system

Abstract

We provide a system that automates the creation and approval process of electronic messages and documents, and supports the aggregation and utilization of knowledge. [Solution] Means for a user to create an electronic message on a terminal; means for the terminal to provide the content of the electronic message to a generative artificial intelligence; means for a server to receive a response from the generative artificial intelligence and automatically optimize the content of the electronic message; means for the server to send the optimized electronic message to an approval workflow module; means for a user to review the electronic message in the approval workflow module and provide approval or feedback; and means for the server to adjust the final electronic message based on the feedback. and means for the server to send the approved electronic message to the recipient.

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 environments, the creation and approval processes for electronic messages (emails) and documents are often labor-intensive and time-consuming. In particular, the need for confirmation and feedback among numerous stakeholders often hinders efficient communication. Furthermore, past knowledge cannot be effectively utilized, and recreating similar documents requires a great deal of effort. For these reasons, there is a demand for a system that automates the creation and approval processes for electronic messages and documents and supports the aggregation and utilization of knowledge. [Means for solving the problem]

[0005] The present invention provides a means for a user to create an electronic message on a terminal and provide the content of the message to a generative artificial intelligence. A server receives a response from the generative artificial intelligence and automatically optimizes the electronic message. The optimized electronic message is sent to an approval workflow module, where the user can provide approval or feedback. The server makes final adjustments based on the feedback and sends the approved electronic message to the recipient.

[0006] Furthermore, the system also includes a means for a user to start creating a document on a terminal and provide the requirements for the document to the generative AI. The generative AI automatically generates the document, and the server sends the document to an approval workflow module. The user can review the document and provide approval or feedback. The server makes final adjustments and distributes the approved document to the relevant parties.

[0007] The present invention stores approved electronic messages and materials in a knowledge base module, allowing users to search and reference past knowledge, and utilize this knowledge to efficiently create new electronic messages and materials.

[0008] These measures automate the creation and approval process of electronic messages and documents, enabling efficient communication. Furthermore, the use of a knowledge base makes it possible to effectively utilize past experience and information.

[0009] "User" means any person or organization that creates, reviews, or approves electronic messages or materials using the System.

[0010] A "terminal" refers to an electronic device such as a computer or smartphone used by a user.

[0011] An "electronic message" is a communication sent in digital form, such as email.

[0012] "Generative AI" refers to AI that generates text or other content based on given input.

[0013] A "server" is a computer that processes and stores data in this system and handles communication with generative artificial intelligence.

[0014] "Optimization" refers to the process of improving and formatting the content of electronic messages and materials based on the output provided by generative artificial intelligence.

[0015] "Approval Workflow Module" means the part of the system that manages the process of reviewing the content of electronic messages or materials and providing approval or feedback.

[0016] "Feedback" means any suggested corrections or improvements provided by a User to the content of an Electronic Message or Material.

[0017] "Final adjustment" refers to the process of making final revisions to the content of electronic messages or materials based on feedback.

[0018] "Recipient" means a person or entity that receives an electronic message or distributed material created by a User.

[0019] "Materials" refers to documents such as reports and presentations created by users.

[0020] "Knowledge Base Module" means a database that stores approved electronic messages and materials and allows users to search and reference past information.

[0021] "Knowledge" refers to the information and experience contained in electronic messages and materials previously created and approved. [Brief explanation of the drawings]

[0022] [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

[0023] 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.

[0024] First, the terms used in the following description will be explained.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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."

[0030] [First embodiment]

[0031] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0032] 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.

[0033] 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).

[0034] 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.

[0035] 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.

[0036] 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.

[0037] 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.

[0038] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0039] 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.

[0040] 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.

[0041] 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.

[0042] 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."

[0043] This invention relates to a system that automates the creation and approval process of electronic messages (emails) and documents, and utilizes a knowledge base. This system realizes efficient communication by linking together the components of the server, terminal, and user.

[0044] Mail Processing Embodiment

[0045] Creating and optimizing electronic messages

[0046] The user begins to compose an electronic message on the terminal. For example, if the user wants to send an email inviting a meeting, he or she inputs the content "Schedule a meeting for next Tuesday" into the terminal. This input is provided to the generative artificial intelligence.

[0047] The server receives the response from the generative AI and automatically optimizes its content. The generative AI generates an optimized electronic message saying, "We've scheduled a meeting for next Tuesday at 10:00 AM. Please respond if you can attend." The server then sends this optimized electronic message to the approval workflow module.

[0048] Approval and feedback

[0049] The user reviews the optimized electronic message in the approval workflow module and provides feedback as needed. For example, if the user provides feedback to add a meeting location, the server adds "Conference Room A" to the email based on the feedback.

[0050] Finally, the server sends the approved electronic message to the recipient, effectively sending unified, organized email.

[0051] Form of document creation

[0052] Document creation and automatic generation

[0053] The user starts creating a new document on their device. For example, when creating a "monthly report," the user inputs this month's sales data and progress on major projects into the device. This input information is provided to the generative AI.

[0054] The server receives the response from the generative AI and automatically generates the document. For example, the generative AI creates a report that includes a "sales data section" and a "project progress section." The server then sends this automatically generated document to the approval workflow module.

[0055] Approval and feedback on materials

[0056] The user reviews the document in the approval workflow module and provides feedback. For example, if a user requests that a sales data graph be added to the document, the server reflects that feedback and generates the final document with the graph added.

[0057] Finally, the server distributes the approved materials to the relevant parties, thereby enabling uniform, high-quality materials to be created and distributed efficiently.

