System

Abstract

A system is provided.SOLUTION: The system includes a means for inputting business-related information from a user, a means for receiving the inputted information and storing it in a database, a means for performing natural language processing based on the stored information and analyzing business contents, a means for preparing a document and generating an answer based on the analysis result, a means for acquiring necessary data from an in-house system and reflecting the acquired data in the generated document and answer, and a means for allowing the user to confirm and correct the generated document and answer.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] In traditional business, businesspeople themselves had to acquire the knowledge and information necessary for their work and then carry out their work based on that knowledge. This method relied on individual ability and was particularly prone to disruptions to work due to illness or family circumstances. It was also difficult to improve work efficiency, requiring a huge amount of time and effort. There is a need for a method that can solve these problems and reduce the workload of businesspeople while still allowing them to carry out their work efficiently. [Means for solving the problem]

[0005] The present invention provides a system that includes a means for inputting work-related information from a user, a means for receiving the input information and storing it in a database, a means for performing natural language processing based on the stored information and analyzing the work content, a means for creating documents and generating answers based on the analysis results, a means for retrieving necessary data from an internal system and reflecting it in the generated documents and answers, and a means for the user to confirm and correct the generated documents and answers. This system allows users to work efficiently without spending time on the work itself. Furthermore, even if a user is unable to participate in work due to illness or family circumstances, the generation AI can take over the work, thereby improving business continuity.

[0006] "Business-related information" refers to all information and data that business people need to carry out their work.

[0007] "User" refers to an individual businessman or business professional who uses the system to input business-related information and utilizes the results.

[0008] The term "means" refers to a combination of hardware or software for realizing a specific function or process.

[0009] "Receiving" refers to the process by which the system takes in information sent by a user.

[0010] A "database" refers to a collection of information designed to store, retrieve, and manage information efficiently.

[0011] "Natural language processing" refers to the technology of understanding, analyzing, and processing human language.

[0012] "Document creation" refers to the process of automatically generating business-related documents, presentation slides, etc.

[0013] "Answer generation" refers to the process of automatically generating appropriate responses to questions or inquiries from users or other systems.

[0014] "Internal systems" refers to various management systems used within a company (e.g., human resources management systems, customer management systems, etc.).

[0015] "Verification and correction" refers to the process in which a user reviews the materials and answers created by the generative AI and manually makes changes as necessary. [Brief explanation of the drawings]

[0016] [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

[0017] 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.

[0018] First, the terms used in the following description will be explained.

[0019] 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).

[0020] 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.

[0021] 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.

[0022] 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.

[0023] 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."

[0024] [First embodiment]

[0025] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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."

[0037] This invention relates to a generation AI system that receives business-related information from users and creates materials and answers based on that information. This system is composed of users, a server, and terminals.

[0038] Overall system configuration

[0039] The system consists of the following main components:

[0040] User: Responsible for entering work-related information and reviewing and correcting generated materials and responses.

[0041] Server: Responsible for receiving, storing, and processing information, and integrating with internal systems.

[0042] Terminal: Provides an interface for users to input information and review / edit generated materials and answers.

[0043] Generative AI: Analyzes input information using natural language processing, and creates materials and generates answers.

[0044] Program processing overview

[0045] The main processing of the system is as follows:

[0046] 1. The user uses the terminal to input work-related information. For example, the user inputs "Best practices for customer service" as training content in text format and clicks the "Submit" button.

[0047] 2. The server receives the entered information. The received information is stored in a database. The server formats the information and stores it appropriately.

[0048] 3. Generative AI takes the stored information and performs natural language processing. For example, it analyzes the content of "Customer Service Best Practices" and extracts key points.

[0049] 4. Generative AI automatically generates materials and answers based on the analysis results. For example, when creating a presentation on customer service, the analyzed key points are placed on the slides. It also generates appropriate answers in email format to departmental questions.

[0050] 5. The server connects to the company's internal systems to obtain the necessary data. For example, it obtains information about new employees from the personnel management system and generates new training materials based on that information.

[0051] 6. The terminal provides an interface that allows users to check the generated materials and responses. Users can check the generated presentation materials and email content on the screen and make corrections as necessary.

[0052] 7. After the user has confirmed and corrected the materials, the device saves or sends the final materials or answers. For example, the finalized presentation materials are saved as a file or a reply email is sent.

[0053] Specific operation example

[0054] 1. The user writes "best practices for customer service" into an input form on a PC terminal and submits it.

[0055] 2. The server receives the information, formats it, and stores it in a database.

[0056] 3. The generative AI retrieves the stored information and performs natural language analysis.

[0057] 4. The AI ​​automatically generates presentation slides, placing key analysis results on each slide. It also generates email responses to questions from the department.

[0058] 5. The server retrieves new employee information from the human resources management system and provides it to the generation AI, which then generates training materials.

[0059] 6. The terminal displays the generated presentation materials and email contents to the user, who then checks and modifies them.

[0060] 7. After the user confirms, the device saves the final document and sends it by email.

[0061] In this way, the present invention realizes a sustainable way of working by efficiently processing work-related information and reducing the workload of users.

[0062] The processing flow will be explained below.

[0063] Step 1:

[0064] Users input business-related information into the generation AI from their terminal.

[0065] Specifically, open the input form on your PC or tablet, enter the training content or a summary of a business book in text format, and click the "Send" button.

[0066] Step 2:

[0067] The server receives the input business-related information.

[0068] Specifically, the information is received using a data reception API connected via the Internet, and the contents are temporarily stored in memory.

[0069] Step 3:

[0070] The server stores the received information in a database.

[0071] Specifically, the received data is formatted and converted into the appropriate format, and then written to the "Business Information" table in the database. The consistency of the information is also checked.

[0072] Step 4:

[0073] The generating AI retrieves stored information from the database.

[0074] Specifically, a query is used to extract the necessary data from the "Business Information" table and pass it to the natural language processing engine.

[0075] Step 5:

[0076] The generative AI performs natural language processing based on the acquired information.

[0077] Specifically, it uses natural language analysis algorithms to understand the context and extract key points, then converts the analysis results into an internal data format.

[0078] Step 6:

[0079] The generative AI generates materials and answers based on the analysis results.

[0080] Specifically, the analysis results are entered into a pre-prepared template, and presentation materials and response emails are automatically generated.

[0081] Step 7:

[0082] The server obtains the necessary data through an API connection with the internal system.

[0083] Specifically, authentication information is used to retrieve data from human resources management systems and customer management systems, format it, and provide it to the generation AI.

[0084] Step 8:

[0085] The generation AI performs additional processing based on the acquired internal data to complement the final documents and answers.

[0086] Specifically, the generated materials and answers are updated using newly acquired information to improve accuracy.

[0087] Step 9:

[0088] The terminal displays the generated materials and answers to the user.

[0089] Specifically, it provides an interface that allows users to check the generated presentation materials and email contents, and displays them on the screen.

[0090] Step 10:

[0091] The user can check and modify the generated materials and answers.

[0092] Specifically, you manually edit the documents or emails displayed on the screen and then click the "Save" or "Send" button to confirm.

[0093] Step 11:

[0094] The terminal saves or transmits the final materials and answers after the user has confirmed and corrected them.

[0095] Specifically, the finalized presentation materials are saved as a file and sent via email to the specified address.

[0096] In this way, this system receives work-related information from the user, and the generation AI automatically creates materials and generates answers based on that information, thereby reducing the user's workload and improving work efficiency.

[0097] Example 1

[0098] 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."

[0099] Conventional business-related information management and document creation systems require users to manually input information, analyze it, and create the necessary documents, which requires a great deal of time and effort. Furthermore, it is difficult to retrieve and link data from multiple internal systems, and the confirmation and correction of generated documents and responses is cumbersome. For these reasons, there was a need for a method to efficiently and quickly manage and process business-related information, automatically generate high-quality documents and responses, and reduce the burden on users.

[0100] 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.

[0101] In this invention, the server includes means for inputting business-related information from a user, means for receiving the input information and saving it in a database, means for analyzing the business content by performing natural language processing based on the saved information, means for creating materials and generating answers based on the analysis results, means for acquiring necessary data from an internal system and reflecting the data in the generated materials and answers, means for allowing the user to check and correct the generated materials and answers, and means for saving the final materials and answers or sending them to an external system. This enables efficient management and processing of business-related information, automatic generation of materials, and reduction of the burden on users.

[0102] "Business-related information" refers to various data and information related to business operations, including, for example, training content, best practices for customer service, and project management information.

[0103] "User" means an individual or entity that uses the System to input work-related information and review and correct generated materials and responses.

[0104] "Means" refers to a method, apparatus, or software component used to perform a particular function or process within a system.

[0105] "Receiving information" refers to the process of obtaining data provided by a user via a network and incorporating it into the system.

[0106] "Database" refers to an organized collection of data, and in this context refers to a system or software for storing input business-related information.

[0107] "Natural language processing" refers to technology that allows computers to analyze, understand, and generate human language, and in this case includes analyzing business content using generative AI models.

[0108] "Document creation" refers to the process of automatically generating documents such as presentations and reports based on input information and analysis results.

[0109] "Answer generation" refers to the process of automatically generating an appropriate response to a question based on input information and analysis results.

[0110] "Internal systems" refers to various information systems used within a company or organization, such as human resources management systems and databases.

[0111] "Confirmation and correction" refers to the process in which the user checks the generated materials and answers and corrects or edits the content as necessary.

[0112] "Saving" refers to recording the final generated materials and answers in file format in storage.

[0113] "Sending to external system" refers to the process of transferring the generated materials and answers to other systems or users using external communication means such as e-mail.

[0114] This invention relates to a generation AI system that receives business-related information from users and creates materials and answers based on that information. This system is composed of users, a server, and terminals.

[0115] Overall system configuration

[0116] The system consists of the following main components:

[0117] User: Responsible for entering work-related information and reviewing and correcting generated materials and responses.

[0118] Server: Responsible for receiving, storing, and processing information and interfacing with internal systems.

[0119] Terminal: Provides an interface for users to input information and review / edit generated materials and answers.

[0120] Generative AI: Analyzes input information using natural language processing, and creates materials and generates answers.

[0121] Program processing overview

[0122] In this generative AI system, users use a PC terminal to input work-related information via a web browser. Specifically, they access a dedicated input form, enter content such as "best practices for customer service" in text format, and click the submit button. This sends the information to the system.

[0123] The server uses Apache HTTP Server to receive information sent by users, formats the data using Python scripts, and stores it in a MySQL database. The received data is converted to JSON format and securely recorded.

[0124] The server then retrieves the stored information and passes it to a generative AI model (e.g., OpenAI's GPT-3) using a prompt. The prompt contains detailed instructions based on the input. For example, a prompt such as "Please create a presentation based on best practices for customer service" could be sent to the generative AI.

[0125] The generative AI analyzes the prompt text and extracts key points based on the input information. For example, it analyzes the key points of "best practices for customer service" and creates presentation materials and email responses to questions. The presentation materials are automatically generated using the API of Microsoft PowerPoint.

[0126] The server also connects to the company's human resources management system (LDAP server) to retrieve additional data, such as new employee information, which is then used to automatically generate training materials for new employees.

[0127] The generated materials and answers are displayed to the user through a web application provided by the device. The user can then check and correct them, and finally confirm the materials and answers. The confirmed materials are saved in the PC's local storage, and an email is sent via the SMTP server.

[0128] Specific examples

[0129] Here are some examples of prompts to input to a generative AI model:

[0130] 1. "Based on the following, create a presentation on best practices for customer service: being respectful, responding quickly, and resolving problems."

[0131] 2. "Create new employee training materials based on the following information: new employee's name, start date, role, and department."

[0132] In this way, this system efficiently processes work-related information, reduces the user's workload, and enables sustainable working styles to be realized.

[0133] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0134] Step 1:

[0135] A user enters work-related information. The user opens a web browser on their PC terminal and accesses a dedicated input form. For example, the user enters information such as "Best practices for customer service" and clicks the submit button. The input data is text data entered by the user and is sent to the system by the submit operation.

[0136] Step 2:

[0137] The server receives the information and stores it in the database. The server receives information sent by the user using Apache HTTP Server. The received text data is converted to JSON format using a Python script and stored in a MySQL database. The received data (input) is text information, and the output is formatted JSON data and stored in the database.

[0138] Step 3:

[0139] The server generates a prompt for the generation AI based on the stored information and passes the information to the AI ​​model. It retrieves the stored JSON data and creates a prompt based on that information: "Please create a presentation based on best practices for customer service." This prompt is sent to the generation AI (for example, GPT-3). The input is JSON data, and the output is the prompt and text data for analysis.

[0140] Step 4:

[0141] The generative AI analyzes the information and automatically generates materials and answers. The generative AI analyzes the text data entered based on the prompt text and extracts important points. For example, it analyzes the key points of "Best Practices for Customer Service," creates presentation materials using Microsoft PowerPoint API, and generates email responses to questions. The input is the prompt text and text data, and the output is the generated presentation materials and email responses.

[0142] Step 5:

[0143] The server retrieves additional data from the internal system and provides it to the generation AI. The server queries the internal human resources management system (LDAP server) to retrieve new employee information (e.g., name, start date, role, department). Based on the retrieved information, it generates training materials for new employees by sending additional prompts to the generation AI. The input is the new employee information retrieved from the LDAP server, and the output is the additional prompts and training materials passed to the generation AI.

[0144] Step 6:

[0145] The terminal displays the generated materials and responses to the user. The generated presentation materials and email content are displayed in the browser on the user's PC terminal via a web application. Presentation materials are displayed in slide format and email content in preview format so that the user can check them. The input is the presentation materials and email data sent from the server, and the output is the content displayed on the browser.

[0146] Step 7:

[0147] The user checks and modifies the generated materials and responses. Using the portal site's editing tools, the user checks the generated presentation materials and email content and makes modifications as necessary. For example, the user may change the title of a presentation slide or add or edit email content. The input is the initially generated materials and email data, and the output is the final data after modifications.

[0148] Step 8:

[0149] The device saves the final materials and answers and sends them to the external system. To save the final data that the user has confirmed and corrected, the presentation materials are saved in local storage by clicking the "Save" button. The final email is sent to the specified address via the SMTP server by clicking the "Send" button. The input is the finalized presentation materials and email data, and the output is the saved file and the sent email.

[0150] (Application example 1)

[0151] 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."

[0152] Conventional document creation systems based on work-related information have had difficulty generating personalized content based on individual user interests and requests. Furthermore, there was a lack of efficient means for checking and correcting content after it had been generated. This meant that users had to spend a lot of time manually creating and editing content, resulting in a decline in labor productivity.

[0153] 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.

[0154] In this invention, the server includes means for inputting work-related information from a user, means for receiving the input information and storing the input information in a database, means for performing natural language processing based on the stored information and analyzing the work content, means for creating materials and generating answers based on the analysis results, means for obtaining necessary data from an internal system and reflecting the data in the generated materials and answers, means for the user to confirm and modify the generated materials and answers, means for the user to input topics and keywords of interest and generate personalized content based on the input, and means for displaying the generated personalized content to the user for confirmation and modification. This enables the user to automatically generate personalized content based not only on work-related information but also on their individual interests, and efficiently confirm and modify it.

[0155] "Business-related information" is a general term for data and knowledge that companies and organizations need to carry out their business.

[0156] "User" refers to a person or organization that uses this system to input business-related information and review and modify the generated content.

[0157] A "database" is a collection of data and a system for efficiently managing, storing, and searching data.

[0158] "Natural language processing" is a general term for technology that enables computers to understand human language and perform tasks such as analysis and generation.

[0159] "Document creation" is the act of combining text and graphics to create a document based on a specific purpose or content.

[0160] "Answer generation" is the process of automatically creating appropriate answers based on questions or requests from users.

[0161] "Internal systems" refers to all information systems used within a company or organization, including databases and management systems.

[0162] "Personalized Content" refers to information and materials that are customized based on a user's individual interests and specific needs.

[0163] The "Internet" is a huge information and communications network that interconnects computers and networks all over the world.

[0164] A "machine learning algorithm" is a type of computer program that learns patterns from data and makes predictions and classifications.

[0165] A specific embodiment for carrying out the present invention will be described.

[0166] The system consists of the following major hardware and software components:

[0167] Hardware: User's smartphone, PC, and server.

[0168] Software: Python programs, generative AI models such as OpenAI GPT-4®, and database systems.

[0169] First, users use their smartphones or PCs to input work-related information and topics and keywords that interest them, which are then sent to a server via the Internet.

[0170] The server does the following:

[0171] 1. Receives input information and stores it in a database system, which is used to support efficient data management and retrieval.

[0172] 2. Based on the stored information, use a Python program to format and analyze the information.

[0173] 3. Based on the formatted and analyzed information, generative AI models such as OpenAI GPT-4 are used to create documents and generate answers, especially personalized content based on the user's topics of interest.

[0174] 4. The generated materials and content are sent to the user's device so that the user can review and modify them.

[0175] The user can check the materials and content generated on the device and make any necessary corrections. The final materials and content after corrections are sent back to the server and stored in a database or sent to a specified destination.

[0176] For example, if a user wants to generate a personalized newsletter about "latest AI technology," here's the prompt:

[0177] Create a personalized newsletter on 'latest AI technologies'.

[0178] By feeding this prompt into a generative AI model such as OpenAI GPT-4, a corresponding newsletter can be automatically generated.

[0179] This allows users to create documents based on business-related information and easily generate personalized content according to their individual interests while significantly reducing the amount of work required.

[0180] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0181] Step 1:

[0182] Users enter work-related information and topics of interest

[0183] Users use their smartphones or PCs to input work-related information, topics of interest, or keywords. For example, consider a user entering information about the latest AI technology. This input is sent to a server via the Internet.

[0184] Input: Work-related information, topics of interest, and keywords

[0185] Output: Sending information to the server

[0186] Step 2:

[0187] The server receives the entered information and stores it in a database

[0188] The server receives the information sent by the user and stores it in a database system, which is used to support efficient data management and retrieval.

[0189] Input: Information submitted by the user

[0190] Output: Saved data for formatting and analysis

[0191] Step 3:

[0192] The server formats and analyzes the stored information

[0193] The server uses a Python program to format the information stored in the database and convert it into a format that is easy for the generative AI model to understand, which includes structuring the information and removing unnecessary information.

[0194] Input: Information stored in a database

[0195] Output: A dataset suitable for formatting and analysis

[0196] Step 4:

[0197] The server uses the generated AI model based on the formatted and analyzed information to create documents and generate answers.

[0198] The server uses generative AI models such as OpenAI GPT-4 to generate documents and answers from the formatted and analyzed information, particularly to generate personalized content based on the user's topics of interest.

[0199] Input: A dataset suitable for formatting and analysis, a prompt

[0200] Output: Auto-generated materials and personalized content

[0201] Step 5:

[0202] The server sends generated materials and content to the user's device.

[0203] The server sends the generated materials and personalized content to the user's device so that the user can review and edit them. The user uses this to review and edit slides and newsletters.

[0204] Input: Auto-generated materials and personalized content

[0205] Output: Content sent to the user's device

[0206] Step 6:

[0207] Review and modify user-generated materials and content

[0208] The user can check the generated materials and content on their device and modify them as necessary. After the user has checked and modified the materials and content, the final materials and content are saved and, in some cases, sent back to the server.

[0209] Input: Generated materials and personalized content

[0210] Output: Final revised materials and content

[0211] Step 7:

[0212] The server stores and transmits the final materials and content.

