System

Abstract

A system is provided.SOLUTION: The system includes a means for receiving text data inputted by a user, a means for analyzing the received text data and extracting main information, a means for generating materials by using artificial intelligence on the basis of the extracted main information, a means for correcting errors and omissions in the generated materials and unifying formats, and a means for providing the generated materials to the user.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] When business people prepare documents for meetings or sales activities, they often spend a lot of time creating them and are so busy reviewing them with their superiors and other stakeholders that they have no time to think. Another problem is that correcting typos and ensuring consistency in tone and manner during the document creation process takes time. The purpose of this invention is to solve these problems and improve the efficiency of document creation, thereby providing business people with time to think. [Means for solving the problem]

[0005] The present invention is a system that includes a means for receiving text data entered by a user, analyzing the received text data, and extracting key information; a means for generating materials using artificial intelligence based on the extracted key information; a means for correcting typos and standardizing the format of the generated materials; and a means for providing the generated materials to the user. This allows users to simply input the content they want to communicate, and the AI ​​automatically generates professional-looking materials, enabling efficient document creation. Furthermore, by including a means for users to review and correct the generated materials, further accuracy and customization can be achieved.

[0006] "User" refers to an individual or organization that uses this system to input text data and receive the generated materials.

[0007] "Text data" refers to textual information that is input by the user to this system and that serves as the basis for creating materials.

[0008] "Reception" means that the server receives data sent from the terminal.

[0009] "Analysis" refers to the process of classifying received text data using techniques such as natural language processing and extracting necessary information.

[0010] "Key information" refers to important elements such as keywords and numerical data necessary for creating materials, extracted from text data by analytical means.

[0011] "Artificial intelligence (AI)" is a technology that learns from large amounts of data, analyzes the data, and automatically generates materials.

[0012] "Materials" refer to documents such as presentations and reports used in meetings and sales activities.

[0013] "Generation" refers to the process by which artificial intelligence automatically creates materials based on extracted key information.

[0014] "Typographical errors" refer to incorrect or missing characters in a document.

[0015] "Revising" means correcting typographical errors and making the material accurate.

[0016] "Format" refers to the uniform components of a document, such as layout, design, color scheme, fonts, etc.

[0017] "Unification" means standardizing the design and presentation of the entire document to maintain consistency in format.

[0018] "Providing" refers to presenting the generated material in a form that is accessible to users. [Brief explanation of the drawings]

[0019] [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. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0020] 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.

[0021] First, the terms used in the following description will be explained.

[0022] 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, a 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), and an APU (Accelerated Processing Unit).

[0023] 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.

[0024] 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.

[0025] 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), Bluetooth (registered trademark), etc.

[0026] 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."

[0027] [First embodiment]

[0028] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0029] 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.

[0030] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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).

[0031] 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.

[0032] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. 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 acquires the data indicating the user input.

[0033] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The 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.

[0034] 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.

[0035] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0036] 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.

[0037] 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.

[0038] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0039] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0040] This invention is a system that efficiently creates materials for business people to use in meetings and sales activities. This system uses AI to automatically generate materials and provides professional-looking materials based on text data entered by the user.

[0041] Users access the system using a terminal and first input text data that will be the basis for creating materials. This text data includes details of meetings and sales, sales data, marketing strategies, competitor analysis, etc. The text data entered by the user is sent from the terminal to the server.

[0042] The server analyzes the received text data and extracts key information. This analysis is performed using natural language processing (NLP) technology to identify keywords and numerical data necessary for document creation. For example, if the text data contains the information "First Quarter Sales: 500 million yen," the server extracts the keyword "First Quarter Sales" and the numerical data "500 million yen."

[0043] The server then uses an artificial intelligence (AI) model to generate a draft of the materials based on the extracted information. The AI ​​model uses the keywords and numerical data extracted earlier to create appropriate slides. For example, a slide titled "Current Sales Status" might include information such as "First Quarter Sales: 500 million yen."

[0044] The server also corrects typos and formatting errors in the generated materials, ensuring a consistent tone and manner throughout the materials, maintaining a professional appearance.

[0045] The generated documents are sent from the server to the terminal, where they are checked by the user and then final corrections are made. The user can check the generated documents on the terminal, input corrections if necessary, and send them back to the server. This allows the user to create highly accurate documents in a short amount of time.

[0046] For example, if a user enters the following information, "1st Quarter Sales: $500,000. 2nd Quarter Projected Sales: $600,000. Marketing Budget: $200,000. Competitor Analysis," the server will generate the following slide:

[0047] 1. Slide 1: Title: "Today's Meeting"

[0048] 2. Slide 2: Title: "Current Sales Status", Content: "First Quarter Sales: 500 million yen. Second Quarter Forecast Sales: 600 million yen."

[0049] 3. Slide 3: Title: "Marketing Strategy", Content: "Marketing Budget: 200 million yen. Competitor Analysis."

[0050] The system allows users to create documents efficiently and significantly reduces the time required for document creation, while the AI-powered automated generation process ensures consistency and professional quality in documents.

[0051] The processing flow will be explained below.

[0052] Step 1: The user accesses the system using a terminal and inputs the text data required to create the document. For example, the user might input information such as "First quarter sales: 500 million yen. Second quarter forecast sales: 600 million yen. Marketing budget: 200 million yen. Competitor analysis."

[0053] Step 2: The terminal receives the input text data and sends it to the server.

[0054] Step 3: The server analyzes the received text data. Specifically, it uses natural language processing technology to break down the text and extract key keywords and numerical data. For example, important information such as "first quarter sales" and "500 million yen" is identified.

[0055] Step 4: The server uses an artificial intelligence (AI) model to generate a draft of the document based on the extracted key information. During this process, the AI ​​applies pre-trained templates and formats to automatically create slides. For example, a slide titled "Current Sales Status" could contain specific data such as "First Quarter Sales: 500 Million Yen."

[0056] Step 5: The server automatically detects and corrects typographical errors in the generated documents, and applies a unified color scheme, font, layout, and other formatting to ensure consistency across the entire document.

[0057] Step 6: The server sends the generated material to the terminal, where it is presented to the user as a finished, professional-looking product.

[0058] Step 7: The terminal visually displays the received information to the user, who can then review the information and make any necessary corrections.

[0059] Step 8: If the user makes any changes to the document, the terminal sends the changes to the server again. The server generates new documents that reflect the changes and sends them to the terminal again.

[0060] Step 9: Finally, the documents that the user has checked and corrected can be used in meetings and sales activities. Users can efficiently create professional documents through the system.

[0061] This series of steps allows users to create high-quality materials in less time, leaving more time for thinking and strategizing.

[0062] Example 1

[0063] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0064] In today's business environment, creating efficient and professional materials is essential, but traditional methods are time-consuming and labor-intensive. They are also prone to quality issues, such as typographical errors and inconsistent formatting. Furthermore, because the content of materials used in meetings and sales activities is specialized, accurate data analysis and appropriate slide composition are required. It is necessary to solve these issues.

[0065] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0066] In this invention, the server includes means for receiving text data entered by a user, means for analyzing the received text data and extracting key information, means for generating materials using a generative AI model based on the extracted key information, means for correcting typos and omissions in the generated materials and standardizing the format, and means for providing the generated materials to the user. This significantly improves the efficiency of document creation, enabling the rapid creation of high-quality, professional-looking materials.

[0067] A "user" is a person who operates the system and inputs text data to create materials to be used in meetings and sales activities.

[0068] "Text data" is character string information input by the user, including meeting details, business data, sales information, marketing strategies, and analysis of competitors.

[0069] A "generative AI model" is an algorithm that uses artificial intelligence to automatically generate materials based on input data, and utilizes natural language processing technology.

[0070] "Materials" are documents such as presentation slides and reports used in meetings and sales activities, and are the final deliverables provided to users.

[0071] "Typographical errors" refer to incorrect or missing characters in a document, and are a factor that reduces the quality of the document.

[0072] "Format" refers to the consistent appearance and layout of a document, including font size, font, paragraph structure, and heading style.

[0073] "Key information" refers to important keywords and numerical data extracted from the analyzed text data, and is the information that forms the basis for creating materials.

[0074] "Means" is a general expression that refers to the specific processes, methods, and technical elements used to realize each function of a system.

[0075] This invention is a system that allows business people to efficiently create materials to be used in meetings and sales activities. This system uses a generative AI model to automatically generate materials and provides professional-looking materials based on text data entered by the user.

[0076] Users access the system using a terminal and first input text data that will be the basis for creating materials. This text data includes details of meetings and sales, sales data, marketing strategies, competitor analysis, etc. The text data entered by the user is sent from the terminal to the server.

[0077] The server analyzes the received text data and extracts key information. This analysis is performed using natural language processing (NLP) technology to identify keywords and numerical data necessary for document creation. For example, if the text data contains the information "First Quarter Sales: 500 million yen," the server extracts the keyword "First Quarter Sales" and the numerical data "500 million yen." This analysis is performed using an NLP module (e.g., spaCy or NLTK).

[0078] The server then uses a generative AI model to generate a draft document based on the extracted information. The generative AI model is realized using technologies such as OpenAI's GPT-3, and inputs the extracted keywords and numerical data as prompts. For example, a prompt such as "First quarter sales: 500 million yen. Second quarter forecast sales: 600 million yen. Marketing budget: 200 million yen. Please generate a slide about competitor analysis" is input. The AI ​​model then creates the appropriate slides, and for example, a slide titled "Current sales" might include information such as "First quarter sales: 500 million yen."

[0079] The server also corrects typos and ensures consistent formatting within the generated documents. It uses Python's textblob library to detect and correct typos. Standardized formatting techniques, such as Markdown and LaTeX, are used. This ensures a consistent tone and manner throughout the documents, maintaining a professional appearance.

[0080] The generated document is sent from the server to the terminal, where it is confirmed by the user and final corrections are made. The user can check the generated document on the terminal and make corrections if necessary. These corrections are also sent back to the server, and the final document is completed.

[0081] For example, if a user enters the following information, "1st Quarter Sales: $500,000. 2nd Quarter Projected Sales: $600,000. Marketing Budget: $200,000. Competitor Analysis," the server will generate the following slide:

[0082] 1. Slide 1: Title: "Today's Meeting"

[0083] 2. Slide 2: Title: "Current Sales Status", Content: "First Quarter Sales: 500 million yen. Second Quarter Forecast Sales: 600 million yen."

[0084] 3. Slide 3: Title: "Marketing Strategy", Content: "Marketing Budget: 200 million yen. Competitor Analysis."

[0085] The system allows users to create documents efficiently and significantly reduces the time required for document creation, while the automated generation process using generative AI models ensures consistency and professional quality in documents.

[0086] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0087] Step 1:

[0088] The user inputs the content of materials to be used in meetings or sales activities into the terminal. This input data includes specific sales data, marketing strategies, competitor analysis, etc. After inputting, the user clicks the "Send" button to send the data to the server. The input is made into a text box, and text data is generated as output.

[0089] Step 2:

[0090] The terminal sends the text data entered by the user to the server. Data transmission is performed using an HTTP POST request. The text data is received from the terminal as input and is received by the server as output.

[0091] Step 3:

[0092] The server analyzes the text data received from the device. Natural language processing (NLP) techniques are used for this analysis. An NLP module (e.g., spaCy, NLTK) is used to tokenize the text data and extract keywords and important numerical data. Using the received text data as input, data analysis is performed, and keywords and numerical data are obtained as output.

[0093] Step 4:

[0094] The server uses keywords and numerical data extracted using NLP techniques to input prompts into a generative AI model (e.g., OpenAI's GPT-3). These prompts include specific content entered by the user, and the model generates materials based on that. Using the prompt as input, the AI ​​model generates a draft of the materials as output.

[0095] Step 5:

[0096] The server checks the draft documents generated by the AI ​​model, corrects typos, and standardizes the format. It uses Python's textblob library to detect typos and standardizes the format using Markdown or LaTeX. It uses the draft documents generated by the AI ​​model as input and outputs the corrected and standardized documents.

[0097] Step 6:

[0098] The server sends the final material to the terminal. The data is transmitted using a secure protocol (e.g., HTTPS) and provided to the user on the terminal. It receives the final material as input and sends the material to the terminal as output.

[0099] Step 7:

[0100] The user checks the data generated on the terminal, corrects it as necessary, and resends the new corrected data to the server. At this time, the corrections are entered as text describing specific points. The user makes the corrections as input, and the corrected data is sent to the server as output.

[0101] Step 8:

[0102] The server receives the revised data again and completes the final document. It generates the document again based on the revisions, corrects typos, and standardizes the format. It then provides the final document to the user. The revised data is received as input, and the final document is completed as output.

[0103] (Application example 1)

[0104] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0105] In today's business world, there is a strong demand for preparing professional materials quickly for meetings and sales activities. However, creating materials manually takes a lot of time and effort, and it is difficult to ensure consistency and accuracy of the materials. In addition, there is a lack of systems that can provide information in real time when responding immediately on-site. As a result, users are unable to provide information efficiently, which leads to a decline in the quality of service provided to customers.

[0106] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0107] In this invention, the server includes means for receiving text data entered by a user, means for analyzing the received text data to extract key information, and means for generating materials using artificial intelligence based on the extracted key information, thereby enabling the real-time generation and visual display of materials.

[0108] The present invention also includes the following means.

[0109] The system includes a means for allowing a user to check and modify the generated material via a visual display device, a means for configuring and generating the content of each slide in real time based on the extracted key information, and a means for allowing a user to operate and modify the generated material using the visual display device when providing it.

[0110] This allows users to efficiently and instantly provide professional materials while working, improving the quality of service to customers.

[0111] 1. "Means for receiving text data entered by a user" refers to a mechanism for sending text data provided by a user to a server and for the server to receive it.

[0112] 2. "Means for analyzing received text data and extracting key information" means a method for analyzing received text data using natural language processing techniques to identify and extract key information.

[0113] 3. "Means for using artificial intelligence to generate documents based on extracted key information" refers to the process of using AI technology to automatically create professional documents using extracted key information as input.

[0114] 4. "Means for correcting typos and omissions in generated documents and standardizing the format" refers to a function that checks generated documents, corrects typos and omissions, and adjusts the overall format to conform to certain standards.

[0115] 5. "Means for providing generated materials to a visual display device in real time" means a structure for displaying generated materials on a visual display device such as smart glasses in a timely manner.

[0116] 6. "Means by which a user can check and modify the generated material via a visual display device" means a mechanism by which a user can check the content of the material via a visual display device and modify it as necessary.

[0117] 7. "Means for composing and generating the content of each slide in real time based on extracted key information" refers to a technique for appropriately arranging the content of slides based on extracted information and instantly creating materials.

[0118] 8. "Means by which the user can manipulate and modify the generated material using a visual display device when providing it" means a function that allows the user to freely manipulate and revise the provided material using a visual display device.

[0119] This invention relates to a system that enables users to generate efficient and professional materials in a short time. In particular, the system of the present invention automatically generates materials based on text data entered by the user and provides them to a visual display device in real time. In this embodiment, each component of the system and its operation will be described.

[0120] The server first receives text data entered by the user. This is done by sending data provided through smart glasses or other input devices to the server. Next, it uses natural language processing (NLP) techniques to analyze the received data and extract important information. Specific NLP toolkits used include SpaCy and NLTK (Natural Language Toolkit).

[0121] The extracted key information is processed by a generative AI model, which automatically generates slides for the materials. This generative AI model organizes appropriate content into slides based on the information extracted earlier. For example, a slide titled "Current Sales Status" might display data such as "First Quarter Sales: 500 million yen." The generated slides are then corrected for typos and formatted uniformly on the server.

[0122] The server then provides the generated materials in real time to a visual display device (e.g., smart glasses). This visual display device can be a device such as Google Glass or Vuzix. The user can not only check the materials through this device, but also make corrections as needed. These corrections are sent from the visual display device to the server, and the materials are generated again.

[0123] For example, if a user asks, "What are the features of this product?", the voice recognition system sends this as text data to the server. The server extracts the keyword "features," and based on that, the generative AI model creates a slide containing supplementary explanations. This slide is displayed in real time on the smart glasses, allowing the user to instantly explain things to the customer.

[0124] An example of a prompt to be input to a generative AI model is, "Prompt: Based on the keyword 'waterproof function' extracted from the conversation, please generate materials containing a detailed description of this product."

[0125] In this way, the user can quickly and accurately create materials while working, thereby improving the quality of service to customers.

[0126] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0127] Step 1:

[0128] The text data entered by the user is sent from the smart glasses to the server.

[0129] Input: Text data is generated by the voice recognition system and provided by the user via the smart glasses.

[0130] Data processing: Converting voice data into text data using a voice recognition system.

[0131] Output: The server receives the text data.

[0132] Step 2:

[0133] The server analyzes the received text data using natural language processing (NLP) technology and extracts key information.

[0134] Input: Text data received by the server.

[0135] Data processing: Analyze text data using an NLP toolkit such as SpaCy to extract keywords and key information.

[0136] Output: Extracted keywords and key information.

[0137] Step 3:

[0138] The server generates presentation slides using a generative AI model based on the extracted key information.

[0139] Input: Extracted keywords and key information.

[0140] Data processing: The generative AI model determines the appropriate template and slide content, and generates the materials.

[0141] Output: The generated slides and materials.

[0142] Step 4:

[0143] The server corrects typographical errors and standardizes formatting in the generated materials.

[0144] Input: Generated slides and materials.

[0145] Data processing: Apply a process to automatically detect and correct typographical errors and ensure consistent formatting across the entire document.

[0146] Output: Corrected and uniformly formatted materials.

[0147] Step 5:

[0148] The server provides the generated materials to the smart glasses in real time.

[0149] Input: Corrected and uniformly formatted materials.

[0150] Data processing: Converting materials into a display format for smart glasses.

[0151] Output: Materials displayed on smart glasses.

[0152] Step 6:

[0153] The user can review and modify the generated material via a visual display device.

[0154] Input: Documents displayed on smart glasses.

[0155] Data processing: The process of checking the data and instructing corrections as necessary.

[0156] Output: Data to be corrected.