[0058] Knowledge base operations

[0059] Accumulation and utilization of knowledge

[0060] The server stores approved electronic messages and materials in a knowledge base module, allowing users to efficiently search and reference past knowledge. When users create new electronic messages or materials by utilizing existing knowledge, they can work quickly and efficiently.

[0061] For example, users can search and refer to past "project progress reports" to quickly find templates and reference information needed to create new reports, significantly reducing time and effort.

[0062] The system of the present invention is expected to significantly improve the efficiency of the process of creating and approving electronic messages and materials, and by making effective use of knowledge, to improve the speed and quality of business communications.

[0063] The processing flow will be explained below.

[0064] Specific processing flow for email processing

[0065] Step 1:

[0066] A user drafts an electronic message on a terminal, specifically, inputs an invitation email for a meeting into the terminal.

[0067] Step 2:

[0068] The device provides the content of the electronic message to the generative artificial intelligence, specifically, sending the phrase "Schedule a meeting for next Tuesday" to the generative artificial intelligence.

[0069] Step 3:

[0070] The server receives a response from the generative artificial intelligence, which generates an optimized electronic message based on the input phrase.

[0071] Step 4:

[0072] The server reviews and formats the optimized electronic message received from the generative AI, improving grammar and expression to say, "We've scheduled a meeting for next Tuesday at 10 a.m. Please reply if you can attend."

[0073] Step 5:

[0074] The server sends the optimized electronic message to the approval workflow module, where the email is added to a queue for review.

[0075] Step 6:

[0076] The user reviews the electronic message at the terminal through an approval workflow module and provides approval or feedback, for example, prompting the addition of a meeting location.

[0077] Step 7:

[0078] The server receives the user's feedback and uses generative AI to reflect the feedback. Specifically, it adds "Meeting location: Meeting Room A."

[0079] Step 8:

[0080] The server then sends the final approved electronic message to the recipient, resulting in a high-quality, approved email.

[0081] Specific process flow for creating materials

[0082] Step 1:

[0083] The user starts creating a new document on the device. Specifically, he opens a new document called "Monthly Report."

[0084] Step 2:

[0085] The device provides the generative AI with the requirements for creating documents. Specifically, it inputs "this month's sales data" and "progress of major projects."

[0086] Step 3:

[0087] The server receives the response from the generative AI, which generates materials with the appropriate sections based on the provided requirements.

[0088] Step 4:

[0089] The server formats the data received from the generative AI, generating a report that includes a sales data section and a project progress section.

[0090] Step 5:

[0091] The server sends the generated material to the approval workflow module, where it is added to a queue for review.

[0092] Step 6:

[0093] The user reviews the document through the approval workflow module on the terminal and provides approval or feedback, for example, requesting the addition of a sales data graph.

[0094] Step 7:

[0095] The server receives the user's feedback and uses generative AI to reflect the feedback. Specifically, it adds a graph of sales data.

[0096] Step 8:

[0097] The server distributes the final approved materials to the relevant parties, thereby ensuring that approved, high-quality materials are distributed.

[0098] Specific process flow for knowledge base operation

[0099] Step 1:

[0100] The server stores the approved electronic messages and materials in a knowledge base module, which stores the information for future reference.

[0101] Step 2:

[0102] The user searches for and references the necessary information from the knowledge base on the terminal. Specifically, the user searches for past "project progress reports."

[0103] Step 3:

[0104] Users can use information retrieved from the knowledge base to efficiently create new electronic messages or documents, for example, creating new reports using templates from the past.

[0105] Through these processing steps, the system efficiently automates the creation, approval, and delivery of electronic messages and materials, and also effectively leverages a knowledge base.

[0106] Example 1

[0107] 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."

[0108] The traditional process of creating and approving electronic messages and documents was often done manually, resulting in inefficiency and a decline in the quality of communication. Furthermore, reusing past data required manual searching and referencing, which was time-consuming and labor-intensive.

[0109] 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.

[0110] In this invention, the server includes: a means for a user to create an electronic message on a terminal; a means for the terminal to provide the content of the electronic message to a generative AI model; a means for the server to receive a response from the generative AI model and automatically optimize the content of the electronic message; a means for the server to send the optimized electronic message to an approval workflow module; a means for a user to review the electronic message in the approval workflow module and provide approval or feedback; a means for the server to adjust the final electronic message based on the feedback; a means for the server to send the approved electronic message to a recipient; and a means for the server to store the approved electronic message in a knowledge base module. This streamlines the creation and approval process of electronic messages and materials, enabling advanced communication that efficiently utilizes knowledge.

[0111] "User" means any person or entity that operates the System and creates, reviews, provides feedback on, and approves electronic messages and materials.

[0112] A "terminal" is an electronic device such as a computer, smartphone, or tablet that is operated by a user.

[0113] "Electronic Message" means a message sent or received by email or other means of electronic communication.

[0114] A "generative AI model" is an artificial intelligence model that generates text data based on prompts entered by a user.

[0115] A "server" is a machine that communicates with devices over a network to provide data to a generative AI model and receive results.

[0116] A "response" is the reply text that a generative AI model generates in response to a prompt.

[0117] "Optimization" refers to converting responses received from a generative AI model into a suitable format for electronic messages or documents.

[0118] An "approval workflow module" is a software module for accepting confirmation, feedback, and approval from users.

[0119] "Feedback" means any instruction for correction or improvement given by a user to a generated electronic message or material.