[0213] The server stores the final materials and content modified by the user in a database and sends them to a specified destination as needed, allowing the user to use the generated content in their actual work.

[0214] Input: Final revised materials and content

[0215] Output: Stored database entries, transmitted materials and content

[0216] This series of processing steps enables efficient creation of materials and generation of personalized content based on business-related information and the individual interests of the user.

[0217] 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.

[0218] This invention combines an emotion engine with a generative AI system that inputs business-related information from users and creates materials and generates answers based on that information. This system is composed of a user, a server, a terminal, a generative AI, and an emotion engine.

[0219] Overall system configuration

[0220] The system consists of the following main components:

[0221] User: Responsible for entering work-related information and reviewing and correcting generated materials and responses.

[0222] Server: Responsible for receiving, storing, and processing information, and integrating with internal systems.

[0223] Terminal: Provides an interface for users to input information and review / edit generated materials and answers.

[0224] Generative AI: Analyzes input information using natural language processing, and creates materials and generates answers.

[0225] Emotion engine: Recognizes the user's emotions and adjusts the output of the generative AI based on those emotions.

[0226] Program processing overview

[0227] The main processing of the system is as follows:

[0228] 1. The user uses the terminal to input work-related information. For example, the user inputs "Best practices for customer service" as training content in text format and clicks the "Submit" button.

[0229] 2. The server receives the entered information. The received information is stored in a database. The server formats the information and stores it appropriately.

[0230] 3. Generative AI retrieves stored information from the database and performs natural language processing. For example, it analyzes the content of "Customer Service Best Practices" and extracts key points.

[0231] 4. The emotion engine infers emotions from user input and voice data. The emotion engine uses machine learning algorithms to parse emotions from the user's context and tone.

[0232] 5. The generation AI adjusts the content of materials and answers based on the emotional data provided by the emotion engine. For example, if the user is feeling stressed, the generated answers will be changed to more gentle expressions.

[0233] 6. Generative AI automatically generates materials and answers based on the analysis results. For example, when creating a presentation on customer service, the analyzed key points are placed on the slides. It also generates appropriate answers in email format to departmental questions.

[0234] 7. The server connects to the company's internal systems to obtain the necessary data. For example, it obtains information about new employees from the personnel management system and generates new training materials based on that information.

[0235] 8. The terminal provides an interface that allows users to check the generated materials and responses. Users can check the generated presentation materials and email content on the screen and make corrections as necessary.

[0236] 9. After the user has confirmed and corrected the materials, the device saves or sends the final materials or answers. For example, the finalized presentation materials are saved as a file or a reply email is sent.

[0237] Specific operation example

[0238] 1. The user writes "best practices for customer service" into an input form on a PC terminal and submits it.

[0239] 2. The server receives the information, formats it, and stores it in a database.

[0240] 3. The generative AI retrieves the stored information and performs natural language analysis.

[0241] 4. The emotion engine infers that the user is tired based on their input and voice data.

[0242] 5. Based on the data from the emotion engine, the generative AI creates presentation materials in a concise format to avoid user fatigue. It also adjusts the wording of email responses to questions from departments to be more pleasant.

[0243] 6. The server retrieves new employee information from the human resources management system and provides it to the generation AI, which then generates training materials.

[0244] 7. The terminal displays the generated presentation materials and email contents to the user, who then checks and modifies them.

[0245] 8. After the user confirms, the device saves the final document and sends it by email.

[0246] In this way, the present invention efficiently processes work-related information while also taking into account the user's emotions, further reducing the user's workload and thereby realizing a sustainable way of working.

[0247] The processing flow will be explained below.

[0248] Step 1:

[0249] Users input business-related information into the generation AI from their terminal.

[0250] Specifically, open the input form on your PC or tablet, enter the training content or a summary of a business book in text format, and click the "Send" button.

[0251] Step 2:

[0252] The server receives the input business-related information.

[0253] Specifically, the information is received using a data reception API connected via the Internet, and the contents are temporarily stored in memory.

[0254] Step 3:

[0255] The server stores the received information in a database.

[0256] Specifically, the received data is formatted and converted into the appropriate format, and then written to the "Business Information" table in the database. The consistency of the information is also checked.

[0257] Step 4:

[0258] The generating AI retrieves stored information from the database.

[0259] Specifically, a query is used to extract the necessary data from the "Business Information" table and pass it to the natural language processing engine.

[0260] Step 5:

[0261] The generative AI performs natural language processing based on the acquired information.

[0262] Specifically, it uses natural language analysis algorithms to understand the context and extract key points, then converts the analysis results into an internal data format.

[0263] Step 6:

[0264] The emotion engine infers emotions from user input and voice data.

[0265] Specifically, it uses machine learning algorithms to analyze the user's context and tone to estimate their emotional state, such as "tiredness," "stress," or "joy."

[0266] Step 7:

[0267] The generative AI adjusts the content of materials and answers based on the emotional data provided by the emotion engine.

[0268] Specifically, if a user is feeling stressed, the content of the reply email will be changed to use gentler vocabulary and writing style, and the format of presentation materials will be made clearer and simpler.

[0269] Step 8:

[0270] The generative AI generates materials and answers based on the analysis results.

[0271] Specifically, the analysis results are entered into a pre-prepared template, and presentation materials and response emails are automatically generated.

[0272] Step 9:

[0273] The server obtains the necessary data through an API connection with the internal system.

[0274] Specifically, authentication information is used to retrieve data from human resources management systems and customer management systems, format it, and provide it to the generation AI.

[0275] Step 10:

[0276] The generation AI performs additional processing based on the acquired internal data to complement the final documents and answers.

[0277] Specifically, the generated materials and answers are updated using newly acquired information to improve accuracy.

[0278] Step 11:

[0279] The terminal displays the generated materials and answers to the user.

[0280] Specifically, it provides an interface that allows users to check the generated presentation materials and email contents, and displays them on the screen.

[0281] Step 12:

[0282] The user can check and modify the generated materials and answers.

[0283] Specifically, you manually edit the documents or emails displayed on the screen and then click the "Save" or "Send" button to confirm.

[0284] Step 13:

[0285] The terminal saves or transmits the final materials and answers after the user has confirmed and corrected them.

[0286] Specifically, the finalized presentation materials are saved as a file and sent via email to the specified address.

[0287] In this way, this system receives work-related information from the user, and the generation AI automatically creates materials and generates answers based on that information, thereby reducing the user's workload and improving work efficiency.In addition, by combining it with an emotion engine, it is possible to respond flexibly and appropriately according to the user's emotional state.

[0288] Example 2

[0289] 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."

[0290] Conventional business-related information processing systems create documents and generate responses in a uniform format without considering the user's emotions, resulting in a lack of consideration for user stress and fatigue. Furthermore, it is often difficult to confirm or correct the generated documents and responses, resulting in a decline in work efficiency. To solve these issues, a flexible approach that takes the user's emotions into account is required.

[0291] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for inputting business-related information from a user, means for receiving the input information and storing it in a database, means for performing natural language processing based on the stored information and analyzing the business content, means for creating materials and generating answers based on the analysis results, means for acquiring necessary data from an internal system and reflecting the data in the generated materials and answers, means for allowing the user to confirm and correct the generated materials and answers, and means for recognizing the user's emotions and adjusting the output content based on the emotions. This makes it possible to efficiently process business-related information while taking the user's emotions into consideration and reduce the workload.

[0292] "Business-related information" refers to all information related to the performance of business within a company or organization, and specifically includes project progress, records of customer service, employee attendance status, etc.

[0293] "User" refers to an individual or organization that uses the system to input business-related information and to review and correct generated materials and responses.

[0294] "Input means" refers to devices or software that provide an interface for users to input work-related information into a system, such as a keyboard, mouse, touchscreen, or voice input.

[0295] "Server" refers to a computer system that receives business-related information entered by users and stores it in a database.

[0296] "Database" refers to an information storage system that structures and stores received information and manages it in a manner that allows for quick search and retrieval as needed.

[0297] "Natural language processing" refers to technology for understanding and analyzing human language, and specifically includes text analysis, summarization, information extraction, and intent understanding.

[0298] "Generative AI" refers to artificial intelligence that uses natural language processing technology to analyze input business-related information and automatically generate the necessary materials and answers.

[0299] An "emotion engine" refers to a system that infers emotions from user input data and voice, and adjusts the output content of the generating AI based on those emotions.

[0300] "Internal systems" refers to various management systems used within a company or organization, including, for example, human resources management systems and project management systems.

[0301] "Network" refers to a communication path used to electronically send and receive information and data, and specifically includes the Internet, an intranet, a LAN (local area network), and the like.

[0302] "Machine learning algorithms" refer to methods or models that allow computers to learn data and make predictions or analyses based on the results. Examples include neural networks and support vector machines.

[0303] MODE FOR CARRYING OUT THE INVENTION

[0304] This invention is a system that combines an emotion engine with a generative AI system that inputs business-related information from users and creates materials and answers based on that information. This system is composed of the following main components:

[0305] User: Responsible for entering work-related information and reviewing and correcting generated materials and responses.

[0306] Server: Responsible for receiving, storing, and processing information and interfacing with internal systems.

[0307] Terminal: Provides an interface for users to input information and review / edit generated materials and answers.

[0308] Generative AI: Uses natural language processing technology to analyze input information and create materials and generate answers.

[0309] Emotion engine: Recognizes the user's emotions and adjusts the output of the generative AI based on those emotions.

[0310] Program processing overview

[0311] Users input work-related information using devices such as PCs and smartphones. For example, a user inputs "best practices for customer service" as training content in text format into the device and clicks the "Send" button.

[0312] The server receives the input information, formats it, and stores it in a database. Specific software used for this purpose includes database management systems (DBMS) such as MySQL and PostgreSQL. The server-side reception process is performed using, for example, a Python server script.

[0313] Generative AI reads information stored in a database and performs natural language processing. A specific generative AI model used is GPT-4. This model analyzes input text, extracts key points, and generates appropriate materials and answers. For example, in "Best Practices for Customer Service," it extracts key points such as "quick response," "honesty," and "importance of follow-up," and creates presentation materials based on these.

[0314] The emotion engine infers emotions from the user's text input and voice data. This engine uses speech recognition software (e.g., Google® Speech-to-Text API) and machine learning algorithms to infer emotions. For example, it can determine the user's stress or fatigue from the tone of the voice data or specific keywords, and provide this as emotion data to the generation AI.

[0315] The generation AI adjusts the content of the generated materials and answers appropriately based on the emotional data provided by the emotion engine. For example, if it estimates that the user is tired, it will use concise and gentle language in its answers. Specifically, it generates gentle emails and presentation materials using phrases such as "Thank you for your hard work" and "Don't worry."

[0316] The terminal provides an interface that displays the generated materials and answers to the user. The user can check the materials and answers on the screen and make corrections as necessary. For example, the generated presentation can be displayed in PowerPoint format and the text in the slides can be corrected.

[0317] Overall, the system works to efficiently process work-related information and automate the creation of documents and responses while taking into account the user's emotions.

[0318] Specific examples

[0319] 1. The user writes "best practices for customer service" into an input form on a PC terminal and submits it.

[0320] 2. The server receives the information, formats it, and stores it in a database.

[0321] 3. The generative AI retrieves the stored information and performs natural language analysis.

[0322] 4. The emotion engine infers that the user is tired based on their input and voice data.

[0323] 5. Based on the data from the emotion engine, the generative AI creates presentation materials in a concise format to avoid user fatigue. It also adjusts the wording of email responses to questions from departments to be more pleasant.

[0324] 6. The server retrieves new employee information from the internal system and provides it to the generation AI, which then generates training materials.

[0325] 7. The terminal displays the generated presentation materials and email contents to the user, who then checks and modifies them.

[0326] 8. After the user confirms, the device saves the final document and sends it by email.

[0327] Example prompt sentence:

[0328] "Based on the topic 'Customer Engagement Best Practices,' please generate a concise and friendly presentation deck and a friendly email response to the department's questions."

[0329] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0330] Step 1:

[0331] A user enters work-related information into an input form on a device such as a PC or smartphone. For example, the user enters "best practices for customer service" in text format and clicks the "Send" button. This input information is sent from the device to the server.

[0332] Step 2:

[0333] The server receives the information sent by the user. At this stage, the text data entered by the user is sent to the server. The server formats the received information and processes the data by removing unnecessary spaces from the string and standardizing the format. The formatted data is then saved in a database. The databases used are MySQL and PostgreSQL.

[0334] Step 3:

[0335] Generative AI reads stored information from a database and performs natural language processing. For example, GPT-4 is used as the generative AI model. It performs sentence analysis on the stored text data and extracts key points. The input is text data from the database, and the output is a list of analyzed key points. Specific operations include the process of calling the GPT-4 model to perform text analysis.

[0336] Step 4:

[0337] The emotion engine infers emotions from text and voice data entered by the user. This emotion engine uses speech recognition software (such as the Google Speech-to-Text API) to convert voice data into text and then performs emotion analysis based on that text. The input is the user's voice and text data, and the output is an inferred emotion label. For example, the output might be "tired" or "stressed."

[0338] Step 5:

[0339] The generative AI adjusts the content of materials and answers based on the emotional data provided by the emotion engine. For example, if it estimates that the user is tired, it will use gentler language in the answer. In this case, the generative AI model receives the analysis results and emotional data as input and generates gentle text. Specifically, it reduces the user's stress by adding phrases such as "Thank you for your hard work."

[0340] Step 6:

[0341] The server interacts with other internal systems to obtain the necessary data. For example, it obtains new employee information from a human resources management system. In this case, the server uses an API to send a request to the human resources management system and provides the obtained data to the generation AI. The input is the API request and response, and the output is the new employee information that is passed to the generation AI. Specific operations include sending an HTTP request to the API endpoint and parsing the result.

[0342] Step 7:

[0343] The terminal provides an interface that displays the generated materials and responses to the user. Specifically, the generated presentation materials and email contents are displayed on the terminal screen, and the user can check and correct them as necessary. The input is the generated content, and the output is the result of the user checking and correcting it.

[0344] Step 8:

[0345] The terminal saves the final documents and responses that the user has reviewed and revised, and sends them as necessary. For example, it saves the revised presentation materials in PDF format or sends a reply email via an SMTP server. The input is the content that the user reviewed and revised, and the output is the saved file or the sent email. Specific operations include disk access for saving files and the use of the SMTP protocol for sending emails.

[0346] (Application example 2)

[0347] 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."

[0348] Conventional generative AI systems were unable to take the user's emotions into consideration when creating documents and generating answers based on work-related information, making it difficult to reduce user stress and provide more appropriate responses. Furthermore, particularly in customer support, responses that take into account the customer's emotions are required, but automating this has been difficult.

[0349] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for inputting business-related information from a user, means for receiving the input information and storing it in a database, means for performing natural language processing based on the stored information and analyzing the business content, emotion analysis means for analyzing the user's emotions, means for adjusting materials and answers generated based on the emotion analysis results, and means for the user to confirm and correct the generated materials and answers. This makes it possible to take the user's emotions into consideration and respond appropriately and in a way that reduces stress.

[0350] "Business-related information" refers to data and information related to the business conducted by a company or organization, and is input by a user.

[0351] "User" means a person who uses the system to input business-related information and check and correct the generated documents and responses.

[0352] "Means for input" refers to a function that provides an interface that allows users to input business-related information into the system.

[0353] "Means for receiving and storing in a database" refers to the function of receiving input information in an appropriate format and storing it in a database.

[0354] "Natural language processing" is a technology that allows computers to understand and analyze human language.

[0355] "Means of analysis" refers to the function of performing natural language processing based on stored information and analyzing business content.

[0356] "Means for creating materials and generating answers" refers to the function of automatically generating materials and answers based on the analysis results.

[0357] "Emotion analysis means" refers to a function that analyzes emotions from user input and voice data.

[0358] "Means for adjustment" refers to the function of adjusting the content of generated materials and answers based on the results of sentiment analysis.

[0359] "Internal systems" is a general term for information systems used within a company.

[0360] "Means for checking and correcting" refers to the function of providing an interface that allows users to check the generated materials and answers and correct them as necessary.

[0361] This system combines emotion analysis functionality with a generative AI system that inputs work-related information from users and creates documents and answers based on that information. Specifically, it consists of the following components:

[0362] 1. A user uses a smartphone or PC to enter business-related information. For example, they enter the details of a customer support inquiry into an input form and submit it.

[0363] 2. The terminal receives the input information and sends it to the server, using communication over the Internet.

[0364] 3. The server stores the received information in a database, where it is formatted for analysis.

[0365] 4. The generative AI installed on the server performs natural language processing based on the stored information and analyzes the business content, using machine learning algorithms.

[0366] 5. The server is also equipped with an emotion analysis function that analyzes emotions from user input. Emotion analysis uses machine learning models based on text and voice data.

[0367] 6. The analyzed emotion data is used to adjust the content of the materials and answers generated by the generative AI. For example, if the user expresses anger or stress, the generated answers will be changed to more gentle expressions.

[0368] 7. The generative AI model uses this data to create materials and generate answers. For example, in customer support email correspondence, an appropriate reply email is generated in response to a customer's inquiry.

[0369] 8. The generated materials and answers are displayed to the user via the terminal, where the user can review them and make corrections as necessary.

[0370] 9. The final confirmed and corrected materials and answers will be returned to the server and stored or transmitted as necessary.

[0371] Examples of specific hardware and software used include a smartphone (ANDROID (registered trademark), iOS), a personal computer, the Python programming language, the Transformers library (Hugging Face), and the EmotionRecognizer library.

[0372] As a specific example, if a user inputs a customer inquiry such as "My product hasn't arrived yet, what's going on?", emotion analysis will determine that the customer is feeling stressed. The generation AI will take this emotional data into consideration and generate a reply in a gentle tone. An example of a generated prompt sentence is as follows:

[0373] Example prompt sentence:

[0374] If your customer is feeling stressed, respond with the following messages: My item hasn't arrived, what's going on?

[0375] In this way, the system automates responses by taking the user's emotions into account, enabling more effective and satisfying customer support.

[0376] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0377] Step 1:

[0378] A user inputs business-related information using a smartphone or PC. This information could be, for example, the contents of a customer support inquiry. By clicking the "Send" button, the input data is sent to the terminal. The input is the inquiry in text format, and the output includes the data to be sent to the terminal.

[0379] Step 2:

[0380] The terminal receives the input information and transmits it to the server. The received data is sent to the server via the Internet. The input includes text data from the user, and the output includes data to be sent to the server.

[0381] Step 3:

[0382] The server stores the received information in a database, where it formats the data appropriately for future analysis and retrieval. The input is the data sent from the device, and the output is the data stored in the database.

[0383] Step 4:

[0384] The generative AI installed on the server retrieves information stored in the database and performs natural language processing. Specifically, it uses a machine learning algorithm to analyze the text of the inquiry and extract key points. The input is text data retrieved from the database, and the output contains the analyzed key points.

[0385] Step 5:

[0386] The server is also equipped with an emotion analysis function that analyzes emotions from user input. Sentiment analysis uses machine learning models based on text and voice data. The input is text and voice data from the user, and the output includes analyzed emotion data.

[0387] Step 6:

[0388] To adjust the generated materials and answers based on the results of emotion analysis, the generation AI takes emotional data into account and adjusts the content. For example, if the user is feeling stressed, the tone of the answer will be changed to a gentler expression. The inputs are emotion analysis data and the analysis results from natural language processing, and the output includes adjusted materials and answers.