[0157] Step 7:

[0158] The server generates the materials again based on the user's correction instructions and provides them to the smart glasses again.

[0159] Input: Correction instruction data.

[0160] Data processing: Regenerate the materials based on instructions, correcting typos and standardizing the format again.

[0161] Output: Final, revised document displayed on smart glasses.

[0162] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0163] This invention is a system that combines text data entered by a user with an emotion engine that recognizes the user's emotions, allowing business people to efficiently and effectively create materials to be used in meetings and sales activities. This system adjusts the tone and wording of the materials based on the user's emotions, and further optimizes the timing and method of reviewing the materials.

[0164] Users access the system using their devices and first input text data that will form the basis for creating materials. This text data includes details of meetings and sales activities, sales data, marketing strategies, and competitor analysis. The text data entered by the user is sent from the device to the server. At the same time, the emotion engine recognizes the user's emotions in real time through facial recognition and voice analysis.

[0165] The server analyzes the received text data. This analysis is performed using natural language processing (NLP) technology to identify keywords and numerical data necessary for document creation. For example, if the text data contains the information "First Quarter Sales: 500 million yen," the server extracts the keyword "First Quarter Sales" and the numerical data "500 million yen." If the emotion engine determines that the user is feeling stressed, the system applies a corresponding tone and phrasing.

[0166] The server then uses the extracted information to generate a draft document using an artificial intelligence (AI) model. The AI ​​model takes into account the output of the emotion engine and selects the appropriate tone and phrasing. For example, if the user has a positive emotion that emphasizes unity and cooperation, the document will include positive expressions such as "success" and "growth."

[0167] The server also corrects typos and typos in the generated documents and standardizes formatting, ensuring a consistent tone and manner throughout the document and maintaining a professional appearance. Furthermore, the emotion engine can suggest the best time and method for reviewing the documents based on the user's emotions. For example, if the user is tired, the system can suggest reviewing the documents later.

[0168] The generated documents are sent from the server to the terminal for user confirmation and final correction. The user can check the generated documents on the terminal, input corrections if necessary, and send them back to the server. This allows the user to create highly accurate documents in a short amount of time.

[0169] For example, if a user enters the following information, "1st Quarter Sales: 500 million yen. 2nd Quarter Projected Sales: 600 million yen. Marketing Budget: 200 million yen. Competitor Analysis," and the emotion engine determines that the user is stressed, the server will generate a slide like this:

[0170] 1. Slide 1: Title: "Today's Meeting", Tone: "Let's be efficient."

[0171] 2. Slide 2: Title: "Current Sales Status", Content: "First Quarter Sales: 500 million yen. Second Quarter Forecast Sales: 600 million yen."

[0172] 3. Slide 3: Title: "Marketing Strategy", Content: "Marketing Budget: 200 million yen. Competitor Analysis."

[0173] This system allows users to create documents efficiently and significantly reduces the time it takes to create them. In addition, by creating documents that reflect the user's emotions, more effective communication can be achieved.

[0174] The processing flow will be explained below.

[0175] Step 1:

[0176] Users access the system using a terminal and log in. After logging in, they input text data that will be the basis for creating materials into the terminal. For example, they might input information such as "First quarter sales: 500 million yen. Second quarter forecast sales: 600 million yen. Marketing budget: 200 million yen. Competitor analysis."

[0177] Step 2:

[0178] The device receives the input text data and sends it to the server, while the emotion engine detects the user's emotions through facial recognition and voice analysis, and also sends this information to the server.

[0179] Step 3:

[0180] The server analyzes the received text data and uses natural language processing (NLP) technology to extract key information needed to create the document, such as identifying important keywords and numerical data like "first quarter sales," "500 million yen," and "competitor analysis."

[0181] Step 4:

[0182] The server analyzes the user's emotional data sent from the emotion engine to understand the user's current emotional state, for example, identifying whether the user is feeling stressed or in a positive mood.

[0183] Step 5:

[0184] The server uses an AI model to generate a draft document based on the extracted key information and emotion data. The AI ​​model selects the appropriate tone and phrasing based on the user's emotions. For example, if the user is feeling stressed, the tone of the document will be set to simple and encouraging.

[0185] Step 6:

[0186] The server automatically detects and corrects typographical errors in the generated documents, and applies a unified color scheme, font, layout, and other formatting to ensure consistency across the entire document.

[0187] Step 7:

[0188] The server sends the generated materials to the terminal, where professional-looking materials are presented to the user.

[0189] Step 8:

[0190] The terminal visually displays the received materials to the user, who can then check the materials and make any necessary corrections.

[0191] Step 9:

[0192] If the user makes any changes to the document, the terminal sends the changes back to the server, which then generates new documents that reflect the changes and sends them back to the terminal.

[0193] Step 10:

[0194] Finally, once the user has completed the review and corrections, the documents can be used in meetings and sales activities. Through the system, users can create professional documents that are efficient and emotionally responsive.

[0195] This series of steps allows users to create high-quality materials in a short amount of time, leaving more time for thinking and strategizing. Furthermore, materials are created that respond to the user's emotions, resulting in more effective communication.

[0196] Example 2

[0197] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0198] Conventional document creation systems struggle to maintain a consistent tone and appropriate expressions when generating documents based on user-entered text data. Furthermore, because documents are created without considering the user's emotional state, they can lack tone and phrasing that matches the user's emotions and intent, reducing the effectiveness of communication. Furthermore, correcting typos and formatting in generated documents requires manual effort, which is inefficient.

[0199] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0200] In this invention, the server includes means for receiving text data entered by a user, means for analyzing the received text data to extract key information, means for generating materials using artificial intelligence based on the extracted key information and user emotion data recognized in real time, means for correcting typos and omissions in the generated materials and standardizing the format, and means for providing the generated materials to the user. This makes it possible to create materials with a consistent tone and expression based on the text and emotion data, and also automates the correction of typos and standardization of the format, improving the effectiveness of communication and increasing the efficiency of document creation.

[0201] A "user" is an individual or entity that operates the system to input text data and review and modify generated materials.

[0202] "Text data" refers to data including text information and numerical information that a user inputs to create a document.

[0203] The "receiving means" is a function or module that allows the server to obtain text data sent by the user.

[0204] The "means for analyzing and extracting key information" refers to a function or module that analyzes received text data using natural language processing technology and identifies and extracts keywords and numerical data necessary for the material.

[0205] "User emotion data recognized in real time" is data indicating the user's emotional state obtained from the user's facial expression, tone of voice, and the like.

[0206] "Means for generating materials using artificial intelligence" refers to a function or module that automatically creates draft materials using an AI model based on extracted key information and sentiment data.

[0207] "Means for correcting typographical errors and standardizing formatting" refers to a function or module that automatically corrects typographical errors in generated materials and standardizes the formatting of the entire document to be consistent.

[0208] The "means for providing" is a function or module for transmitting the generated materials to the user's terminal and providing them.

[0209] "Materials" are documents such as letters, slides, and reports created for use in meetings and sales activities.

[0210] This invention is a system that combines text data entered by a user with an emotion engine that recognizes the user's emotions. Its purpose is to enable businesspeople to efficiently and effectively create materials to be used in meetings and sales activities. This system adjusts the tone and wording of materials based on the user's emotions, and also optimizes the timing and method of reviewing materials.

[0211] Users access the system using a terminal and first input text data that will form the basis for creating materials. This text data includes details of meetings and sales activities, sales data, marketing strategies, and competitor analysis. The text data entered by the user is sent from the terminal to the server. At the same time, the terminal analyzes the user's face and voice in real time and obtains the user's emotional data using an emotion engine.

[0212] The server analyzes the received text data. This analysis is performed using natural language processing (NLP) technology to extract keywords and numerical data necessary for creating documents from the text data. For example, if the text data contains the information "First Quarter Sales: 500 million yen," the server extracts the keyword "First Quarter Sales" and the numerical data "500 million yen." If the emotion engine indicates that the user is feeling stressed, the system applies a corresponding tone and phrasing.

[0213] Next, the server uses the extracted information to generate a draft document using a generative AI model. This AI model could be GPT-3 or BERT, for example. The AI ​​model takes into account the output of the emotion engine and selects an appropriate tone and phrasing. For example, if the user has positive emotions, such as wanting to emphasize unity and cooperation, the document will include positive expressions like "success" and "growth."

[0214] The server also corrects typos and unifies formatting in the generated documents, ensuring a consistent tone and manner throughout the document and maintaining a professional appearance. Furthermore, the emotion engine can suggest the best time and method for reviewing the documents based on the user's emotions. For example, if the user is tired, the system suggests reviewing them later.

[0215] The generated documents are sent from the server to the terminal, where the user confirms and makes any final corrections. The user confirms the generated documents on the terminal, enters any necessary corrections, and sends them back to the server. In this way, the user can create highly accurate documents in a short amount of time.

[0216] For example, if a user enters the following information, "1st Quarter Sales: 500 million yen. 2nd Quarter Projected Sales: 600 million yen. Marketing Budget: 200 million yen. Competitor Analysis," and the emotion engine determines that the user is stressed, the server will generate a slide like this:

[0217] 1. Slide 1: Title: "Today's Meeting", Tone: "Let's be efficient."

[0218] 2. Slide 2: Title: "Current Sales Status", Content: "First Quarter Sales: 500 million yen. Second Quarter Forecast Sales: 600 million yen."

[0219] 3. Slide 3: Title: "Marketing Strategy", Content: "Marketing Budget: 200 million yen. Competitor Analysis."

[0220] This allows users to create materials efficiently and significantly reduces the time it takes to create them. In addition, by creating materials that reflect the user's emotions, more effective communication can be achieved.

[0221] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0222] Specific processing steps of the program

[0223] Step 1:

[0224] A user operates a terminal to access the system. First, they access the login page using a web browser or a dedicated application and enter their authentication information to log in. The input of this step is the user's authentication information, and the output is the login status (success or failure).

[0225] Step 2:

[0226] The user inputs text data for creating materials into the terminal. Information such as the contents of the meeting, sales data, and marketing strategies is entered into a dedicated input form, and the user presses the send button. The input in this step is the text data entered by the user, and the output is that text data is sent from the terminal to the server.

[0227] Step 3:

[0228] The terminal sends the entered text data to the server. The text data is sent to the server in JSON format via the Web API as a POST request. The input in this step is the text data entered by the user, and the output is the text data received by the server.

[0229] Step 4:

[0230] The device acquires emotional data in real time from the user's face and voice. Using the device's built-in camera and microphone, the device analyzes the user's emotional data using facial recognition software (e.g., OpenCV) and voice analysis software (e.g., Praat). The input for this step is the user's facial expression and tone of voice, and the output is emotional data.

[0231] Step 5:

[0232] The device sends the acquired emotion data to the server. The emotion data analyzed by the emotion engine is sent to the server in JSON format. The input of this step is the emotion data acquired by the device, and the output is the emotion data received by the server.

[0233] Step 6:

[0234] The server analyzes the received text data using natural language processing (NLP) techniques. It uses Python scripts and NLP libraries (spaCy, NLTK) to extract keywords and numerical data from the text data. The input for this step is the text data entered by the user, and the output is the extracted key information.

[0235] Step 7:

[0236] The server adjusts the tone and wording of the document based on the emotion data. It analyzes the output of the emotion engine and selects a tone that will relax the user if they are feeling stressed, and reflects this in the document. The inputs for this step are emotion data and extracted key information, and the output is the adjusted key information.

[0237] Step 8:

[0238] The server uses a generative AI model to generate a draft document based on the extracted information. For example, a prompt sentence is input into an AI model such as GPT-3 to generate a draft document. The input in this step is the adjusted key information and prompt sentence, and the output is the generated draft document. Example: Prompt sentence: "Please create a document based on first quarter sales."

[0239] Step 9:

[0240] The server corrects typos and standardizes the format of the generated document. It uses a proofreading tool (such as Grammarly or Hemingway) to ensure a consistent tone and format throughout the document. The input to this step is a draft of the generated document, and the output is the corrected and standardized document.

[0241] Step 10:

[0242] The server sends the generated documents to the terminal in JSON or PDF format and provides them to the user. The input of this step is the corrected and formatted documents, and the output is the documents provided to the user's terminal.

[0243] Step 11:

[0244] The user checks the generated data on the terminal and makes corrections as necessary. The data is displayed on the terminal and corrections are entered through the user interface. The input of this step is the provided data, and the output is the correction data entered by the user.

[0245] Step 12:

[0246] The terminal sends the corrected document back to the server. The document with the corrected data is sent back to the server for final confirmation. The input of this step is the document with the corrected data, and the output is the final confirmed document.

[0247] These steps allow users to create materials efficiently and with sensitivity.

[0248] (Application example 2)

[0249] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0250] Conventional document creation systems automatically process large amounts of data without considering the user's emotions, often failing to reflect the optimal tone or phrasing, resulting in stressful situations for users. Furthermore, the timing of review and revision after document creation is uniform and not dependent on the user's state, making it difficult to provide an efficient work environment. Therefore, there is a need for a system that appropriately reflects the user's emotions and provides more efficient review timing.

[0251] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0252] In this invention, the server includes means for receiving text data entered by a user, means for analyzing the received text data and extracting key information, means for generating materials using artificial intelligence based on the extracted key information, means for correcting typos and omissions in the generated materials and standardizing the format, means for recognizing the user's emotions and adjusting the tone and wording of the materials in response to the emotions, means for suggesting the timing and method of reviewing the materials based on the user's emotions, and means for recognizing emotions using the user's voice or video data and reflecting the recognition results in the generation of materials. This makes it possible to create effective materials that appropriately reflect the user's emotions and provide an efficient work environment.

[0253] "User" refers to an individual or organization that uses the System to create materials.

[0254] "Text data" refers to textual information entered by users, including details of meetings and sales, sales data, marketing strategies, and competitor analysis.

[0255] "Key information" refers to information extracted from text data, such as important keywords and numerical data.

[0256] "Artificial intelligence" refers to computer systems that have algorithms and models for analyzing text data and generating materials.

[0257] "Emotion engine" refers to a software or hardware system that recognizes a user's emotional state and adjusts the tone and phrasing of material accordingly.

[0258] "Error correction" refers to the process of automatically correcting incorrect characters or omissions in generated materials.

[0259] "Format unification" refers to the process of ensuring that the format and layout of generated materials are consistent.

[0260] "Document review timing" refers to a function that suggests the appropriate time or duration for a user to review and revise documents.

[0261] "Voice data" refers to voice information that records what a user says.

[0262] "Video data" refers to video information such as the user's face and posture.

[0263] "Tone" refers to the tone of the language and expressions used in a document.

[0264] The present invention is a system that combines text data entered by a user with an emotion engine that recognizes the user's emotions, allowing a robot used in a factory to recognize the emotional state of factory staff in real time and provide work instructions and feedback efficiently based on that information. Specific embodiments of the present invention will be described below.

[0265] The basic configuration of the system consists of a means for receiving text data entered by a user, a means for analyzing the received text data and extracting key information, a means for generating materials using artificial intelligence (AI) based on the extracted key information, a means for correcting typos and omissions in the generated materials and standardizing the format, a means for providing the generated materials to the user, a means for recognizing the user's emotions and adjusting the tone and wording of the materials in response to the emotions, a means for suggesting the timing and method for reviewing the materials based on the user's emotions, and a means for recognizing emotions using the user's voice or video data and reflecting the recognition results in the generation of materials.

[0266] Hardware and software used

[0267] The system uses the following hardware and software:

[0268] Hardware: Camera, microphone

[0269] Software: EmotionRecognition (emotion recognition engine), TextGenerator (text generation engine), NLPProcessor (natural language processing engine)

[0270] Processing flow

[0271] 1. Receiving user input: The user inputs text data about the factory status and tasks into the system, for example, receiving instructions such as "Please report on the progress of the production line."

[0272] 2. Emotion recognition: The emotion recognition engine analyzes the user's face and voice in real time through the camera and microphone to determine the user's emotional state.

[0273] 3. Text data analysis: The received text data is analyzed using a natural language processing engine to extract key keywords and numerical data.

[0274] 4. Document generation: Based on the extracted information, the text generation engine generates documents, taking into account the results of the emotion recognition engine and selecting the appropriate tone and wording.

[0275] 5. Correct typos and standardize formatting: Generated materials are automatically corrected for typos and formatted consistently.

[0276] 6. Provision to user: The generated materials are provided to the user, who can review and modify them.

[0277] 7. Review timing suggestion: Suggest the optimal review timing and method based on the user's emotional state.

[0278] Specific examples

[0279] For example, when checking the progress of a factory line, if the robot detects that a staff member is under stress, it will say in an encouraging tone, "Today is tough again, but let's do our best together!" and instruct the staff to transform negative emotions into positive ones.

[0280] Example prompts to input to a generative AI model:

[0281] "What words would be effective to say to factory staff when they are feeling stressed?"

[0282] This makes it possible to create effective materials that appropriately reflect the user's emotions and to provide an efficient work environment.

[0283] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0284] Step 1:

[0285] The terminal receives text data from the user about the factory status and tasks. For example, an instruction such as "Please report on the progress of the production line" is input. This text data is sent from the terminal to the server. The input data is in raw text format and is not processed or analyzed.

[0286] Step 2:

[0287] The server analyzes the received text data using a natural language processing engine (NLPProcessor). During the analysis, key keywords and numerical data are extracted. For example, keywords such as "production line" and "progress" and their associated numerical data are identified. The output is the key information in a structured data format.

[0288] Step 3:

[0289] The server uses the EmotionRecognition engine to analyze the user's video and audio data sent from the device via the camera and microphone. The analysis determines the user's emotional state (e.g., stress, relaxation). The input data is in the form of video and audio, and the output is a label indicating the emotional state.

[0290] Step 4:

[0291] The server receives the extracted key information and emotional state labels as input and generates materials using a text generation engine (TextGenerator). During this process, appropriate tone and phrasing are selected depending on the emotional state. For example, if the user is feeling stressed, an encouraging tone is used. The output is a draft document with adjusted tone and phrasing.

