysis unit can also use cloud storage such as AWS S3 or Google Drive. Furthermore, the analysis unit can save the input content in appropriate cloud storage, making it accessible to the recipient at any time. This improves the convenience of information storage and access by automatically saving the input content of the sender in cloud storage and making it accessible to the recipient at any time.

[0043] The analysis unit can automatically distribute information via email or social media based on the sender's text input. For example, if the sender inputs "meeting minutes," the generation AI will automatically distribute the information via email. The analysis unit can also use email and social media such as Gmail, Twitter, and Facebook. Furthermore, the analysis unit can also distribute information via appropriate email or social media depending on the input content. This improves the efficiency of information transmission by automatically distributing information via email or social media based on the sender's text input.

[0044] The analysis unit can automatically send push notifications based on the text input of the sender, delivering information to the recipient instantly. For example, if the sender inputs "meeting minutes," the generation AI automatically sends a push notification, delivering the information to the recipient instantly. The analysis unit can also use push notification technologies such as mobile app notifications and browser notifications. Furthermore, the analysis unit can also send appropriate push notifications depending on the input content, delivering the information to the recipient instantly. This allows for automatic push notifications to be sent based on the text input of the sender, delivering the information to the recipient instantly, improving the speed and efficiency of information transmission.

[0045] The output unit can automatically store the information received by the recipient in cloud storage so that it can be accessed when needed. For example, when the recipient receives "meeting minutes," the generation AI automatically stores the information in cloud storage so that it can be accessed when needed. The output unit can also use cloud storage such as AWS S3 or Google Drive. Furthermore, the output unit can also store the received information in appropriate cloud storage so that it can be accessed when needed. This improves the convenience of information storage and access by automatically storing the information received by the recipient in cloud storage so that it can be accessed when needed.

[0046] The output unit can automatically tag the information received by the recipient, making it easier to search. For example, when a recipient receives "meeting minutes," the generation AI automatically tags the information, making it easier to search. The output unit can also perform keyword-based tagging or category-based tagging. Furthermore, the output unit can appropriately tag the received information, making it easier to search. This allows the recipient to automatically tag the information they receive, making it easier to search, thereby improving the efficiency of information management and search.

[0047] The output unit automatically links the information received by the recipient to a calendar or task management app, enabling efficient management. For example, when the recipient receives "meeting minutes," the generation AI automatically links the information to the calendar, enabling efficient management. The output unit can also use calendar and task management apps such as Google Calendar and Microsoft To-Do. Furthermore, the output unit can also appropriately link the received information to a calendar or task management app, enabling efficient management. This allows the recipient to automatically link the information received to a calendar or task management app, enabling efficient management, thereby improving the convenience of information management and use.

[0048] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0049] The analysis unit can automatically generate graphs and charts based on the sender's input and provide information visually. For example, if the sender inputs "sales data," the generation AI automatically analyzes the sales data and displays it visually in formats such as bar graphs and pie charts. The analysis unit can also select the optimal graph or chart format depending on the type of data. Furthermore, the analysis unit can add animation effects to visually show data fluctuations and trends. This allows the information to be automatically generated based on the sender's input and provided visually, improving the understandability and ease of acceptance of the information.

[0050] The analysis unit can automatically generate presentation slides based on the sender's input and convey information visually. For example, if the sender inputs "introducing a new product," the generation AI automatically creates presentation slides that visually display the product's features and advantages. The analysis unit can also automatically adjust the slide design and layout. Furthermore, the analysis unit can select the optimal slide template based on the input and automatically generate a presentation. This allows the sender to automatically generate presentation slides based on their input and convey information visually, improving the ease of information reception and understanding.

[0051] The analysis unit can automatically generate a voice message based on the input content of the caller and provide the information to the recipient by voice. For example, if the caller inputs "meeting minutes," the generation AI automatically creates a voice message and conveys the contents of the meeting by voice. The analysis unit can also generate natural-sounding voices using speech synthesis technology. Furthermore, the analysis unit can set the optimal voice tone and speed according to the input content and automatically generate a voice message. This allows the system to automatically generate a voice message based on the input content of the caller and provide information by voice to the recipient, improving the ease of receiving and understanding the information.