[0389] Step 7:

[0390] The generated materials and answers are displayed to the user via the terminal. The user can check them and make corrections as necessary. The input is the materials and answer data sent from the server, and the output includes data that reflects the user's corrections.

[0391] Step 8:

[0392] Finally, the confirmed and corrected materials and responses are returned to the server and stored or transmitted as necessary. This streamlines business processes such as customer support and improves customer satisfaction. The input is the data modified by the user, and the output is the data that is finally stored or transmitted.

[0393] 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.

[0394] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (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.

[0395] 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.

[0396] [Second embodiment]

[0397] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0398] 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.

[0399] 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).

[0400] 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.

[0401] 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.

[0402] 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).

[0403] 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.

[0404] 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.

[0405] 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.

[0406] 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.

[0407] 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.

[0408] 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."

[0409] This invention relates to a generation AI system that receives business-related information from users and creates materials and answers based on that information. This system is composed of users, a server, and terminals.

[0410] Overall system configuration

[0411] The system consists of the following main components:

[0412] User: Responsible for entering work-related information and reviewing and correcting generated materials and responses.

[0413] Server: Responsible for receiving, storing, and processing information, and integrating with internal systems.

[0414] Terminal: Provides an interface for users to input information and review / edit generated materials and answers.

[0415] Generative AI: Analyzes input information using natural language processing, and creates materials and generates answers.

[0416] Program processing overview

[0417] The main processing of the system is as follows:

[0418] 1. The user uses the terminal to input work-related information. For example, the user inputs "Best practices for customer service" as training content in text format and clicks the "Submit" button.

[0419] 2. The server receives the entered information. The received information is stored in a database. The server formats the information and stores it appropriately.

[0420] 3. Generative AI takes the stored information and performs natural language processing. For example, it analyzes the content of "Customer Service Best Practices" and extracts key points.

[0421] 4. Generative AI automatically generates materials and answers based on the analysis results. For example, when creating a presentation on customer service, the analyzed key points are placed on the slides. It also generates appropriate answers in email format to departmental questions.

[0422] 5. The server connects to the company's internal systems to obtain the necessary data. For example, it obtains information about new employees from the personnel management system and generates new training materials based on that information.

[0423] 6. The terminal provides an interface that allows users to check the generated materials and responses. Users can check the generated presentation materials and email content on the screen and make corrections as necessary.

[0424] 7. After the user has confirmed and corrected the materials, the device saves or sends the final materials or answers. For example, the finalized presentation materials are saved as a file or a reply email is sent.

[0425] Specific operation example

[0426] 1. The user writes "best practices for customer service" into an input form on a PC terminal and submits it.

[0427] 2. The server receives the information, formats it, and stores it in a database.

[0428] 3. The generative AI retrieves the stored information and performs natural language analysis.

[0429] 4. The AI ​​automatically generates presentation slides, placing key analysis results on each slide. It also generates email responses to questions from the department.

[0430] 5. The server retrieves new employee information from the human resources management system and provides it to the generation AI, which then generates training materials.

[0431] 6. The terminal displays the generated presentation materials and email contents to the user, who then checks and modifies them.

[0432] 7. After the user confirms, the device saves the final document and sends it by email.

[0433] In this way, the present invention realizes a sustainable way of working by efficiently processing work-related information and reducing the workload of users.

[0434] The processing flow will be explained below.

[0435] Step 1:

[0436] Users input business-related information into the generation AI from their terminal.

[0437] Specifically, open the input form on your PC or tablet, enter the training content or a summary of a business book in text format, and click the "Send" button.

[0438] Step 2:

[0439] The server receives the input business-related information.

[0440] Specifically, the information is received using a data reception API connected via the Internet, and the contents are temporarily stored in memory.

[0441] Step 3:

[0442] The server stores the received information in a database.

[0443] Specifically, the received data is formatted and converted into the appropriate format, and then written to the "Business Information" table in the database. The consistency of the information is also checked.

[0444] Step 4:

[0445] The generating AI retrieves stored information from the database.

[0446] Specifically, a query is used to extract the necessary data from the "Business Information" table and pass it to the natural language processing engine.

[0447] Step 5:

[0448] The generative AI performs natural language processing based on the acquired information.

[0449] Specifically, it uses natural language analysis algorithms to understand the context and extract key points, then converts the analysis results into an internal data format.

[0450] Step 6:

[0451] The generative AI generates materials and answers based on the analysis results.

[0452] Specifically, the analysis results are entered into a pre-prepared template, and presentation materials and response emails are automatically generated.

[0453] Step 7:

[0454] The server obtains the necessary data through an API connection with the internal system.

[0455] Specifically, authentication information is used to retrieve data from human resources management systems and customer management systems, format it, and provide it to the generation AI.

[0456] Step 8:

[0457] The generation AI performs additional processing based on the acquired internal data to complement the final documents and answers.

[0458] Specifically, the generated materials and answers are updated using newly acquired information to improve accuracy.

[0459] Step 9:

[0460] The terminal displays the generated materials and answers to the user.

[0461] Specifically, it provides an interface that allows users to check the generated presentation materials and email contents, and displays them on the screen.

[0462] Step 10:

[0463] The user can check and modify the generated materials and answers.

[0464] Specifically, you manually edit the documents or emails displayed on the screen and then click the "Save" or "Send" button to confirm.

[0465] Step 11:

[0466] The terminal saves or transmits the final materials and answers after the user has confirmed and corrected them.

[0467] Specifically, the finalized presentation materials are saved as a file and sent via email to the specified address.

[0468] In this way, this system receives work-related information from the user, and the generation AI automatically creates materials and generates answers based on that information, thereby reducing the user's workload and improving work efficiency.

[0469] Example 1

[0470] 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."

[0471] Conventional business-related information management and document creation systems require users to manually input information, analyze it, and create the necessary documents, which requires a great deal of time and effort. Furthermore, it is difficult to retrieve and link data from multiple internal systems, and the confirmation and correction of generated documents and responses is cumbersome. For these reasons, there was a need for a method to efficiently and quickly manage and process business-related information, automatically generate high-quality documents and responses, and reduce the burden on users.

[0472] 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.

[0473] In this invention, the server includes means for inputting business-related information from a user, means for receiving the input information and saving it in a database, means for analyzing the business content by performing natural language processing based on the saved information, means for creating materials and generating answers based on the analysis results, means for acquiring necessary data from an internal system and reflecting the data in the generated materials and answers, means for allowing the user to check and correct the generated materials and answers, and means for saving the final materials and answers or sending them to an external system. This enables efficient management and processing of business-related information, automatic generation of materials, and reduction of the burden on users.

[0474] "Business-related information" refers to various data and information related to business operations, including, for example, training content, best practices for customer service, and project management information.

[0475] "User" means an individual or entity that uses the System to input work-related information and review and correct generated materials and responses.

[0476] "Means" refers to a method, apparatus, or software component used to perform a particular function or process within a system.

[0477] "Receiving information" refers to the process of obtaining data provided by a user via a network and incorporating it into the system.

[0478] "Database" refers to an organized collection of data, and in this context refers to a system or software for storing input business-related information.

[0479] "Natural language processing" refers to technology that allows computers to analyze, understand, and generate human language, and in this case includes analyzing business content using generative AI models.

[0480] "Document creation" refers to the process of automatically generating documents such as presentations and reports based on input information and analysis results.

[0481] "Answer generation" refers to the process of automatically generating an appropriate response to a question based on input information and analysis results.

[0482] "Internal systems" refers to various information systems used within a company or organization, such as human resources management systems and databases.

[0483] "Confirmation and correction" refers to the process in which the user checks the generated materials and answers and corrects or edits the content as necessary.

[0484] "Saving" refers to recording the final generated materials and answers in file format in storage.

[0485] "Sending to external system" refers to the process of transferring the generated materials and answers to other systems or users using external communication means such as e-mail.

[0486] This invention relates to a generation AI system that receives business-related information from users and creates materials and answers based on that information. This system is composed of users, a server, and terminals.

[0487] Overall system configuration

[0488] The system consists of the following main components:

[0489] User: Responsible for entering work-related information and reviewing and correcting generated materials and responses.

[0490] Server: Responsible for receiving, storing, and processing information and interfacing with internal systems.

[0491] Terminal: Provides an interface for users to input information and review / edit generated materials and answers.

[0492] Generative AI: Analyzes input information using natural language processing, and creates materials and generates answers.

[0493] Program processing overview

[0494] In this generative AI system, users use a PC terminal to input work-related information via a web browser. Specifically, they access a dedicated input form, enter content such as "best practices for customer service" in text format, and click the submit button. This sends the information to the system.

[0495] The server uses Apache HTTP Server to receive information submitted by users, formats the data using Python scripts, and stores it in a MySQL database, where it is converted to JSON format and securely recorded.

[0496] The server then retrieves the stored information and passes it to a generative AI model (e.g., OpenAI's GPT-3) using a prompt. The prompt contains detailed instructions based on the input. For example, the prompt could say, "Please create a presentation based on best practices for customer service."

[0497] The generative AI analyzes prompts and extracts key points based on the input information. For example, it analyzes the key points of "customer service best practices" and creates presentation materials and email responses to questions. The presentation materials are automatically generated using the Microsoft PowerPoint API.

[0498] The server also connects to the company's human resources management system (LDAP server) to retrieve additional data, such as new employee information, which is then used to automatically generate training materials for new employees.

[0499] The generated materials and answers are displayed to the user through a web application provided by the device. The user can then check and correct them, and finally confirm the materials and answers. The confirmed materials are saved in the PC's local storage, and an email is sent via the SMTP server.

[0500] Specific examples

[0501] Here are some examples of prompts to input to a generative AI model:

[0502] 1. "Based on the following, create a presentation on best practices for customer service: being respectful, responding quickly, and resolving problems."

[0503] 2. "Create new employee training materials based on the following information: new employee's name, start date, role, and department."

[0504] In this way, this system efficiently processes work-related information, reduces the user's workload, and enables sustainable working styles to be realized.

[0505] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0506] Step 1:

[0507] A user enters work-related information. The user opens a web browser on their PC terminal and accesses a dedicated input form. For example, the user enters information such as "Best practices for customer service" and clicks the submit button. The input data is text data entered by the user and is sent to the system by the submit operation.

[0508] Step 2:

[0509] The server receives the information and stores it in the database. The server receives information sent by the user using Apache HTTP Server. The received text data is converted to JSON format using a Python script and stored in a MySQL database. The received data (input) is text information, and the output is formatted JSON data and stored in the database.

[0510] Step 3:

[0511] The server generates a prompt for the generation AI based on the stored information and passes the information to the AI ​​model. It retrieves the stored JSON data and creates a prompt based on that information: "Please create a presentation based on best practices for customer service." This prompt is sent to the generation AI (for example, GPT-3). The input is JSON data, and the output is the prompt and text data for analysis.

[0512] Step 4:

[0513] The generative AI analyzes the information and automatically generates materials and answers. The generative AI analyzes the text data entered based on the prompt text and extracts important points. For example, it analyzes the key points of "Best Practices for Customer Service," creates presentation materials using Microsoft PowerPoint API, and generates email responses to questions. The input is the prompt text and text data, and the output is the generated presentation materials and email responses.

[0514] Step 5:

[0515] The server retrieves additional data from the internal system and provides it to the generation AI. The server queries the internal human resources management system (LDAP server) to retrieve new employee information (e.g., name, start date, role, department). Based on the retrieved information, it generates training materials for new employees by sending additional prompts to the generation AI. The input is the new employee information retrieved from the LDAP server, and the output is the additional prompts and training materials passed to the generation AI.

[0516] Step 6:

[0517] The terminal displays the generated materials and responses to the user. The generated presentation materials and email content are displayed in the browser on the user's PC terminal via a web application. Presentation materials are displayed in slide format and email content in preview format so that the user can check them. The input is the presentation materials and email data sent from the server, and the output is the content displayed on the browser.

[0518] Step 7:

[0519] The user checks and modifies the generated materials and responses. Using the portal site's editing tools, the user checks the generated presentation materials and email content and makes modifications as necessary. For example, the user may change the title of a presentation slide or add or edit email content. The input is the initially generated materials and email data, and the output is the final data after modifications.

[0520] Step 8:

[0521] The device saves the final materials and answers and sends them to the external system. To save the final data that the user has confirmed and corrected, the presentation materials are saved in local storage by clicking the "Save" button. The final email is sent to the specified address via the SMTP server by clicking the "Send" button. The input is the finalized presentation materials and email data, and the output is the saved file and the sent email.

[0522] (Application example 1)

[0523] 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."

[0524] Conventional document creation systems based on work-related information have had difficulty generating personalized content based on individual user interests and requests. Furthermore, there was a lack of efficient means for checking and correcting content after it had been generated. This meant that users had to spend a lot of time manually creating and editing content, resulting in a decline in labor productivity.

[0525] 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.

[0526] In this invention, the server includes means for inputting work-related information from a user, means for receiving the input information and storing the input information in a database, means for performing natural language processing based on the stored information and analyzing the work content, means for creating materials and generating answers based on the analysis results, means for obtaining necessary data from an internal system and reflecting the data in the generated materials and answers, means for the user to confirm and modify the generated materials and answers, means for the user to input topics and keywords of interest and generate personalized content based on the input, and means for displaying the generated personalized content to the user for confirmation and modification. This enables the user to automatically generate personalized content based not only on work-related information but also on their individual interests, and efficiently confirm and modify it.

[0527] "Business-related information" is a general term for data and knowledge that companies and organizations need to carry out their business.

[0528] "User" refers to a person or organization that uses this system to input business-related information and review and modify the generated content.

[0529] A "database" is a collection of data and a system for efficiently managing, storing, and searching data.

[0530] "Natural language processing" is a general term for technology that enables computers to understand human language and perform tasks such as analysis and generation.

[0531] "Document creation" is the act of combining text and graphics to create a document based on a specific purpose or content.

[0532] "Answer generation" is the process of automatically creating appropriate answers based on questions or requests from users.

[0533] "Internal systems" refers to all information systems used within a company or organization, including databases and management systems.

[0534] "Personalized Content" refers to information and materials that are customized based on a user's individual interests and specific needs.

[0535] The "Internet" is a huge information and communications network that interconnects computers and networks all over the world.

[0536] A "machine learning algorithm" is a type of computer program that learns patterns from data and makes predictions and classifications.

[0537] A specific embodiment for carrying out the present invention will be described.

[0538] The system consists of the following major hardware and software components:

[0539] Hardware: User's smartphone, PC, and server.

[0540] Software: Python programs, generative AI models such as OpenAI GPT-4, and database systems.

[0541] First, users use their smartphones or PCs to input work-related information and topics and keywords that interest them, which are then sent to a server via the Internet.

[0542] The server does the following:

[0543] 1. Receives input information and stores it in a database system, which is used to support efficient data management and retrieval.

[0544] 2. Based on the stored information, use a Python program to format and analyze the information.

[0545] 3. Based on the formatted and analyzed information, generative AI models such as OpenAI GPT-4 are used to create documents and generate answers, especially personalized content based on the user's topics of interest.

[0546] 4. The generated materials and content are sent to the user's device so that the user can review and modify them.

[0547] The user can check the materials and content generated on the device and make any necessary corrections. The final materials and content after corrections are sent back to the server and stored in a database or sent to a specified destination.

[0548] For example, if a user wants to generate a personalized newsletter about "latest AI technology," here's the prompt:

[0549] Create a personalized newsletter on 'latest AI technologies'.

[0550] By feeding this prompt into a generative AI model such as OpenAI GPT-4, a corresponding newsletter can be automatically generated.

[0551] This allows users to create documents based on business-related information and easily generate personalized content according to their individual interests while significantly reducing the amount of work required.

[0552] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0553] Step 1:

[0554] Users enter work-related information and topics of interest

[0555] Users use their smartphones or PCs to input work-related information, topics of interest, or keywords. For example, consider a user entering information about the latest AI technology. This input is sent to a server via the Internet.

[0556] Input: Work-related information, topics of interest, and keywords

[0557] Output: Sending information to the server

[0558] Step 2:

[0559] The server receives the entered information and stores it in a database

[0560] The server receives the information sent by the user and stores it in a database system, which is used to support efficient data management and retrieval.

[0561] Input: Information submitted by the user

[0562] Output: Saved data for formatting and analysis

[0563] Step 3:

[0564] The server formats and analyzes the stored information

[0565] The server uses a Python program to format the information stored in the database and convert it into a format that is easy for the generative AI model to understand, which includes structuring the information and removing unnecessary information.

[0566] Input: Information stored in a database

[0567] Output: A dataset suitable for formatting and analysis

[0568] Step 4:

[0569] The server uses the generated AI model based on the formatted and analyzed information to create documents and generate answers.

[0570] The server uses generative AI models such as OpenAI GPT-4 to generate documents and answers from the formatted and analyzed information, particularly to generate personalized content based on the user's topics of interest.

[0571] Input: A dataset suitable for formatting and analysis, a prompt

[0572] Output: Auto-generated materials and personalized content

[0573] Step 5:

[0574] The server sends generated materials and content to the user's device.

[0575] The server sends the generated materials and personalized content to the user's device so that the user can review and edit them. The user uses this to review and edit slides and newsletters.

[0576] Input: Auto-generated materials and personalized content

[0577] Output: Content sent to the user's device

[0578] Step 6:

[0579] Review and modify user-generated materials and content

[0580] The user can check the generated materials and content on their device and modify them as necessary. After the user has checked and modified the materials and content, the final materials and content are saved and, in some cases, sent back to the server.

[0581] Input: Generated materials and personalized content

[0582] Output: Final revised materials and content

[0583] Step 7:

[0584] The server stores and transmits the final materials and content.

[0585] The server stores the final materials and content modified by the user in a database and sends them to a specified destination as needed, allowing the user to use the generated content in their actual work.

[0586] Input: Final revised materials and content

[0587] Output: Stored database entries, transmitted materials and content

[0588] This series of processing steps enables efficient creation of materials and generation of personalized content based on business-related information and the individual interests of the user.

[0589] 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.

[0590] This invention combines an emotion engine with a generative AI system that inputs business-related information from users and creates materials and generates answers based on that information. This system is composed of a user, a server, a terminal, a generative AI, and an emotion engine.

[0591] Overall system configuration

[0592] The system consists of the following main components:

[0593] User: Responsible for entering work-related information and reviewing and correcting generated materials and responses.

[0594] Server: Responsible for receiving, storing, and processing information, and integrating with internal systems.

[0595] Terminal: Provides an interface for users to input information and review / edit generated materials and answers.

[0596] Generative AI: Analyzes input information using natural language processing, and creates materials and generates answers.

[0597] Emotion engine: Recognizes the user's emotions and adjusts the output of the generative AI based on those emotions.

[0598] Program processing overview

[0599] The main processing of the system is as follows:

[0600] 1. The user uses the terminal to input work-related information. For example, the user inputs "Best practices for customer service" as training content in text format and clicks the "Submit" button.

[0601] 2. The server receives the entered information. The received information is stored in a database. The server formats the information and stores it appropriately.

[0602] 3. Generative AI retrieves stored information from the database and performs natural language processing. For example, it analyzes the content of "Customer Service Best Practices" and extracts key points.

[0603] 4. The emotion engine infers emotions from user input and voice data. The emotion engine uses machine learning algorithms to parse emotions from the user's context and tone.

[0604] 5. The generation AI adjusts the content of materials and answers based on the emotional data provided by the emotion engine. For example, if the user is feeling stressed, the generated answers will be changed to more gentle expressions.