[0120] "Knowledge Base Module" means a software module for storing approved electronic messages and materials for future retrieval and reuse.

[0121] The present invention is a system for automating the creation and approval process of electronic messages and documents and utilizing a knowledge base. This system is composed of a server, terminals, a generative AI model, and various software modules. Each component and its operation are described in detail below.

[0122] Creating and optimizing electronic messages

[0123] A user uses a device to create an electronic message. For example, when creating a meeting invitation email, the user types something like "Schedule a meeting for next Tuesday." This input is formatted as a prompt by the device and provided to a generative AI model (e.g., GPT-4 (registered trademark)).

[0124] The server receives the response from the generative AI model, analyzes its content, and generates an optimized message, such as "We've scheduled a meeting for next Tuesday at 10:00 AM. Please reply if you can attend."

[0125] The server then sends the generated optimization message to the approval workflow module, where the user can review the message and provide feedback if necessary, for example, to add "Conference Room A."

[0126] The server adjusts the final electronic message based on the feedback and sends the approved message to the recipient, a process that ensures a uniform message is sent efficiently.

[0127] Prompt Sentence Examples

[0128] Set up a meeting for next Tuesday

[0129] Document creation and automatic generation

[0130] The user starts creating a new document using the device. For example, to create a "monthly report," the user inputs this month's sales data and the progress of major projects into the device. This input information is provided to the generative AI model as a prompt.

[0131] The server receives the response from the generative AI model and automatically generates the document. For example, a report containing a "sales data section" and a "project progress section" is generated. The server sends this report to the approval workflow module and notifies the user.

[0132] The user reviews the document in the approval workflow module and provides feedback as needed, such as "add a chart of sales data."

[0133] The server then distributes the final revised documents to the relevant parties, ensuring efficient distribution of high-quality, uniform documents.

[0134] Prompt Sentence Examples

[0135] Enter this month's sales data and progress on major projects

[0136] Knowledge base operations

[0137] The server stores approved electronic messages and materials in a knowledge base module, allowing users to access the knowledge base from their terminals and search and refer to past electronic messages and materials.

[0138] Users can efficiently create new electronic messages and documents by utilizing referenced knowledge. For example, by referencing a past "Project Progress Report" and using it as a template for a new report, users can significantly reduce time and effort.

[0139] This system is expected to significantly streamline the process of creating and approving electronic messages and documents, and by making effective use of knowledge, to result in faster, higher-quality business communications.

[0140] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0141] Creating and optimizing electronic messages

[0142] Step 1:

[0143] A user begins composing an electronic message on a terminal, for example, typing "Schedule a meeting for next Tuesday." The input data is formatted as a prompt sentence.

[0144] Step 2:

[0145] The device provides the prompt text entered by the user to the generative AI model. Specifically, it sends the prompt text "Schedule a meeting for next Tuesday" to the generative AI model.

[0146] Step 3:

[0147] The server receives a response from the generative AI model. For example, the generative AI model generates an optimized message such as, "We've scheduled a meeting for next Tuesday at 10:00 AM. Please reply if you can attend." This response is then analyzed to obtain the optimized message.

[0148] Step 4:

[0149] The server sends the optimized electronic message to the approval workflow module, specifically, stores the generated optimized message in a database of the approval workflow module, and notifies the user.

[0150] Step 5:

[0151] The user checks the message in the approval workflow module and provides feedback if necessary. For example, the user enters feedback such as adding "Conference Room A" and submits it to the approval workflow module.

[0152] Step 6:

[0153] The server receives feedback from the approval workflow module and adjusts the final electronic message, for example, adding "Conference Room A" based on the feedback, and completes the final message.

[0154] Step 7:

[0155] The server sends the approved electronic message to the recipient, sends the final electronic message based on the recipient list, and notifies the user.

[0156] Step 8:

[0157] The server stores the approved electronic message in the knowledge base module, and accumulates the final message data in the knowledge base database.

[0158] Document creation and automatic generation

[0159] Step 1:

[0160] The user starts creating a document on the terminal. For example, to create a "monthly report," the user inputs this month's sales data and the progress of major projects.

[0161] Step 2:

[0162] The device generates the input information as a prompt and provides it to the generative AI model. Specifically, the prompt "Enter this month's sales data and progress on major projects" is sent to the generative AI model.

[0163] Step 3:

[0164] The server receives a response from the generative AI model. The generated document contains a "sales data section" and a "project progress section." This response is analyzed to obtain the automatically generated document.

[0165] Step 4:

[0166] The server sends the automatically generated material to the approval workflow module, stores the generated material in the database of the approval workflow module, and notifies the user.

[0167] Step 5:

[0168] The user reviews the material in the approval workflow module and provides feedback. For example, the user inputs an instruction to "add a graph of sales data" and submits it to the approval workflow module.

[0169] Step 6:

[0170] The server receives feedback from the approval workflow module and adjusts the final document, adding graphs based on the feedback and completing the final document.

[0171] Step 7:

[0172] The server distributes the approved final documents to the parties concerned, sends the final documents based on the parties list, and notifies the users.

[0173] Step 8:

[0174] The server stores the approved materials in the knowledge base module, and accumulates the final material data in the knowledge base database.

[0175] Knowledge base operations

[0176] Step 1:

[0177] The server stores approved electronic messages and materials in a knowledge base module, organizing and centralizing the data.

[0178] Step 2:

[0179] Users can access the knowledge base from their devices to search and refer to past electronic messages and documents, quickly finding the information they need.