[0052] The analysis unit can automatically generate infographics based on the sender's input and provide information visually. For example, if the sender inputs "market research results," the generation AI automatically creates an infographic and visually displays the survey results. The analysis unit can also select the optimal infographic format depending on the type of data. Furthermore, the analysis unit can add design elements to visually show data fluctuations and trends. This allows the information to be automatically generated based on the sender's input and provided visually, improving the understandability and ease of acceptance of the information.

[0053] The analysis unit can automatically provide information through the digital assistant based on the input content of the caller. For example, if the caller inputs "schedule a meeting tomorrow," the generation AI will automatically notify the digital assistant of the scheduled meeting. The analysis unit can also provide information using the digital assistant's voice or text. Furthermore, the analysis unit can also provide information through the digital assistant at the optimal timing depending on the input content. This makes it possible to automatically provide information through the digital assistant based on the input content of the caller, thereby improving the ease and convenience of receiving information.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The text input unit inputs text from the information sender. For example, it provides an interface for the user to input text. The text input unit can also support voice input and handwritten input. Step 2: The analysis unit analyzes the text entered by the text input unit. For example, the generation AI can understand the context of the text and automatically complete or correct it as needed. The generation AI can also automatically suggest related additional information based on the caller's input. Furthermore, the generation AI can analyze the caller's emotions and convert them into an appropriate tone and expression. Step 3: The output unit outputs the information analyzed by the analysis unit in the format optimal for the recipient. For example, the generation AI can provide information in the form of a chat, image, reminder, or other format depending on the recipient's needs. The generation AI can also output information in the optimal format depending on the recipient's device or application environment.

[0056] (Example 2) The communication tool according to an embodiment of the present invention is a system in which the sender of information inputs information in text format and the generation AI outputs it in any format. This reduces the effort required for creating and storing documents, and allows the recipient to receive information in a way that is easy to understand.

[0057] A communication tool according to an embodiment includes a text input unit, an analysis unit, and an output unit. The text input unit inputs text from a sender of information. For example, it provides an interface for a user to input text. The text input unit can also support voice input and handwriting input. The analysis unit analyzes the text input by the text input unit. For example, the generation AI understands the context of the text and automatically completes or corrects it as necessary. The generation AI can also automatically suggest related additional information based on the sender's input. Furthermore, the generation AI can analyze the sender's emotions and convert them into an appropriate tone or expression. The output unit outputs the information analyzed by the analysis unit in a format optimal for the recipient. For example, the generation AI provides information in the form of a chat, image, reminder, or other format depending on the recipient's needs. The generation AI can also output information in an optimal format depending on the recipient's device or application environment. This allows the communication tool according to an embodiment to reduce the effort required for document creation and storage and provide information in a manner that is easy for recipients to understand. For example, when a school teacher wants to inform a class schedule, they only need to input the text, and the generation AI will automatically analyze the information and output it in the optimal format for the recipient, significantly reducing the effort required for document creation, printing, and distribution. Recipients also no longer need to store documents, making information management easier.

[0058] The analysis unit understands the context of the text entered by the text input unit and can automatically complete or correct it as needed. For example, if the sender enters "Tomorrow's meeting is in the afternoon," the generation AI will understand the context and automatically complete it as "Tomorrow's meeting starts at 2:00 p.m." The analysis unit can also perform context analysis and extract related topics to understand the context. Furthermore, the analysis unit can perform dictionary-based completion and correction using machine learning models. This allows the context of the sender's text input to be understood and automatically completed or corrected as needed, maintaining the accuracy and consistency of the information.