[0292] Step 5:

[0293] The server detects and corrects typographical errors in the generated documents and standardizes the format to ensure consistency. The input data is a draft document, and the output is the final document that has been corrected and formatted.

[0294] Step 6:

[0295] The server sends the final document to the terminal and provides an interface for the user to review and modify it. The user reviews the document, inputs any necessary modifications, and then sends it back to the server. The input data is the final document and the user's proposed modifications, and the output is the final document modified by the user.

[0296] Step 7:

[0297] The server then suggests when and how to review the material based on the user's emotional state. For example, if the server determines that the user is tired, it suggests reviewing the material later. The input data for this step is the emotional state label, and the output is the suggestion.

[0298] This series of processes enables the creation of effective materials that appropriately reflect the user's emotions and provides an efficient work environment.

[0299] 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.

[0300] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[0301] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0302] [Second embodiment]

[0303] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0304] 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.

[0305] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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).

[0306] 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.

[0307] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0308] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0309] 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.

[0310] 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.

[0311] 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 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.

[0312] 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.

[0313] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0314] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0315] This invention is a system that efficiently creates materials for business people to use in meetings and sales activities. This system uses AI to automatically generate materials and provides professional-looking materials based on text data entered by the user.

[0316] Users access the system using a terminal and first input text data that will be the basis for creating materials. This text data includes details of meetings and sales, sales data, marketing strategies, competitor analysis, etc. The text data entered by the user is sent from the terminal to the server.

[0317] The server analyzes the received text data and extracts key information. This analysis is performed using natural language processing (NLP) technology to identify keywords and numerical data necessary for document creation. For example, if the text data contains the information "First Quarter Sales: 500 million yen," the server extracts the keyword "First Quarter Sales" and the numerical data "500 million yen."

[0318] The server then uses an artificial intelligence (AI) model to generate a draft of the materials based on the extracted information. The AI ​​model uses the keywords and numerical data extracted earlier to create appropriate slides. For example, a slide titled "Current Sales Status" might include information such as "First Quarter Sales: 500 million yen."

[0319] The server also corrects typos and formatting errors in the generated materials, ensuring a consistent tone and manner throughout the materials, maintaining a professional appearance.

[0320] The generated documents are sent from the server to the terminal, where they are checked by the user and then final corrections are made. The user can check the generated documents on the terminal, input corrections if necessary, and send them back to the server. This allows the user to create highly accurate documents in a short amount of time.

[0321] For example, if a user enters the following information, "1st Quarter Sales: $500,000. 2nd Quarter Projected Sales: $600,000. Marketing Budget: $200,000. Competitor Analysis," the server will generate the following slide:

[0322] 1. Slide 1: Title: "Today's Meeting"

[0323] 2. Slide 2: Title: "Current Sales Status", Content: "First Quarter Sales: 500 million yen. Second Quarter Forecast Sales: 600 million yen."

[0324] 3. Slide 3: Title: "Marketing Strategy", Content: "Marketing Budget: 200 million yen. Competitor Analysis."

[0325] The system allows users to create documents efficiently and significantly reduces the time required for document creation, while the AI-powered automated generation process ensures consistency and professional quality in documents.

[0326] The processing flow will be explained below.

[0327] Step 1: The user accesses the system using a terminal and inputs the text data required to create the document. For example, the user might input information such as "First quarter sales: 500 million yen. Second quarter forecast sales: 600 million yen. Marketing budget: 200 million yen. Competitor analysis."

[0328] Step 2: The terminal receives the input text data and sends it to the server.

[0329] Step 3: The server analyzes the received text data. Specifically, it uses natural language processing technology to break down the text and extract key keywords and numerical data. For example, important information such as "first quarter sales" and "500 million yen" is identified.

[0330] Step 4: The server uses an artificial intelligence (AI) model to generate a draft of the document based on the extracted key information. During this process, the AI ​​applies pre-trained templates and formats to automatically create slides. For example, a slide titled "Current Sales Status" could contain specific data such as "First Quarter Sales: 500 Million Yen."

[0331] Step 5: The server automatically detects and corrects typographical errors in the generated documents, and applies a unified color scheme, font, layout, and other formatting to ensure consistency across the entire document.

[0332] Step 6: The server sends the generated material to the terminal, where it is presented to the user as a finished, professional-looking product.

[0333] Step 7: The terminal visually displays the received information to the user, who can then review the information and make any necessary corrections.

[0334] Step 8: If the user makes any changes to the document, the terminal sends the changes to the server again. The server generates new documents that reflect the changes and sends them to the terminal again.

[0335] Step 9: Finally, the documents that the user has checked and corrected can be used in meetings and sales activities. Users can efficiently create professional documents through the system.

[0336] This series of steps allows users to create high-quality materials in less time, leaving more time for thinking and strategizing.

[0337] Example 1

[0338] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0339] In today's business environment, creating efficient and professional materials is essential, but traditional methods are time-consuming and labor-intensive. They are also prone to quality issues, such as typographical errors and inconsistent formatting. Furthermore, because the content of materials used in meetings and sales activities is specialized, accurate data analysis and appropriate slide composition are required. It is necessary to solve these issues.

[0340] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0341] In this invention, the server includes means for receiving text data entered by a user, means for analyzing the received text data and extracting key information, means for generating materials using a generative AI model based on the extracted key information, means for correcting typos and omissions in the generated materials and standardizing the format, and means for providing the generated materials to the user. This significantly improves the efficiency of document creation, enabling the rapid creation of high-quality, professional-looking materials.

[0342] A "user" is a person who operates the system and inputs text data to create materials to be used in meetings and sales activities.

[0343] "Text data" is character string information input by the user, including meeting details, business data, sales information, marketing strategies, and analysis of competitors.

[0344] A "generative AI model" is an algorithm that uses artificial intelligence to automatically generate materials based on input data, and utilizes natural language processing technology.

[0345] "Materials" are documents such as presentation slides and reports used in meetings and sales activities, and are the final deliverables provided to users.

[0346] "Typographical errors" refer to incorrect or missing characters in a document, and are a factor that reduces the quality of the document.

[0347] "Format" refers to the consistent appearance and layout of a document, including font size, font, paragraph structure, and heading style.

[0348] "Key information" refers to important keywords and numerical data extracted from the analyzed text data, and is the information that forms the basis for creating materials.

[0349] "Means" is a general expression that refers to the specific processes, methods, and technical elements used to realize each function of a system.

[0350] This invention is a system that allows business people to efficiently create materials to be used in meetings and sales activities. This system uses a generative AI model to automatically generate materials and provides professional-looking materials based on text data entered by the user.

[0351] Users access the system using a terminal and first input text data that will be the basis for creating materials. This text data includes details of meetings and sales, sales data, marketing strategies, competitor analysis, etc. The text data entered by the user is sent from the terminal to the server.

[0352] The server analyzes the received text data and extracts key information. This analysis is performed using natural language processing (NLP) technology to identify keywords and numerical data necessary for document creation. For example, if the text data contains the information "First Quarter Sales: 500 million yen," the server extracts the keyword "First Quarter Sales" and the numerical data "500 million yen." This analysis is performed using an NLP module (e.g., spaCy or NLTK).

[0353] The server then uses a generative AI model to generate a draft document based on the extracted information. The generative AI model is realized using technologies such as OpenAI's GPT-3, and inputs the extracted keywords and numerical data as prompts. For example, a prompt such as "First quarter sales: 500 million yen. Second quarter forecast sales: 600 million yen. Marketing budget: 200 million yen. Please generate a slide about competitor analysis" is input. The AI ​​model then creates the appropriate slides, and for example, a slide titled "Current sales" might include information such as "First quarter sales: 500 million yen."

[0354] The server also corrects typos and ensures consistent formatting within the generated documents. It uses Python's textblob library to detect and correct typos. Standardized formatting techniques, such as Markdown and LaTeX, are used. This ensures a consistent tone and manner throughout the documents, maintaining a professional appearance.

[0355] The generated document is sent from the server to the terminal, where it is confirmed by the user and final corrections are made. The user can check the generated document on the terminal and make corrections if necessary. These corrections are also sent back to the server, and the final document is completed.

[0356] For example, if a user enters the following information, "1st Quarter Sales: $500,000. 2nd Quarter Projected Sales: $600,000. Marketing Budget: $200,000. Competitor Analysis," the server will generate the following slide:

[0357] 1. Slide 1: Title: "Today's Meeting"

[0358] 2. Slide 2: Title: "Current Sales Status", Content: "First Quarter Sales: 500 million yen. Second Quarter Forecast Sales: 600 million yen."

[0359] 3. Slide 3: Title: "Marketing Strategy", Content: "Marketing Budget: 200 million yen. Competitor Analysis."

[0360] The system allows users to create documents efficiently and significantly reduces the time required for document creation, while the automated generation process using generative AI models ensures consistency and professional quality in documents.

[0361] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0362] Step 1:

[0363] The user inputs the content of materials to be used in meetings or sales activities into the terminal. This input data includes specific sales data, marketing strategies, competitor analysis, etc. After inputting, the user clicks the "Send" button to send the data to the server. The input is made into a text box, and text data is generated as output.

[0364] Step 2:

[0365] The terminal sends the text data entered by the user to the server. Data transmission is performed using an HTTP POST request. The text data is received from the terminal as input and is received by the server as output.

[0366] Step 3:

[0367] The server analyzes the text data received from the device. Natural language processing (NLP) techniques are used for this analysis. An NLP module (e.g., spaCy, NLTK) is used to tokenize the text data and extract keywords and important numerical data. Using the received text data as input, data analysis is performed, and keywords and numerical data are obtained as output.

[0368] Step 4:

[0369] The server uses keywords and numerical data extracted using NLP techniques to input prompts into a generative AI model (e.g., OpenAI's GPT-3). These prompts include specific content entered by the user, and the model generates materials based on that. Using the prompt as input, the AI ​​model generates a draft of the materials as output.

[0370] Step 5:

[0371] The server checks the draft documents generated by the AI ​​model, corrects typos, and standardizes the format. It uses Python's textblob library to detect typos and standardizes the format using Markdown or LaTeX. It uses the draft documents generated by the AI ​​model as input and outputs the corrected and standardized documents.

[0372] Step 6:

[0373] The server sends the final material to the terminal. The data is transmitted using a secure protocol (e.g., HTTPS) and provided to the user on the terminal. It receives the final material as input and sends the material to the terminal as output.

[0374] Step 7:

[0375] The user checks the data generated on the terminal, corrects it as necessary, and resends the new corrected data to the server. At this time, the corrections are entered as text describing specific points. The user makes the corrections as input, and the corrected data is sent to the server as output.

[0376] Step 8:

[0377] The server receives the revised data again and completes the final document. It generates the document again based on the revisions, corrects typos, and standardizes the format. It then provides the final document to the user. The revised data is received as input, and the final document is completed as output.

[0378] (Application example 1)

[0379] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0380] In today's business world, there is a strong demand for preparing professional materials quickly for meetings and sales activities. However, creating materials manually takes a lot of time and effort, and it is difficult to ensure consistency and accuracy of the materials. In addition, there is a lack of systems that can provide information in real time when responding immediately on-site. As a result, users are unable to provide information efficiently, which leads to a decline in the quality of service provided to customers.

[0381] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0382] In this invention, the server includes means for receiving text data entered by a user, means for analyzing the received text data to extract key information, and means for generating materials using artificial intelligence based on the extracted key information, thereby enabling the real-time generation and visual display of materials.

[0383] The present invention also includes the following means.

[0384] The system includes a means for allowing a user to check and modify the generated material via a visual display device, a means for configuring and generating the content of each slide in real time based on the extracted key information, and a means for allowing a user to operate and modify the generated material using the visual display device when providing it.

[0385] This allows users to efficiently and instantly provide professional materials while working, improving the quality of service to customers.

[0386] 1. "Means for receiving text data entered by a user" refers to a mechanism for sending text data provided by a user to a server and for the server to receive it.

[0387] 2. "Means for analyzing received text data and extracting key information" means a method for analyzing received text data using natural language processing techniques to identify and extract key information.

[0388] 3. "Means for using artificial intelligence to generate documents based on extracted key information" refers to the process of using AI technology to automatically create professional documents using extracted key information as input.

[0389] 4. "Means for correcting typos and omissions in generated documents and standardizing the format" refers to a function that checks generated documents, corrects typos and omissions, and adjusts the overall format to conform to certain standards.

[0390] 5. "Means for providing generated materials to a visual display device in real time" means a structure for displaying generated materials on a visual display device such as smart glasses in a timely manner.

[0391] 6. "Means by which a user can check and modify the generated material via a visual display device" means a mechanism by which a user can check the content of the material via a visual display device and modify it as necessary.

[0392] 7. "Means for composing and generating the content of each slide in real time based on extracted key information" refers to a technique for appropriately arranging the content of slides based on extracted information and instantly creating materials.

[0393] 8. "Means by which the user can manipulate and modify the generated material using a visual display device when providing it" means a function that allows the user to freely manipulate and revise the provided material using a visual display device.

[0394] This invention relates to a system that enables users to generate efficient and professional materials in a short time. In particular, the system of the present invention automatically generates materials based on text data entered by the user and provides them to a visual display device in real time. In this embodiment, each component of the system and its operation will be described.

[0395] The server first receives text data entered by the user. This is done by sending data provided through smart glasses or other input devices to the server. Next, it uses natural language processing (NLP) techniques to analyze the received data and extract important information. Specific NLP toolkits used include SpaCy and NLTK (Natural Language Toolkit).

[0396] The extracted key information is processed by a generative AI model, which automatically generates slides for the materials. This generative AI model organizes appropriate content into slides based on the information extracted earlier. For example, a slide titled "Current Sales Status" might display data such as "First Quarter Sales: 500 million yen." The generated slides are then corrected for typos and formatted uniformly on the server.

[0397] The server then provides the generated materials in real time to a visual display device (e.g., smart glasses). This visual display device can be a device such as Google Glass or Vuzix. The user can not only check the materials through this device, but also make corrections as needed. These corrections are sent from the visual display device to the server, and the materials are generated again.

[0398] For example, if a user asks, "What are the features of this product?", the voice recognition system sends this as text data to the server. The server extracts the keyword "features," and based on that, the generative AI model creates a slide containing supplementary explanations. This slide is displayed in real time on the smart glasses, allowing the user to instantly explain things to the customer.

[0399] An example of a prompt to be input to a generative AI model is, "Prompt: Based on the keyword 'waterproof function' extracted from the conversation, please generate materials containing a detailed description of this product."

[0400] In this way, the user can quickly and accurately create materials while working, thereby improving the quality of service to customers.

[0401] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0402] Step 1:

[0403] The text data entered by the user is sent from the smart glasses to the server.

[0404] Input: Text data is generated by the voice recognition system and provided by the user via the smart glasses.

[0405] Data processing: Converting voice data into text data using a voice recognition system.

[0406] Output: The server receives the text data.

[0407] Step 2:

[0408] The server analyzes the received text data using natural language processing (NLP) technology and extracts key information.

[0409] Input: Text data received by the server.

[0410] Data processing: Analyze text data using an NLP toolkit such as SpaCy to extract keywords and key information.

[0411] Output: Extracted keywords and key information.

[0412] Step 3:

[0413] The server generates presentation slides using a generative AI model based on the extracted key information.

[0414] Input: Extracted keywords and key information.

[0415] Data processing: The generative AI model determines the appropriate template and slide content, and generates the materials.

[0416] Output: The generated slides and materials.

[0417] Step 4:

[0418] The server corrects typographical errors and standardizes formatting in the generated materials.

[0419] Input: Generated slides and materials.

[0420] Data processing: Apply a process to automatically detect and correct typographical errors and ensure consistent formatting across the entire document.

[0421] Output: Corrected and uniformly formatted materials.

[0422] Step 5:

[0423] The server provides the generated materials to the smart glasses in real time.

[0424] Input: Corrected and uniformly formatted materials.

[0425] Data processing: Converting materials into a display format for smart glasses.

[0426] Output: Materials displayed on smart glasses.

[0427] Step 6:

[0428] The user can review and modify the generated material via a visual display device.

[0429] Input: Documents displayed on smart glasses.

[0430] Data processing: The process of checking the data and instructing corrections as necessary.

[0431] Output: Data to be corrected.

[0432] Step 7:

[0433] The server generates the materials again based on the user's correction instructions and provides them to the smart glasses again.

[0434] Input: Correction instruction data.

[0435] Data processing: Regenerate the materials based on instructions, correcting typos and standardizing the format again.

[0436] Output: Final, revised document displayed on smart glasses.

[0437] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0438] This invention is a system that combines text data entered by a user with an emotion engine that recognizes the user's emotions, allowing business people to efficiently and effectively create materials to be used in meetings and sales activities. This system adjusts the tone and wording of the materials based on the user's emotions, and further optimizes the timing and method of reviewing the materials.

[0439] Users access the system using their devices and first input text data that will form the basis for creating materials. This text data includes details of meetings and sales activities, sales data, marketing strategies, and competitor analysis. The text data entered by the user is sent from the device to the server. At the same time, the emotion engine recognizes the user's emotions in real time through facial recognition and voice analysis.

[0440] The server analyzes the received text data. This analysis is performed using natural language processing (NLP) technology to identify keywords and numerical data necessary for document creation. For example, if the text data contains the information "First Quarter Sales: 500 million yen," the server extracts the keyword "First Quarter Sales" and the numerical data "500 million yen." If the emotion engine determines that the user is feeling stressed, the system applies a corresponding tone and phrasing.

[0441] The server then uses the extracted information to generate a draft document using an artificial intelligence (AI) model. The AI ​​model takes into account the output of the emotion engine and selects the appropriate tone and phrasing. For example, if the user has a positive emotion that emphasizes unity and cooperation, the document will include positive expressions such as "success" and "growth."

[0442] The server also corrects typos and typos in the generated documents and standardizes formatting, ensuring a consistent tone and manner throughout the document and maintaining a professional appearance. Furthermore, the emotion engine can suggest the best time and method for reviewing the documents based on the user's emotions. For example, if the user is tired, the system can suggest reviewing the documents later.