[0180] Step 3:

[0181] Users can leverage knowledge to efficiently create new electronic messages and materials, and save time by leveraging existing templates and reference information.

[0182] Through these steps, the system streamlines the creation and approval process of electronic messages and documents, and makes effective use of knowledge.

[0183] (Application example 1)

[0184] 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."

[0185] In the traditional manufacturing industry, the creation and approval process of work instructions was often performed manually, resulting in time-consuming and labor-intensive tasks. Furthermore, incorporating feedback required significant manual operations, increasing the likelihood of errors and delays. This often resulted in inconsistent quality of the generated instructions. Furthermore, past instructions and documents were not efficiently stored and searched in a knowledge base, resulting in insufficient utilization of past knowledge. To address these issues, the present invention provides a system that automates the creation and approval process of electronic messages and documents, enabling efficient utilization of knowledge.

[0186] 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.

[0187] In this invention, the server includes: a means for a user to create an electronic message on a terminal; a means for the terminal to provide the content of the electronic message to a generative artificial intelligence; a means for the server to receive a response from the generative artificial intelligence and automatically optimize the content of the electronic message; a means for transmitting the electronic message to an approval workflow module; a means for a user to review and approve or provide feedback in the approval workflow module; a means for adjusting the final electronic message based on the feedback; a means for transmitting the approved electronic message to a recipient; a means for receiving a response from the generative artificial intelligence to generate an automated work instruction and generating an optimized work instruction; a means for a user to review and approve or provide feedback in the approval workflow module; a means for adjusting the final work instruction based on the feedback and storing the optimized work instruction in a knowledge base. This reduces errors and delays through automated processes and enables efficient work by utilizing past knowledge while maintaining consistent quality of electronic messages and work instructions.

[0188] A "terminal" is a device that a user operates to create, edit, and input electronic messages, documents, and the like.

[0189] "Generative AI" is an AI system that generates optimized electronic messages and materials based on user input.

[0190] A "server" is a central processing unit that receives responses from generative artificial intelligence and performs various data processing and transmission.

[0191] "Approval Workflow Module" means a software module that manages the process of providing approval or feedback on the content of an electronic message or document.

[0192] "Feedback" refers to additional information or requests for corrections provided by a user through the approval workflow module.

[0193] A "knowledge base" is a database that stores and manages information such as past electronic messages and documents, allowing users to efficiently search and reference them.

[0194] A "work instruction manual" is a document that details the specific work content and procedures for manufacturing robots and workers.

[0195] A "prompt sentence" is an input sentence for obtaining a specific output from a generative artificial intelligence.

[0196] This invention is a system that automates the creation and approval process of electronic messages and documents, utilizing generative artificial intelligence and a knowledge base. This system operates in cooperation with a user terminal, a server that acts as a central processing unit, and an approval workflow module.

[0197] Mail Processing Embodiment

[0198] Creating and optimizing electronic messages

[0199] A user begins to compose an electronic message on a terminal. For example, when the user wants to send an invitation email for a meeting, the user inputs the content "Schedule a meeting for next Tuesday" into the terminal. This input is provided to the generative AI. The server receives a response from the generative AI and automatically optimizes the content. The generative AI generates an optimized electronic message saying "We have scheduled a meeting for next Tuesday at 10:00 AM. Please reply if you can attend." The server then sends this optimized electronic message to the approval workflow module.

[0200] Approval and feedback

[0201] The user reviews the optimized electronic message in the approval workflow module and provides feedback as needed. For example, if the user provides feedback to add a meeting location, the server adds "Conference Room A" to the email based on that feedback. Finally, the server sends the approved electronic message to the recipient, thereby efficiently sending a well-organized email.

[0202] Form of document creation

[0203] Document creation and automatic generation

[0204] A user starts creating a new document on a terminal. For example, when creating a "monthly report," the user inputs this month's sales data and the progress of major projects into the terminal. This input information is provided to the generative AI. The server receives a response from the generative AI and automatically generates the document. For example, the generative AI creates a report that includes a "sales data section" and a "project progress section." The server sends this automatically generated document to an approval workflow module.

[0205] Approval and feedback on materials

[0206] Users review documents and provide feedback in the approval workflow module. For example, if a user requests that a sales data graph be added to the document, the server incorporates that feedback and generates the final document with the graph added. Finally, the server distributes the approved document to the relevant parties. This ensures that consistent, high-quality documents are created and distributed efficiently.

[0207] Generating work instructions for factory robots

[0208] Create and approve work orders

[0209] The user starts creating a work instruction on a terminal. For example, if the work instruction for a manufacturing robot is to "assemble parts A and B," this content is provided to the generative AI. The server receives the response from the generative AI and generates an optimized work instruction. This is then sent to the approval workflow module.

[0210] Users review the instructions in the approval workflow module and provide approval or feedback. If the user provides feedback, the server adjusts and generates the final work instructions based on that feedback. This final work instruction is saved in the knowledge base. This allows for efficient work with fewer errors by utilizing past knowledge.

[0211] As a specific example, if the user inputs "Today's manufactured product will be the assembly of parts A and B. 10 parts A and 20 parts B will be used. Quality checks will be performed," the generated work instruction will be "Today's manufactured product will be the assembly of parts A (10 pieces) and parts B (20 pieces). Please be sure to perform quality checks."

[0212] Example prompt sentence:

[0213] Create a work instruction for your manufacturing robot:

[0214] Today's production involves assembling parts A and B. 10 parts A and 20 parts B are used. Quality check is performed.