[0059] The analysis unit can automatically suggest related additional information based on the text entered by the text input unit. For example, if the sender enters "Regarding the start of a new project," the generation AI automatically suggests communication content related to past project starts. The analysis unit can also retrieve related additional information from a database. Furthermore, the analysis unit can also retrieve and suggest data from an external API. This makes it possible to automatically suggest related additional information based on the sender's input, thereby improving the richness and convenience of the information.

[0060] The analysis unit can analyze the sender's emotions and convert them into an appropriate tone and expression. For example, if the sender types, "This problem is very troubling," the generation AI analyzes the emotion and converts it into an appropriate tone, such as, "I would like your cooperation on this problem." The analysis unit can also analyze the sender's emotions using emotion analysis algorithms and facial expression recognition technology. Furthermore, the analysis unit can set standards for appropriate tones and expressions, such as formal or casual expressions. This allows the quality of communication to be improved by analyzing the sender's emotions and converting them into appropriate tones and expressions.

[0061] The text input unit supports voice input and handwritten input, which the generation AI can analyze and convert into text. For example, if the caller voice-inputs, "Tomorrow's meeting starts at 2:00 PM," the generation AI analyzes the voice and converts it into text. The text input unit can also input handwritten characters with a digital pen, which the generation AI analyzes and converts into text. Furthermore, the text input unit can analyze input using voice recognition technology and handwritten character recognition technology. This allows for voice input and handwritten input, which the generation AI can analyze and convert into text, providing a variety of input methods and improving convenience.

[0062] The analysis unit translates the input content of the sender in real time and can output it in an appropriate format for recipients who speak different languages. For example, if the sender inputs "Tomorrow's meeting will start at 2 PM" in Japanese, the generation AI will translate it into English in real time and output it as "The meeting will start at 2 PM tomorrow." The analysis unit can also perform translation in real time using a translation algorithm. Furthermore, the analysis unit can also set different supported language types. This allows the sender to translate the input content in real time and output it in an appropriate format for recipients who speak different languages, making international communication smoother.

[0063] The analysis unit can provide feedback in real time based on the sender's emotions and encourage improvement of the content of the message. For example, if the sender inputs, "This problem is very troubling," the generation AI analyzes the emotion and provides feedback such as, "I would like to ask for your cooperation on this problem." The analysis unit can also use its emotion estimation function to analyze the sender's emotions and provide appropriate feedback. Furthermore, the analysis unit can set criteria for positive and negative feedback. This allows the system to provide feedback in real time based on the sender's emotions and encourage improvement of the content of the message, thereby improving the quality of communication.

[0064] The output unit can automatically select the optimal output format based on the recipient's past behavioral history and preferences. For example, if the recipient has often received information in chat format in the past, the generation AI will automatically output the information in chat format. The output unit can also select the optimal output format based on the recipient's browsing history and purchase history. Furthermore, the output unit can also understand the recipient's preferences based on survey results and select the optimal output format. This makes it possible to provide the recipient with the most appropriate information by automatically selecting the optimal output format based on the recipient's past behavioral history and preferences.

[0065] The output unit can output information in the optimal format depending on the device and application environment of the recipient. For example, if the recipient is using a smartphone, the generation AI can output information in a format optimal for the smartphone. Also, if the recipient is using a tablet, the output unit can output information in a format optimal for the tablet. Furthermore, the output unit can also output information in the optimal format depending on a specific application environment. This makes it possible to improve the ease of receiving information by outputting information in the optimal format depending on the device and application environment of the recipient.

[0066] The output unit can select an output format according to the receiver's emotional state and elicit a positive response. For example, if the receiver is feeling stressed, the generation AI can output information in a tone that will relax the receiver. Also, if the receiver is happy, the generation AI can output information in a positive tone. Furthermore, the output unit can analyze the receiver's emotional state using an emotion estimation function and select the optimal output format. This allows the quality of communication to be improved by selecting an output format according to the receiver's emotional state and eliciting a positive response.