[0443] The generated documents are sent from the server to the terminal for user confirmation and final correction. The user can check the generated documents on the terminal, input corrections if necessary, and send them back to the server. This allows the user to create highly accurate documents in a short amount of time.

[0444] For example, if a user enters the following information, "1st Quarter Sales: 500 million yen. 2nd Quarter Projected Sales: 600 million yen. Marketing Budget: 200 million yen. Competitor Analysis," and the emotion engine determines that the user is stressed, the server will generate a slide like this:

[0445] 1. Slide 1: Title: "Today's Meeting", Tone: "Let's be efficient."

[0446] 2. Slide 2: Title: "Current Sales Status", Content: "First Quarter Sales: 500 million yen. Second Quarter Forecast Sales: 600 million yen."

[0447] 3. Slide 3: Title: "Marketing Strategy", Content: "Marketing Budget: 200 million yen. Competitor Analysis."

[0448] This system allows users to create documents efficiently and significantly reduces the time it takes to create them. In addition, by creating documents that reflect the user's emotions, more effective communication can be achieved.

[0449] The processing flow will be explained below.

[0450] Step 1:

[0451] Users access the system using a terminal and log in. After logging in, they input text data that will be the basis for creating materials into the terminal. For example, they might input information such as "First quarter sales: 500 million yen. Second quarter forecast sales: 600 million yen. Marketing budget: 200 million yen. Competitor analysis."

[0452] Step 2:

[0453] The device receives the input text data and sends it to the server, while the emotion engine detects the user's emotions through facial recognition and voice analysis, and also sends this information to the server.

[0454] Step 3:

[0455] The server analyzes the received text data and uses natural language processing (NLP) technology to extract key information needed to create the document, such as identifying important keywords and numerical data like "first quarter sales," "500 million yen," and "competitor analysis."

[0456] Step 4:

[0457] The server analyzes the user's emotional data sent from the emotion engine to understand the user's current emotional state, for example, identifying whether the user is feeling stressed or in a positive mood.

[0458] Step 5:

[0459] The server uses an AI model to generate a draft document based on the extracted key information and emotion data. The AI ​​model selects the appropriate tone and phrasing based on the user's emotions. For example, if the user is feeling stressed, the tone of the document will be set to simple and encouraging.

[0460] Step 6:

[0461] The server automatically detects and corrects typographical errors in the generated documents, and applies a unified color scheme, font, layout, and other formatting to ensure consistency across the entire document.

[0462] Step 7:

[0463] The server sends the generated materials to the terminal, where professional-looking materials are presented to the user.

[0464] Step 8:

[0465] The terminal visually displays the received materials to the user, who can then check the materials and make any necessary corrections.

[0466] Step 9:

[0467] If the user makes any changes to the document, the terminal sends the changes back to the server, which then generates new documents that reflect the changes and sends them back to the terminal.

[0468] Step 10:

[0469] Finally, once the user has completed the review and corrections, the documents can be used in meetings and sales activities. Through the system, users can create professional documents that are efficient and emotionally responsive.

[0470] This series of steps allows users to create high-quality materials in a short amount of time, leaving more time for thinking and strategizing. Furthermore, materials are created that respond to the user's emotions, resulting in more effective communication.

[0471] Example 2

[0472] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0473] Conventional document creation systems struggle to maintain a consistent tone and appropriate expressions when generating documents based on user-entered text data. Furthermore, because documents are created without considering the user's emotional state, they can lack tone and phrasing that matches the user's emotions and intent, reducing the effectiveness of communication. Furthermore, correcting typos and formatting in generated documents requires manual effort, which is inefficient.

[0474] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0475] In this invention, the server includes means for receiving text data entered by a user, means for analyzing the received text data to extract key information, means for generating materials using artificial intelligence based on the extracted key information and user emotion data recognized in real time, means for correcting typos and omissions in the generated materials and standardizing the format, and means for providing the generated materials to the user. This makes it possible to create materials with a consistent tone and expression based on the text and emotion data, and also automates the correction of typos and standardization of the format, improving the effectiveness of communication and increasing the efficiency of document creation.

[0476] A "user" is an individual or entity that operates the system to input text data and review and modify generated materials.

[0477] "Text data" refers to data including text information and numerical information that a user inputs to create a document.

[0478] The "receiving means" is a function or module that allows the server to obtain text data sent by the user.

[0479] The "means for analyzing and extracting key information" refers to a function or module that analyzes received text data using natural language processing technology and identifies and extracts keywords and numerical data necessary for the material.

[0480] "User emotion data recognized in real time" is data indicating the user's emotional state obtained from the user's facial expression, tone of voice, and the like.

[0481] "Means for generating materials using artificial intelligence" refers to a function or module that automatically creates draft materials using an AI model based on extracted key information and sentiment data.

[0482] "Means for correcting typographical errors and standardizing formatting" refers to a function or module that automatically corrects typographical errors in generated materials and standardizes the formatting of the entire document to be consistent.

[0483] The "means for providing" is a function or module for transmitting the generated materials to the user's terminal and providing them.

[0484] "Materials" are documents such as letters, slides, and reports created for use in meetings and sales activities.

[0485] This invention is a system that combines text data entered by a user with an emotion engine that recognizes the user's emotions. Its purpose is to enable businesspeople to efficiently and effectively create materials to be used in meetings and sales activities. This system adjusts the tone and wording of materials based on the user's emotions, and also optimizes the timing and method of reviewing materials.

[0486] Users access the system using a terminal and first input text data that will form the basis for creating materials. This text data includes details of meetings and sales activities, sales data, marketing strategies, and competitor analysis. The text data entered by the user is sent from the terminal to the server. At the same time, the terminal analyzes the user's face and voice in real time and obtains the user's emotional data using an emotion engine.

[0487] The server analyzes the received text data. This analysis is performed using natural language processing (NLP) technology to extract keywords and numerical data necessary for creating documents from the text data. For example, if the text data contains the information "First Quarter Sales: 500 million yen," the server extracts the keyword "First Quarter Sales" and the numerical data "500 million yen." If the emotion engine indicates that the user is feeling stressed, the system applies a corresponding tone and phrasing.

[0488] Next, the server uses the extracted information to generate a draft document using a generative AI model. This AI model could be GPT-3 or BERT, for example. The AI ​​model takes into account the output of the emotion engine and selects an appropriate tone and phrasing. For example, if the user has positive emotions, such as wanting to emphasize unity and cooperation, the document will include positive expressions like "success" and "growth."

[0489] The server also corrects typos and unifies formatting in the generated documents, ensuring a consistent tone and manner throughout the document and maintaining a professional appearance. Furthermore, the emotion engine can suggest the best time and method for reviewing the documents based on the user's emotions. For example, if the user is tired, the system suggests reviewing them later.

[0490] The generated documents are sent from the server to the terminal, where the user confirms and makes any final corrections. The user confirms the generated documents on the terminal, enters any necessary corrections, and sends them back to the server. In this way, the user can create highly accurate documents in a short amount of time.

[0491] For example, if a user enters the following information, "1st Quarter Sales: 500 million yen. 2nd Quarter Projected Sales: 600 million yen. Marketing Budget: 200 million yen. Competitor Analysis," and the emotion engine determines that the user is stressed, the server will generate a slide like this:

[0492] 1. Slide 1: Title: "Today's Meeting", Tone: "Let's be efficient."

[0493] 2. Slide 2: Title: "Current Sales Status", Content: "First Quarter Sales: 500 million yen. Second Quarter Forecast Sales: 600 million yen."

[0494] 3. Slide 3: Title: "Marketing Strategy", Content: "Marketing Budget: 200 million yen. Competitor Analysis."

[0495] This allows users to create materials efficiently and significantly reduces the time it takes to create them. In addition, by creating materials that reflect the user's emotions, more effective communication can be achieved.

[0496] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0497] Specific processing steps of the program

[0498] Step 1:

[0499] A user operates a terminal to access the system. First, they access the login page using a web browser or a dedicated application and enter their authentication information to log in. The input of this step is the user's authentication information, and the output is the login status (success or failure).

[0500] Step 2:

[0501] The user inputs text data for creating materials into the terminal. Information such as the contents of the meeting, sales data, and marketing strategies is entered into a dedicated input form, and the user presses the send button. The input in this step is the text data entered by the user, and the output is that text data is sent from the terminal to the server.

[0502] Step 3:

[0503] The terminal sends the entered text data to the server. The text data is sent to the server in JSON format via the Web API as a POST request. The input in this step is the text data entered by the user, and the output is the text data received by the server.

[0504] Step 4:

[0505] The device acquires emotional data in real time from the user's face and voice. Using the device's built-in camera and microphone, the device analyzes the user's emotional data using facial recognition software (e.g., OpenCV) and voice analysis software (e.g., Praat). The input for this step is the user's facial expression and tone of voice, and the output is emotional data.

[0506] Step 5:

[0507] The device sends the acquired emotion data to the server. The emotion data analyzed by the emotion engine is sent to the server in JSON format. The input of this step is the emotion data acquired by the device, and the output is the emotion data received by the server.

[0508] Step 6:

[0509] The server analyzes the received text data using natural language processing (NLP) techniques. It uses Python scripts and NLP libraries (spaCy, NLTK) to extract keywords and numerical data from the text data. The input for this step is the text data entered by the user, and the output is the extracted key information.

[0510] Step 7:

[0511] The server adjusts the tone and wording of the document based on the emotion data. It analyzes the output of the emotion engine and selects a tone that will relax the user if they are feeling stressed, and reflects this in the document. The inputs for this step are emotion data and extracted key information, and the output is the adjusted key information.

[0512] Step 8:

[0513] The server uses a generative AI model to generate a draft document based on the extracted information. For example, a prompt sentence is input into an AI model such as GPT-3 to generate a draft document. The input in this step is the adjusted key information and prompt sentence, and the output is the generated draft document. Example: Prompt sentence: "Please create a document based on first quarter sales."

[0514] Step 9:

[0515] The server corrects typos and standardizes the format of the generated document. It uses a proofreading tool (such as Grammarly or Hemingway) to ensure a consistent tone and format throughout the document. The input to this step is a draft of the generated document, and the output is the corrected and standardized document.

[0516] Step 10:

[0517] The server sends the generated documents to the terminal in JSON or PDF format and provides them to the user. The input of this step is the corrected and formatted documents, and the output is the documents provided to the user's terminal.

[0518] Step 11:

[0519] The user checks the generated data on the terminal and makes corrections as necessary. The data is displayed on the terminal and corrections are entered through the user interface. The input of this step is the provided data, and the output is the correction data entered by the user.

[0520] Step 12:

[0521] The terminal sends the corrected document back to the server. The document with the corrected data is sent back to the server for final confirmation. The input of this step is the document with the corrected data, and the output is the final confirmed document.

[0522] These steps allow users to create materials efficiently and with sensitivity.

[0523] (Application example 2)

[0524] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0525] Conventional document creation systems automatically process large amounts of data without considering the user's emotions, often failing to reflect the optimal tone or phrasing, resulting in stressful situations for users. Furthermore, the timing of review and revision after document creation is uniform and not dependent on the user's state, making it difficult to provide an efficient work environment. Therefore, there is a need for a system that appropriately reflects the user's emotions and provides more efficient review timing.

[0526] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0527] In this invention, the server includes means for receiving text data entered by a user, means for analyzing the received text data and extracting key information, means for generating materials using artificial intelligence based on the extracted key information, means for correcting typos and omissions in the generated materials and standardizing the format, means for recognizing the user's emotions and adjusting the tone and wording of the materials in response to the emotions, means for suggesting the timing and method of reviewing the materials based on the user's emotions, and means for recognizing emotions using the user's voice or video data and reflecting the recognition results in the generation of materials. This makes it possible to create effective materials that appropriately reflect the user's emotions and provide an efficient work environment.

[0528] "User" refers to an individual or organization that uses the System to create materials.

[0529] "Text data" refers to textual information entered by users, including details of meetings and sales, sales data, marketing strategies, and competitor analysis.

[0530] "Key information" refers to information extracted from text data, such as important keywords and numerical data.

[0531] "Artificial intelligence" refers to computer systems that have algorithms and models for analyzing text data and generating materials.

[0532] "Emotion engine" refers to a software or hardware system that recognizes a user's emotional state and adjusts the tone and phrasing of material accordingly.

[0533] "Error correction" refers to the process of automatically correcting incorrect characters or omissions in generated materials.

[0534] "Format unification" refers to the process of ensuring that the format and layout of generated materials are consistent.

[0535] "Document review timing" refers to a function that suggests the appropriate time or duration for a user to review and revise documents.

[0536] "Voice data" refers to voice information that records what a user says.

[0537] "Video data" refers to video information such as the user's face and posture.

[0538] "Tone" refers to the tone of the language and expressions used in a document.

[0539] The present invention is a system that combines text data entered by a user with an emotion engine that recognizes the user's emotions, allowing a robot used in a factory to recognize the emotional state of factory staff in real time and provide work instructions and feedback efficiently based on that information. Specific embodiments of the present invention will be described below.

[0540] The basic configuration of the system consists of a means for receiving text data entered by a user, a means for analyzing the received text data and extracting key information, a means for generating materials using artificial intelligence (AI) based on the extracted key information, a means for correcting typos and omissions in the generated materials and standardizing the format, a means for providing the generated materials to the user, a means for recognizing the user's emotions and adjusting the tone and wording of the materials in response to the emotions, a means for suggesting the timing and method for reviewing the materials based on the user's emotions, and a means for recognizing emotions using the user's voice or video data and reflecting the recognition results in the generation of materials.

[0541] Hardware and software used

[0542] The system uses the following hardware and software:

[0543] Hardware: Camera, microphone

[0544] Software: EmotionRecognition (emotion recognition engine), TextGenerator (text generation engine), NLPProcessor (natural language processing engine)

[0545] Processing flow

[0546] 1. Receiving user input: The user inputs text data about the factory status and tasks into the system, for example, receiving instructions such as "Please report on the progress of the production line."

[0547] 2. Emotion recognition: The emotion recognition engine analyzes the user's face and voice in real time through the camera and microphone to determine the user's emotional state.

[0548] 3. Text data analysis: The received text data is analyzed using a natural language processing engine to extract key keywords and numerical data.

[0549] 4. Document generation: Based on the extracted information, the text generation engine generates documents, taking into account the results of the emotion recognition engine and selecting the appropriate tone and wording.

[0550] 5. Correct typos and standardize formatting: Generated materials are automatically corrected for typos and formatted consistently.

[0551] 6. Provision to user: The generated materials are provided to the user, who can review and modify them.

[0552] 7. Review timing suggestion: Suggest the optimal review timing and method based on the user's emotional state.

[0553] Specific examples

[0554] For example, when checking the progress of a factory line, if the robot detects that a staff member is under stress, it will say in an encouraging tone, "Today is tough again, but let's do our best together!" and instruct the staff to transform negative emotions into positive ones.

[0555] Example prompts to input to a generative AI model:

[0556] "What words would be effective to say to factory staff when they are feeling stressed?"

[0557] This makes it possible to create effective materials that appropriately reflect the user's emotions and to provide an efficient work environment.

[0558] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0559] Step 1:

[0560] The terminal receives text data from the user about the factory status and tasks. For example, an instruction such as "Please report on the progress of the production line" is input. This text data is sent from the terminal to the server. The input data is in raw text format and is not processed or analyzed.

[0561] Step 2:

[0562] The server analyzes the received text data using a natural language processing engine (NLPProcessor). During the analysis, key keywords and numerical data are extracted. For example, keywords such as "production line" and "progress" and their associated numerical data are identified. The output is the key information in a structured data format.

[0563] Step 3:

[0564] The server uses the EmotionRecognition engine to analyze the user's video and audio data sent from the device via the camera and microphone. The analysis determines the user's emotional state (e.g., stress, relaxation). The input data is in the form of video and audio, and the output is a label indicating the emotional state.

[0565] Step 4:

[0566] The server receives the extracted key information and emotional state labels as input and generates materials using a text generation engine (TextGenerator). During this process, appropriate tone and phrasing are selected depending on the emotional state. For example, if the user is feeling stressed, an encouraging tone is used. The output is a draft document with adjusted tone and phrasing.

[0567] Step 5:

[0568] The server detects and corrects typographical errors in the generated documents and standardizes the format to ensure consistency. The input data is a draft document, and the output is the final document that has been corrected and formatted.

[0569] Step 6:

[0570] The server sends the final document to the terminal and provides an interface for the user to review and modify it. The user reviews the document, inputs any necessary modifications, and then sends it back to the server. The input data is the final document and the user's proposed modifications, and the output is the final document modified by the user.

[0571] Step 7:

[0572] The server then suggests when and how to review the material based on the user's emotional state. For example, if the server determines that the user is tired, it suggests reviewing the material later. The input data for this step is the emotional state label, and the output is the suggestion.

[0573] This series of processes enables the creation of effective materials that appropriately reflect the user's emotions and provides an efficient work environment.

[0574] 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.

[0575] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[0576] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0577] [Third embodiment]

[0578] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0579] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0580] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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).

[0581] 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.

[0582] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0583] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0584] 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.

[0585] 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.

[0586] 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 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.

[0587] 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.

[0588] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0589] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0590] This invention is a system that efficiently creates materials for business people to use in meetings and sales activities. This system uses AI to automatically generate materials and provides professional-looking materials based on text data entered by the user.

[0591] Users access the system using a terminal and first input text data that will be the basis for creating materials. This text data includes details of meetings and sales, sales data, marketing strategies, competitor analysis, etc. The text data entered by the user is sent from the terminal to the server.

[0592] The server analyzes the received text data and extracts key information. This analysis is performed using natural language processing (NLP) technology to identify keywords and numerical data necessary for document creation. For example, if the text data contains the information "First Quarter Sales: 500 million yen," the server extracts the keyword "First Quarter Sales" and the numerical data "500 million yen."

[0593] The server then uses an artificial intelligence (AI) model to generate a draft of the materials based on the extracted information. The AI ​​model uses the keywords and numerical data extracted earlier to create appropriate slides. For example, a slide titled "Current Sales Status" might include information such as "First Quarter Sales: 500 million yen."