[0215] Prepared instructions:

[0216] The present invention automates the process of creating electronic messages, documents, and even work instructions for factory robots, and ensures consistent quality, enabling efficient work with fewer errors and delays.

[0217] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0218] Step 1:

[0219] The user begins composing an electronic message on a terminal. The user types something like "Schedule a meeting for next Tuesday." This input is provided to the generative artificial intelligence as a prompt.

[0220] Input: Message content created by the user

[0221] Output: Prompt sentence to be provided to the generative AI

[0222] Step 2:

[0223] The server sends the prompt to the generative AI and receives a response, which generates an optimized electronic message based on the input: "We've scheduled a meeting for next Tuesday at 10:00 AM. Please reply if you can attend."

[0224] Input: prompt statement

[0225] Output: Optimized electronic message

[0226] Step 3:

[0227] The server sends the optimized electronic message to an approval workflow module, where the electronic message awaits approval or feedback.

[0228] Input: Optimized electronic message

[0229] Output: Waiting for confirmation in the approval workflow module

[0230] Step 4:

[0231] A user reviews the electronic message through the approval workflow module and provides feedback if necessary, for example, "Please add conference room A."

[0232] Input: Feedback

[0233] Output: Feedback content

[0234] Step 5:

[0235] The server adjusts the electronic message based on the feedback, specifically generating a final electronic message with "Conference Room A" added.

[0236] Input: Feedback

[0237] Output: Final corrected electronic message

[0238] Step 6:

[0239] The server sends the final approved electronic message to the recipient, which delivers the completed electronic message to the recipient.

[0240] Input: Final electronic message

[0241] Output: Email sent to recipient

[0242] Step 7:

[0243] The user starts creating a new document on the device. For example, to create a "monthly report," the user inputs this month's sales data and the progress of major projects into the device. This input is provided to the generative AI.

[0244] Input: Original data for document creation

[0245] Output: Prompt sentence to be provided to the generative AI

[0246] Step 8:

[0247] The server receives the response from the generative AI and automatically generates the document, which then creates a report that includes sections on sales data and project progress.

[0248] Input: prompt statement

[0249] Output: Generated material

[0250] Step 9:

[0251] The server sends the generated material to an approval workflow module, where it awaits approval or feedback.

[0252] Input: Generated material

[0253] Output: Waiting for confirmation in the approval workflow module

[0254] Step 10:

[0255] Users review the materials through an approval workflow module and provide feedback, such as "Please add a chart of the sales data."

[0256] Input: Feedback

[0257] Output: Feedback content

[0258] Step 11:

[0259] The server adjusts the materials based on the feedback, specifically generating the final materials with the sales data graph added.

[0260] Input: Feedback

[0261] Output: Final revised document

[0262] Step 12:

[0263] The server distributes the final approved documents to the relevant parties, ensuring that consistent, high-quality documents are delivered to the relevant parties.

[0264] Input: Final material

[0265] Output: Materials distributed to stakeholders

[0266] Step 13:

[0267] The user begins creating work instructions for a manufacturing robot on a terminal. For example, they input, "Today's production task is to assemble parts A and B." This input is provided to the generative artificial intelligence.

[0268] Input: Work instruction source data

[0269] Output: Prompt sentence to be provided to the generative AI

[0270] Step 14:

[0271] The server receives the response from the generative AI and generates an optimized work instruction. The generative AI generates the following: "Today's production involves assembling parts A (10 units) and B (20 units). Please be sure to perform a quality check."

[0272] Input: prompt statement

[0273] Output: Optimized work instructions

[0274] Step 15:

[0275] The server sends the optimized work order to an approval workflow module, where the work order awaits approval or feedback.

[0276] Input: Optimized Work Instruction

[0277] Output: Waiting for confirmation in the approval workflow module

[0278] Step 16:

[0279] A user reviews the work order in the approval workflow module and provides feedback, such as "Please also add a quality check item."

[0280] Input: Feedback

[0281] Output: Feedback content

[0282] Step 17:

[0283] The server adjusts the work instructions based on the feedback, specifically generating the final work instructions with the "quality check items" added.

[0284] Input: Feedback

[0285] Output: Final revised work instructions

[0286] Step 18:

[0287] The server saves the final approved work order in the knowledge base, which accumulates historical data for future work.

[0288] Input: Final Work Order

[0289] Output: Work instructions stored in the knowledge base

[0290] These steps automate the creation and approval process for electronic messages, documents, and work instructions, facilitating effective knowledge utilization.

[0291] 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.

[0292] This invention combines a knowledge-based system that automates the creation and approval process of electronic messages (emails) and documents with an emotion engine. This system, which involves the collaboration of server, terminal, and user components, realizes efficient and emotion-sensitive communication.

[0293] Mail Processing Embodiment

[0294] Creating and optimizing electronic messages

[0295] A user creates a draft of an electronic message on a terminal. For example, if the user wants to send an invitation email to a meeting, the user inputs the content "Schedule a meeting for next Tuesday" into the terminal.

[0296] The terminal provides the contents of the electronic message to the generative AI, and the emotion engine analyzes the user's emotions. This input data and emotion data are provided to the generative AI.

[0297] The server receives the response from the generative AI and automatically optimizes its content. For example, the generative AI generates an optimized message such as, "We have scheduled a meeting for next Tuesday at 10:00 AM. Please reply if you can attend." The user's emotions, analyzed by the emotion engine, are also reflected in this message.

[0298] Approval and feedback

[0299] The server sends the optimized electronic message to the approval workflow module, which adds it to a queue for review.