[0067] The output unit can add interactive elements to deepen the recipient's understanding when receiving information. For example, the output unit can provide information in the form of a quiz to check the recipient's level of understanding when receiving information. The output unit can also provide information using interactive content. Furthermore, the output unit can provide information in the form of a game to attract the recipient's interest. This can improve the recipient's ease of receiving and understanding of information by adding interactive elements to deepen their understanding when receiving information.

[0068] The output unit can use AR or VR to make the information visually easier to understand when the recipient receives it. For example, the output unit can use AR to visually display the information when the recipient receives it, deepening their understanding. The output unit can also provide the information using VR. Furthermore, the output unit can also use an AR kit or a VR headset to make the information visually easier to understand. This makes it possible to improve the recipient's understanding of the information by making the information visually easier to understand when the recipient receives it using AR or VR.

[0069] The analysis unit can automatically format the text input from the sender and generate professional documents. For example, if the sender inputs "meeting minutes," the generation AI automatically formats the text and generates professional minutes. The analysis unit can also set standards and formats for professional documents such as business letters and reports. Furthermore, the analysis unit can select and automatically format the appropriate format depending on the content of the document. This automatically formats the text input from the sender and generates professional documents, improving the efficiency of document creation.

[0070] The analysis unit automatically selects a template based on the input from the sender, enabling efficient document creation. For example, if the sender inputs "meeting minutes," the generation AI automatically selects an appropriate template and efficiently creates minutes. The analysis unit can also set types such as business document templates and presentation templates. Furthermore, the analysis unit can also select the optimal template based on the input and automatically create documents. This allows for automatic template selection based on the sender's input and efficient document creation, improving the efficiency of document creation.

[0071] The analysis unit can automatically adjust the tone and style of a document according to the sender's emotions. For example, if the sender types, "This problem is very troubling," the generation AI analyzes the emotion and adjusts the tone to, "I would like to ask for your cooperation on this problem." The analysis unit can also set standards such as a formal tone or a casual style. Furthermore, the analysis unit can analyze the sender's emotions using an emotion estimation function and adjust the tone and style appropriately. This automatically adjusts the tone and style of a document according to the sender's emotions, improving the readability and effectiveness of the document.

[0072] The analysis unit can automatically generate video content based on the sender's text input and convey information visually. For example, if the sender inputs "introduction to a new product," the analysis unit's generation AI automatically creates a video that visually displays the product's features and advantages. The analysis unit can also set the type of video, such as animated video or live-action video. Furthermore, the analysis unit can generate optimal video content based on the input content and convey information visually. This allows the sender to automatically generate video content based on the sender's text input and convey information visually, improving the ease of information reception and comprehension.

[0073] The analysis unit can provide real-time feedback on the document based on the sender's emotions and encourage improvements. For example, if the sender enters, "This problem is very troubling," the generation AI analyzes the emotion and provides feedback such as, "I would appreciate your help with this problem." The analysis unit can also provide feedback such as grammar checks and suggestions for content improvement. Furthermore, the analysis unit can analyze the sender's emotions using an emotion estimation function and provide appropriate feedback in real time. This allows the quality of documents to be improved by providing real-time feedback on the document based on the sender's emotions and encouraging improvements.

[0074] The analysis unit can automatically add a digital signature to the text input from the sender and authenticate it as an official document. For example, if the sender inputs "minutes of the meeting," the generation AI automatically adds a digital signature and authenticates it as official minutes. The analysis unit can also use digital signature technologies such as public key cryptography and digital certificates. Furthermore, the analysis unit can also add an appropriate digital signature depending on the content of the document and authenticate it as an official document. This allows the reliability and officialness of the document to be improved by automatically adding a digital signature to the text input from the sender and authenticating it as an official document.

[0075] The analysis unit can automatically save the input content of the sender in cloud storage, making it accessible to the recipient at any time. For example, if the sender inputs "meeting minutes," the generation AI automatically saves it in cloud storage, making it accessible to the recipient at any time. The analysis unit can also use cloud storage such as AWS S3 or Google Drive. Furthermore, the analysis unit can save the input content in appropriate cloud storage, making it accessible to the recipient at any time. This improves the convenience of information storage and access by automatically saving the input content of the sender in cloud storage and making it accessible to the recipient at any time.