[0605] 6. Generative AI automatically generates materials and answers based on the analysis results. For example, when creating a presentation on customer service, the analyzed key points are placed on the slides. It also generates appropriate answers in email format to departmental questions.

[0606] 7. The server connects to the company's internal systems to obtain the necessary data. For example, it obtains information about new employees from the personnel management system and generates new training materials based on that information.

[0607] 8. The terminal provides an interface that allows users to check the generated materials and responses. Users can check the generated presentation materials and email content on the screen and make corrections as necessary.

[0608] 9. After the user has confirmed and corrected the materials, the device saves or sends the final materials or answers. For example, the finalized presentation materials are saved as a file or a reply email is sent.

[0609] Specific operation example

[0610] 1. The user writes "best practices for customer service" into an input form on a PC terminal and submits it.

[0611] 2. The server receives the information, formats it, and stores it in a database.

[0612] 3. The generative AI retrieves the stored information and performs natural language analysis.

[0613] 4. The emotion engine infers that the user is tired based on their input and voice data.

[0614] 5. Based on the data from the emotion engine, the generative AI creates presentation materials in a concise format to avoid user fatigue. It also adjusts the wording of email responses to questions from departments to be more pleasant.

[0615] 6. The server retrieves new employee information from the human resources management system and provides it to the generation AI, which then generates training materials.

[0616] 7. The terminal displays the generated presentation materials and email contents to the user, who then checks and modifies them.

[0617] 8. After the user confirms, the device saves the final document and sends it by email.

[0618] In this way, the present invention efficiently processes work-related information while also taking into account the user's emotions, further reducing the user's workload and thereby realizing a sustainable way of working.

[0619] The processing flow will be explained below.

[0620] Step 1:

[0621] Users input business-related information into the generation AI from their terminal.

[0622] Specifically, open the input form on your PC or tablet, enter the training content or a summary of a business book in text format, and click the "Send" button.

[0623] Step 2:

[0624] The server receives the input business-related information.

[0625] Specifically, the information is received using a data reception API connected via the Internet, and the contents are temporarily stored in memory.

[0626] Step 3:

[0627] The server stores the received information in a database.

[0628] Specifically, the received data is formatted and converted into the appropriate format, and then written to the "Business Information" table in the database. The consistency of the information is also checked.

[0629] Step 4:

[0630] The generating AI retrieves stored information from the database.

[0631] Specifically, a query is used to extract the necessary data from the "Business Information" table and pass it to the natural language processing engine.

[0632] Step 5:

[0633] The generative AI performs natural language processing based on the acquired information.

[0634] Specifically, it uses natural language analysis algorithms to understand the context and extract key points, then converts the analysis results into an internal data format.

[0635] Step 6:

[0636] The emotion engine infers emotions from user input and voice data.

[0637] Specifically, it uses machine learning algorithms to analyze the user's context and tone to estimate their emotional state, such as "tiredness," "stress," or "joy."

[0638] Step 7:

[0639] The generative AI adjusts the content of materials and answers based on the emotional data provided by the emotion engine.

[0640] Specifically, if a user is feeling stressed, the content of the reply email will be changed to use gentler vocabulary and writing style, and the format of presentation materials will be made clearer and simpler.

[0641] Step 8:

[0642] The generative AI generates materials and answers based on the analysis results.

[0643] Specifically, the analysis results are entered into a pre-prepared template, and presentation materials and response emails are automatically generated.

[0644] Step 9:

[0645] The server obtains the necessary data through an API connection with the internal system.

[0646] Specifically, authentication information is used to retrieve data from human resources management systems and customer management systems, format it, and provide it to the generation AI.

[0647] Step 10:

[0648] The generation AI performs additional processing based on the acquired internal data to complement the final documents and answers.

[0649] Specifically, the generated materials and answers are updated using newly acquired information to improve accuracy.

[0650] Step 11:

[0651] The terminal displays the generated materials and answers to the user.

[0652] Specifically, it provides an interface that allows users to check the generated presentation materials and email contents, and displays them on the screen.

[0653] Step 12:

[0654] The user can check and modify the generated materials and answers.

[0655] Specifically, you manually edit the documents or emails displayed on the screen and then click the "Save" or "Send" button to confirm.

[0656] Step 13:

[0657] The terminal saves or transmits the final materials and answers after the user has confirmed and corrected them.

[0658] Specifically, the finalized presentation materials are saved as a file and sent via email to the specified address.

[0659] In this way, this system receives work-related information from the user, and the generation AI automatically creates materials and generates answers based on that information, thereby reducing the user's workload and improving work efficiency.In addition, by combining it with an emotion engine, it is possible to respond flexibly and appropriately according to the user's emotional state.

[0660] Example 2

[0661] 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."

[0662] Conventional business-related information processing systems create documents and generate responses in a uniform format without considering the user's emotions, resulting in a lack of consideration for user stress and fatigue. Furthermore, it is often difficult to confirm or correct the generated documents and responses, resulting in a decline in work efficiency. To solve these issues, a flexible approach that takes the user's emotions into account is required.

[0663] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for inputting business-related information from a user, means for receiving the input information and storing it in a database, means for performing natural language processing based on the stored information and analyzing the business content, means for creating materials and generating answers based on the analysis results, means for acquiring necessary data from an internal system and reflecting the data in the generated materials and answers, means for allowing the user to confirm and correct the generated materials and answers, and means for recognizing the user's emotions and adjusting the output content based on the emotions. This makes it possible to efficiently process business-related information while taking the user's emotions into consideration and reduce the workload.

[0664] "Business-related information" refers to all information related to the performance of business within a company or organization, and specifically includes project progress, records of customer service, employee attendance status, etc.

[0665] "User" refers to an individual or organization that uses the system to input business-related information and to review and correct generated materials and responses.

[0666] "Input means" refers to devices or software that provide an interface for users to input work-related information into a system, such as a keyboard, mouse, touchscreen, or voice input.

[0667] "Server" refers to a computer system that receives business-related information entered by users and stores it in a database.

[0668] "Database" refers to an information storage system that structures and stores received information and manages it in a manner that allows for quick search and retrieval as needed.

[0669] "Natural language processing" refers to technology for understanding and analyzing human language, and specifically includes text analysis, summarization, information extraction, and intent understanding.

[0670] "Generative AI" refers to artificial intelligence that uses natural language processing technology to analyze input business-related information and automatically generate the necessary materials and answers.

[0671] An "emotion engine" refers to a system that infers emotions from user input data and voice, and adjusts the output content of the generating AI based on those emotions.

[0672] "Internal systems" refers to various management systems used within a company or organization, including, for example, human resources management systems and project management systems.

[0673] "Network" refers to a communication path used to electronically send and receive information and data, and specifically includes the Internet, an intranet, a LAN (local area network), and the like.

[0674] "Machine learning algorithms" refer to methods or models that allow computers to learn data and make predictions or analyses based on the results. Examples include neural networks and support vector machines.

[0675] MODE FOR CARRYING OUT THE INVENTION

[0676] This invention is a system that combines an emotion engine with a generative AI system that inputs business-related information from users and creates materials and answers based on that information. This system is composed of the following main components:

[0677] User: Responsible for entering work-related information and reviewing and correcting generated materials and responses.

[0678] Server: Responsible for receiving, storing, and processing information and interfacing with internal systems.

[0679] Terminal: Provides an interface for users to input information and review / edit generated materials and answers.

[0680] Generative AI: Uses natural language processing technology to analyze input information and create materials and generate answers.

[0681] Emotion engine: Recognizes the user's emotions and adjusts the output of the generative AI based on those emotions.

[0682] Program processing overview

[0683] Users input work-related information using devices such as PCs and smartphones. For example, a user inputs "best practices for customer service" as training content in text format into the device and clicks the "Send" button.

[0684] The server receives the input information, formats it, and stores it in a database. Specific software used for this purpose includes database management systems (DBMS) such as MySQL and PostgreSQL. The server-side reception process is performed using, for example, a Python server script.

[0685] Generative AI reads information stored in a database and performs natural language processing. A specific generative AI model used is GPT-4. This model analyzes input text, extracts key points, and generates appropriate materials and answers. For example, in "Best Practices for Customer Service," it extracts key points such as "quick response," "honesty," and "importance of follow-up," and creates presentation materials based on these.

[0686] The emotion engine infers emotions from the user's text input and voice data. This engine uses voice recognition software (such as the Google Speech-to-Text API) and machine learning algorithms to infer emotions. For example, it can determine the user's stress or fatigue from the tone of the voice data or specific keywords, and provide this as emotion data to the generation AI.

[0687] The generation AI adjusts the content of the generated materials and answers appropriately based on the emotional data provided by the emotion engine. For example, if it estimates that the user is tired, it will use concise and gentle language in its answers. Specifically, it generates gentle emails and presentation materials using phrases such as "Thank you for your hard work" and "Don't worry."

[0688] The terminal provides an interface that displays the generated materials and answers to the user. The user can check the materials and answers on the screen and make corrections as necessary. For example, the generated presentation can be displayed in PowerPoint format and the text in the slides can be corrected.

[0689] Overall, the system works to efficiently process work-related information and automate the creation of documents and responses while taking into account the user's emotions.

[0690] Specific examples

[0691] 1. The user writes "best practices for customer service" into an input form on a PC terminal and submits it.

[0692] 2. The server receives the information, formats it, and stores it in a database.

[0693] 3. The generative AI retrieves the stored information and performs natural language analysis.

[0694] 4. The emotion engine infers that the user is tired based on their input and voice data.

[0695] 5. Based on the data from the emotion engine, the generative AI creates presentation materials in a concise format to avoid user fatigue. It also adjusts the wording of email responses to questions from departments to be more pleasant.

[0696] 6. The server retrieves new employee information from the internal system and provides it to the generation AI, which then generates training materials.

[0697] 7. The terminal displays the generated presentation materials and email contents to the user, who then checks and modifies them.

[0698] 8. After the user confirms, the device saves the final document and sends it by email.

[0699] Example prompt sentence:

[0700] "Based on the topic 'Customer Engagement Best Practices,' please generate a concise and friendly presentation deck and a friendly email response to the department's questions."

[0701] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0702] Step 1:

[0703] A user enters work-related information into an input form on a device such as a PC or smartphone. For example, the user enters "best practices for customer service" in text format and clicks the "Send" button. This input information is sent from the device to the server.

[0704] Step 2:

[0705] The server receives the information sent by the user. At this stage, the text data entered by the user is sent to the server. The server formats the received information and processes the data by removing unnecessary spaces from the string and standardizing the format. The formatted data is then saved in a database. The databases used are MySQL and PostgreSQL.

[0706] Step 3:

[0707] Generative AI reads stored information from a database and performs natural language processing. For example, GPT-4 is used as the generative AI model. It performs sentence analysis on the stored text data and extracts key points. The input is text data from the database, and the output is a list of analyzed key points. Specific operations include the process of calling the GPT-4 model to perform text analysis.

[0708] Step 4:

[0709] The emotion engine infers emotions from text and voice data entered by the user. This emotion engine uses speech recognition software (such as the Google Speech-to-Text API) to convert voice data into text and then performs emotion analysis based on that text. The input is the user's voice and text data, and the output is an inferred emotion label. For example, the output might be "tired" or "stressed."

[0710] Step 5:

[0711] The generative AI adjusts the content of materials and answers based on the emotional data provided by the emotion engine. For example, if it estimates that the user is tired, it will use gentler language in the answer. In this case, the generative AI model receives the analysis results and emotional data as input and generates gentle text. Specifically, it reduces the user's stress by adding phrases such as "Thank you for your hard work."

[0712] Step 6:

[0713] The server interacts with other internal systems to obtain the necessary data. For example, it obtains new employee information from a human resources management system. In this case, the server uses an API to send a request to the human resources management system and provides the obtained data to the generation AI. The input is the API request and response, and the output is the new employee information that is passed to the generation AI. Specific operations include sending an HTTP request to the API endpoint and parsing the result.

[0714] Step 7:

[0715] The terminal provides an interface that displays the generated materials and responses to the user. Specifically, the generated presentation materials and email contents are displayed on the terminal screen, and the user can check and correct them as necessary. The input is the generated content, and the output is the result of the user checking and correcting it.

[0716] Step 8:

[0717] The terminal saves the final documents and responses that the user has reviewed and revised, and sends them as necessary. For example, it saves the revised presentation materials in PDF format or sends a reply email via an SMTP server. The input is the content that the user reviewed and revised, and the output is the saved file or the sent email. Specific operations include disk access for saving files and the use of the SMTP protocol for sending emails.

[0718] (Application example 2)

[0719] 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."

[0720] Conventional generative AI systems were unable to take the user's emotions into consideration when creating documents and generating answers based on work-related information, making it difficult to reduce user stress and provide more appropriate responses. Furthermore, particularly in customer support, responses that take into account the customer's emotions are required, but automating this has been difficult.

[0721] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for inputting business-related information from a user, means for receiving the input information and storing it in a database, means for performing natural language processing based on the stored information and analyzing the business content, emotion analysis means for analyzing the user's emotions, means for adjusting materials and answers generated based on the emotion analysis results, and means for the user to confirm and correct the generated materials and answers. This makes it possible to take the user's emotions into consideration and respond appropriately and in a way that reduces stress.

[0722] "Business-related information" refers to data and information related to the business conducted by a company or organization, and is input by a user.

[0723] "User" means a person who uses the system to input business-related information and check and correct the generated documents and responses.

[0724] "Means for input" refers to a function that provides an interface that allows users to input business-related information into the system.

[0725] "Means for receiving and storing in a database" refers to the function of receiving input information in an appropriate format and storing it in a database.

[0726] "Natural language processing" is a technology that allows computers to understand and analyze human language.

[0727] "Means of analysis" refers to the function of performing natural language processing based on stored information and analyzing business content.

[0728] "Means for creating materials and generating answers" refers to the function of automatically generating materials and answers based on the analysis results.

[0729] "Emotion analysis means" refers to a function that analyzes emotions from user input and voice data.

[0730] "Means for adjustment" refers to the function of adjusting the content of generated materials and answers based on the results of sentiment analysis.

[0731] "Internal systems" is a general term for information systems used within a company.

[0732] "Means for checking and correcting" refers to the function of providing an interface that allows users to check the generated materials and answers and correct them as necessary.

[0733] This system combines emotion analysis functionality with a generative AI system that inputs work-related information from users and creates documents and answers based on that information. Specifically, it consists of the following components:

[0734] 1. A user uses a smartphone or PC to enter business-related information. For example, they enter the details of a customer support inquiry into an input form and submit it.

[0735] 2. The terminal receives the input information and sends it to the server, using communication over the Internet.

[0736] 3. The server stores the received information in a database, where it is formatted for analysis.

[0737] 4. The generative AI installed on the server performs natural language processing based on the stored information and analyzes the business content, using machine learning algorithms.

[0738] 5. The server is also equipped with an emotion analysis function that analyzes emotions from user input. Emotion analysis uses machine learning models based on text and voice data.

[0739] 6. The analyzed emotion data is used to adjust the content of the materials and answers generated by the generative AI. For example, if the user expresses anger or stress, the generated answers will be changed to more gentle expressions.

[0740] 7. The generative AI model uses this data to create materials and generate answers. For example, in customer support email correspondence, an appropriate reply email is generated in response to a customer's inquiry.

[0741] 8. The generated materials and answers are displayed to the user via the terminal, where the user can review them and make corrections as necessary.

[0742] 9. The final confirmed and corrected materials and answers will be returned to the server and stored or transmitted as necessary.

[0743] Examples of specific hardware and software used include smartphones (Android, iOS), personal computers, the Python programming language, the Transformers library (Hugging Face), and the EmotionRecognizer library.

[0744] As a specific example, if a user inputs a customer inquiry such as "My product hasn't arrived yet, what's going on?", emotion analysis will determine that the customer is feeling stressed. The generation AI will take this emotional data into consideration and generate a reply in a gentle tone. An example of a generated prompt sentence is as follows:

[0745] Example prompt sentence:

[0746] If your customer is feeling stressed, respond with the following messages: My item hasn't arrived, what's going on?

[0747] In this way, the system automates responses by taking the user's emotions into account, enabling more effective and satisfying customer support.

[0748] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0749] Step 1:

[0750] A user inputs business-related information using a smartphone or PC. This information could be, for example, the contents of a customer support inquiry. By clicking the "Send" button, the input data is sent to the terminal. The input is the inquiry in text format, and the output includes the data to be sent to the terminal.

[0751] Step 2:

[0752] The terminal receives the input information and transmits it to the server. The received data is sent to the server via the Internet. The input includes text data from the user, and the output includes data to be sent to the server.

[0753] Step 3:

[0754] The server stores the received information in a database, where it formats the data appropriately for future analysis and retrieval. The input is the data sent from the device, and the output is the data stored in the database.

[0755] Step 4:

[0756] The generative AI installed on the server retrieves information stored in the database and performs natural language processing. Specifically, it uses a machine learning algorithm to analyze the text of the inquiry and extract key points. The input is text data retrieved from the database, and the output contains the analyzed key points.

[0757] Step 5:

[0758] The server is also equipped with an emotion analysis function that analyzes emotions from user input. Sentiment analysis uses machine learning models based on text and voice data. The input is text and voice data from the user, and the output includes analyzed emotion data.

[0759] Step 6:

[0760] To adjust the generated materials and answers based on the results of emotion analysis, the generation AI takes emotional data into account and adjusts the content. For example, if the user is feeling stressed, the tone of the answer will be changed to a gentler expression. The inputs are emotion analysis data and the analysis results from natural language processing, and the output includes adjusted materials and answers.

[0761] Step 7:

[0762] The generated materials and answers are displayed to the user via the terminal. The user can check them and make corrections as necessary. The input is the materials and answer data sent from the server, and the output includes data that reflects the user's corrections.

[0763] Step 8:

[0764] Finally, the confirmed and corrected materials and responses are returned to the server and stored or transmitted as necessary. This streamlines business processes such as customer support and improves customer satisfaction. The input is the data modified by the user, and the output is the data that is finally stored or transmitted.

[0765] 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.

[0766] 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.

[0767] 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.

[0768] [Third embodiment]

[0769] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0770] 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.

[0771] 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).

[0772] 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.

[0773] 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.

[0774] 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).

[0775] 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.

[0776] 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.

[0777] 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.

[0778] 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.

[0779] 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.

[0780] 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."

[0781] This invention relates to a generation AI system that receives business-related information from users and creates materials and answers based on that information. This system is composed of users, a server, and terminals.

[0782] Overall system configuration

[0783] The system consists of the following main components:

[0784] User: Responsible for entering work-related information and reviewing and correcting generated materials and responses.

[0785] Server: Responsible for receiving, storing, and processing information, and integrating with internal systems.

[0786] Terminal: Provides an interface for users to input information and review / edit generated materials and answers.

[0787] Generative AI: Analyzes input information using natural language processing, and creates materials and generates answers.

[0788] Program processing overview

[0789] The main processing of the system is as follows:

[0790] 1. The user uses the terminal to input work-related information. For example, the user inputs "Best practices for customer service" as training content in text format and clicks the "Submit" button.

[0791] 2. The server receives the entered information. The received information is stored in a database. The server formats the information and stores it appropriately.

[0792] 3. Generative AI takes the stored information and performs natural language processing. For example, it analyzes the content of "Customer Service Best Practices" and extracts key points.