[0300] The user reviews the electronic message at the terminal through an approval workflow module and provides approval or feedback, for example, prompting the addition of a meeting location.

[0301] The server receives the user's feedback and uses regenerative artificial intelligence to reflect the feedback. At this time, the emotion engine also analyzes the user's emotions and adds appropriate emotional expressions. Specifically, it adds "Meeting location: Meeting Room A."

[0302] Finally, the server sends the approved electronic message to the recipient, resulting in efficient delivery of high-quality, sensitive emails.

[0303] Form of document creation

[0304] Document creation and automatic generation

[0305] The user starts creating a new document on the terminal. For example, when creating a "monthly report," the user inputs this month's sales data and progress on major projects into the terminal.

[0306] The device provides the requirements for creating documents to the generative AI. The emotion engine also analyzes the user's emotions. This data and emotional information are provided to the generative AI.

[0307] The server receives the response from the generative AI and automatically generates the materials. Specifically, it creates a report that includes a "sales data section" and a "project progress section." At this time, expressions based on the emotions analyzed by the emotion engine are applied.

[0308] Approval and feedback on materials

[0309] The server sends the generated document to the approval workflow module, where it is added to a queue for review.

[0310] The user reviews the document through the approval workflow module on the terminal and provides approval or feedback, for example, requesting the addition of a sales data graph.

[0311] The server receives user feedback and uses regenerative artificial intelligence to reflect the feedback. The emotion engine analyzes the user's emotions and adds appropriate emotional expressions to the document. Specifically, it adds a graph of sales data.

[0312] Finally, the server distributes the approved materials to the relevant parties. High-quality materials that take emotional impact into consideration are efficiently created and distributed.

[0313] Knowledge base operation embodiment

[0314] Accumulation and utilization of knowledge

[0315] The server stores the approved electronic messages and materials in a knowledge base module, which stores the information for future reference. Emotion data analyzed by the emotion engine is also stored in the knowledge base.

[0316] The user searches for and references the information they need from the knowledge base on their device. For example, they search for a past "project progress report." At this time, the emotion engine uses the emotion data in the knowledge base to make emotion-based recommendations.

[0317] Users can efficiently create new electronic messages or documents by utilizing information obtained from the knowledge base. For example, they can create new reports using past templates. In this case, too, they can utilize the emotional information provided by the emotion engine.

[0318] Through these processing steps, the system efficiently automates the creation, approval, and transmission of electronic messages and materials, and also utilizes an emotion engine to realize emotion-sensitive communication.In addition, by utilizing a knowledge base, it is possible to effectively utilize past experience and information.

[0319] The processing flow will be explained below.

[0320] Specific processing flow for email processing

[0321] Step 1:

[0322] A user drafts an electronic message on a terminal, specifically, inputs an invitation email for a meeting into the terminal.

[0323] Step 2:

[0324] The device provides the content of the electronic message to the generative artificial intelligence, specifically, sending the phrase "Schedule a meeting for next Tuesday" to the generative artificial intelligence.

[0325] Step 3:

[0326] The emotion engine recognizes and analyzes the user's emotions. For example, if the user is feeling stressed, the emotion engine will analyze the information.

[0327] Step 4:

[0328] The server receives a response from the generative artificial intelligence, which generates an optimized electronic message based on the input phrase.

[0329] Step 5:

[0330] The server acquires the user's emotion data from the emotion engine and reflects it in an optimized electronic message, for example, adding a phrase such as "This meeting is very important and we look forward to your participation."

[0331] Step 6:

[0332] The server sends the optimized electronic message to the approval workflow module, where the email is added to a queue for review.

[0333] Step 7:

[0334] The user reviews the electronic message at the terminal through an approval workflow module and provides approval or feedback, for example, prompting the addition of a meeting location.

[0335] Step 8:

[0336] The server receives the user's feedback and uses generative AI to reflect the feedback. Specifically, it adds "Meeting location: Meeting Room A."

[0337] Step 9:

[0338] The server uses an emotion engine to double-check the final electronic message and reinforce any necessary emotional expressions.

[0339] Step 10:

[0340] The server then sends the final approved electronic message to the recipient, ensuring that the email is sent with high quality, emotional sensitivity, and approval.

[0341] Specific process flow for creating materials

[0342] Step 1:

[0343] The user starts creating a new document on the device. Specifically, he opens a new document called "Monthly Report."

[0344] Step 2:

[0345] The device provides the generative AI with the requirements for creating documents. Specifically, it inputs "this month's sales data" and "progress of major projects."

[0346] Step 3:

[0347] The emotion engine recognizes and analyzes the user's emotion, for example, if the user is happy, the information will be analyzed by the emotion engine.

[0348] Step 4:

[0349] The server receives the response from the generative AI, which generates materials with the appropriate sections based on the provided requirements.

[0350] Step 5:

[0351] The server retrieves the user's emotional data from the emotion engine and reflects it in the document. Specifically, it adds a phrase such as, "This monthly report shows very good results."

[0352] Step 6:

[0353] The server sends the generated material to the approval workflow module, where it is added to a queue for review.

[0354] Step 7:

[0355] The user reviews the document through the approval workflow module on the terminal and provides approval or feedback, for example, requesting the addition of a sales data graph.

[0356] Step 8:

[0357] The server receives the user's feedback and uses generative AI to reflect the feedback, for example adding a graph of sales data.

[0358] Step 9:

[0359] The server uses an emotion engine to double-check the final material and reinforce any necessary emotional expressions.