[0076] The analysis unit can select a distribution method according to the sender's emotions and deliver the information in the most optimal way. For example, if the sender enters, "This problem is very troubling," the generation AI analyzes the emotion and selects the most optimal distribution method. The analysis unit can also set distribution methods such as email delivery or social media posting. Furthermore, the analysis unit can analyze the sender's emotions using an emotion estimation function and select the most optimal distribution method. This allows the distribution method to be selected according to the sender's emotions and delivers the information in the most optimal way, improving the ease and effectiveness of information reception.

[0077] The analysis unit can automatically distribute information via email or social media based on the sender's text input. For example, if the sender inputs "meeting minutes," the generation AI will automatically distribute the information via email. The analysis unit can also use email and social media such as Gmail, Twitter, and Facebook. Furthermore, the analysis unit can also distribute information via appropriate email or social media depending on the input content. This improves the efficiency of information transmission by automatically distributing information via email or social media based on the sender's text input.

[0078] The analysis unit can automatically send push notifications based on the text input of the sender, delivering information to the recipient instantly. For example, if the sender inputs "meeting minutes," the generation AI automatically sends a push notification, delivering the information to the recipient instantly. The analysis unit can also use push notification technologies such as mobile app notifications and browser notifications. Furthermore, the analysis unit can also send appropriate push notifications depending on the input content, delivering the information to the recipient instantly. This allows for automatic push notifications to be sent based on the text input of the sender, delivering the information to the recipient instantly, improving the speed and efficiency of information transmission.

[0079] The output unit can automatically store the information received by the recipient in cloud storage so that it can be accessed when needed. For example, when the recipient receives "meeting minutes," the generation AI automatically stores the information in cloud storage so that it can be accessed when needed. The output unit can also use cloud storage such as AWS S3 or Google Drive. Furthermore, the output unit can also store the received information in appropriate cloud storage so that it can be accessed when needed. This improves the convenience of information storage and access by automatically storing the information received by the recipient in cloud storage so that it can be accessed when needed.

[0080] The output unit can automatically tag the information received by the recipient, making it easier to search. For example, when a recipient receives "meeting minutes," the generation AI automatically tags the information, making it easier to search. The output unit can also perform keyword-based tagging or category-based tagging. Furthermore, the output unit can appropriately tag the received information, making it easier to search. This allows the recipient to automatically tag the information they receive, making it easier to search, thereby improving the efficiency of information management and search.

[0081] The output unit can suggest ways to organize information according to the recipient's emotions and encourage efficient management. For example, if the recipient is feeling stressed, the generation AI can suggest ways to organize information that will help them relax. The output unit can also set information organization methods such as folder organization, tagging, and prioritization. Furthermore, the output unit can analyze the recipient's emotions using an emotion estimation function and suggest the optimal way to organize information. This can improve the convenience of information management and use by suggesting ways to organize information according to the recipient's emotions and encouraging efficient management.

[0082] The output unit automatically links the information received by the recipient to a calendar or task management app, enabling efficient management. For example, when the recipient receives "meeting minutes," the generation AI automatically links the information to the calendar, enabling efficient management. The output unit can also use calendar and task management apps such as Google Calendar and Microsoft To-Do. Furthermore, the output unit can also appropriately link the received information to a calendar or task management app, enabling efficient management. This allows the recipient to automatically link the information received to a calendar or task management app, enabling efficient management, thereby improving the convenience of information management and use.

[0083] The output unit can prioritize information according to the recipient's emotions, preventing them from missing important information. For example, if the recipient is feeling stressed, the generation AI can prioritize information that will help them relax. The output unit can also prioritize information based on importance, urgency, and the recipient's level of interest. Furthermore, the output unit can analyze the recipient's emotions using an emotion estimation function and prioritize optimal information. This allows the recipient to prioritize information according to their emotions and prevent them from missing important information, improving the efficiency of information management and use.