[0793] 4. Generative AI automatically generates materials and answers based on the analysis results. For example, when creating a presentation on customer service, the analyzed key points are placed on the slides. It also generates appropriate answers in email format to departmental questions.

[0794] 5. The server connects to the company's internal systems to obtain the necessary data. For example, it obtains information about new employees from the personnel management system and generates new training materials based on that information.

[0795] 6. The terminal provides an interface that allows users to check the generated materials and responses. Users can check the generated presentation materials and email content on the screen and make corrections as necessary.

[0796] 7. After the user has confirmed and corrected the materials, the device saves or sends the final materials or answers. For example, the finalized presentation materials are saved as a file or a reply email is sent.

[0797] Specific operation example

[0798] 1. The user writes "best practices for customer service" into an input form on a PC terminal and submits it.

[0799] 2. The server receives the information, formats it, and stores it in a database.

[0800] 3. The generative AI retrieves the stored information and performs natural language analysis.

[0801] 4. The AI ​​automatically generates presentation slides, placing key analysis results on each slide. It also generates email responses to questions from the department.

[0802] 5. The server retrieves new employee information from the human resources management system and provides it to the generation AI, which then generates training materials.

[0803] 6. The terminal displays the generated presentation materials and email contents to the user, who then checks and modifies them.

[0804] 7. After the user confirms, the device saves the final document and sends it by email.

[0805] In this way, the present invention realizes a sustainable way of working by efficiently processing work-related information and reducing the workload of users.

[0806] The processing flow will be explained below.

[0807] Step 1:

[0808] Users input business-related information into the generation AI from their terminal.

[0809] Specifically, open the input form on your PC or tablet, enter the training content or a summary of a business book in text format, and click the "Send" button.

[0810] Step 2:

[0811] The server receives the input business-related information.

[0812] Specifically, the information is received using a data reception API connected via the Internet, and the contents are temporarily stored in memory.

[0813] Step 3:

[0814] The server stores the received information in a database.

[0815] Specifically, the received data is formatted and converted into the appropriate format, and then written to the "Business Information" table in the database. The consistency of the information is also checked.

[0816] Step 4:

[0817] The generating AI retrieves stored information from the database.

[0818] Specifically, a query is used to extract the necessary data from the "Business Information" table and pass it to the natural language processing engine.

[0819] Step 5:

[0820] The generative AI performs natural language processing based on the acquired information.

[0821] Specifically, it uses natural language analysis algorithms to understand the context and extract key points, then converts the analysis results into an internal data format.

[0822] Step 6:

[0823] The generative AI generates materials and answers based on the analysis results.

[0824] Specifically, the analysis results are entered into a pre-prepared template, and presentation materials and response emails are automatically generated.

[0825] Step 7:

[0826] The server obtains the necessary data through an API connection with the internal system.

[0827] Specifically, authentication information is used to retrieve data from human resources management systems and customer management systems, format it, and provide it to the generation AI.

[0828] Step 8:

[0829] The generation AI performs additional processing based on the acquired internal data to complement the final documents and answers.

[0830] Specifically, the generated materials and answers are updated using newly acquired information to improve accuracy.

[0831] Step 9:

[0832] The terminal displays the generated materials and answers to the user.

[0833] Specifically, it provides an interface that allows users to check the generated presentation materials and email contents, and displays them on the screen.

[0834] Step 10:

[0835] The user can check and modify the generated materials and answers.

[0836] Specifically, you manually edit the documents or emails displayed on the screen and then click the "Save" or "Send" button to confirm.

[0837] Step 11:

[0838] The terminal saves or transmits the final materials and answers after the user has confirmed and corrected them.

[0839] Specifically, the finalized presentation materials are saved as a file and sent via email to the specified address.

[0840] In this way, this system receives work-related information from the user, and the generation AI automatically creates materials and generates answers based on that information, thereby reducing the user's workload and improving work efficiency.

[0841] Example 1

[0842] 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."

[0843] Conventional business-related information management and document creation systems require users to manually input information, analyze it, and create the necessary documents, which requires a great deal of time and effort. Furthermore, it is difficult to retrieve and link data from multiple internal systems, and the confirmation and correction of generated documents and responses is cumbersome. For these reasons, there was a need for a method to efficiently and quickly manage and process business-related information, automatically generate high-quality documents and responses, and reduce the burden on users.

[0844] 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.

[0845] In this invention, the server includes means for inputting business-related information from a user, means for receiving the input information and saving it in a database, means for analyzing the business content by performing natural language processing based on the saved information, means for creating materials and generating answers based on the analysis results, means for acquiring necessary data from an internal system and reflecting the data in the generated materials and answers, means for allowing the user to check and correct the generated materials and answers, and means for saving the final materials and answers or sending them to an external system. This enables efficient management and processing of business-related information, automatic generation of materials, and reduction of the burden on users.

[0846] "Business-related information" refers to various data and information related to business operations, including, for example, training content, best practices for customer service, and project management information.

[0847] "User" means an individual or entity that uses the System to input work-related information and review and correct generated materials and responses.

[0848] "Means" refers to a method, apparatus, or software component used to perform a particular function or process within a system.

[0849] "Receiving information" refers to the process of obtaining data provided by a user via a network and incorporating it into the system.

[0850] "Database" refers to an organized collection of data, and in this context refers to a system or software for storing input business-related information.

[0851] "Natural language processing" refers to technology that allows computers to analyze, understand, and generate human language, and in this case includes analyzing business content using generative AI models.

[0852] "Document creation" refers to the process of automatically generating documents such as presentations and reports based on input information and analysis results.

[0853] "Answer generation" refers to the process of automatically generating an appropriate response to a question based on input information and analysis results.

[0854] "Internal systems" refers to various information systems used within a company or organization, such as human resources management systems and databases.

[0855] "Confirmation and correction" refers to the process in which the user checks the generated materials and answers and corrects or edits the content as necessary.

[0856] "Saving" refers to recording the final generated materials and answers in file format in storage.

[0857] "Sending to external system" refers to the process of transferring the generated materials and answers to other systems or users using external communication means such as e-mail.

[0858] This invention relates to a generation AI system that receives business-related information from users and creates materials and answers based on that information. This system is composed of users, a server, and terminals.

[0859] Overall system configuration

[0860] The system consists of the following main components:

[0861] User: Responsible for entering work-related information and reviewing and correcting generated materials and responses.

[0862] Server: Responsible for receiving, storing, and processing information and interfacing with internal systems.

[0863] Terminal: Provides an interface for users to input information and review / edit generated materials and answers.

[0864] Generative AI: Analyzes input information using natural language processing, and creates materials and generates answers.

[0865] Program processing overview

[0866] In this generative AI system, users use a PC terminal to input work-related information via a web browser. Specifically, they access a dedicated input form, enter content such as "best practices for customer service" in text format, and click the submit button. This sends the information to the system.

[0867] The server uses Apache HTTP Server to receive information submitted by users, formats the data using Python scripts, and stores it in a MySQL database, where it is converted to JSON format and securely recorded.

[0868] The server then retrieves the stored information and passes it to a generative AI model (e.g., OpenAI's GPT-3) using a prompt. The prompt contains detailed instructions based on the input. For example, the prompt could say, "Please create a presentation based on best practices for customer service."

[0869] The generative AI analyzes prompts and extracts key points based on the input information. For example, it analyzes the key points of "customer service best practices" and creates presentation materials and email responses to questions. The presentation materials are automatically generated using the Microsoft PowerPoint API.

[0870] The server also connects to the company's human resources management system (LDAP server) to retrieve additional data, such as new employee information, which is then used to automatically generate training materials for new employees.

[0871] The generated materials and answers are displayed to the user through a web application provided by the device. The user can then check and correct them, and finally confirm the materials and answers. The confirmed materials are saved in the PC's local storage, and an email is sent via the SMTP server.

[0872] Specific examples

[0873] Here are some examples of prompts to input to a generative AI model:

[0874] 1. "Based on the following, create a presentation on best practices for customer service: being respectful, responding quickly, and resolving problems."

[0875] 2. "Create new employee training materials based on the following information: new employee's name, start date, role, and department."

[0876] In this way, this system efficiently processes work-related information, reduces the user's workload, and enables sustainable working styles to be realized.

[0877] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0878] Step 1:

[0879] A user enters work-related information. The user opens a web browser on their PC terminal and accesses a dedicated input form. For example, the user enters information such as "Best practices for customer service" and clicks the submit button. The input data is text data entered by the user and is sent to the system by the submit operation.

[0880] Step 2:

[0881] The server receives the information and stores it in the database. The server receives information sent by the user using Apache HTTP Server. The received text data is converted to JSON format using a Python script and stored in a MySQL database. The received data (input) is text information, and the output is formatted JSON data and stored in the database.

[0882] Step 3:

[0883] The server generates a prompt for the generation AI based on the stored information and passes the information to the AI ​​model. It retrieves the stored JSON data and creates a prompt based on that information: "Please create a presentation based on best practices for customer service." This prompt is sent to the generation AI (for example, GPT-3). The input is JSON data, and the output is the prompt and text data for analysis.

[0884] Step 4:

[0885] The generative AI analyzes the information and automatically generates materials and answers. The generative AI analyzes the text data entered based on the prompt text and extracts important points. For example, it analyzes the key points of "Best Practices for Customer Service," creates presentation materials using Microsoft PowerPoint API, and generates email responses to questions. The input is the prompt text and text data, and the output is the generated presentation materials and email responses.

[0886] Step 5:

[0887] The server retrieves additional data from the internal system and provides it to the generation AI. The server queries the internal human resources management system (LDAP server) to retrieve new employee information (e.g., name, start date, role, department). Based on the retrieved information, it generates training materials for new employees by sending additional prompts to the generation AI. The input is the new employee information retrieved from the LDAP server, and the output is the additional prompts and training materials passed to the generation AI.

[0888] Step 6:

[0889] The terminal displays the generated materials and responses to the user. The generated presentation materials and email content are displayed in the browser on the user's PC terminal via a web application. Presentation materials are displayed in slide format and email content in preview format so that the user can check them. The input is the presentation materials and email data sent from the server, and the output is the content displayed on the browser.

[0890] Step 7:

[0891] The user checks and modifies the generated materials and responses. Using the portal site's editing tools, the user checks the generated presentation materials and email content and makes modifications as necessary. For example, the user may change the title of a presentation slide or add or edit email content. The input is the initially generated materials and email data, and the output is the final data after modifications.

[0892] Step 8:

[0893] The device saves the final materials and answers and sends them to the external system. To save the final data that the user has confirmed and corrected, the presentation materials are saved in local storage by clicking the "Save" button. The final email is sent to the specified address via the SMTP server by clicking the "Send" button. The input is the finalized presentation materials and email data, and the output is the saved file and the sent email.

[0894] (Application example 1)

[0895] 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."

[0896] Conventional document creation systems based on work-related information have had difficulty generating personalized content based on individual user interests and requests. Furthermore, there was a lack of efficient means for checking and correcting content after it had been generated. This meant that users had to spend a lot of time manually creating and editing content, resulting in a decline in labor productivity.

[0897] 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.

[0898] In this invention, the server includes means for inputting work-related information from a user, means for receiving the input information and storing the input information in a database, means for performing natural language processing based on the stored information and analyzing the work content, means for creating materials and generating answers based on the analysis results, means for obtaining necessary data from an internal system and reflecting the data in the generated materials and answers, means for the user to confirm and modify the generated materials and answers, means for the user to input topics and keywords of interest and generate personalized content based on the input, and means for displaying the generated personalized content to the user for confirmation and modification. This enables the user to automatically generate personalized content based not only on work-related information but also on their individual interests, and efficiently confirm and modify it.

[0899] "Business-related information" is a general term for data and knowledge that companies and organizations need to carry out their business.

[0900] "User" refers to a person or organization that uses this system to input business-related information and review and modify the generated content.

[0901] A "database" is a collection of data and a system for efficiently managing, storing, and searching data.

[0902] "Natural language processing" is a general term for technology that enables computers to understand human language and perform tasks such as analysis and generation.

[0903] "Document creation" is the act of combining text and graphics to create a document based on a specific purpose or content.

[0904] "Answer generation" is the process of automatically creating appropriate answers based on questions or requests from users.

[0905] "Internal systems" refers to all information systems used within a company or organization, including databases and management systems.

[0906] "Personalized Content" refers to information and materials that are customized based on a user's individual interests and specific needs.

[0907] The "Internet" is a huge information and communications network that interconnects computers and networks all over the world.

[0908] A "machine learning algorithm" is a type of computer program that learns patterns from data and makes predictions and classifications.

[0909] A specific embodiment for carrying out the present invention will be described.

[0910] The system consists of the following major hardware and software components:

[0911] Hardware: User's smartphone, PC, and server.

[0912] Software: Python programs, generative AI models such as OpenAI GPT-4, and database systems.

[0913] First, users use their smartphones or PCs to input work-related information and topics and keywords that interest them, which are then sent to a server via the Internet.

[0914] The server does the following:

[0915] 1. Receives input information and stores it in a database system, which is used to support efficient data management and retrieval.

[0916] 2. Based on the stored information, use a Python program to format and analyze the information.

[0917] 3. Based on the formatted and analyzed information, generative AI models such as OpenAI GPT-4 are used to create documents and generate answers, especially personalized content based on the user's topics of interest.

[0918] 4. The generated materials and content are sent to the user's device so that the user can review and modify them.

[0919] The user can check the materials and content generated on the device and make any necessary corrections. The final materials and content after corrections are sent back to the server and stored in a database or sent to a specified destination.

[0920] For example, if a user wants to generate a personalized newsletter about "latest AI technology," here's the prompt:

[0921] Create a personalized newsletter on 'latest AI technologies'.

[0922] By feeding this prompt into a generative AI model such as OpenAI GPT-4, a corresponding newsletter can be automatically generated.

[0923] This allows users to create documents based on business-related information and easily generate personalized content according to their individual interests while significantly reducing the amount of work required.

[0924] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0925] Step 1:

[0926] Users enter work-related information and topics of interest

[0927] Users use their smartphones or PCs to input work-related information, topics of interest, or keywords. For example, consider a user entering information about the latest AI technology. This input is sent to a server via the Internet.

[0928] Input: Work-related information, topics of interest, and keywords

[0929] Output: Sending information to the server

[0930] Step 2:

[0931] The server receives the entered information and stores it in a database

[0932] The server receives the information sent by the user and stores it in a database system, which is used to support efficient data management and retrieval.

[0933] Input: Information submitted by the user

[0934] Output: Saved data for formatting and analysis

[0935] Step 3:

[0936] The server formats and analyzes the stored information

[0937] The server uses a Python program to format the information stored in the database and convert it into a format that is easy for the generative AI model to understand, which includes structuring the information and removing unnecessary information.

[0938] Input: Information stored in a database

[0939] Output: A dataset suitable for formatting and analysis

[0940] Step 4:

[0941] The server uses the generated AI model based on the formatted and analyzed information to create documents and generate answers.

[0942] The server uses generative AI models such as OpenAI GPT-4 to generate documents and answers from the formatted and analyzed information, particularly to generate personalized content based on the user's topics of interest.

[0943] Input: A dataset suitable for formatting and analysis, a prompt

[0944] Output: Auto-generated materials and personalized content

[0945] Step 5:

[0946] The server sends generated materials and content to the user's device.

[0947] The server sends the generated materials and personalized content to the user's device so that the user can review and edit them. The user uses this to review and edit slides and newsletters.

[0948] Input: Auto-generated materials and personalized content

[0949] Output: Content sent to the user's device

[0950] Step 6:

[0951] Review and modify user-generated materials and content

[0952] The user can check the generated materials and content on their device and modify them as necessary. After the user has checked and modified the materials and content, the final materials and content are saved and, in some cases, sent back to the server.

[0953] Input: Generated materials and personalized content

[0954] Output: Final revised materials and content

[0955] Step 7:

[0956] The server stores and transmits the final materials and content.

[0957] The server stores the final materials and content modified by the user in a database and sends them to a specified destination as needed, allowing the user to use the generated content in their actual work.

[0958] Input: Final revised materials and content

[0959] Output: Stored database entries, transmitted materials and content

[0960] This series of processing steps enables efficient creation of materials and generation of personalized content based on business-related information and the individual interests of the user.

[0961] 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.

[0962] This invention combines an emotion engine with a generative AI system that inputs business-related information from users and creates materials and generates answers based on that information. This system is composed of a user, a server, a terminal, a generative AI, and an emotion engine.

[0963] Overall system configuration

[0964] The system consists of the following main components:

[0965] User: Responsible for entering work-related information and reviewing and correcting generated materials and responses.

[0966] Server: Responsible for receiving, storing, and processing information, and integrating with internal systems.

[0967] Terminal: Provides an interface for users to input information and review / edit generated materials and answers.

[0968] Generative AI: Analyzes input information using natural language processing, and creates materials and generates answers.

[0969] Emotion engine: Recognizes the user's emotions and adjusts the output of the generative AI based on those emotions.

[0970] Program processing overview

[0971] The main processing of the system is as follows:

[0972] 1. The user uses the terminal to input work-related information. For example, the user inputs "Best practices for customer service" as training content in text format and clicks the "Submit" button.

[0973] 2. The server receives the entered information. The received information is stored in a database. The server formats the information and stores it appropriately.

[0974] 3. Generative AI retrieves stored information from the database and performs natural language processing. For example, it analyzes the content of "Customer Service Best Practices" and extracts key points.

[0975] 4. The emotion engine infers emotions from user input and voice data. The emotion engine uses machine learning algorithms to parse emotions from the user's context and tone.

[0976] 5. The generation AI adjusts the content of materials and answers based on the emotional data provided by the emotion engine. For example, if the user is feeling stressed, the generated answers will be changed to more gentle expressions.

[0977] 6. Generative AI automatically generates materials and answers based on the analysis results. For example, when creating a presentation on customer service, the analyzed key points are placed on the slides. It also generates appropriate answers in email format to departmental questions.

[0978] 7. The server connects to the company's internal systems to obtain the necessary data. For example, it obtains information about new employees from the personnel management system and generates new training materials based on that information.

[0979] 8. The terminal provides an interface that allows users to check the generated materials and responses. Users can check the generated presentation materials and email content on the screen and make corrections as necessary.

[0980] 9. After the user has confirmed and corrected the materials, the device saves or sends the final materials or answers. For example, the finalized presentation materials are saved as a file or a reply email is sent.

[0981] Specific operation example

[0982] 1. The user writes "best practices for customer service" into an input form on a PC terminal and submits it.

[0983] 2. The server receives the information, formats it, and stores it in a database.

[0984] 3. The generative AI retrieves the stored information and performs natural language analysis.

[0985] 4. The emotion engine infers that the user is tired based on their input and voice data.

[0986] 5. Based on the data from the emotion engine, the generative AI creates presentation materials in a concise format to avoid user fatigue. It also adjusts the wording of email responses to questions from departments to be more pleasant.

[0987] 6. The server retrieves new employee information from the human resources management system and provides it to the generation AI, which then generates training materials.

[0988] 7. The terminal displays the generated presentation materials and email contents to the user, who then checks and modifies them.

[0989] 8. After the user confirms, the device saves the final document and sends it by email.

[0990] In this way, the present invention efficiently processes work-related information while also taking into account the user's emotions, further reducing the user's workload and thereby realizing a sustainable way of working.

[0991] The processing flow will be explained below.

[0992] Step 1:

[0993] Users input business-related information into the generation AI from their terminal.

[0994] Specifically, open the input form on your PC or tablet, enter the training content or a summary of a business book in text format, and click the "Send" button.