[0360] Step 10:

[0361] The server distributes the final approved materials to the relevant parties, ensuring that approved, high-quality, and emotionally sensitive materials are delivered.

[0362] Specific process flow for knowledge base operation

[0363] Step 1:

[0364] The server stores the approved electronic messages and materials in a knowledge base module, which stores the information for future reference.

[0365] Step 2:

[0366] The emotion engine stores the emotional data contained in these approved electronic messages and materials in a knowledge base, allowing for emotion-based search and referencing.

[0367] Step 3:

[0368] The user searches for and references the necessary information from the knowledge base on the terminal. For example, the user searches for a past "project progress report."

[0369] Step 4:

[0370] Users can use information retrieved from the knowledge base to efficiently create new electronic messages or documents, for example, creating new reports using templates from the past.

[0371] Step 5:

[0372] The server updates the knowledge base with newly approved electronic messages and materials and their associated emotional data.

[0373] Step 6:

[0374] An emotion engine analyzes the information in the knowledge base and makes emotion-based recommendations, such as recommending the best email template for a particular situation or emotion.

[0375] Through these processing steps, the system efficiently automates the creation, approval, and transmission of electronic messages and materials, and utilizes an emotion engine to ensure emotion-sensitive communication.It also utilizes a knowledge base to effectively utilize past experience and information.

[0376] Example 2

[0377] 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."

[0378] The traditional process of creating electronic messages and materials requires many manual operations, resulting in inefficiencies and human error. It is also difficult to communicate with recipients in a way that takes their emotions into account, which can lead to inappropriate messaging and a lack of consideration for the recipient. Furthermore, there is no established method for effectively utilizing past data and knowledge, which limits the reusability of materials and messages. A system that can solve these issues is needed.

[0379] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0380] In this invention, the server includes: a means for a user to create an electronic message on a terminal; a means for the terminal to provide the content of the electronic message to a generative AI model; a means for an emotion engine to analyze the user's emotions and provide the data to the generative AI model; a means for the server to receive a response from the generative AI model and automatically optimize the content of the electronic message; a means for the server to send the optimized electronic message to an approval workflow module; a means for a user to review the electronic message in the approval workflow module and provide approval or feedback; a means for the server to receive the feedback and adjust the final electronic message using a regenerative AI; and a means for the server to send the approved electronic message to a recipient. This allows for efficient automation of the creation, approval, and sending of electronic messages and materials, and further enables emotion-conscious communication using an emotion engine.

[0381] An "electronic message" is a communication message in digital form for a user to send through a terminal.

[0382] A "generative AI model" is a model that uses artificial intelligence to generate and optimize text data.

[0383] An "emotion engine" is a system that analyzes emotions from user input data and provides that emotion data.

[0384] "Terminal" means a user-operated device and hardware for creating, editing, and transmitting electronic messages and materials.

[0385] "Server" means a central computer that receives responses from the generative AI model and automates the processing of electronic messages and materials.

[0386] An "approval workflow module" is a system for reviewing user-created electronic messages and materials and providing approval or feedback.

[0387] "Feedback" means any corrections or additional instructions or suggestions provided by a User to an Electronic Message or Material.

[0388] "Knowledge Base Module" means a system for storing approved electronic messages and materials and allowing users to search and reference historical data.

[0389] "Optimization" is the process of improving the content of electronic messages and materials provided by generative AI models, tailoring them to be efficient and emotionally sensitive.

[0390] "Reconciliation" is the process of modifying electronic messages and materials to reflect user feedback.

[0391] A "prompt" is text data and instructions input to a generative AI model to obtain a specific output.

[0392] The present invention is a system that automates the creation and approval process of electronic messages and documents, and realizes emotion-conscious communication by combining it with an emotion engine. This system operates in cooperation with the user, terminal, and server components. Specific embodiments of the present invention are described below.

[0393] Creating and optimizing electronic messages

[0394] A user drafts an electronic message, such as a meeting invitation email, on a terminal. For example, the user types, "Schedule a meeting for next Tuesday." The terminal provides this input to a generative AI (e.g., GPT-3®) in the form of a prompt. At the same time, an emotion engine (e.g., IBM Watson®) analyzes the user's emotions, and this data is also sent to the generative AI.

[0395] The server receives the response from the generative AI and automatically optimizes its content. For example, it receives an optimized message such as, "We've scheduled a meeting for next Tuesday at 10:00 AM. Please reply if you can attend." This message also appropriately reflects the results of user sentiment analysis.

[0396] Approval and feedback

[0397] The server sends the optimized electronic message to the approval workflow module and adds it to a queue for review. The user reviews the electronic message through the approval workflow module at the terminal and provides approval or feedback, such as requesting the addition of a meeting location.

[0398] The server receives the user's feedback and incorporates it using the generative AI. The generative AI and emotion engine run again to add a new element to the message, specifically "Meeting location: Meeting Room A." Finally, the server sends the approved electronic message to the recipient.

[0399] Form of document creation

[0400] A user creates a new document, such as a monthly report, on a terminal. The user inputs this month's sales data and progress on major projects into the terminal. The terminal provides the data necessary to create the document and the results of sentiment analysis to the generative AI, and the server receives the generated document. For example, a report is generated that includes a "sales data section" and a "project progress section."

[0401] Similarly, the server sends the generated material to the approval workflow module, receives user feedback and regenerates it, and finally, the server distributes the approved material to the relevant parties.