[0594] The server also corrects typos and formatting errors in the generated materials, ensuring a consistent tone and manner throughout the materials, maintaining a professional appearance.

[0595] The generated documents are sent from the server to the terminal, where they are checked by the user and then final corrections are made. The user can check the generated documents on the terminal, input corrections if necessary, and send them back to the server. This allows the user to create highly accurate documents in a short amount of time.

[0596] For example, if a user enters the following information, "1st Quarter Sales: $500,000. 2nd Quarter Projected Sales: $600,000. Marketing Budget: $200,000. Competitor Analysis," the server will generate the following slide:

[0597] 1. Slide 1: Title: "Today's Meeting"

[0598] 2. Slide 2: Title: "Current Sales Status", Content: "First Quarter Sales: 500 million yen. Second Quarter Forecast Sales: 600 million yen."

[0599] 3. Slide 3: Title: "Marketing Strategy", Content: "Marketing Budget: 200 million yen. Competitor Analysis."

[0600] The system allows users to create documents efficiently and significantly reduces the time required for document creation, while the AI-powered automated generation process ensures consistency and professional quality in documents.

[0601] The processing flow will be explained below.

[0602] Step 1: The user accesses the system using a terminal and inputs the text data required to create the document. For example, the user might input information such as "First quarter sales: 500 million yen. Second quarter forecast sales: 600 million yen. Marketing budget: 200 million yen. Competitor analysis."

[0603] Step 2: The terminal receives the input text data and sends it to the server.

[0604] Step 3: The server analyzes the received text data. Specifically, it uses natural language processing technology to break down the text and extract key keywords and numerical data. For example, important information such as "first quarter sales" and "500 million yen" is identified.

[0605] Step 4: The server uses an artificial intelligence (AI) model to generate a draft of the document based on the extracted key information. During this process, the AI ​​applies pre-trained templates and formats to automatically create slides. For example, a slide titled "Current Sales Status" could contain specific data such as "First Quarter Sales: 500 Million Yen."

[0606] Step 5: The server automatically detects and corrects typographical errors in the generated documents, and applies a unified color scheme, font, layout, and other formatting to ensure consistency across the entire document.

[0607] Step 6: The server sends the generated material to the terminal, where it is presented to the user as a finished, professional-looking product.

[0608] Step 7: The terminal visually displays the received information to the user, who can then review the information and make any necessary corrections.

[0609] Step 8: If the user makes any changes to the document, the terminal sends the changes to the server again. The server generates new documents that reflect the changes and sends them to the terminal again.

[0610] Step 9: Finally, the documents that the user has checked and corrected can be used in meetings and sales activities. Users can efficiently create professional documents through the system.

[0611] This series of steps allows users to create high-quality materials in less time, leaving more time for thinking and strategizing.

[0612] Example 1

[0613] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0614] In today's business environment, creating efficient and professional materials is essential, but traditional methods are time-consuming and labor-intensive. They are also prone to quality issues, such as typographical errors and inconsistent formatting. Furthermore, because the content of materials used in meetings and sales activities is specialized, accurate data analysis and appropriate slide composition are required. It is necessary to solve these issues.

[0615] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0616] In this invention, the server includes means for receiving text data entered by a user, means for analyzing the received text data and extracting key information, means for generating materials using a generative AI model based on the extracted key information, means for correcting typos and omissions in the generated materials and standardizing the format, and means for providing the generated materials to the user. This significantly improves the efficiency of document creation, enabling the rapid creation of high-quality, professional-looking materials.

[0617] A "user" is a person who operates the system and inputs text data to create materials to be used in meetings and sales activities.

[0618] "Text data" is character string information input by the user, including meeting details, business data, sales information, marketing strategies, and analysis of competitors.

[0619] A "generative AI model" is an algorithm that uses artificial intelligence to automatically generate materials based on input data, and utilizes natural language processing technology.

[0620] "Materials" are documents such as presentation slides and reports used in meetings and sales activities, and are the final deliverables provided to users.

[0621] "Typographical errors" refer to incorrect or missing characters in a document, and are a factor that reduces the quality of the document.

[0622] "Format" refers to the consistent appearance and layout of a document, including font size, font, paragraph structure, and heading style.

[0623] "Key information" refers to important keywords and numerical data extracted from the analyzed text data, and is the information that forms the basis for creating materials.

[0624] "Means" is a general expression that refers to the specific processes, methods, and technical elements used to realize each function of a system.

[0625] This invention is a system that allows business people to efficiently create materials to be used in meetings and sales activities. This system uses a generative AI model to automatically generate materials and provides professional-looking materials based on text data entered by the user.

[0626] Users access the system using a terminal and first input text data that will be the basis for creating materials. This text data includes details of meetings and sales, sales data, marketing strategies, competitor analysis, etc. The text data entered by the user is sent from the terminal to the server.

[0627] The server analyzes the received text data and extracts key information. This analysis is performed using natural language processing (NLP) technology to identify keywords and numerical data necessary for document creation. For example, if the text data contains the information "First Quarter Sales: 500 million yen," the server extracts the keyword "First Quarter Sales" and the numerical data "500 million yen." This analysis is performed using an NLP module (e.g., spaCy or NLTK).

[0628] The server then uses a generative AI model to generate a draft document based on the extracted information. The generative AI model is realized using technologies such as OpenAI's GPT-3, and inputs the extracted keywords and numerical data as prompts. For example, a prompt such as "First quarter sales: 500 million yen. Second quarter forecast sales: 600 million yen. Marketing budget: 200 million yen. Please generate a slide about competitor analysis" is input. The AI ​​model then creates the appropriate slides, and for example, a slide titled "Current sales" might include information such as "First quarter sales: 500 million yen."

[0629] The server also corrects typos and ensures consistent formatting within the generated documents. It uses Python's textblob library to detect and correct typos. Standardized formatting techniques, such as Markdown and LaTeX, are used. This ensures a consistent tone and manner throughout the documents, maintaining a professional appearance.

[0630] The generated document is sent from the server to the terminal, where it is confirmed by the user and final corrections are made. The user can check the generated document on the terminal and make corrections if necessary. These corrections are also sent back to the server, and the final document is completed.

[0631] For example, if a user enters the following information, "1st Quarter Sales: $500,000. 2nd Quarter Projected Sales: $600,000. Marketing Budget: $200,000. Competitor Analysis," the server will generate the following slide:

[0632] 1. Slide 1: Title: "Today's Meeting"

[0633] 2. Slide 2: Title: "Current Sales Status", Content: "First Quarter Sales: 500 million yen. Second Quarter Forecast Sales: 600 million yen."

[0634] 3. Slide 3: Title: "Marketing Strategy", Content: "Marketing Budget: 200 million yen. Competitor Analysis."

[0635] The system allows users to create documents efficiently and significantly reduces the time required for document creation, while the automated generation process using generative AI models ensures consistency and professional quality in documents.

[0636] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0637] Step 1:

[0638] The user inputs the content of materials to be used in meetings or sales activities into the terminal. This input data includes specific sales data, marketing strategies, competitor analysis, etc. After inputting, the user clicks the "Send" button to send the data to the server. The input is made into a text box, and text data is generated as output.

[0639] Step 2:

[0640] The terminal sends the text data entered by the user to the server. Data transmission is performed using an HTTP POST request. The text data is received from the terminal as input and is received by the server as output.

[0641] Step 3:

[0642] The server analyzes the text data received from the device. Natural language processing (NLP) techniques are used for this analysis. An NLP module (e.g., spaCy, NLTK) is used to tokenize the text data and extract keywords and important numerical data. Using the received text data as input, data analysis is performed, and keywords and numerical data are obtained as output.

[0643] Step 4:

[0644] The server uses keywords and numerical data extracted using NLP techniques to input prompts into a generative AI model (e.g., OpenAI's GPT-3). These prompts include specific content entered by the user, and the model generates materials based on that. Using the prompt as input, the AI ​​model generates a draft of the materials as output.

[0645] Step 5:

[0646] The server checks the draft documents generated by the AI ​​model, corrects typos, and standardizes the format. It uses Python's textblob library to detect typos and standardizes the format using Markdown or LaTeX. It uses the draft documents generated by the AI ​​model as input and outputs the corrected and standardized documents.

[0647] Step 6:

[0648] The server sends the final material to the terminal. The data is transmitted using a secure protocol (e.g., HTTPS) and provided to the user on the terminal. It receives the final material as input and sends the material to the terminal as output.

[0649] Step 7:

[0650] The user checks the data generated on the terminal, corrects it as necessary, and resends the new corrected data to the server. At this time, the corrections are entered as text describing specific points. The user makes the corrections as input, and the corrected data is sent to the server as output.

[0651] Step 8:

[0652] The server receives the revised data again and completes the final document. It generates the document again based on the revisions, corrects typos, and standardizes the format. It then provides the final document to the user. The revised data is received as input, and the final document is completed as output.

[0653] (Application example 1)

[0654] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0655] In today's business world, there is a strong demand for preparing professional materials quickly for meetings and sales activities. However, creating materials manually takes a lot of time and effort, and it is difficult to ensure consistency and accuracy of the materials. In addition, there is a lack of systems that can provide information in real time when responding immediately on-site. As a result, users are unable to provide information efficiently, which leads to a decline in the quality of service provided to customers.

[0656] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0657] In this invention, the server includes means for receiving text data entered by a user, means for analyzing the received text data to extract key information, and means for generating materials using artificial intelligence based on the extracted key information, thereby enabling the real-time generation and visual display of materials.

[0658] The present invention also includes the following means.

[0659] The system includes a means for allowing a user to check and modify the generated material via a visual display device, a means for configuring and generating the content of each slide in real time based on the extracted key information, and a means for allowing a user to operate and modify the generated material using the visual display device when providing it.

[0660] This allows users to efficiently and instantly provide professional materials while working, improving the quality of service to customers.

[0661] 1. "Means for receiving text data entered by a user" refers to a mechanism for sending text data provided by a user to a server and for the server to receive it.

[0662] 2. "Means for analyzing received text data and extracting key information" means a method for analyzing received text data using natural language processing techniques to identify and extract key information.

[0663] 3. "Means for using artificial intelligence to generate documents based on extracted key information" refers to the process of using AI technology to automatically create professional documents using extracted key information as input.

[0664] 4. "Means for correcting typos and omissions in generated documents and standardizing the format" refers to a function that checks generated documents, corrects typos and omissions, and adjusts the overall format to conform to certain standards.

[0665] 5. "Means for providing generated materials to a visual display device in real time" means a structure for displaying generated materials on a visual display device such as smart glasses in a timely manner.

[0666] 6. "Means by which a user can check and modify the generated material via a visual display device" means a mechanism by which a user can check the content of the material via a visual display device and modify it as necessary.

[0667] 7. "Means for composing and generating the content of each slide in real time based on extracted key information" refers to a technique for appropriately arranging the content of slides based on extracted information and instantly creating materials.

[0668] 8. "Means by which the user can manipulate and modify the generated material using a visual display device when providing it" means a function that allows the user to freely manipulate and revise the provided material using a visual display device.

[0669] This invention relates to a system that enables users to generate efficient and professional materials in a short time. In particular, the system of the present invention automatically generates materials based on text data entered by the user and provides them to a visual display device in real time. In this embodiment, each component of the system and its operation will be described.

[0670] The server first receives text data entered by the user. This is done by sending data provided through smart glasses or other input devices to the server. Next, it uses natural language processing (NLP) techniques to analyze the received data and extract important information. Specific NLP toolkits used include SpaCy and NLTK (Natural Language Toolkit).

[0671] The extracted key information is processed by a generative AI model, which automatically generates slides for the materials. This generative AI model organizes appropriate content into slides based on the information extracted earlier. For example, a slide titled "Current Sales Status" might display data such as "First Quarter Sales: 500 million yen." The generated slides are then corrected for typos and formatted uniformly on the server.

[0672] The server then provides the generated materials in real time to a visual display device (e.g., smart glasses). This visual display device can be a device such as Google Glass or Vuzix. The user can not only check the materials through this device, but also make corrections as needed. These corrections are sent from the visual display device to the server, and the materials are generated again.

[0673] For example, if a user asks, "What are the features of this product?", the voice recognition system sends this as text data to the server. The server extracts the keyword "features," and based on that, the generative AI model creates a slide containing supplementary explanations. This slide is displayed in real time on the smart glasses, allowing the user to instantly explain things to the customer.

[0674] An example of a prompt to be input to a generative AI model is, "Prompt: Based on the keyword 'waterproof function' extracted from the conversation, please generate materials containing a detailed description of this product."

[0675] In this way, the user can quickly and accurately create materials while working, thereby improving the quality of service to customers.

[0676] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0677] Step 1:

[0678] The text data entered by the user is sent from the smart glasses to the server.

[0679] Input: Text data is generated by the voice recognition system and provided by the user via the smart glasses.

[0680] Data processing: Converting voice data into text data using a voice recognition system.

[0681] Output: The server receives the text data.

[0682] Step 2:

[0683] The server analyzes the received text data using natural language processing (NLP) technology and extracts key information.

[0684] Input: Text data received by the server.

[0685] Data processing: Analyze text data using an NLP toolkit such as SpaCy to extract keywords and key information.

[0686] Output: Extracted keywords and key information.

[0687] Step 3:

[0688] The server generates presentation slides using a generative AI model based on the extracted key information.

[0689] Input: Extracted keywords and key information.

[0690] Data processing: The generative AI model determines the appropriate template and slide content, and generates the materials.

[0691] Output: The generated slides and materials.

[0692] Step 4:

[0693] The server corrects typographical errors and standardizes formatting in the generated materials.

[0694] Input: Generated slides and materials.

[0695] Data processing: Apply a process to automatically detect and correct typographical errors and ensure consistent formatting across the entire document.

[0696] Output: Corrected and uniformly formatted materials.

[0697] Step 5:

[0698] The server provides the generated materials to the smart glasses in real time.

[0699] Input: Corrected and uniformly formatted materials.

[0700] Data processing: Converting materials into a display format for smart glasses.

[0701] Output: Materials displayed on smart glasses.

[0702] Step 6:

[0703] The user can review and modify the generated material via a visual display device.

[0704] Input: Documents displayed on smart glasses.

[0705] Data processing: The process of checking the data and instructing corrections as necessary.

[0706] Output: Data to be corrected.

[0707] Step 7:

[0708] The server generates the materials again based on the user's correction instructions and provides them to the smart glasses again.

[0709] Input: Correction instruction data.

[0710] Data processing: Regenerate the materials based on instructions, correcting typos and standardizing the format again.

[0711] Output: Final, revised document displayed on smart glasses.

[0712] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0713] This invention is a system that combines text data entered by a user with an emotion engine that recognizes the user's emotions, allowing business people to efficiently and effectively create materials to be used in meetings and sales activities. This system adjusts the tone and wording of the materials based on the user's emotions, and further optimizes the timing and method of reviewing the materials.

[0714] Users access the system using their devices and first input text data that will form the basis for creating materials. This text data includes details of meetings and sales activities, sales data, marketing strategies, and competitor analysis. The text data entered by the user is sent from the device to the server. At the same time, the emotion engine recognizes the user's emotions in real time through facial recognition and voice analysis.

[0715] The server analyzes the received text data. This analysis is performed using natural language processing (NLP) technology to identify keywords and numerical data necessary for document creation. For example, if the text data contains the information "First Quarter Sales: 500 million yen," the server extracts the keyword "First Quarter Sales" and the numerical data "500 million yen." If the emotion engine determines that the user is feeling stressed, the system applies a corresponding tone and phrasing.

[0716] The server then uses the extracted information to generate a draft document using an artificial intelligence (AI) model. The AI ​​model takes into account the output of the emotion engine and selects the appropriate tone and phrasing. For example, if the user has a positive emotion that emphasizes unity and cooperation, the document will include positive expressions such as "success" and "growth."

[0717] The server also corrects typos and typos in the generated documents and standardizes formatting, ensuring a consistent tone and manner throughout the document and maintaining a professional appearance. Furthermore, the emotion engine can suggest the best time and method for reviewing the documents based on the user's emotions. For example, if the user is tired, the system can suggest reviewing the documents later.

[0718] The generated documents are sent from the server to the terminal for user confirmation and final correction. The user can check the generated documents on the terminal, input corrections if necessary, and send them back to the server. This allows the user to create highly accurate documents in a short amount of time.

[0719] For example, if a user enters the following information, "1st Quarter Sales: 500 million yen. 2nd Quarter Projected Sales: 600 million yen. Marketing Budget: 200 million yen. Competitor Analysis," and the emotion engine determines that the user is stressed, the server will generate a slide like this:

[0720] 1. Slide 1: Title: "Today's Meeting", Tone: "Let's be efficient."

[0721] 2. Slide 2: Title: "Current Sales Status", Content: "First Quarter Sales: 500 million yen. Second Quarter Forecast Sales: 600 million yen."

[0722] 3. Slide 3: Title: "Marketing Strategy", Content: "Marketing Budget: 200 million yen. Competitor Analysis."

[0723] This system allows users to create documents efficiently and significantly reduces the time it takes to create them. In addition, by creating documents that reflect the user's emotions, more effective communication can be achieved.

[0724] The processing flow will be explained below.

[0725] Step 1:

[0726] Users access the system using a terminal and log in. After logging in, they input text data that will be the basis for creating materials into the terminal. For example, they might input information such as "First quarter sales: 500 million yen. Second quarter forecast sales: 600 million yen. Marketing budget: 200 million yen. Competitor analysis."

[0727] Step 2:

[0728] The device receives the input text data and sends it to the server, while the emotion engine detects the user's emotions through facial recognition and voice analysis, and also sends this information to the server.

[0729] Step 3:

[0730] The server analyzes the received text data and uses natural language processing (NLP) technology to extract key information needed to create the document, such as identifying important keywords and numerical data like "first quarter sales," "500 million yen," and "competitor analysis."

[0731] Step 4:

[0732] The server analyzes the user's emotional data sent from the emotion engine to understand the user's current emotional state, for example, identifying whether the user is feeling stressed or in a positive mood.