[0995] Step 2:

[0996] The server receives the input business-related information.

[0997] Specifically, the information is received using a data reception API connected via the Internet, and the contents are temporarily stored in memory.

[0998] Step 3:

[0999] The server stores the received information in a database.

[1000] Specifically, the received data is formatted and converted into the appropriate format, and then written to the "Business Information" table in the database. The consistency of the information is also checked.

[1001] Step 4:

[1002] The generating AI retrieves stored information from the database.

[1003] Specifically, a query is used to extract the necessary data from the "Business Information" table and pass it to the natural language processing engine.

[1004] Step 5:

[1005] The generative AI performs natural language processing based on the acquired information.

[1006] Specifically, it uses natural language analysis algorithms to understand the context and extract key points, then converts the analysis results into an internal data format.

[1007] Step 6:

[1008] The emotion engine infers emotions from user input and voice data.

[1009] Specifically, it uses machine learning algorithms to analyze the user's context and tone to estimate their emotional state, such as "tiredness," "stress," or "joy."

[1010] Step 7:

[1011] The generative AI adjusts the content of materials and answers based on the emotional data provided by the emotion engine.

[1012] Specifically, if a user is feeling stressed, the content of the reply email will be changed to use gentler vocabulary and writing style, and the format of presentation materials will be made clearer and simpler.

[1013] Step 8:

[1014] The generative AI generates materials and answers based on the analysis results.

[1015] Specifically, the analysis results are entered into a pre-prepared template, and presentation materials and response emails are automatically generated.

[1016] Step 9:

[1017] The server obtains the necessary data through an API connection with the internal system.

[1018] Specifically, authentication information is used to retrieve data from human resources management systems and customer management systems, format it, and provide it to the generation AI.

[1019] Step 10:

[1020] The generation AI performs additional processing based on the acquired internal data to complement the final documents and answers.

[1021] Specifically, the generated materials and answers are updated using newly acquired information to improve accuracy.

[1022] Step 11:

[1023] The terminal displays the generated materials and answers to the user.

[1024] Specifically, it provides an interface that allows users to check the generated presentation materials and email contents, and displays them on the screen.

[1025] Step 12:

[1026] The user can check and modify the generated materials and answers.

[1027] Specifically, you manually edit the documents or emails displayed on the screen and then click the "Save" or "Send" button to confirm.

[1028] Step 13:

[1029] The terminal saves or transmits the final materials and answers after the user has confirmed and corrected them.

[1030] Specifically, the finalized presentation materials are saved as a file and sent via email to the specified address.

[1031] In this way, this system receives work-related information from the user, and the generation AI automatically creates materials and generates answers based on that information, thereby reducing the user's workload and improving work efficiency.In addition, by combining it with an emotion engine, it is possible to respond flexibly and appropriately according to the user's emotional state.

[1032] Example 2

[1033] 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."

[1034] Conventional business-related information processing systems create documents and generate responses in a uniform format without considering the user's emotions, resulting in a lack of consideration for user stress and fatigue. Furthermore, it is often difficult to confirm or correct the generated documents and responses, resulting in a decline in work efficiency. To solve these issues, a flexible approach that takes the user's emotions into account is required.

[1035] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for inputting business-related information from a user, means for receiving the input information and storing it in a database, means for performing natural language processing based on the stored information and analyzing the business content, means for creating materials and generating answers based on the analysis results, means for acquiring necessary data from an internal system and reflecting the data in the generated materials and answers, means for allowing the user to confirm and correct the generated materials and answers, and means for recognizing the user's emotions and adjusting the output content based on the emotions. This makes it possible to efficiently process business-related information while taking the user's emotions into consideration and reduce the workload.

[1036] "Business-related information" refers to all information related to the performance of business within a company or organization, and specifically includes project progress, records of customer service, employee attendance status, etc.

[1037] "User" refers to an individual or organization that uses the system to input business-related information and to review and correct generated materials and responses.

[1038] "Input means" refers to devices or software that provide an interface for users to input work-related information into a system, such as a keyboard, mouse, touchscreen, or voice input.

[1039] "Server" refers to a computer system that receives business-related information entered by users and stores it in a database.

[1040] "Database" refers to an information storage system that structures and stores received information and manages it in a manner that allows for quick search and retrieval as needed.

[1041] "Natural language processing" refers to technology for understanding and analyzing human language, and specifically includes text analysis, summarization, information extraction, and intent understanding.

[1042] "Generative AI" refers to artificial intelligence that uses natural language processing technology to analyze input business-related information and automatically generate the necessary materials and answers.

[1043] An "emotion engine" refers to a system that infers emotions from user input data and voice, and adjusts the output content of the generating AI based on those emotions.

[1044] "Internal systems" refers to various management systems used within a company or organization, including, for example, human resources management systems and project management systems.

[1045] "Network" refers to a communication path used to electronically send and receive information and data, and specifically includes the Internet, an intranet, a LAN (local area network), and the like.

[1046] "Machine learning algorithms" refer to methods or models that allow computers to learn data and make predictions or analyses based on the results. Examples include neural networks and support vector machines.

[1047] MODE FOR CARRYING OUT THE INVENTION

[1048] This invention is a system that combines an emotion engine with a generative AI system that inputs business-related information from users and creates materials and answers based on that information. This system is composed of the following main components:

[1049] User: Responsible for entering work-related information and reviewing and correcting generated materials and responses.

[1050] Server: Responsible for receiving, storing, and processing information and interfacing with internal systems.

[1051] Terminal: Provides an interface for users to input information and review / edit generated materials and answers.

[1052] Generative AI: Uses natural language processing technology to analyze input information and create materials and generate answers.

[1053] Emotion engine: Recognizes the user's emotions and adjusts the output of the generative AI based on those emotions.

[1054] Program processing overview

[1055] Users input work-related information using devices such as PCs and smartphones. For example, a user inputs "best practices for customer service" as training content in text format into the device and clicks the "Send" button.

[1056] The server receives the input information, formats it, and stores it in a database. Specific software used for this purpose includes database management systems (DBMS) such as MySQL and PostgreSQL. The server-side reception process is performed using, for example, a Python server script.

[1057] Generative AI reads information stored in a database and performs natural language processing. A specific generative AI model used is GPT-4. This model analyzes input text, extracts key points, and generates appropriate materials and answers. For example, in "Best Practices for Customer Service," it extracts key points such as "quick response," "honesty," and "importance of follow-up," and creates presentation materials based on these.

[1058] The emotion engine infers emotions from the user's text input and voice data. This engine uses voice recognition software (such as the Google Speech-to-Text API) and machine learning algorithms to infer emotions. For example, it can determine the user's stress or fatigue from the tone of the voice data or specific keywords, and provide this as emotion data to the generation AI.

[1059] The generation AI adjusts the content of the generated materials and answers appropriately based on the emotional data provided by the emotion engine. For example, if it estimates that the user is tired, it will use concise and gentle language in its answers. Specifically, it generates gentle emails and presentation materials using phrases such as "Thank you for your hard work" and "Don't worry."

[1060] The terminal provides an interface that displays the generated materials and answers to the user. The user can check the materials and answers on the screen and make corrections as necessary. For example, the generated presentation can be displayed in PowerPoint format and the text in the slides can be corrected.

[1061] Overall, the system works to efficiently process work-related information and automate the creation of documents and responses while taking into account the user's emotions.

[1062] Specific examples

[1063] 1. The user writes "best practices for customer service" into an input form on a PC terminal and submits it.

[1064] 2. The server receives the information, formats it, and stores it in a database.

[1065] 3. The generative AI retrieves the stored information and performs natural language analysis.

[1066] 4. The emotion engine infers that the user is tired based on their input and voice data.

[1067] 5. Based on the data from the emotion engine, the generative AI creates presentation materials in a concise format to avoid user fatigue. It also adjusts the wording of email responses to questions from departments to be more pleasant.

[1068] 6. The server retrieves new employee information from the internal system and provides it to the generation AI, which then generates training materials.

[1069] 7. The terminal displays the generated presentation materials and email contents to the user, who then checks and modifies them.

[1070] 8. After the user confirms, the device saves the final document and sends it by email.

[1071] Example prompt sentence:

[1072] "Based on the topic 'Customer Engagement Best Practices,' please generate a concise and friendly presentation deck and a friendly email response to the department's questions."

[1073] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1074] Step 1:

[1075] A user enters work-related information into an input form on a device such as a PC or smartphone. For example, the user enters "best practices for customer service" in text format and clicks the "Send" button. This input information is sent from the device to the server.

[1076] Step 2:

[1077] The server receives the information sent by the user. At this stage, the text data entered by the user is sent to the server. The server formats the received information and processes the data by removing unnecessary spaces from the string and standardizing the format. The formatted data is then saved in a database. The databases used are MySQL and PostgreSQL.

[1078] Step 3:

[1079] Generative AI reads stored information from a database and performs natural language processing. For example, GPT-4 is used as the generative AI model. It performs sentence analysis on the stored text data and extracts key points. The input is text data from the database, and the output is a list of analyzed key points. Specific operations include the process of calling the GPT-4 model to perform text analysis.

[1080] Step 4:

[1081] The emotion engine infers emotions from text and voice data entered by the user. This emotion engine uses speech recognition software (such as the Google Speech-to-Text API) to convert voice data into text and then performs emotion analysis based on that text. The input is the user's voice and text data, and the output is an inferred emotion label. For example, the output might be "tired" or "stressed."

[1082] Step 5:

[1083] The generative AI adjusts the content of materials and answers based on the emotional data provided by the emotion engine. For example, if it estimates that the user is tired, it will use gentler language in the answer. In this case, the generative AI model receives the analysis results and emotional data as input and generates gentle text. Specifically, it reduces the user's stress by adding phrases such as "Thank you for your hard work."

[1084] Step 6:

[1085] The server interacts with other internal systems to obtain the necessary data. For example, it obtains new employee information from a human resources management system. In this case, the server uses an API to send a request to the human resources management system and provides the obtained data to the generation AI. The input is the API request and response, and the output is the new employee information that is passed to the generation AI. Specific operations include sending an HTTP request to the API endpoint and parsing the result.

[1086] Step 7:

[1087] The terminal provides an interface that displays the generated materials and responses to the user. Specifically, the generated presentation materials and email contents are displayed on the terminal screen, and the user can check and correct them as necessary. The input is the generated content, and the output is the result of the user checking and correcting it.

[1088] Step 8:

[1089] The terminal saves the final documents and responses that the user has reviewed and revised, and sends them as necessary. For example, it saves the revised presentation materials in PDF format or sends a reply email via an SMTP server. The input is the content that the user reviewed and revised, and the output is the saved file or the sent email. Specific operations include disk access for saving files and the use of the SMTP protocol for sending emails.

[1090] (Application example 2)

[1091] 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."

[1092] Conventional generative AI systems were unable to take the user's emotions into consideration when creating documents and generating answers based on work-related information, making it difficult to reduce user stress and provide more appropriate responses. Furthermore, particularly in customer support, responses that take into account the customer's emotions are required, but automating this has been difficult.

[1093] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for inputting business-related information from a user, means for receiving the input information and storing it in a database, means for performing natural language processing based on the stored information and analyzing the business content, emotion analysis means for analyzing the user's emotions, means for adjusting materials and answers generated based on the emotion analysis results, and means for the user to confirm and correct the generated materials and answers. This makes it possible to take the user's emotions into consideration and respond appropriately and in a way that reduces stress.

[1094] "Business-related information" refers to data and information related to the business conducted by a company or organization, and is input by a user.

[1095] "User" means a person who uses the system to input business-related information and check and correct the generated documents and responses.

[1096] "Means for input" refers to a function that provides an interface that allows users to input business-related information into the system.

[1097] "Means for receiving and storing in a database" refers to the function of receiving input information in an appropriate format and storing it in a database.

[1098] "Natural language processing" is a technology that allows computers to understand and analyze human language.

[1099] "Means of analysis" refers to the function of performing natural language processing based on stored information and analyzing business content.

[1100] "Means for creating materials and generating answers" refers to the function of automatically generating materials and answers based on the analysis results.

[1101] "Emotion analysis means" refers to a function that analyzes emotions from user input and voice data.

[1102] "Means for adjustment" refers to the function of adjusting the content of generated materials and answers based on the results of sentiment analysis.

[1103] "Internal systems" is a general term for information systems used within a company.

[1104] "Means for checking and correcting" refers to the function of providing an interface that allows users to check the generated materials and answers and correct them as necessary.

[1105] This system combines emotion analysis functionality with a generative AI system that inputs work-related information from users and creates documents and answers based on that information. Specifically, it consists of the following components:

[1106] 1. A user uses a smartphone or PC to enter business-related information. For example, they enter the details of a customer support inquiry into an input form and submit it.

[1107] 2. The terminal receives the input information and sends it to the server, using communication over the Internet.

[1108] 3. The server stores the received information in a database, where it is formatted for analysis.

[1109] 4. The generative AI installed on the server performs natural language processing based on the stored information and analyzes the business content, using machine learning algorithms.

[1110] 5. The server is also equipped with an emotion analysis function that analyzes emotions from user input. Emotion analysis uses machine learning models based on text and voice data.

[1111] 6. The analyzed emotion data is used to adjust the content of the materials and answers generated by the generative AI. For example, if the user expresses anger or stress, the generated answers will be changed to more gentle expressions.

[1112] 7. The generative AI model uses this data to create materials and generate answers. For example, in customer support email correspondence, an appropriate reply email is generated in response to a customer's inquiry.

[1113] 8. The generated materials and answers are displayed to the user via the terminal, where the user can review them and make corrections as necessary.

[1114] 9. The final confirmed and corrected materials and answers will be returned to the server and stored or transmitted as necessary.

[1115] Examples of specific hardware and software used include smartphones (Android, iOS), personal computers, the Python programming language, the Transformers library (Hugging Face), and the EmotionRecognizer library.

[1116] As a specific example, if a user inputs a customer inquiry such as "My product hasn't arrived yet, what's going on?", emotion analysis will determine that the customer is feeling stressed. The generation AI will take this emotional data into consideration and generate a reply in a gentle tone. An example of a generated prompt sentence is as follows:

[1117] Example prompt sentence:

[1118] If your customer is feeling stressed, respond with the following messages: My item hasn't arrived, what's going on?

[1119] In this way, the system automates responses by taking the user's emotions into account, enabling more effective and satisfying customer support.

[1120] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1121] Step 1:

[1122] A user inputs business-related information using a smartphone or PC. This information could be, for example, the contents of a customer support inquiry. By clicking the "Send" button, the input data is sent to the terminal. The input is the inquiry in text format, and the output includes the data to be sent to the terminal.

[1123] Step 2:

[1124] The terminal receives the input information and transmits it to the server. The received data is sent to the server via the Internet. The input includes text data from the user, and the output includes data to be sent to the server.

[1125] Step 3:

[1126] The server stores the received information in a database, where it formats the data appropriately for future analysis and retrieval. The input is the data sent from the device, and the output is the data stored in the database.

[1127] Step 4:

[1128] The generative AI installed on the server retrieves information stored in the database and performs natural language processing. Specifically, it uses a machine learning algorithm to analyze the text of the inquiry and extract key points. The input is text data retrieved from the database, and the output contains the analyzed key points.

[1129] Step 5:

[1130] The server is also equipped with an emotion analysis function that analyzes emotions from user input. Sentiment analysis uses machine learning models based on text and voice data. The input is text and voice data from the user, and the output includes analyzed emotion data.

[1131] Step 6:

[1132] To adjust the generated materials and answers based on the results of emotion analysis, the generation AI takes emotional data into account and adjusts the content. For example, if the user is feeling stressed, the tone of the answer will be changed to a gentler expression. The inputs are emotion analysis data and the analysis results from natural language processing, and the output includes adjusted materials and answers.

[1133] Step 7:

[1134] The generated materials and answers are displayed to the user via the terminal. The user can check them and make corrections as necessary. The input is the materials and answer data sent from the server, and the output includes data that reflects the user's corrections.

[1135] Step 8:

[1136] Finally, the confirmed and corrected materials and responses are returned to the server and stored or transmitted as necessary. This streamlines business processes such as customer support and improves customer satisfaction. The input is the data modified by the user, and the output is the data that is finally stored or transmitted.

[1137] 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.

[1138] 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.

[1139] 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.

[1140] [Fourth embodiment]

[1141] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1142] 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.

[1143] 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).

[1144] 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.

[1145] 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.

[1146] 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).

[1147] 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.

[1148] 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.

[1149] 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.

[1150] 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.

[1151] 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.

[1152] 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.

[1153] 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."

[1154] This invention relates to a generation AI system that receives business-related information from users and creates materials and answers based on that information. This system is composed of users, a server, and terminals.

[1155] Overall system configuration

[1156] The system consists of the following main components:

[1157] User: Responsible for entering work-related information and reviewing and correcting generated materials and responses.

[1158] Server: Responsible for receiving, storing, and processing information, and integrating with internal systems.

[1159] Terminal: Provides an interface for users to input information and review / edit generated materials and answers.

[1160] Generative AI: Analyzes input information using natural language processing, and creates materials and generates answers.

[1161] Program processing overview

[1162] The main processing of the system is as follows:

[1163] 1. The user uses the terminal to input work-related information. For example, the user inputs "Best practices for customer service" as training content in text format and clicks the "Submit" button.

[1164] 2. The server receives the entered information. The received information is stored in a database. The server formats the information and stores it appropriately.

[1165] 3. Generative AI takes the stored information and performs natural language processing. For example, it analyzes the content of "Customer Service Best Practices" and extracts key points.

[1166] 4. Generative AI automatically generates materials and answers based on the analysis results. For example, when creating a presentation on customer service, the analyzed key points are placed on the slides. It also generates appropriate answers in email format to departmental questions.

[1167] 5. The server connects to the company's internal systems to obtain the necessary data. For example, it obtains information about new employees from the personnel management system and generates new training materials based on that information.

[1168] 6. The terminal provides an interface that allows users to check the generated materials and responses. Users can check the generated presentation materials and email content on the screen and make corrections as necessary.

[1169] 7. After the user has confirmed and corrected the materials, the device saves or sends the final materials or answers. For example, the finalized presentation materials are saved as a file or a reply email is sent.

[1170] Specific operation example

[1171] 1. The user writes "best practices for customer service" into an input form on a PC terminal and submits it.

[1172] 2. The server receives the information, formats it, and stores it in a database.

[1173] 3. The generative AI retrieves the stored information and performs natural language analysis.

[1174] 4. The AI ​​automatically generates presentation slides, placing key analysis results on each slide. It also generates email responses to questions from the department.

[1175] 5. The server retrieves new employee information from the human resources management system and provides it to the generation AI, which then generates training materials.

[1176] 6. The terminal displays the generated presentation materials and email contents to the user, who then checks and modifies them.

[1177] 7. After the user confirms, the device saves the final document and sends it by email.

[1178] In this way, the present invention realizes a sustainable way of working by efficiently processing work-related information and reducing the workload of users.

[1179] The processing flow will be explained below.

[1180] Step 1:

[1181] Users input business-related information into the generation AI from their terminal.

[1182] Specifically, open the input form on your PC or tablet, enter the training content or a summary of a business book in text format, and click the "Send" button.

[1183] Step 2:

[1184] The server receives the input business-related information.

[1185] Specifically, the information is received using a data reception API connected via the Internet, and the contents are temporarily stored in memory.

[1186] Step 3:

[1187] The server stores the received information in a database.