[0084] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0085] The analysis unit can automatically generate graphs and charts based on the sender's input and provide information visually. For example, if the sender inputs "sales data," the generation AI automatically analyzes the sales data and displays it visually in formats such as bar graphs and pie charts. The analysis unit can also select the optimal graph or chart format depending on the type of data. Furthermore, the analysis unit can add animation effects to visually show data fluctuations and trends. This allows the information to be automatically generated based on the sender's input and provided visually, improving the understandability and ease of acceptance of the information.

[0086] The analysis unit can automatically generate presentation slides based on the sender's input and convey information visually. For example, if the sender inputs "introducing a new product," the generation AI automatically creates presentation slides that visually display the product's features and advantages. The analysis unit can also automatically adjust the slide design and layout. Furthermore, the analysis unit can select the optimal slide template based on the input and automatically generate a presentation. This allows the sender to automatically generate presentation slides based on their input and convey information visually, improving the ease of information reception and understanding.

[0087] The analysis unit can automatically generate a voice message based on the input content of the caller and provide the information to the recipient by voice. For example, if the caller inputs "meeting minutes," the generation AI automatically creates a voice message and conveys the contents of the meeting by voice. The analysis unit can also generate natural-sounding voices using speech synthesis technology. Furthermore, the analysis unit can set the optimal voice tone and speed according to the input content and automatically generate a voice message. This allows the system to automatically generate a voice message based on the input content of the caller and provide information by voice to the recipient, improving the ease of receiving and understanding the information.

[0088] The analysis unit can automatically generate infographics based on the sender's input and provide information visually. For example, if the sender inputs "market research results," the generation AI automatically creates an infographic and visually displays the survey results. The analysis unit can also select the optimal infographic format depending on the type of data. Furthermore, the analysis unit can add design elements to visually show data fluctuations and trends. This allows the information to be automatically generated based on the sender's input and provided visually, improving the understandability and ease of acceptance of the information.

[0089] The analysis unit can automatically provide information through the digital assistant based on the input content of the caller. For example, if the caller inputs "schedule a meeting tomorrow," the generation AI will automatically notify the digital assistant of the scheduled meeting. The analysis unit can also provide information using the digital assistant's voice or text. Furthermore, the analysis unit can also provide information through the digital assistant at the optimal timing depending on the input content. This makes it possible to automatically provide information through the digital assistant based on the input content of the caller, thereby improving the ease and convenience of receiving information.

[0090] The analysis unit analyzes the sender's emotions and provides appropriate feedback to encourage improvement of the message content. For example, if the sender types, "This problem is very troubling," the generation AI analyzes the emotion and provides feedback such as, "I would like to ask for your cooperation on this problem." The analysis unit can also use its emotion estimation function to analyze the sender's emotions and provide appropriate feedback. Furthermore, the analysis unit can set criteria for positive and negative feedback. This allows the quality of communication to be improved by providing feedback according to the sender's emotions and encouraging improvement of the message content.

[0091] The analysis unit can improve the quality of communication by analyzing the sender's emotions and converting them into an appropriate tone and expression. For example, if the sender types, "I'm having a lot of trouble with this problem," the generation AI analyzes the emotion and converts it into an appropriate tone, such as, "I'd like your cooperation on this problem." The analysis unit can also analyze the sender's emotions using emotion analysis algorithms and facial expression recognition technology. Furthermore, the analysis unit can set standards for appropriate tones and expressions, such as formal or casual expressions. This allows the quality of communication to be improved by analyzing the sender's emotions and converting them into an appropriate tone and expression.