[0733] Step 5:

[0734] The server uses an AI model to generate a draft document based on the extracted key information and emotion data. The AI ​​model selects the appropriate tone and phrasing based on the user's emotions. For example, if the user is feeling stressed, the tone of the document will be set to simple and encouraging.

[0735] Step 6:

[0736] The server automatically detects and corrects typographical errors in the generated documents, and applies a unified color scheme, font, layout, and other formatting to ensure consistency across the entire document.

[0737] Step 7:

[0738] The server sends the generated materials to the terminal, where professional-looking materials are presented to the user.

[0739] Step 8:

[0740] The terminal visually displays the received materials to the user, who can then check the materials and make any necessary corrections.

[0741] Step 9:

[0742] If the user makes any changes to the document, the terminal sends the changes back to the server, which then generates new documents that reflect the changes and sends them back to the terminal.

[0743] Step 10:

[0744] Finally, once the user has completed the review and corrections, the documents can be used in meetings and sales activities. Through the system, users can create professional documents that are efficient and emotionally responsive.

[0745] This series of steps allows users to create high-quality materials in a short amount of time, leaving more time for thinking and strategizing. Furthermore, materials are created that respond to the user's emotions, resulting in more effective communication.

[0746] Example 2

[0747] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0748] Conventional document creation systems struggle to maintain a consistent tone and appropriate expressions when generating documents based on user-entered text data. Furthermore, because documents are created without considering the user's emotional state, they can lack tone and phrasing that matches the user's emotions and intent, reducing the effectiveness of communication. Furthermore, correcting typos and formatting in generated documents requires manual effort, which is inefficient.

[0749] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0750] In this invention, the server includes means for receiving text data entered by a user, means for analyzing the received text data to extract key information, means for generating materials using artificial intelligence based on the extracted key information and user emotion data recognized in real time, means for correcting typos and omissions in the generated materials and standardizing the format, and means for providing the generated materials to the user. This makes it possible to create materials with a consistent tone and expression based on the text and emotion data, and also automates the correction of typos and standardization of the format, improving the effectiveness of communication and increasing the efficiency of document creation.

[0751] A "user" is an individual or entity that operates the system to input text data and review and modify generated materials.

[0752] "Text data" refers to data including text information and numerical information that a user inputs to create a document.

[0753] The "receiving means" is a function or module that allows the server to obtain text data sent by the user.

[0754] The "means for analyzing and extracting key information" refers to a function or module that analyzes received text data using natural language processing technology and identifies and extracts keywords and numerical data necessary for the material.

[0755] "User emotion data recognized in real time" is data indicating the user's emotional state obtained from the user's facial expression, tone of voice, and the like.

[0756] "Means for generating materials using artificial intelligence" refers to a function or module that automatically creates draft materials using an AI model based on extracted key information and sentiment data.

[0757] "Means for correcting typographical errors and standardizing formatting" refers to a function or module that automatically corrects typographical errors in generated materials and standardizes the formatting of the entire document to be consistent.

[0758] The "means for providing" is a function or module for transmitting the generated materials to the user's terminal and providing them.

[0759] "Materials" are documents such as letters, slides, and reports created for use in meetings and sales activities.

[0760] This invention is a system that combines text data entered by a user with an emotion engine that recognizes the user's emotions. Its purpose is to enable businesspeople to efficiently and effectively create materials to be used in meetings and sales activities. This system adjusts the tone and wording of materials based on the user's emotions, and also optimizes the timing and method of reviewing materials.

[0761] Users access the system using a terminal and first input text data that will form the basis for creating materials. This text data includes details of meetings and sales activities, sales data, marketing strategies, and competitor analysis. The text data entered by the user is sent from the terminal to the server. At the same time, the terminal analyzes the user's face and voice in real time and obtains the user's emotional data using an emotion engine.

[0762] The server analyzes the received text data. This analysis is performed using natural language processing (NLP) technology to extract keywords and numerical data necessary for creating documents from the text data. For example, if the text data contains the information "First Quarter Sales: 500 million yen," the server extracts the keyword "First Quarter Sales" and the numerical data "500 million yen." If the emotion engine indicates that the user is feeling stressed, the system applies a corresponding tone and phrasing.

[0763] Next, the server uses the extracted information to generate a draft document using a generative AI model. This AI model could be GPT-3 or BERT, for example. The AI ​​model takes into account the output of the emotion engine and selects an appropriate tone and phrasing. For example, if the user has positive emotions, such as wanting to emphasize unity and cooperation, the document will include positive expressions like "success" and "growth."

[0764] The server also corrects typos and unifies formatting in the generated documents, ensuring a consistent tone and manner throughout the document and maintaining a professional appearance. Furthermore, the emotion engine can suggest the best time and method for reviewing the documents based on the user's emotions. For example, if the user is tired, the system suggests reviewing them later.

[0765] The generated documents are sent from the server to the terminal, where the user confirms and makes any final corrections. The user confirms the generated documents on the terminal, enters any necessary corrections, and sends them back to the server. In this way, the user can create highly accurate documents in a short amount of time.

[0766] For example, if a user enters the following information, "1st Quarter Sales: 500 million yen. 2nd Quarter Projected Sales: 600 million yen. Marketing Budget: 200 million yen. Competitor Analysis," and the emotion engine determines that the user is stressed, the server will generate a slide like this:

[0767] 1. Slide 1: Title: "Today's Meeting", Tone: "Let's be efficient."

[0768] 2. Slide 2: Title: "Current Sales Status", Content: "First Quarter Sales: 500 million yen. Second Quarter Forecast Sales: 600 million yen."

[0769] 3. Slide 3: Title: "Marketing Strategy", Content: "Marketing Budget: 200 million yen. Competitor Analysis."

[0770] This allows users to create materials efficiently and significantly reduces the time it takes to create them. In addition, by creating materials that reflect the user's emotions, more effective communication can be achieved.

[0771] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0772] Specific processing steps of the program

[0773] Step 1:

[0774] A user operates a terminal to access the system. First, they access the login page using a web browser or a dedicated application and enter their authentication information to log in. The input of this step is the user's authentication information, and the output is the login status (success or failure).

[0775] Step 2:

[0776] The user inputs text data for creating materials into the terminal. Information such as the contents of the meeting, sales data, and marketing strategies is entered into a dedicated input form, and the user presses the send button. The input in this step is the text data entered by the user, and the output is that text data is sent from the terminal to the server.

[0777] Step 3:

[0778] The terminal sends the entered text data to the server. The text data is sent to the server in JSON format via the Web API as a POST request. The input in this step is the text data entered by the user, and the output is the text data received by the server.

[0779] Step 4:

[0780] The device acquires emotional data in real time from the user's face and voice. Using the device's built-in camera and microphone, the device analyzes the user's emotional data using facial recognition software (e.g., OpenCV) and voice analysis software (e.g., Praat). The input for this step is the user's facial expression and tone of voice, and the output is emotional data.

[0781] Step 5:

[0782] The device sends the acquired emotion data to the server. The emotion data analyzed by the emotion engine is sent to the server in JSON format. The input of this step is the emotion data acquired by the device, and the output is the emotion data received by the server.

[0783] Step 6:

[0784] The server analyzes the received text data using natural language processing (NLP) techniques. It uses Python scripts and NLP libraries (spaCy, NLTK) to extract keywords and numerical data from the text data. The input for this step is the text data entered by the user, and the output is the extracted key information.

[0785] Step 7:

[0786] The server adjusts the tone and wording of the document based on the emotion data. It analyzes the output of the emotion engine and selects a tone that will relax the user if they are feeling stressed, and reflects this in the document. The inputs for this step are emotion data and extracted key information, and the output is the adjusted key information.

[0787] Step 8:

[0788] The server uses a generative AI model to generate a draft document based on the extracted information. For example, a prompt sentence is input into an AI model such as GPT-3 to generate a draft document. The input in this step is the adjusted key information and prompt sentence, and the output is the generated draft document. Example: Prompt sentence: "Please create a document based on first quarter sales."

[0789] Step 9:

[0790] The server corrects typos and standardizes the format of the generated document. It uses a proofreading tool (such as Grammarly or Hemingway) to ensure a consistent tone and format throughout the document. The input to this step is a draft of the generated document, and the output is the corrected and standardized document.

[0791] Step 10:

[0792] The server sends the generated documents to the terminal in JSON or PDF format and provides them to the user. The input of this step is the corrected and formatted documents, and the output is the documents provided to the user's terminal.

[0793] Step 11:

[0794] The user checks the generated data on the terminal and makes corrections as necessary. The data is displayed on the terminal and corrections are entered through the user interface. The input of this step is the provided data, and the output is the correction data entered by the user.

[0795] Step 12:

[0796] The terminal sends the corrected document back to the server. The document with the corrected data is sent back to the server for final confirmation. The input of this step is the document with the corrected data, and the output is the final confirmed document.

[0797] These steps allow users to create materials efficiently and with sensitivity.

[0798] (Application example 2)

[0799] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0800] Conventional document creation systems automatically process large amounts of data without considering the user's emotions, often failing to reflect the optimal tone or phrasing, resulting in stressful situations for users. Furthermore, the timing of review and revision after document creation is uniform and not dependent on the user's state, making it difficult to provide an efficient work environment. Therefore, there is a need for a system that appropriately reflects the user's emotions and provides more efficient review timing.

[0801] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0802] In this invention, the server includes means for receiving text data entered by a user, means for analyzing the received text data and extracting key information, means for generating materials using artificial intelligence based on the extracted key information, means for correcting typos and omissions in the generated materials and standardizing the format, means for recognizing the user's emotions and adjusting the tone and wording of the materials in response to the emotions, means for suggesting the timing and method of reviewing the materials based on the user's emotions, and means for recognizing emotions using the user's voice or video data and reflecting the recognition results in the generation of materials. This makes it possible to create effective materials that appropriately reflect the user's emotions and provide an efficient work environment.

[0803] "User" refers to an individual or organization that uses the System to create materials.

[0804] "Text data" refers to textual information entered by users, including details of meetings and sales, sales data, marketing strategies, and competitor analysis.

[0805] "Key information" refers to information extracted from text data, such as important keywords and numerical data.

[0806] "Artificial intelligence" refers to computer systems that have algorithms and models for analyzing text data and generating materials.

[0807] "Emotion engine" refers to a software or hardware system that recognizes a user's emotional state and adjusts the tone and phrasing of material accordingly.

[0808] "Error correction" refers to the process of automatically correcting incorrect characters or omissions in generated materials.

[0809] "Format unification" refers to the process of ensuring that the format and layout of generated materials are consistent.

[0810] "Document review timing" refers to a function that suggests the appropriate time or duration for a user to review and revise documents.

[0811] "Voice data" refers to voice information that records what a user says.

[0812] "Video data" refers to video information such as the user's face and posture.

[0813] "Tone" refers to the tone of the language and expressions used in a document.

[0814] The present invention is a system that combines text data entered by a user with an emotion engine that recognizes the user's emotions, allowing a robot used in a factory to recognize the emotional state of factory staff in real time and provide work instructions and feedback efficiently based on that information. Specific embodiments of the present invention will be described below.

[0815] The basic configuration of the system consists of a means for receiving text data entered by a user, a means for analyzing the received text data and extracting key information, a means for generating materials using artificial intelligence (AI) based on the extracted key information, a means for correcting typos and omissions in the generated materials and standardizing the format, a means for providing the generated materials to the user, a means for recognizing the user's emotions and adjusting the tone and wording of the materials in response to the emotions, a means for suggesting the timing and method for reviewing the materials based on the user's emotions, and a means for recognizing emotions using the user's voice or video data and reflecting the recognition results in the generation of materials.

[0816] Hardware and software used

[0817] The system uses the following hardware and software:

[0818] Hardware: Camera, microphone

[0819] Software: EmotionRecognition (emotion recognition engine), TextGenerator (text generation engine), NLPProcessor (natural language processing engine)

[0820] Processing flow

[0821] 1. Receiving user input: The user inputs text data about the factory status and tasks into the system, for example, receiving instructions such as "Please report on the progress of the production line."

[0822] 2. Emotion recognition: The emotion recognition engine analyzes the user's face and voice in real time through the camera and microphone to determine the user's emotional state.

[0823] 3. Text data analysis: The received text data is analyzed using a natural language processing engine to extract key keywords and numerical data.

[0824] 4. Document generation: Based on the extracted information, the text generation engine generates documents, taking into account the results of the emotion recognition engine and selecting the appropriate tone and wording.

[0825] 5. Correct typos and standardize formatting: Generated materials are automatically corrected for typos and formatted consistently.

[0826] 6. Provision to user: The generated materials are provided to the user, who can review and modify them.

[0827] 7. Review timing suggestion: Suggest the optimal review timing and method based on the user's emotional state.

[0828] Specific examples

[0829] For example, when checking the progress of a factory line, if the robot detects that a staff member is under stress, it will say in an encouraging tone, "Today is tough again, but let's do our best together!" and instruct the staff to transform negative emotions into positive ones.

[0830] Example prompts to input to a generative AI model:

[0831] "What words would be effective to say to factory staff when they are feeling stressed?"

[0832] This makes it possible to create effective materials that appropriately reflect the user's emotions and to provide an efficient work environment.

[0833] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0834] Step 1:

[0835] The terminal receives text data from the user about the factory status and tasks. For example, an instruction such as "Please report on the progress of the production line" is input. This text data is sent from the terminal to the server. The input data is in raw text format and is not processed or analyzed.

[0836] Step 2:

[0837] The server analyzes the received text data using a natural language processing engine (NLPProcessor). During the analysis, key keywords and numerical data are extracted. For example, keywords such as "production line" and "progress" and their associated numerical data are identified. The output is the key information in a structured data format.

[0838] Step 3:

[0839] The server uses the EmotionRecognition engine to analyze the user's video and audio data sent from the device via the camera and microphone. The analysis determines the user's emotional state (e.g., stress, relaxation). The input data is in the form of video and audio, and the output is a label indicating the emotional state.

[0840] Step 4:

[0841] The server receives the extracted key information and emotional state labels as input and generates materials using a text generation engine (TextGenerator). During this process, appropriate tone and phrasing are selected depending on the emotional state. For example, if the user is feeling stressed, an encouraging tone is used. The output is a draft document with adjusted tone and phrasing.

[0842] Step 5:

[0843] The server detects and corrects typographical errors in the generated documents and standardizes the format to ensure consistency. The input data is a draft document, and the output is the final document that has been corrected and formatted.

[0844] Step 6:

[0845] The server sends the final document to the terminal and provides an interface for the user to review and modify it. The user reviews the document, inputs any necessary modifications, and then sends it back to the server. The input data is the final document and the user's proposed modifications, and the output is the final document modified by the user.

[0846] Step 7:

[0847] The server then suggests when and how to review the material based on the user's emotional state. For example, if the server determines that the user is tired, it suggests reviewing the material later. The input data for this step is the emotional state label, and the output is the suggestion.

[0848] This series of processes enables the creation of effective materials that appropriately reflect the user's emotions and provides an efficient work environment.

[0849] 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.

[0850] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[0851] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[0852] [Fourth embodiment]

[0853] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0854] 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.

[0855] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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).

[0856] 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.

[0857] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0858] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0859] 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.

[0860] The control object 443 includes a display device, LEDs in the eyes, and motors for driving 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.

[0861] 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.

[0862] 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 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.

[0863] 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.

[0864] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0865] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[0866] This invention is a system that efficiently creates materials for business people to use in meetings and sales activities. This system uses AI to automatically generate materials and provides professional-looking materials based on text data entered by the user.

[0867] Users access the system using a terminal and first input text data that will be the basis for creating materials. This text data includes details of meetings and sales, sales data, marketing strategies, competitor analysis, etc. The text data entered by the user is sent from the terminal to the server.

[0868] The server analyzes the received text data and extracts key information. This analysis is performed using natural language processing (NLP) technology to identify keywords and numerical data necessary for document creation. For example, if the text data contains the information "First Quarter Sales: 500 million yen," the server extracts the keyword "First Quarter Sales" and the numerical data "500 million yen."

[0869] The server then uses an artificial intelligence (AI) model to generate a draft of the materials based on the extracted information. The AI ​​model uses the keywords and numerical data extracted earlier to create appropriate slides. For example, a slide titled "Current Sales Status" might include information such as "First Quarter Sales: 500 million yen."

[0870] The server also corrects typos and formatting errors in the generated materials, ensuring a consistent tone and manner throughout the materials, maintaining a professional appearance.

[0871] The generated documents are sent from the server to the terminal, where they are checked by the user and then final corrections are made. The user can check the generated documents on the terminal, input corrections if necessary, and send them back to the server. This allows the user to create highly accurate documents in a short amount of time.

[0872] For example, if a user enters the following information, "1st Quarter Sales: $500,000. 2nd Quarter Projected Sales: $600,000. Marketing Budget: $200,000. Competitor Analysis," the server will generate the following slide:

[0873] 1. Slide 1: Title: "Today's Meeting"

[0874] 2. Slide 2: Title: "Current Sales Status", Content: "First Quarter Sales: 500 million yen. Second Quarter Forecast Sales: 600 million yen."

[0875] 3. Slide 3: Title: "Marketing Strategy", Content: "Marketing Budget: 200 million yen. Competitor Analysis."

[0876] The system allows users to create documents efficiently and significantly reduces the time required for document creation, while the AI-powered automated generation process ensures consistency and professional quality in documents.

[0877] The processing flow will be explained below.

[0878] Step 1: The user accesses the system using a terminal and inputs the text data required to create the document. For example, the user might input information such as "First quarter sales: 500 million yen. Second quarter forecast sales: 600 million yen. Marketing budget: 200 million yen. Competitor analysis."

[0879] Step 2: The terminal receives the input text data and sends it to the server.

[0880] Step 3: The server analyzes the received text data. Specifically, it uses natural language processing technology to break down the text and extract key keywords and numerical data. For example, important information such as "first quarter sales" and "500 million yen" is identified.

[0881] Step 4: The server uses an artificial intelligence (AI) model to generate a draft of the document based on the extracted key information. During this process, the AI ​​applies pre-trained templates and formats to automatically create slides. For example, a slide titled "Current Sales Status" could contain specific data such as "First Quarter Sales: 500 Million Yen."