[1188] Specifically, the received data is formatted and converted into the appropriate format, and then written to the "Business Information" table in the database. The consistency of the information is also checked.

[1189] Step 4:

[1190] The generating AI retrieves stored information from the database.

[1191] Specifically, a query is used to extract the necessary data from the "Business Information" table and pass it to the natural language processing engine.

[1192] Step 5:

[1193] The generative AI performs natural language processing based on the acquired information.

[1194] Specifically, it uses natural language analysis algorithms to understand the context and extract key points, then converts the analysis results into an internal data format.

[1195] Step 6:

[1196] The generative AI generates materials and answers based on the analysis results.

[1197] Specifically, the analysis results are entered into a pre-prepared template, and presentation materials and response emails are automatically generated.

[1198] Step 7:

[1199] The server obtains the necessary data through an API connection with the internal system.

[1200] Specifically, authentication information is used to retrieve data from human resources management systems and customer management systems, format it, and provide it to the generation AI.

[1201] Step 8:

[1202] The generation AI performs additional processing based on the acquired internal data to complement the final documents and answers.

[1203] Specifically, the generated materials and answers are updated using newly acquired information to improve accuracy.

[1204] Step 9:

[1205] The terminal displays the generated materials and answers to the user.

[1206] Specifically, it provides an interface that allows users to check the generated presentation materials and email contents, and displays them on the screen.

[1207] Step 10:

[1208] The user can check and modify the generated materials and answers.

[1209] Specifically, you manually edit the documents or emails displayed on the screen and then click the "Save" or "Send" button to confirm.

[1210] Step 11:

[1211] The terminal saves or transmits the final materials and answers after the user has confirmed and corrected them.

[1212] Specifically, the finalized presentation materials are saved as a file and sent via email to the specified address.

[1213] In this way, this system receives work-related information from the user, and the generation AI automatically creates materials and generates answers based on that information, thereby reducing the user's workload and improving work efficiency.

[1214] Example 1

[1215] 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."

[1216] Conventional business-related information management and document creation systems require users to manually input information, analyze it, and create the necessary documents, which requires a great deal of time and effort. Furthermore, it is difficult to retrieve and link data from multiple internal systems, and the confirmation and correction of generated documents and responses is cumbersome. For these reasons, there was a need for a method to efficiently and quickly manage and process business-related information, automatically generate high-quality documents and responses, and reduce the burden on users.

[1217] 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.

[1218] In this invention, the server includes means for inputting business-related information from a user, means for receiving the input information and saving it in a database, means for analyzing the business content by performing natural language processing based on the saved information, means for creating materials and generating answers based on the analysis results, means for acquiring necessary data from an internal system and reflecting the data in the generated materials and answers, means for allowing the user to check and correct the generated materials and answers, and means for saving the final materials and answers or sending them to an external system. This enables efficient management and processing of business-related information, automatic generation of materials, and reduction of the burden on users.

[1219] "Business-related information" refers to various data and information related to business operations, including, for example, training content, best practices for customer service, and project management information.

[1220] "User" means an individual or entity that uses the System to input work-related information and review and correct generated materials and responses.

[1221] "Means" refers to a method, apparatus, or software component used to perform a particular function or process within a system.

[1222] "Receiving information" refers to the process of obtaining data provided by a user via a network and incorporating it into the system.

[1223] "Database" refers to an organized collection of data, and in this context refers to a system or software for storing input business-related information.

[1224] "Natural language processing" refers to technology that allows computers to analyze, understand, and generate human language, and in this case includes analyzing business content using generative AI models.

[1225] "Document creation" refers to the process of automatically generating documents such as presentations and reports based on input information and analysis results.

[1226] "Answer generation" refers to the process of automatically generating an appropriate response to a question based on input information and analysis results.

[1227] "Internal systems" refers to various information systems used within a company or organization, such as human resources management systems and databases.

[1228] "Confirmation and correction" refers to the process in which the user checks the generated materials and answers and corrects or edits the content as necessary.

[1229] "Saving" refers to recording the final generated materials and answers in file format in storage.

[1230] "Sending to external system" refers to the process of transferring the generated materials and answers to other systems or users using external communication means such as e-mail.

[1231] This invention relates to a generation AI system that receives business-related information from users and creates materials and answers based on that information. This system is composed of users, a server, and terminals.

[1232] Overall system configuration

[1233] The system consists of the following main components:

[1234] User: Responsible for entering work-related information and reviewing and correcting generated materials and responses.

[1235] Server: Responsible for receiving, storing, and processing information and interfacing with internal systems.

[1236] Terminal: Provides an interface for users to input information and review / edit generated materials and answers.

[1237] Generative AI: Analyzes input information using natural language processing, and creates materials and generates answers.

[1238] Program processing overview

[1239] In this generative AI system, users use a PC terminal to input work-related information via a web browser. Specifically, they access a dedicated input form, enter content such as "best practices for customer service" in text format, and click the submit button. This sends the information to the system.

[1240] The server uses Apache HTTP Server to receive information submitted by users, formats the data using Python scripts, and stores it in a MySQL database, where it is converted to JSON format and securely recorded.

[1241] The server then retrieves the stored information and passes it to a generative AI model (e.g., OpenAI's GPT-3) using a prompt. The prompt contains detailed instructions based on the input. For example, the prompt could say, "Please create a presentation based on best practices for customer service."

[1242] The generative AI analyzes prompts and extracts key points based on the input information. For example, it analyzes the key points of "customer service best practices" and creates presentation materials and email responses to questions. The presentation materials are automatically generated using the Microsoft PowerPoint API.

[1243] The server also connects to the company's human resources management system (LDAP server) to retrieve additional data, such as new employee information, which is then used to automatically generate training materials for new employees.

[1244] The generated materials and answers are displayed to the user through a web application provided by the device. The user can then check and correct them, and finally confirm the materials and answers. The confirmed materials are saved in the PC's local storage, and an email is sent via the SMTP server.

[1245] Specific examples

[1246] Here are some examples of prompts to input to a generative AI model:

[1247] 1. "Based on the following, create a presentation on best practices for customer service: being respectful, responding quickly, and resolving problems."

[1248] 2. "Create new employee training materials based on the following information: new employee's name, start date, role, and department."

[1249] In this way, this system efficiently processes work-related information, reduces the user's workload, and enables sustainable working styles to be realized.

[1250] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1251] Step 1:

[1252] A user enters work-related information. The user opens a web browser on their PC terminal and accesses a dedicated input form. For example, the user enters information such as "Best practices for customer service" and clicks the submit button. The input data is text data entered by the user and is sent to the system by the submit operation.

[1253] Step 2:

[1254] The server receives the information and stores it in the database. The server receives information sent by the user using Apache HTTP Server. The received text data is converted to JSON format using a Python script and stored in a MySQL database. The received data (input) is text information, and the output is formatted JSON data and stored in the database.

[1255] Step 3:

[1256] The server generates a prompt for the generation AI based on the stored information and passes the information to the AI ​​model. It retrieves the stored JSON data and creates a prompt based on that information: "Please create a presentation based on best practices for customer service." This prompt is sent to the generation AI (for example, GPT-3). The input is JSON data, and the output is the prompt and text data for analysis.

[1257] Step 4:

[1258] The generative AI analyzes the information and automatically generates materials and answers. The generative AI analyzes the text data entered based on the prompt text and extracts important points. For example, it analyzes the key points of "Best Practices for Customer Service," creates presentation materials using Microsoft PowerPoint API, and generates email responses to questions. The input is the prompt text and text data, and the output is the generated presentation materials and email responses.

[1259] Step 5:

[1260] The server retrieves additional data from the internal system and provides it to the generation AI. The server queries the internal human resources management system (LDAP server) to retrieve new employee information (e.g., name, start date, role, department). Based on the retrieved information, it generates training materials for new employees by sending additional prompts to the generation AI. The input is the new employee information retrieved from the LDAP server, and the output is the additional prompts and training materials passed to the generation AI.

[1261] Step 6:

[1262] The terminal displays the generated materials and responses to the user. The generated presentation materials and email content are displayed in the browser on the user's PC terminal via a web application. Presentation materials are displayed in slide format and email content in preview format so that the user can check them. The input is the presentation materials and email data sent from the server, and the output is the content displayed on the browser.

[1263] Step 7:

[1264] The user checks and modifies the generated materials and responses. Using the portal site's editing tools, the user checks the generated presentation materials and email content and makes modifications as necessary. For example, the user may change the title of a presentation slide or add or edit email content. The input is the initially generated materials and email data, and the output is the final data after modifications.

[1265] Step 8:

[1266] The device saves the final materials and answers and sends them to the external system. To save the final data that the user has confirmed and corrected, the presentation materials are saved in local storage by clicking the "Save" button. The final email is sent to the specified address via the SMTP server by clicking the "Send" button. The input is the finalized presentation materials and email data, and the output is the saved file and the sent email.

[1267] (Application example 1)

[1268] 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."

[1269] Conventional document creation systems based on work-related information have had difficulty generating personalized content based on individual user interests and requests. Furthermore, there was a lack of efficient means for checking and correcting content after it had been generated. This meant that users had to spend a lot of time manually creating and editing content, resulting in a decline in labor productivity.

[1270] 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.

[1271] In this invention, the server includes means for inputting work-related information from a user, means for receiving the input information and storing the input information in a database, means for performing natural language processing based on the stored information and analyzing the work content, means for creating materials and generating answers based on the analysis results, means for obtaining necessary data from an internal system and reflecting the data in the generated materials and answers, means for the user to confirm and modify the generated materials and answers, means for the user to input topics and keywords of interest and generate personalized content based on the input, and means for displaying the generated personalized content to the user for confirmation and modification. This enables the user to automatically generate personalized content based not only on work-related information but also on their individual interests, and efficiently confirm and modify it.

[1272] "Business-related information" is a general term for data and knowledge that companies and organizations need to carry out their business.

[1273] "User" refers to a person or organization that uses this system to input business-related information and review and modify the generated content.

[1274] A "database" is a collection of data and a system for efficiently managing, storing, and searching data.

[1275] "Natural language processing" is a general term for technology that enables computers to understand human language and perform tasks such as analysis and generation.

[1276] "Document creation" is the act of combining text and graphics to create a document based on a specific purpose or content.

[1277] "Answer generation" is the process of automatically creating appropriate answers based on questions or requests from users.

[1278] "Internal systems" refers to all information systems used within a company or organization, including databases and management systems.

[1279] "Personalized Content" refers to information and materials that are customized based on a user's individual interests and specific needs.

[1280] The "Internet" is a huge information and communications network that interconnects computers and networks all over the world.

[1281] A "machine learning algorithm" is a type of computer program that learns patterns from data and makes predictions and classifications.

[1282] A specific embodiment for carrying out the present invention will be described.

[1283] The system consists of the following major hardware and software components:

[1284] Hardware: User's smartphone, PC, and server.

[1285] Software: Python programs, generative AI models such as OpenAI GPT-4, and database systems.

[1286] First, users use their smartphones or PCs to input work-related information and topics and keywords that interest them, which are then sent to a server via the Internet.

[1287] The server does the following:

[1288] 1. Receives input information and stores it in a database system, which is used to support efficient data management and retrieval.

[1289] 2. Based on the stored information, use a Python program to format and analyze the information.

[1290] 3. Based on the formatted and analyzed information, generative AI models such as OpenAI GPT-4 are used to create documents and generate answers, especially personalized content based on the user's topics of interest.

[1291] 4. The generated materials and content are sent to the user's device so that the user can review and modify them.

[1292] The user can check the materials and content generated on the device and make any necessary corrections. The final materials and content after corrections are sent back to the server and stored in a database or sent to a specified destination.

[1293] For example, if a user wants to generate a personalized newsletter about "latest AI technology," here's the prompt:

[1294] Create a personalized newsletter on 'latest AI technologies'.

[1295] By feeding this prompt into a generative AI model such as OpenAI GPT-4, a corresponding newsletter can be automatically generated.

[1296] This allows users to create documents based on business-related information and easily generate personalized content according to their individual interests while significantly reducing the amount of work required.

[1297] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1298] Step 1:

[1299] Users enter work-related information and topics of interest

[1300] Users use their smartphones or PCs to input work-related information, topics of interest, or keywords. For example, consider a user entering information about the latest AI technology. This input is sent to a server via the Internet.

[1301] Input: Work-related information, topics of interest, and keywords

[1302] Output: Sending information to the server

[1303] Step 2:

[1304] The server receives the entered information and stores it in a database

[1305] The server receives the information sent by the user and stores it in a database system, which is used to support efficient data management and retrieval.

[1306] Input: Information submitted by the user

[1307] Output: Saved data for formatting and analysis

[1308] Step 3:

[1309] The server formats and analyzes the stored information

[1310] The server uses a Python program to format the information stored in the database and convert it into a format that is easy for the generative AI model to understand, which includes structuring the information and removing unnecessary information.

[1311] Input: Information stored in a database

[1312] Output: A dataset suitable for formatting and analysis

[1313] Step 4:

[1314] The server uses the generated AI model based on the formatted and analyzed information to create documents and generate answers.

[1315] The server uses generative AI models such as OpenAI GPT-4 to generate documents and answers from the formatted and analyzed information, particularly to generate personalized content based on the user's topics of interest.

[1316] Input: A dataset suitable for formatting and analysis, a prompt

[1317] Output: Auto-generated materials and personalized content

[1318] Step 5:

[1319] The server sends generated materials and content to the user's device.

[1320] The server sends the generated materials and personalized content to the user's device so that the user can review and edit them. The user uses this to review and edit slides and newsletters.

[1321] Input: Auto-generated materials and personalized content

[1322] Output: Content sent to the user's device

[1323] Step 6:

[1324] Review and modify user-generated materials and content

[1325] The user can check the generated materials and content on their device and modify them as necessary. After the user has checked and modified the materials and content, the final materials and content are saved and, in some cases, sent back to the server.

[1326] Input: Generated materials and personalized content

[1327] Output: Final revised materials and content

[1328] Step 7:

[1329] The server stores and transmits the final materials and content.

[1330] The server stores the final materials and content modified by the user in a database and sends them to a specified destination as needed, allowing the user to use the generated content in their actual work.

[1331] Input: Final revised materials and content

[1332] Output: Stored database entries, transmitted materials and content

[1333] This series of processing steps enables efficient creation of materials and generation of personalized content based on business-related information and the individual interests of the user.

[1334] 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.

[1335] This invention combines an emotion engine with a generative AI system that inputs business-related information from users and creates materials and generates answers based on that information. This system is composed of a user, a server, a terminal, a generative AI, and an emotion engine.

[1336] Overall system configuration

[1337] The system consists of the following main components:

[1338] User: Responsible for entering work-related information and reviewing and correcting generated materials and responses.

[1339] Server: Responsible for receiving, storing, and processing information, and integrating with internal systems.

[1340] Terminal: Provides an interface for users to input information and review / edit generated materials and answers.

[1341] Generative AI: Analyzes input information using natural language processing, and creates materials and generates answers.

[1342] Emotion engine: Recognizes the user's emotions and adjusts the output of the generative AI based on those emotions.

[1343] Program processing overview

[1344] The main processing of the system is as follows:

[1345] 1. The user uses the terminal to input work-related information. For example, the user inputs "Best practices for customer service" as training content in text format and clicks the "Submit" button.

[1346] 2. The server receives the entered information. The received information is stored in a database. The server formats the information and stores it appropriately.

[1347] 3. Generative AI retrieves stored information from the database and performs natural language processing. For example, it analyzes the content of "Customer Service Best Practices" and extracts key points.

[1348] 4. The emotion engine infers emotions from user input and voice data. The emotion engine uses machine learning algorithms to parse emotions from the user's context and tone.

[1349] 5. The generation AI adjusts the content of materials and answers based on the emotional data provided by the emotion engine. For example, if the user is feeling stressed, the generated answers will be changed to more gentle expressions.

[1350] 6. Generative AI automatically generates materials and answers based on the analysis results. For example, when creating a presentation on customer service, the analyzed key points are placed on the slides. It also generates appropriate answers in email format to departmental questions.

[1351] 7. The server connects to the company's internal systems to obtain the necessary data. For example, it obtains information about new employees from the personnel management system and generates new training materials based on that information.

[1352] 8. The terminal provides an interface that allows users to check the generated materials and responses. Users can check the generated presentation materials and email content on the screen and make corrections as necessary.

[1353] 9. After the user has confirmed and corrected the materials, the device saves or sends the final materials or answers. For example, the finalized presentation materials are saved as a file or a reply email is sent.

[1354] Specific operation example

[1355] 1. The user writes "best practices for customer service" into an input form on a PC terminal and submits it.

[1356] 2. The server receives the information, formats it, and stores it in a database.

[1357] 3. The generative AI retrieves the stored information and performs natural language analysis.

[1358] 4. The emotion engine infers that the user is tired based on their input and voice data.

[1359] 5. Based on the data from the emotion engine, the generative AI creates presentation materials in a concise format to avoid user fatigue. It also adjusts the wording of email responses to questions from departments to be more pleasant.

[1360] 6. The server retrieves new employee information from the human resources management system and provides it to the generation AI, which then generates training materials.

[1361] 7. The terminal displays the generated presentation materials and email contents to the user, who then checks and modifies them.

[1362] 8. After the user confirms, the device saves the final document and sends it by email.

[1363] In this way, the present invention efficiently processes work-related information while also taking into account the user's emotions, further reducing the user's workload and thereby realizing a sustainable way of working.

[1364] The processing flow will be explained below.

[1365] Step 1:

[1366] Users input business-related information into the generation AI from their terminal.

[1367] Specifically, open the input form on your PC or tablet, enter the training content or a summary of a business book in text format, and click the "Send" button.

[1368] Step 2:

[1369] The server receives the input business-related information.

[1370] Specifically, the information is received using a data reception API connected via the Internet, and the contents are temporarily stored in memory.

[1371] Step 3:

[1372] The server stores the received information in a database.

[1373] Specifically, the received data is formatted and converted into the appropriate format, and then written to the "Business Information" table in the database. The consistency of the information is also checked.

[1374] Step 4:

[1375] The generating AI retrieves stored information from the database.

[1376] Specifically, a query is used to extract the necessary data from the "Business Information" table and pass it to the natural language processing engine.

[1377] Step 5:

[1378] The generative AI performs natural language processing based on the acquired information.

[1379] Specifically, it uses natural language analysis algorithms to understand the context and extract key points, then converts the analysis results into an internal data format.

[1380] Step 6:

[1381] The emotion engine infers emotions from user input and voice data.

[1382] Specifically, it uses machine learning algorithms to analyze the user's context and tone to estimate their emotional state, such as "tiredness," "stress," or "joy."

[1383] Step 7:

[1384] The generative AI adjusts the content of materials and answers based on the emotional data provided by the emotion engine.

[1385] Specifically, if a user is feeling stressed, the content of the reply email will be changed to use gentler vocabulary and writing style, and the format of presentation materials will be made clearer and simpler.

[1386] Step 8:

[1387] The generative AI generates materials and answers based on the analysis results.

[1388] Specifically, the analysis results are entered into a pre-prepared template, and presentation materials and response emails are automatically generated.

[1389] Step 9:

[1390] The server obtains the necessary data through an API connection with the internal system.

[1391] Specifically, authentication information is used to retrieve data from human resources management systems and customer management systems, format it, and provide it to the generation AI.

[1392] Step 10:

[1393] The generation AI performs additional processing based on the acquired internal data to complement the final documents and answers.