[0092] The analysis unit analyzes the sender's emotions and selects the appropriate distribution method, thereby improving the ease of information acceptance and effectiveness. For example, if the sender enters, "This problem is extremely troubling," the generation AI analyzes the emotion and selects the optimal distribution method. The analysis unit can also set distribution methods such as email distribution or social media posting. Furthermore, the analysis unit can analyze the sender's emotions using an emotion estimation function and select the optimal distribution method. This allows the distribution method to be selected according to the sender's emotions, and the information to be delivered in the optimal way, thereby improving the ease of information acceptance and effectiveness.

[0093] The analysis unit analyzes the sender's emotions and provides appropriate feedback in real time, encouraging them to improve their message content. For example, if the sender types, "This problem is very troubling me," the generation AI analyzes the emotion and provides feedback such as, "I would appreciate your help with this problem." The analysis unit can also provide feedback such as grammar checks and suggestions for improving the content. Furthermore, the analysis unit can use its emotion estimation function to analyze the sender's emotions and provide appropriate feedback in real time. This allows the system to provide feedback in real time based on the sender's emotions and encourage them to improve their message content, thereby improving the quality of documents.

[0094] The analysis unit analyzes the sender's emotions and adjusts the tone and style appropriately, improving the readability and effectiveness of documents. For example, if the sender types, "I'm very troubled by this problem," the generation AI analyzes the emotion and adjusts the tone to, "I'd like to ask for your cooperation on this problem." The analysis unit can also set standards such as a formal tone or a casual style. Furthermore, the analysis unit can use emotion estimation to analyze the sender's emotions and adjust the tone and style appropriately. This automatically adjusts the tone and style of documents according to the sender's emotions, improving the readability and effectiveness of documents.

[0095] The processing flow of the second embodiment will be briefly explained below.

[0096] Step 1: The text input unit inputs text from the information sender. For example, it provides an interface for the user to input text. The text input unit can also support voice input and handwritten input. Step 2: The analysis unit analyzes the text entered by the text input unit. For example, the generation AI can understand the context of the text and automatically complete or correct it as needed. The generation AI can also automatically suggest related additional information based on the caller's input. Furthermore, the generation AI can analyze the caller's emotions and convert them into an appropriate tone and expression. Step 3: The output unit outputs the information analyzed by the analysis unit in the format optimal for the recipient. For example, the generation AI can provide information in the form of a chat, image, reminder, or other format depending on the recipient's needs. The generation AI can also output information in the optimal format depending on the recipient's device or application environment.

[0097] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0098] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0099] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0100] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0101] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0102] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0103] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0104] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0105] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0106] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0107] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0108] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0109] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0110] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0111] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0112] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0113] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0114] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0115] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0116] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0117] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0118] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0119] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0121] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0122] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0123] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0124] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0125] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0126] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0127] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0128] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0129] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0131] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0132] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0133] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0134] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0135] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0136] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0137] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0138] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0139] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0140] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0141] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0142] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0144] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0146] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0147] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0148] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0149] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0150] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0151] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0152] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0153] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0154] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0155] 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.

[0156] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0157] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0158] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0159] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0160] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0161] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0162] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0163] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0164] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a text input unit for inputting text from the information sender; an analysis unit that analyzes the text input by the text input unit; an output unit that outputs the information analyzed by the analysis unit in a format optimal for the recipient; A system characterized by:

2. The text input unit It supports voice input and handwritten input, and the generative AI analyzes it and converts it into text.

2. The system of claim 1.

3. The output unit Automatically selects the optimal output format based on the recipient's past behavioral history and preferences 2. The system of claim 1.

4. The analysis unit Automatically format and generate professional-looking documents based on incoming text input 2. The system of claim 1.

5. The analysis unit Automatically digitally sign text input by the sender, authenticating it as an official document 2. The system of claim 1.

6. The analysis unit Analyze the caller's emotions and adapt the tone and expression accordingly 2. The system of claim 1.

7. The output unit Selecting output formats that correspond to the recipient's emotional state and eliciting positive responses 2. The system of claim 1.

8. The analysis unit Automatically adjust the tone and style of your writing based on the sender's sentiment 2. The system of claim 1.

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