[0882] Step 5: The server automatically detects and corrects typographical errors in the generated documents, and applies a unified color scheme, font, layout, and other formatting to ensure consistency across the entire document.

[0883] Step 6: The server sends the generated material to the terminal, where it is presented to the user as a finished, professional-looking product.

[0884] Step 7: The terminal visually displays the received information to the user, who can then review the information and make any necessary corrections.

[0885] Step 8: If the user makes any changes to the document, the terminal sends the changes to the server again. The server generates new documents that reflect the changes and sends them to the terminal again.

[0886] Step 9: Finally, the documents that the user has checked and corrected can be used in meetings and sales activities. Users can efficiently create professional documents through the system.

[0887] This series of steps allows users to create high-quality materials in less time, leaving more time for thinking and strategizing.

[0888] Example 1

[0889] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[0890] In today's business environment, creating efficient and professional materials is essential, but traditional methods are time-consuming and labor-intensive. They are also prone to quality issues, such as typographical errors and inconsistent formatting. Furthermore, because the content of materials used in meetings and sales activities is specialized, accurate data analysis and appropriate slide composition are required. It is necessary to solve these issues.

[0891] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0892] In this invention, the server includes means for receiving text data entered by a user, means for analyzing the received text data and extracting key information, means for generating materials using a generative AI model based on the extracted key information, means for correcting typos and omissions in the generated materials and standardizing the format, and means for providing the generated materials to the user. This significantly improves the efficiency of document creation, enabling the rapid creation of high-quality, professional-looking materials.

[0893] A "user" is a person who operates the system and inputs text data to create materials to be used in meetings and sales activities.

[0894] "Text data" is character string information input by the user, including meeting details, business data, sales information, marketing strategies, and analysis of competitors.

[0895] A "generative AI model" is an algorithm that uses artificial intelligence to automatically generate materials based on input data, and utilizes natural language processing technology.

[0896] "Materials" are documents such as presentation slides and reports used in meetings and sales activities, and are the final deliverables provided to users.

[0897] "Typographical errors" refer to incorrect or missing characters in a document, and are a factor that reduces the quality of the document.

[0898] "Format" refers to the consistent appearance and layout of a document, including font size, font, paragraph structure, and heading style.

[0899] "Key information" refers to important keywords and numerical data extracted from the analyzed text data, and is the information that forms the basis for creating materials.

[0900] "Means" is a general expression that refers to the specific processes, methods, and technical elements used to realize each function of a system.

[0901] This invention is a system that allows business people to efficiently create materials to be used in meetings and sales activities. This system uses a generative AI model to automatically generate materials and provides professional-looking materials based on text data entered by the user.

[0902] Users access the system using a terminal and first input text data that will be the basis for creating materials. This text data includes details of meetings and sales, sales data, marketing strategies, competitor analysis, etc. The text data entered by the user is sent from the terminal to the server.

[0903] The server analyzes the received text data and extracts key information. This analysis is performed using natural language processing (NLP) technology to identify keywords and numerical data necessary for document creation. For example, if the text data contains the information "First Quarter Sales: 500 million yen," the server extracts the keyword "First Quarter Sales" and the numerical data "500 million yen." This analysis is performed using an NLP module (e.g., spaCy or NLTK).

[0904] The server then uses a generative AI model to generate a draft document based on the extracted information. The generative AI model is realized using technologies such as OpenAI's GPT-3, and inputs the extracted keywords and numerical data as prompts. For example, a prompt such as "First quarter sales: 500 million yen. Second quarter forecast sales: 600 million yen. Marketing budget: 200 million yen. Please generate a slide about competitor analysis" is input. The AI ​​model then creates the appropriate slides, and for example, a slide titled "Current sales" might include information such as "First quarter sales: 500 million yen."

[0905] The server also corrects typos and ensures consistent formatting within the generated documents. It uses Python's textblob library to detect and correct typos. Standardized formatting techniques, such as Markdown and LaTeX, are used. This ensures a consistent tone and manner throughout the documents, maintaining a professional appearance.

[0906] The generated document is sent from the server to the terminal, where it is confirmed by the user and final corrections are made. The user can check the generated document on the terminal and make corrections if necessary. These corrections are also sent back to the server, and the final document is completed.

[0907] For example, if a user enters the following information, "1st Quarter Sales: $500,000. 2nd Quarter Projected Sales: $600,000. Marketing Budget: $200,000. Competitor Analysis," the server will generate the following slide:

[0908] 1. Slide 1: Title: "Today's Meeting"

[0909] 2. Slide 2: Title: "Current Sales Status", Content: "First Quarter Sales: 500 million yen. Second Quarter Forecast Sales: 600 million yen."

[0910] 3. Slide 3: Title: "Marketing Strategy", Content: "Marketing Budget: 200 million yen. Competitor Analysis."

[0911] The system allows users to create documents efficiently and significantly reduces the time required for document creation, while the automated generation process using generative AI models ensures consistency and professional quality in documents.

[0912] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0913] Step 1:

[0914] The user inputs the content of materials to be used in meetings or sales activities into the terminal. This input data includes specific sales data, marketing strategies, competitor analysis, etc. After inputting, the user clicks the "Send" button to send the data to the server. The input is made into a text box, and text data is generated as output.

[0915] Step 2:

[0916] The terminal sends the text data entered by the user to the server. Data transmission is performed using an HTTP POST request. The text data is received from the terminal as input and is received by the server as output.

[0917] Step 3:

[0918] The server analyzes the text data received from the device. Natural language processing (NLP) techniques are used for this analysis. An NLP module (e.g., spaCy, NLTK) is used to tokenize the text data and extract keywords and important numerical data. Using the received text data as input, data analysis is performed, and keywords and numerical data are obtained as output.

[0919] Step 4:

[0920] The server uses keywords and numerical data extracted using NLP techniques to input prompts into a generative AI model (e.g., OpenAI's GPT-3). These prompts include specific content entered by the user, and the model generates materials based on that. Using the prompt as input, the AI ​​model generates a draft of the materials as output.

[0921] Step 5:

[0922] The server checks the draft documents generated by the AI ​​model, corrects typos, and standardizes the format. It uses Python's textblob library to detect typos and standardizes the format using Markdown or LaTeX. It uses the draft documents generated by the AI ​​model as input and outputs the corrected and standardized documents.

[0923] Step 6:

[0924] The server sends the final material to the terminal. The data is transmitted using a secure protocol (e.g., HTTPS) and provided to the user on the terminal. It receives the final material as input and sends the material to the terminal as output.

[0925] Step 7:

[0926] The user checks the data generated on the terminal, corrects it as necessary, and resends the new corrected data to the server. At this time, the corrections are entered as text describing specific points. The user makes the corrections as input, and the corrected data is sent to the server as output.

[0927] Step 8:

[0928] The server receives the revised data again and completes the final document. It generates the document again based on the revisions, corrects typos, and standardizes the format. It then provides the final document to the user. The revised data is received as input, and the final document is completed as output.

[0929] (Application example 1)

[0930] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[0931] In today's business world, there is a strong demand for preparing professional materials quickly for meetings and sales activities. However, creating materials manually takes a lot of time and effort, and it is difficult to ensure consistency and accuracy of the materials. In addition, there is a lack of systems that can provide information in real time when responding immediately on-site. As a result, users are unable to provide information efficiently, which leads to a decline in the quality of service provided to customers.

[0932] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0933] In this invention, the server includes means for receiving text data entered by a user, means for analyzing the received text data to extract key information, and means for generating materials using artificial intelligence based on the extracted key information, thereby enabling the real-time generation and visual display of materials.

[0934] The present invention also includes the following means.

[0935] The system includes a means for allowing a user to check and modify the generated material via a visual display device, a means for configuring and generating the content of each slide in real time based on the extracted key information, and a means for allowing a user to operate and modify the generated material using the visual display device when providing it.

[0936] This allows users to efficiently and instantly provide professional materials while working, improving the quality of service to customers.

[0937] 1. "Means for receiving text data entered by a user" refers to a mechanism for sending text data provided by a user to a server and for the server to receive it.

[0938] 2. "Means for analyzing received text data and extracting key information" means a method for analyzing received text data using natural language processing techniques to identify and extract key information.

[0939] 3. "Means for using artificial intelligence to generate documents based on extracted key information" refers to the process of using AI technology to automatically create professional documents using extracted key information as input.

[0940] 4. "Means for correcting typos and omissions in generated documents and standardizing the format" refers to a function that checks generated documents, corrects typos and omissions, and adjusts the overall format to conform to certain standards.

[0941] 5. "Means for providing generated materials to a visual display device in real time" means a structure for displaying generated materials on a visual display device such as smart glasses in a timely manner.

[0942] 6. "Means by which a user can check and modify the generated material via a visual display device" means a mechanism by which a user can check the content of the material via a visual display device and modify it as necessary.

[0943] 7. "Means for composing and generating the content of each slide in real time based on extracted key information" refers to a technique for appropriately arranging the content of slides based on extracted information and instantly creating materials.

[0944] 8. "Means by which the user can manipulate and modify the generated material using a visual display device when providing it" means a function that allows the user to freely manipulate and revise the provided material using a visual display device.

[0945] This invention relates to a system that enables users to generate efficient and professional materials in a short time. In particular, the system of the present invention automatically generates materials based on text data entered by the user and provides them to a visual display device in real time. In this embodiment, each component of the system and its operation will be described.

[0946] The server first receives text data entered by the user. This is done by sending data provided through smart glasses or other input devices to the server. Next, it uses natural language processing (NLP) techniques to analyze the received data and extract important information. Specific NLP toolkits used include SpaCy and NLTK (Natural Language Toolkit).

[0947] The extracted key information is processed by a generative AI model, which automatically generates slides for the materials. This generative AI model organizes appropriate content into slides based on the information extracted earlier. For example, a slide titled "Current Sales Status" might display data such as "First Quarter Sales: 500 million yen." The generated slides are then corrected for typos and formatted uniformly on the server.

[0948] The server then provides the generated materials in real time to a visual display device (e.g., smart glasses). This visual display device can be a device such as Google Glass or Vuzix. The user can not only check the materials through this device, but also make corrections as needed. These corrections are sent from the visual display device to the server, and the materials are generated again.

[0949] For example, if a user asks, "What are the features of this product?", the voice recognition system sends this as text data to the server. The server extracts the keyword "features," and based on that, the generative AI model creates a slide containing supplementary explanations. This slide is displayed in real time on the smart glasses, allowing the user to instantly explain things to the customer.

[0950] An example of a prompt to be input to a generative AI model is, "Prompt: Based on the keyword 'waterproof function' extracted from the conversation, please generate materials containing a detailed description of this product."

[0951] In this way, the user can quickly and accurately create materials while working, thereby improving the quality of service to customers.

[0952] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0953] Step 1:

[0954] The text data entered by the user is sent from the smart glasses to the server.

[0955] Input: Text data is generated by the voice recognition system and provided by the user via the smart glasses.

[0956] Data processing: Converting voice data into text data using a voice recognition system.

[0957] Output: The server receives the text data.

[0958] Step 2:

[0959] The server analyzes the received text data using natural language processing (NLP) technology and extracts key information.

[0960] Input: Text data received by the server.

[0961] Data processing: Analyze text data using an NLP toolkit such as SpaCy to extract keywords and key information.

[0962] Output: Extracted keywords and key information.

[0963] Step 3:

[0964] The server generates presentation slides using a generative AI model based on the extracted key information.

[0965] Input: Extracted keywords and key information.

[0966] Data processing: The generative AI model determines the appropriate template and slide content, and generates the materials.

[0967] Output: The generated slides and materials.

[0968] Step 4:

[0969] The server corrects typographical errors and standardizes formatting in the generated materials.

[0970] Input: Generated slides and materials.

[0971] Data processing: Apply a process to automatically detect and correct typographical errors and ensure consistent formatting across the entire document.

[0972] Output: Corrected and uniformly formatted materials.

[0973] Step 5:

[0974] The server provides the generated materials to the smart glasses in real time.

[0975] Input: Corrected and uniformly formatted materials.

[0976] Data processing: Converting materials into a display format for smart glasses.

[0977] Output: Materials displayed on smart glasses.

[0978] Step 6:

[0979] The user can review and modify the generated material via a visual display device.

[0980] Input: Documents displayed on smart glasses.

[0981] Data processing: The process of checking the data and instructing corrections as necessary.

[0982] Output: Data to be corrected.

[0983] Step 7:

[0984] The server generates the materials again based on the user's correction instructions and provides them to the smart glasses again.

[0985] Input: Correction instruction data.

[0986] Data processing: Regenerate the materials based on instructions, correcting typos and standardizing the format again.

[0987] Output: Final, revised document displayed on smart glasses.

[0988] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0989] This invention is a system that combines text data entered by a user with an emotion engine that recognizes the user's emotions, allowing business people to efficiently and effectively create materials to be used in meetings and sales activities. This system adjusts the tone and wording of the materials based on the user's emotions, and further optimizes the timing and method of reviewing the materials.

[0990] Users access the system using their devices and first input text data that will form the basis for creating materials. This text data includes details of meetings and sales activities, sales data, marketing strategies, and competitor analysis. The text data entered by the user is sent from the device to the server. At the same time, the emotion engine recognizes the user's emotions in real time through facial recognition and voice analysis.

[0991] The server analyzes the received text data. This analysis is performed using natural language processing (NLP) technology to identify keywords and numerical data necessary for document creation. For example, if the text data contains the information "First Quarter Sales: 500 million yen," the server extracts the keyword "First Quarter Sales" and the numerical data "500 million yen." If the emotion engine determines that the user is feeling stressed, the system applies a corresponding tone and phrasing.

[0992] The server then uses the extracted information to generate a draft document using an artificial intelligence (AI) model. The AI ​​model takes into account the output of the emotion engine and selects the appropriate tone and phrasing. For example, if the user has a positive emotion that emphasizes unity and cooperation, the document will include positive expressions such as "success" and "growth."

[0993] The server also corrects typos and typos in the generated documents and standardizes formatting, ensuring a consistent tone and manner throughout the document and maintaining a professional appearance. Furthermore, the emotion engine can suggest the best time and method for reviewing the documents based on the user's emotions. For example, if the user is tired, the system can suggest reviewing the documents later.

[0994] The generated documents are sent from the server to the terminal for user confirmation and final correction. The user can check the generated documents on the terminal, input corrections if necessary, and send them back to the server. This allows the user to create highly accurate documents in a short amount of time.

[0995] For example, if a user enters the following information, "1st Quarter Sales: 500 million yen. 2nd Quarter Projected Sales: 600 million yen. Marketing Budget: 200 million yen. Competitor Analysis," and the emotion engine determines that the user is stressed, the server will generate a slide like this:

[0996] 1. Slide 1: Title: "Today's Meeting", Tone: "Let's be efficient."

[0997] 2. Slide 2: Title: "Current Sales Status", Content: "First Quarter Sales: 500 million yen. Second Quarter Forecast Sales: 600 million yen."

[0998] 3. Slide 3: Title: "Marketing Strategy", Content: "Marketing Budget: 200 million yen. Competitor Analysis."

[0999] This system allows users to create documents efficiently and significantly reduces the time it takes to create them. In addition, by creating documents that reflect the user's emotions, more effective communication can be achieved.

[1000] The processing flow will be explained below.

[1001] Step 1:

[1002] Users access the system using a terminal and log in. After logging in, they input text data that will be the basis for creating materials into the terminal. For example, they might input information such as "First quarter sales: 500 million yen. Second quarter forecast sales: 600 million yen. Marketing budget: 200 million yen. Competitor analysis."

[1003] Step 2:

[1004] The device receives the input text data and sends it to the server, while the emotion engine detects the user's emotions through facial recognition and voice analysis, and also sends this information to the server.

[1005] Step 3:

[1006] The server analyzes the received text data and uses natural language processing (NLP) technology to extract key information needed to create the document, such as identifying important keywords and numerical data like "first quarter sales," "500 million yen," and "competitor analysis."

[1007] Step 4:

[1008] The server analyzes the user's emotional data sent from the emotion engine to understand the user's current emotional state, for example, identifying whether the user is feeling stressed or in a positive mood.

[1009] Step 5:

[1010] The server uses an AI model to generate a draft document based on the extracted key information and emotion data. The AI ​​model selects the appropriate tone and phrasing based on the user's emotions. For example, if the user is feeling stressed, the tone of the document will be set to simple and encouraging.

[1011] Step 6:

[1012] The server automatically detects and corrects typographical errors in the generated documents, and applies a unified color scheme, font, layout, and other formatting to ensure consistency across the entire document.

[1013] Step 7:

[1014] The server sends the generated materials to the terminal, where professional-looking materials are presented to the user.

[1015] Step 8:

[1016] The terminal visually displays the received materials to the user, who can then check the materials and make any necessary corrections.

[1017] Step 9:

[1018] If the user makes any changes to the document, the terminal sends the changes back to the server, which then generates new documents that reflect the changes and sends them back to the terminal.

[1019] Step 10:

[1020] Finally, once the user has completed the review and corrections, the documents can be used in meetings and sales activities. Through the system, users can create professional documents that are efficient and emotionally responsive.

[1021] This series of steps allows users to create high-quality materials in a short amount of time, leaving more time for thinking and strategizing. Furthermore, materials are created that respond to the user's emotions, resulting in more effective communication.

[1022] Example 2

[1023] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1024] Conventional document creation systems struggle to maintain a consistent tone and appropriate expressions when generating documents based on user-entered text data. Furthermore, because documents are created without considering the user's emotional state, they can lack tone and phrasing that matches the user's emotions and intent, reducing the effectiveness of communication. Furthermore, correcting typos and formatting in generated documents requires manual effort, which is inefficient.

[1025] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1026] In this invention, the server includes means for receiving text data entered by a user, means for analyzing the received text data to extract key information, means for generating materials using artificial intelligence based on the extracted key information and user emotion data recognized in real time, means for correcting typos and omissions in the generated materials and standardizing the format, and means for providing the generated materials to the user. This makes it possible to create materials with a consistent tone and expression based on the text and emotion data, and also automates the correction of typos and standardization of the format, improving the effectiveness of communication and increasing the efficiency of document creation.