[1394] Specifically, the generated materials and answers are updated using newly acquired information to improve accuracy.

[1395] Step 11:

[1396] The terminal displays the generated materials and answers to the user.

[1397] Specifically, it provides an interface that allows users to check the generated presentation materials and email contents, and displays them on the screen.

[1398] Step 12:

[1399] The user can check and modify the generated materials and answers.

[1400] Specifically, you manually edit the documents or emails displayed on the screen and then click the "Save" or "Send" button to confirm.

[1401] Step 13:

[1402] The terminal saves or transmits the final materials and answers after the user has confirmed and corrected them.

[1403] Specifically, the finalized presentation materials are saved as a file and sent via email to the specified address.

[1404] In this way, this system receives work-related information from the user, and the generation AI automatically creates materials and generates answers based on that information, thereby reducing the user's workload and improving work efficiency.In addition, by combining it with an emotion engine, it is possible to respond flexibly and appropriately according to the user's emotional state.

[1405] Example 2

[1406] 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."

[1407] Conventional business-related information processing systems create documents and generate responses in a uniform format without considering the user's emotions, resulting in a lack of consideration for user stress and fatigue. Furthermore, it is often difficult to confirm or correct the generated documents and responses, resulting in a decline in work efficiency. To solve these issues, a flexible approach that takes the user's emotions into account is required.

[1408] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for inputting business-related information from a user, means for receiving the input information and storing it in a database, means for performing natural language processing based on the stored information and analyzing the business content, means for creating materials and generating answers based on the analysis results, means for acquiring necessary data from an internal system and reflecting the data in the generated materials and answers, means for allowing the user to confirm and correct the generated materials and answers, and means for recognizing the user's emotions and adjusting the output content based on the emotions. This makes it possible to efficiently process business-related information while taking the user's emotions into consideration and reduce the workload.

[1409] "Business-related information" refers to all information related to the performance of business within a company or organization, and specifically includes project progress, records of customer service, employee attendance status, etc.

[1410] "User" refers to an individual or organization that uses the system to input business-related information and to review and correct generated materials and responses.

[1411] "Input means" refers to devices or software that provide an interface for users to input work-related information into a system, such as a keyboard, mouse, touchscreen, or voice input.

[1412] "Server" refers to a computer system that receives business-related information entered by users and stores it in a database.

[1413] "Database" refers to an information storage system that structures and stores received information and manages it in a manner that allows for quick search and retrieval as needed.

[1414] "Natural language processing" refers to technology for understanding and analyzing human language, and specifically includes text analysis, summarization, information extraction, and intent understanding.

[1415] "Generative AI" refers to artificial intelligence that uses natural language processing technology to analyze input business-related information and automatically generate the necessary materials and answers.

[1416] An "emotion engine" refers to a system that infers emotions from user input data and voice, and adjusts the output content of the generating AI based on those emotions.

[1417] "Internal systems" refers to various management systems used within a company or organization, including, for example, human resources management systems and project management systems.

[1418] "Network" refers to a communication path used to electronically send and receive information and data, and specifically includes the Internet, an intranet, a LAN (local area network), and the like.

[1419] "Machine learning algorithms" refer to methods or models that allow computers to learn data and make predictions or analyses based on the results. Examples include neural networks and support vector machines.

[1420] MODE FOR CARRYING OUT THE INVENTION

[1421] This invention is a system that combines an emotion engine with a generative AI system that inputs business-related information from users and creates materials and answers based on that information. This system is composed of the following main components:

[1422] User: Responsible for entering work-related information and reviewing and correcting generated materials and responses.

[1423] Server: Responsible for receiving, storing, and processing information and interfacing with internal systems.

[1424] Terminal: Provides an interface for users to input information and review / edit generated materials and answers.

[1425] Generative AI: Uses natural language processing technology to analyze input information and create materials and generate answers.

[1426] Emotion engine: Recognizes the user's emotions and adjusts the output of the generative AI based on those emotions.

[1427] Program processing overview

[1428] Users input work-related information using devices such as PCs and smartphones. For example, a user inputs "best practices for customer service" as training content in text format into the device and clicks the "Send" button.

[1429] The server receives the input information, formats it, and stores it in a database. Specific software used for this purpose includes database management systems (DBMS) such as MySQL and PostgreSQL. The server-side reception process is performed using, for example, a Python server script.

[1430] Generative AI reads information stored in a database and performs natural language processing. A specific generative AI model used is GPT-4. This model analyzes input text, extracts key points, and generates appropriate materials and answers. For example, in "Best Practices for Customer Service," it extracts key points such as "quick response," "honesty," and "importance of follow-up," and creates presentation materials based on these.

[1431] The emotion engine infers emotions from the user's text input and voice data. This engine uses voice recognition software (such as the Google Speech-to-Text API) and machine learning algorithms to infer emotions. For example, it can determine the user's stress or fatigue from the tone of the voice data or specific keywords, and provide this as emotion data to the generation AI.

[1432] The generation AI adjusts the content of the generated materials and answers appropriately based on the emotional data provided by the emotion engine. For example, if it estimates that the user is tired, it will use concise and gentle language in its answers. Specifically, it generates gentle emails and presentation materials using phrases such as "Thank you for your hard work" and "Don't worry."

[1433] The terminal provides an interface that displays the generated materials and answers to the user. The user can check the materials and answers on the screen and make corrections as necessary. For example, the generated presentation can be displayed in PowerPoint format and the text in the slides can be corrected.

[1434] Overall, the system works to efficiently process work-related information and automate the creation of documents and responses while taking into account the user's emotions.

[1435] Specific examples

[1436] 1. The user writes "best practices for customer service" into an input form on a PC terminal and submits it.

[1437] 2. The server receives the information, formats it, and stores it in a database.

[1438] 3. The generative AI retrieves the stored information and performs natural language analysis.

[1439] 4. The emotion engine infers that the user is tired based on their input and voice data.

[1440] 5. Based on the data from the emotion engine, the generative AI creates presentation materials in a concise format to avoid user fatigue. It also adjusts the wording of email responses to questions from departments to be more pleasant.

[1441] 6. The server retrieves new employee information from the internal system and provides it to the generation AI, which then generates training materials.

[1442] 7. The terminal displays the generated presentation materials and email contents to the user, who then checks and modifies them.

[1443] 8. After the user confirms, the device saves the final document and sends it by email.

[1444] Example prompt sentence:

[1445] "Based on the topic 'Customer Engagement Best Practices,' please generate a concise and friendly presentation deck and a friendly email response to the department's questions."

[1446] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1447] Step 1:

[1448] A user enters work-related information into an input form on a device such as a PC or smartphone. For example, the user enters "best practices for customer service" in text format and clicks the "Send" button. This input information is sent from the device to the server.

[1449] Step 2:

[1450] The server receives the information sent by the user. At this stage, the text data entered by the user is sent to the server. The server formats the received information and processes the data by removing unnecessary spaces from the string and standardizing the format. The formatted data is then saved in a database. The databases used are MySQL and PostgreSQL.

[1451] Step 3:

[1452] Generative AI reads stored information from a database and performs natural language processing. For example, GPT-4 is used as the generative AI model. It performs sentence analysis on the stored text data and extracts key points. The input is text data from the database, and the output is a list of analyzed key points. Specific operations include the process of calling the GPT-4 model to perform text analysis.

[1453] Step 4:

[1454] The emotion engine infers emotions from text and voice data entered by the user. This emotion engine uses speech recognition software (such as the Google Speech-to-Text API) to convert voice data into text and then performs emotion analysis based on that text. The input is the user's voice and text data, and the output is an inferred emotion label. For example, the output might be "tired" or "stressed."

[1455] Step 5:

[1456] The generative AI adjusts the content of materials and answers based on the emotional data provided by the emotion engine. For example, if it estimates that the user is tired, it will use gentler language in the answer. In this case, the generative AI model receives the analysis results and emotional data as input and generates gentle text. Specifically, it reduces the user's stress by adding phrases such as "Thank you for your hard work."

[1457] Step 6:

[1458] The server interacts with other internal systems to obtain the necessary data. For example, it obtains new employee information from a human resources management system. In this case, the server uses an API to send a request to the human resources management system and provides the obtained data to the generation AI. The input is the API request and response, and the output is the new employee information that is passed to the generation AI. Specific operations include sending an HTTP request to the API endpoint and parsing the result.

[1459] Step 7:

[1460] The terminal provides an interface that displays the generated materials and responses to the user. Specifically, the generated presentation materials and email contents are displayed on the terminal screen, and the user can check and correct them as necessary. The input is the generated content, and the output is the result of the user checking and correcting it.

[1461] Step 8:

[1462] The terminal saves the final documents and responses that the user has reviewed and revised, and sends them as necessary. For example, it saves the revised presentation materials in PDF format or sends a reply email via an SMTP server. The input is the content that the user reviewed and revised, and the output is the saved file or the sent email. Specific operations include disk access for saving files and the use of the SMTP protocol for sending emails.

[1463] (Application example 2)

[1464] 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."

[1465] Conventional generative AI systems were unable to take the user's emotions into consideration when creating documents and generating answers based on work-related information, making it difficult to reduce user stress and provide more appropriate responses. Furthermore, particularly in customer support, responses that take into account the customer's emotions are required, but automating this has been difficult.

[1466] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for inputting business-related information from a user, means for receiving the input information and storing it in a database, means for performing natural language processing based on the stored information and analyzing the business content, emotion analysis means for analyzing the user's emotions, means for adjusting materials and answers generated based on the emotion analysis results, and means for the user to confirm and correct the generated materials and answers. This makes it possible to take the user's emotions into consideration and respond appropriately and in a way that reduces stress.

[1467] "Business-related information" refers to data and information related to the business conducted by a company or organization, and is input by a user.

[1468] "User" means a person who uses the system to input business-related information and check and correct the generated documents and responses.

[1469] "Means for input" refers to a function that provides an interface that allows users to input business-related information into the system.

[1470] "Means for receiving and storing in a database" refers to the function of receiving input information in an appropriate format and storing it in a database.

[1471] "Natural language processing" is a technology that allows computers to understand and analyze human language.

[1472] "Means of analysis" refers to the function of performing natural language processing based on stored information and analyzing business content.

[1473] "Means for creating materials and generating answers" refers to the function of automatically generating materials and answers based on the analysis results.

[1474] "Emotion analysis means" refers to a function that analyzes emotions from user input and voice data.

[1475] "Means for adjustment" refers to the function of adjusting the content of generated materials and answers based on the results of sentiment analysis.

[1476] "Internal systems" is a general term for information systems used within a company.

[1477] "Means for checking and correcting" refers to the function of providing an interface that allows users to check the generated materials and answers and correct them as necessary.

[1478] This system combines emotion analysis functionality with a generative AI system that inputs work-related information from users and creates documents and answers based on that information. Specifically, it consists of the following components:

[1479] 1. A user uses a smartphone or PC to enter business-related information. For example, they enter the details of a customer support inquiry into an input form and submit it.

[1480] 2. The terminal receives the input information and sends it to the server, using communication over the Internet.

[1481] 3. The server stores the received information in a database, where it is formatted for analysis.

[1482] 4. The generative AI installed on the server performs natural language processing based on the stored information and analyzes the business content, using machine learning algorithms.

[1483] 5. The server is also equipped with an emotion analysis function that analyzes emotions from user input. Emotion analysis uses machine learning models based on text and voice data.

[1484] 6. The analyzed emotion data is used to adjust the content of the materials and answers generated by the generative AI. For example, if the user expresses anger or stress, the generated answers will be changed to more gentle expressions.

[1485] 7. The generative AI model uses this data to create materials and generate answers. For example, in customer support email correspondence, an appropriate reply email is generated in response to a customer's inquiry.

[1486] 8. The generated materials and answers are displayed to the user via the terminal, where the user can review them and make corrections as necessary.

[1487] 9. The final confirmed and corrected materials and answers will be returned to the server and stored or transmitted as necessary.

[1488] Examples of specific hardware and software used include smartphones (Android, iOS), personal computers, the Python programming language, the Transformers library (Hugging Face), and the EmotionRecognizer library.

[1489] As a specific example, if a user inputs a customer inquiry such as "My product hasn't arrived yet, what's going on?", emotion analysis will determine that the customer is feeling stressed. The generation AI will take this emotional data into consideration and generate a reply in a gentle tone. An example of a generated prompt sentence is as follows:

[1490] Example prompt sentence:

[1491] If your customer is feeling stressed, respond with the following messages: My item hasn't arrived, what's going on?

[1492] In this way, the system automates responses by taking the user's emotions into account, enabling more effective and satisfying customer support.

[1493] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1494] Step 1:

[1495] A user inputs business-related information using a smartphone or PC. This information could be, for example, the contents of a customer support inquiry. By clicking the "Send" button, the input data is sent to the terminal. The input is the inquiry in text format, and the output includes the data to be sent to the terminal.

[1496] Step 2:

[1497] The terminal receives the input information and transmits it to the server. The received data is sent to the server via the Internet. The input includes text data from the user, and the output includes data to be sent to the server.

[1498] Step 3:

[1499] The server stores the received information in a database, where it formats the data appropriately for future analysis and retrieval. The input is the data sent from the device, and the output is the data stored in the database.

[1500] Step 4:

[1501] The generative AI installed on the server retrieves information stored in the database and performs natural language processing. Specifically, it uses a machine learning algorithm to analyze the text of the inquiry and extract key points. The input is text data retrieved from the database, and the output contains the analyzed key points.

[1502] Step 5:

[1503] The server is also equipped with an emotion analysis function that analyzes emotions from user input. Sentiment analysis uses machine learning models based on text and voice data. The input is text and voice data from the user, and the output includes analyzed emotion data.

[1504] Step 6:

[1505] To adjust the generated materials and answers based on the results of emotion analysis, the generation AI takes emotional data into account and adjusts the content. For example, if the user is feeling stressed, the tone of the answer will be changed to a gentler expression. The inputs are emotion analysis data and the analysis results from natural language processing, and the output includes adjusted materials and answers.

[1506] Step 7:

[1507] The generated materials and answers are displayed to the user via the terminal. The user can check them and make corrections as necessary. The input is the materials and answer data sent from the server, and the output includes data that reflects the user's corrections.

[1508] Step 8:

[1509] Finally, the confirmed and corrected materials and responses are returned to the server and stored or transmitted as necessary. This streamlines business processes such as customer support and improves customer satisfaction. The input is the data modified by the user, and the output is the data that is finally stored or transmitted.

[1510] 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.

[1511] 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.

[1512] 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.

[1513] 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.

[1514] 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.

[1515] 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.

[1516] 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).

[1517] 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.

[1518] 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."

[1519] 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.

[1520] 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).

[1521] 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.

[1522] 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.

[1523] 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.

[1524] 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.

[1525] 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.

[1526] 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.

[1527] 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.

[1528] 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.

[1529] 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.

[1530] 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.

[1531] The following is further disclosed regarding the above embodiment.

[1532] (Claim 1)

[1533] means for inputting business-related information from a user;

[1534] means for receiving and storing the input information in a database;

[1535] A means of analyzing business operations by performing natural language processing based on the stored information;

[1536] A means for creating materials and generating answers based on the analysis results;

[1537] A means to obtain the necessary data from the company's internal systems and reflect it in the generated documents and responses,

[1538] A means for users to check and correct the generated materials and answers,

[1539] A system including:

[1540] (Claim 2)

[1541] 10. The system of claim 1, wherein business-related information is entered, received, and stored over the Internet.

[1542] (Claim 3)

[1543] 2. The system of claim 1, wherein the natural language processing uses a machine learning algorithm to analyze business content.

[1544] "Example 1"

[1545] (Claim 1)

[1546] means for inputting business-related information from a user;

[1547] means for receiving and storing the input information in a database;

[1548] A means of analyzing business operations by performing natural language processing based on the stored information;

[1549] A means for creating materials and generating answers based on the analysis results;

[1550] A means to retrieve necessary data from internal systems and reflect it in generated materials and responses,

[1551] A means for users to check and correct the generated materials and answers,

[1552] A means of storing or transmitting final materials and responses to an external system;

[1553] A system including:

[1554] (Claim 2)

[1555] 10. The system of claim 1, wherein the business-related information is entered, received, and stored over a communications network.

[1556] (Claim 3)

[1557] The system of claim 1, wherein the natural language processing analyzes business content using a generative AI model.

[1558] "Application Example 1"

[1559] (Claim 1)

[1560] means for inputting business-related information from a user;

[1561] means for receiving and storing the input information in a database;

[1562] A means of analyzing business operations by performing natural language processing based on the stored information;

[1563] A means for creating materials and generating answers based on the analysis results;

[1564] A means to obtain the necessary data from the company's internal systems and reflect it in the generated documents and responses,

[1565] A means for users to check and correct the generated materials and answers,

[1566] A means for users to input topics or keywords of interest and generate personalized content based on them;

[1567] A means for displaying the generated personalized content to the user so that the user can check and modify it;

[1568] A system including:

[1569] (Claim 2)

[1570] 10. The system of claim 1, wherein the input, reception, and storage of business-related information and topics and keywords of interest to the user are performed over the Internet.

[1571] (Claim 3)

[1572] 10. The system of claim 1, wherein the natural language processing uses machine learning algorithms to analyze and generate personalized content based on business context and user interests.

[1573] "Example 2: Combining Emotion Engines"

[1574] (Claim 1)

[1575] means for inputting business-related information from a user;

[1576] means for receiving and storing the input information in a database;

[1577] A means of analyzing business operations by performing natural language processing based on the stored information;

[1578] A means for creating materials and generating answers based on the analysis results;

[1579] A means to retrieve necessary data from internal systems and reflect it in generated materials and responses,

[1580] A means for users to check and correct the generated materials and answers,

[1581] means for recognizing a user's emotion and adjusting output content based on the emotion;

[1582] A system including:

[1583] (Claim 2)

[1584] 10. The system of claim 1, wherein the business-related information is entered, received, and stored over a network.

[1585] (Claim 3)

[1586] 2. The system of claim 1, wherein the natural language processing uses a machine learning algorithm to analyze business content.

[1587] "Application example 2 when combining emotion engines"

[1588] (Claim 1)

[1589] means for inputting business-related information from a user;

[1590] means for receiving and storing the input information in a database;

[1591] A means of analyzing business operations by performing natural language processing based on the stored information;

[1592] A means for creating materials and generating answers based on the analysis results;

[1593] emotion analysis means for analyzing the emotions of a user;

[1594] A means of adjusting materials and responses generated based on the results of sentiment analysis;

[1595] A means to obtain the necessary data from the company's internal systems and reflect it in the generated documents and responses,

[1596] A means for users to check and correct the generated materials and answers,

[1597] A system including:

[1598] (Claim 2)

[1599] 10. The system of claim 1, wherein business-related information is entered, received, and stored over the Internet.

[1600] (Claim 3)

[1601] 2. The system of claim 1, wherein the natural language processing uses a machine learning algorithm to analyze business content. [Explanation of symbols]

[1602] 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 inputting business-related information from a user; means for receiving and storing the input information in a database; A means of analyzing business operations by performing natural language processing based on the stored information; A means for creating materials and generating answers based on the analysis results; A means to obtain the necessary data from the company's internal systems and reflect it in the generated documents and responses, A means for users to check and modify the generated materials and answers, and A system including:

2. 10. The system of claim 1, wherein business-related information is entered, received, and stored over the Internet.

3. The system according to claim 1 , wherein the natural language processing analyzes business content using a machine learning algorithm.

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