[1027] A "user" is an individual or entity that operates the system to input text data and review and modify generated materials.

[1028] "Text data" refers to data including text information and numerical information that a user inputs to create a document.

[1029] The "receiving means" is a function or module that allows the server to obtain text data sent by the user.

[1030] The "means for analyzing and extracting key information" refers to a function or module that analyzes received text data using natural language processing technology and identifies and extracts keywords and numerical data necessary for the material.

[1031] "User emotion data recognized in real time" is data indicating the user's emotional state obtained from the user's facial expression, tone of voice, and the like.

[1032] "Means for generating materials using artificial intelligence" refers to a function or module that automatically creates draft materials using an AI model based on extracted key information and sentiment data.

[1033] "Means for correcting typographical errors and standardizing formatting" refers to a function or module that automatically corrects typographical errors in generated materials and standardizes the formatting of the entire document to be consistent.

[1034] The "means for providing" is a function or module for transmitting the generated materials to the user's terminal and providing them.

[1035] "Materials" are documents such as letters, slides, and reports created for use in meetings and sales activities.

[1036] This invention is a system that combines text data entered by a user with an emotion engine that recognizes the user's emotions. Its purpose is to enable businesspeople to efficiently and effectively create materials to be used in meetings and sales activities. This system adjusts the tone and wording of materials based on the user's emotions, and also optimizes the timing and method of reviewing materials.

[1037] Users access the system using a terminal and first input text data that will form the basis for creating materials. This text data includes details of meetings and sales activities, sales data, marketing strategies, and competitor analysis. The text data entered by the user is sent from the terminal to the server. At the same time, the terminal analyzes the user's face and voice in real time and obtains the user's emotional data using an emotion engine.

[1038] The server analyzes the received text data. This analysis is performed using natural language processing (NLP) technology to extract keywords and numerical data necessary for creating documents from the text data. For example, if the text data contains the information "First Quarter Sales: 500 million yen," the server extracts the keyword "First Quarter Sales" and the numerical data "500 million yen." If the emotion engine indicates that the user is feeling stressed, the system applies a corresponding tone and phrasing.

[1039] Next, the server uses the extracted information to generate a draft document using a generative AI model. This AI model could be GPT-3 or BERT, for example. The AI ​​model takes into account the output of the emotion engine and selects an appropriate tone and phrasing. For example, if the user has positive emotions, such as wanting to emphasize unity and cooperation, the document will include positive expressions like "success" and "growth."

[1040] The server also corrects typos and unifies formatting in the generated documents, ensuring a consistent tone and manner throughout the document and maintaining a professional appearance. Furthermore, the emotion engine can suggest the best time and method for reviewing the documents based on the user's emotions. For example, if the user is tired, the system suggests reviewing them later.

[1041] The generated documents are sent from the server to the terminal, where the user confirms and makes any final corrections. The user confirms the generated documents on the terminal, enters any necessary corrections, and sends them back to the server. In this way, the user can create highly accurate documents in a short amount of time.

[1042] For example, if a user enters the following information, "1st Quarter Sales: 500 million yen. 2nd Quarter Projected Sales: 600 million yen. Marketing Budget: 200 million yen. Competitor Analysis," and the emotion engine determines that the user is stressed, the server will generate a slide like this:

[1043] 1. Slide 1: Title: "Today's Meeting", Tone: "Let's be efficient."

[1044] 2. Slide 2: Title: "Current Sales Status", Content: "First Quarter Sales: 500 million yen. Second Quarter Forecast Sales: 600 million yen."

[1045] 3. Slide 3: Title: "Marketing Strategy", Content: "Marketing Budget: 200 million yen. Competitor Analysis."

[1046] This allows users to create materials efficiently and significantly reduces the time it takes to create them. In addition, by creating materials that reflect the user's emotions, more effective communication can be achieved.

[1047] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1048] Specific processing steps of the program

[1049] Step 1:

[1050] A user operates a terminal to access the system. First, they access the login page using a web browser or a dedicated application and enter their authentication information to log in. The input of this step is the user's authentication information, and the output is the login status (success or failure).

[1051] Step 2:

[1052] The user inputs text data for creating materials into the terminal. Information such as the contents of the meeting, sales data, and marketing strategies is entered into a dedicated input form, and the user presses the send button. The input in this step is the text data entered by the user, and the output is that text data is sent from the terminal to the server.

[1053] Step 3:

[1054] The terminal sends the entered text data to the server. The text data is sent to the server in JSON format via the Web API as a POST request. The input in this step is the text data entered by the user, and the output is the text data received by the server.

[1055] Step 4:

[1056] The device acquires emotional data in real time from the user's face and voice. Using the device's built-in camera and microphone, the device analyzes the user's emotional data using facial recognition software (e.g., OpenCV) and voice analysis software (e.g., Praat). The input for this step is the user's facial expression and tone of voice, and the output is emotional data.

[1057] Step 5:

[1058] The device sends the acquired emotion data to the server. The emotion data analyzed by the emotion engine is sent to the server in JSON format. The input of this step is the emotion data acquired by the device, and the output is the emotion data received by the server.

[1059] Step 6:

[1060] The server analyzes the received text data using natural language processing (NLP) techniques. It uses Python scripts and NLP libraries (spaCy, NLTK) to extract keywords and numerical data from the text data. The input for this step is the text data entered by the user, and the output is the extracted key information.

[1061] Step 7:

[1062] The server adjusts the tone and wording of the document based on the emotion data. It analyzes the output of the emotion engine and selects a tone that will relax the user if they are feeling stressed, and reflects this in the document. The inputs for this step are emotion data and extracted key information, and the output is the adjusted key information.

[1063] Step 8:

[1064] The server uses a generative AI model to generate a draft document based on the extracted information. For example, a prompt sentence is input into an AI model such as GPT-3 to generate a draft document. The input in this step is the adjusted key information and prompt sentence, and the output is the generated draft document. Example: Prompt sentence: "Please create a document based on first quarter sales."

[1065] Step 9:

[1066] The server corrects typos and standardizes the format of the generated document. It uses a proofreading tool (such as Grammarly or Hemingway) to ensure a consistent tone and format throughout the document. The input to this step is a draft of the generated document, and the output is the corrected and standardized document.

[1067] Step 10:

[1068] The server sends the generated documents to the terminal in JSON or PDF format and provides them to the user. The input of this step is the corrected and formatted documents, and the output is the documents provided to the user's terminal.

[1069] Step 11:

[1070] The user checks the generated data on the terminal and makes corrections as necessary. The data is displayed on the terminal and corrections are entered through the user interface. The input of this step is the provided data, and the output is the correction data entered by the user.

[1071] Step 12:

[1072] The terminal sends the corrected document back to the server. The document with the corrected data is sent back to the server for final confirmation. The input of this step is the document with the corrected data, and the output is the final confirmed document.

[1073] These steps allow users to create materials efficiently and with sensitivity.

[1074] (Application example 2)

[1075] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1076] Conventional document creation systems automatically process large amounts of data without considering the user's emotions, often failing to reflect the optimal tone or phrasing, resulting in stressful situations for users. Furthermore, the timing of review and revision after document creation is uniform and not dependent on the user's state, making it difficult to provide an efficient work environment. Therefore, there is a need for a system that appropriately reflects the user's emotions and provides more efficient review timing.

[1077] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1078] In this invention, the server includes means for receiving text data entered by a user, means for analyzing the received text data and extracting key information, means for generating materials using artificial intelligence based on the extracted key information, means for correcting typos and omissions in the generated materials and standardizing the format, means for recognizing the user's emotions and adjusting the tone and wording of the materials in response to the emotions, means for suggesting the timing and method of reviewing the materials based on the user's emotions, and means for recognizing emotions using the user's voice or video data and reflecting the recognition results in the generation of materials. This makes it possible to create effective materials that appropriately reflect the user's emotions and provide an efficient work environment.

[1079] "User" refers to an individual or organization that uses the System to create materials.

[1080] "Text data" refers to textual information entered by users, including details of meetings and sales, sales data, marketing strategies, and competitor analysis.

[1081] "Key information" refers to information extracted from text data, such as important keywords and numerical data.

[1082] "Artificial intelligence" refers to computer systems that have algorithms and models for analyzing text data and generating materials.

[1083] "Emotion engine" refers to a software or hardware system that recognizes a user's emotional state and adjusts the tone and phrasing of material accordingly.

[1084] "Error correction" refers to the process of automatically correcting incorrect characters or omissions in generated materials.

[1085] "Format unification" refers to the process of ensuring that the format and layout of generated materials are consistent.

[1086] "Document review timing" refers to a function that suggests the appropriate time or duration for a user to review and revise documents.

[1087] "Voice data" refers to voice information that records what a user says.

[1088] "Video data" refers to video information such as the user's face and posture.

[1089] "Tone" refers to the tone of the language and expressions used in a document.

[1090] The present invention is a system that combines text data entered by a user with an emotion engine that recognizes the user's emotions, allowing a robot used in a factory to recognize the emotional state of factory staff in real time and provide work instructions and feedback efficiently based on that information. Specific embodiments of the present invention will be described below.

[1091] The basic configuration of the system consists of a means for receiving text data entered by a user, a means for analyzing the received text data and extracting key information, a means for generating materials using artificial intelligence (AI) based on the extracted key information, a means for correcting typos and omissions in the generated materials and standardizing the format, a means for providing the generated materials to the user, a means for recognizing the user's emotions and adjusting the tone and wording of the materials in response to the emotions, a means for suggesting the timing and method for reviewing the materials based on the user's emotions, and a means for recognizing emotions using the user's voice or video data and reflecting the recognition results in the generation of materials.

[1092] Hardware and software used

[1093] The system uses the following hardware and software:

[1094] Hardware: Camera, microphone

[1095] Software: EmotionRecognition (emotion recognition engine), TextGenerator (text generation engine), NLPProcessor (natural language processing engine)

[1096] Processing flow

[1097] 1. Receiving user input: The user inputs text data about the factory status and tasks into the system, for example, receiving instructions such as "Please report on the progress of the production line."

[1098] 2. Emotion recognition: The emotion recognition engine analyzes the user's face and voice in real time through the camera and microphone to determine the user's emotional state.

[1099] 3. Text data analysis: The received text data is analyzed using a natural language processing engine to extract key keywords and numerical data.

[1100] 4. Document generation: Based on the extracted information, the text generation engine generates documents, taking into account the results of the emotion recognition engine and selecting the appropriate tone and wording.

[1101] 5. Correct typos and standardize formatting: Generated materials are automatically corrected for typos and formatted consistently.

[1102] 6. Provision to user: The generated materials are provided to the user, who can review and modify them.

[1103] 7. Review timing suggestion: Suggest the optimal review timing and method based on the user's emotional state.

[1104] Specific examples

[1105] For example, when checking the progress of a factory line, if the robot detects that a staff member is under stress, it will say in an encouraging tone, "Today is tough again, but let's do our best together!" and instruct the staff to transform negative emotions into positive ones.

[1106] Example prompts to input to a generative AI model:

[1107] "What words would be effective to say to factory staff when they are feeling stressed?"

[1108] This makes it possible to create effective materials that appropriately reflect the user's emotions and to provide an efficient work environment.

[1109] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1110] Step 1:

[1111] The terminal receives text data from the user about the factory status and tasks. For example, an instruction such as "Please report on the progress of the production line" is input. This text data is sent from the terminal to the server. The input data is in raw text format and is not processed or analyzed.

[1112] Step 2:

[1113] The server analyzes the received text data using a natural language processing engine (NLPProcessor). During the analysis, key keywords and numerical data are extracted. For example, keywords such as "production line" and "progress" and their associated numerical data are identified. The output is the key information in a structured data format.

[1114] Step 3:

[1115] The server uses the EmotionRecognition engine to analyze the user's video and audio data sent from the device via the camera and microphone. The analysis determines the user's emotional state (e.g., stress, relaxation). The input data is in the form of video and audio, and the output is a label indicating the emotional state.

[1116] Step 4:

[1117] The server receives the extracted key information and emotional state labels as input and generates materials using a text generation engine (TextGenerator). During this process, appropriate tone and phrasing are selected depending on the emotional state. For example, if the user is feeling stressed, an encouraging tone is used. The output is a draft document with adjusted tone and phrasing.

[1118] Step 5:

[1119] The server detects and corrects typographical errors in the generated documents and standardizes the format to ensure consistency. The input data is a draft document, and the output is the final document that has been corrected and formatted.

[1120] Step 6:

[1121] The server sends the final document to the terminal and provides an interface for the user to review and modify it. The user reviews the document, inputs any necessary modifications, and then sends it back to the server. The input data is the final document and the user's proposed modifications, and the output is the final document modified by the user.

[1122] Step 7:

[1123] The server then suggests when and how to review the material based on the user's emotional state. For example, if the server determines that the user is tired, it suggests reviewing the material later. The input data for this step is the emotional state label, and the output is the suggestion.

[1124] This series of processes enables the creation of effective materials that appropriately reflect the user's emotions and provides an efficient work environment.

[1125] 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.

[1126] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[1127] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1128] 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.

[1129] FIG. 9 is a diagram illustrating 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 actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect 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.

[1130] 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.

[1131] 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).

[1132] 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 indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, 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 indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1133] 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."

[1134] 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.

[1135] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1136] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1137] 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.

[1138] 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.

[1139] 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.

[1140] The hardware resource for executing a specific process can be any of the following processors: An example of a processor 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. Another example of a processor is 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.

[1141] The hardware resource that executes the specific processing 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 processing may be a single processor.

[1142] 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.

[1143] 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.

[1144] 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.

[1145] 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.

[1146] The following is further disclosed regarding the above embodiment.

[1147] (Claim 1)

[1148] means for receiving text data entered by a user;

[1149] means for analyzing the received text data to extract key information;

[1150] A means of generating materials using artificial intelligence based on the extracted key information;

[1151] A means to correct typos and standardize formatting in the generated materials;

[1152] means for providing the generated material to a user;

[1153] A system including:

[1154] (Claim 2)

[1155] 2. The system according to claim 1, further comprising means for sorting the content of each slide and generating a slide configuration based on the extracted main information.

[1156] (Claim 3)

[1157] 2. The system according to claim 1, further comprising means for allowing a user to confirm and correct the generated material when it is provided to the user.

[1158] "Example 1"

[1159] (Claim 1)

[1160] means for receiving text data entered by a user;

[1161] means for analyzing the received text data to extract key information;

[1162] A means of generating materials using a generative AI model based on the extracted key information;

[1163] A means to correct typos and standardize formatting in the generated materials;

[1164] means for providing the generated material to a user;

[1165] A system including:

[1166] (Claim 2)

[1167] 2. The system according to claim 1, further comprising means for sorting the contents of each document based on the extracted main information and generating a document structure.

[1168] (Claim 3)

[1169] 2. The system according to claim 1, further comprising means for allowing a user to confirm and correct the generated material when it is provided to the user.

[1170] "Application Example 1"

[1171] (Claim 1)

[1172] means for receiving text data entered by a user;

[1173] means for analyzing the received text data to extract key information;

[1174] A means of generating materials using artificial intelligence based on the extracted key information;

[1175] A means to correct typos and standardize formatting in the generated materials;

[1176] means for providing the generated material in real time to a visual display device;

[1177] means for allowing a user to review and modify the generated material via a visual display device;

[1178] A system including:

[1179] (Claim 2)

[1180] 10. The system of claim 1, further comprising means for structuring and generating in real time the content of each slide based on the extracted key information.

[1181] (Claim 3)

[1182] 10. The system of claim 1, further comprising means for allowing a user to manipulate and modify the presentation of the generated material using a visual display device.

[1183] "Example 2: Combining Emotion Engines"

[1184] (Claim 1)

[1185] means for receiving text data entered by a user;

[1186] means for analyzing the received text data to extract key information;

[1187] A means for generating materials using artificial intelligence based on the extracted key information and real-time recognized user emotion data;

[1188] A means to correct typos and standardize formatting in the generated materials;

[1189] means for providing the generated material to a user;

[1190] A system including:

[1191] (Claim 2)

[1192] 2. The system according to claim 1, further comprising means for allocating the content of each slide and generating a slide configuration based on the extracted main information and the user's emotion data.

[1193] (Claim 3)

[1194] 10. The system of claim 1, further comprising means for allowing a user to review and modify the generated material when it is provided to the user.

[1195] "Application example 2 when combining emotion engines"

[1196] (Claim 1)

[1197] means for receiving text data entered by a user;

[1198] means for analyzing the received text data to extract key information;

[1199] A means of generating materials using artificial intelligence based on the extracted key information;

[1200] A means to correct typos and standardize formatting in the generated materials;

[1201] means for providing the generated material to a user;

[1202] a means of recognizing a user's emotions and adjusting the tone and phrasing of the material accordingly;

[1203] A system including:

[1204] (Claim 2)

[1205] 2. The system according to claim 1, further comprising means for sorting the content of each slide and generating a slide configuration based on the extracted main information.

[1206] (Claim 3)

[1207] 2. The system according to claim 1, further comprising means for allowing a user to confirm and correct the generated material when it is provided to the user.

[1208] (Claim 4)

[1209] 10. The system of claim 1, further comprising means for suggesting timing and method for reviewing the material based on the user's sentiment.

[1210] (Claim 5)

[1211] 2. The system according to claim 1, further comprising means for recognizing emotions using voice or video data of a user and reflecting the recognition results in material generation. [Explanation of symbols]

[1212] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for receiving text data entered by a user; means for analyzing the received text data to extract key information; A means of generating materials using artificial intelligence based on the extracted key information; A means to correct typos and standardize formatting in the generated materials; means for providing the generated material to a user; A system including:

2. 2. The system according to claim 1, further comprising means for sorting the contents of each slide and generating a slide configuration based on the extracted main information.

3. 2. The system according to claim 1, further comprising means for allowing a user to confirm and correct the generated material when it is provided to the user.

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