[0402] Knowledge base operations

[0403] The server stores approved electronic messages and materials in a knowledge base module, which stores information for future reference. Emotional data analyzed by the emotion engine is also stored in the knowledge base. Users can search and reference the knowledge base from their devices and use past information to efficiently create new electronic messages and materials. Recommendations based on emotions are also made.

[0404] Examples of prompt statements

[0405] 1. Generate a meeting invitation email

[0406] An electronic message typed "Schedule a meeting for next Tuesday." User sentiment: Polite and cooperative.

[0407] 2. Preparation of monthly reports

[0408] Data entered: "This month's sales data: $1 million" and "Major project progress: 80% complete." User emotion: Pride and accomplishment.

[0409] As described above, the present invention is a system that efficiently automates a series of processes from creating, approving, and sending electronic messages and materials, and realizes communication that takes emotions into consideration.

[0410] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0411] Step 1:

[0412] A user drafts an electronic message at a terminal.

[0413] Type: A user types "Schedule a meeting for next Tuesday" into a terminal.

[0414] What it does: A user creates and saves a draft using the text input field on their device.

[0415] Output: The created draft of the electronic message is saved in the terminal.

[0416] Step 2:

[0417] The device provides a draft electronic message to the generative AI model.

[0418] Input: The created draft of the electronic message.

[0419] How it works: The device sends the draft content to the generative AI model via API. At the same time, the emotion engine parses the emotion data from the user's input and sends this emotion data to the generative AI model as well.

[0420] Output: The draft content and sentiment data are fed into the generative AI model.

[0421] Step 3:

[0422] The server receives the response from the generative AI model and automatically optimizes the content of the electronic message.

[0423] Input: Optimized message and sentiment data from the generative AI model.

[0424] Action: The server receives the generated AI model's response and checks the generated optimization message.

[0425] Output: The optimized electronic message is stored on the server.

[0426] Step 4:

[0427] The server sends the optimized electronic message to the approval workflow module.

[0428] Input: Optimized electronic message.

[0429] What happens: The server sends the optimized message to the approval workflow module and adds it to the queue for review.

[0430] Output: A message is added to the Approval Workflow module.

[0431] Step 5:

[0432] A user reviews the electronic message in the approval workflow module and provides approval or feedback.

[0433] Input: Electronic messages added to the Approval Workflow module.

[0434] Action: The user accesses the approval workflow module on their device, checks the electronic message, and optionally enters feedback, such as "Please add a meeting location."

[0435] Output: The approval or feedback is saved in the Approval Workflow module.

[0436] Step 6:

[0437] The server receives user feedback and uses regenerative artificial intelligence to adjust the final electronic message.

[0438] Input: User feedback.

[0439] Action: The server calls the generative AI model again based on the feedback, and uses the emotion engine to generate a new message that reflects the feedback. "Meeting location: Meeting Room A" is added.

[0440] Output: The final reconciled electronic message.

[0441] Step 7:

[0442] The server sends the approved electronic message to the recipient.

[0443] Input: The final reconciled electronic message.

[0444] What happens: The server sends the final message to the email server for delivery to the appropriate recipients.

[0445] Output: The recipient receives the optimized electronic message.

[0446] Step 8:

[0447] The user starts creating a new document on the device.

[0448] Input: The user enters sales data and project progress into the terminal.

[0449] Action: The user uses the input fields on the device to enter the required data and begin creating a document.

[0450] Output: The created draft document is saved on the device.

[0451] Step 9:

[0452] The device provides the generative AI model with the data necessary to create the document.

[0453] Input: Draft of the material created.

[0454] How it works: The device uses an API to send the draft and sentiment data to the generative AI model.

[0455] Output: The outline of the material and content requirements are provided to the generative AI model.

[0456] Step 10:

[0457] The server receives the response from the generative AI model and automatically generates the materials.

[0458] Input: Generated material from a generative AI model.

[0459] How it works: The server receives the response from the generative AI model and automatically generates the final version of the material.

[0460] Output: The automatically generated materials are saved on the server.

[0461] Step 11:

[0462] The server sends the generated materials to an approval workflow module.

[0463] Input: Auto-generated materials.

[0464] What happens: The server sends the document to the approval workflow module and adds it to the queue for review.

[0465] Output: The document is added to the approval workflow module.

[0466] Step 12:

[0467] The user reviews the material in the approval workflow module and provides approval or feedback.

[0468] Input: Materials added to the Approval Workflow module.

[0469] Action: The user reviews the material on their device and provides feedback, such as adding graphs, if necessary.

[0470] Output: The approval or feedback is saved in the Approval Workflow module.

[0471] Step 13:

[0472] The server receives user feedback and uses regenerative artificial intelligence to adjust the final material.

[0473] Input: User feedback.

[0474] Action: The server invokes the regenerative AI model and generates materials that reflect the feedback. A sales data graph is added.

[0475] Output: Final, adjusted materials.

[0476] Step 14:

[0477] The server distributes the approved materials to the relevant parties.

[0478] Input: Final adjusted materials.

[0479] How it works: The server distributes the materials to interested parties via email or shared file services.

[0480] Output: Stakeholders receive the final materials.

[0481] Step 15:

[0482] The server stores the approved electronic messages and materials in a knowledge base module.

[0483] Input: Authorized electronic messages and materials.

[0484] How it works: The server stores these data in a knowledge base module, along with the emotion data.

[0485] Output: Stored electronic messages and materials and emotional data.

[0486] Step 16:

[0487] A user searches and references past electronic messages and documents from the knowledge base.

[0488] Input: The user's search query.

[0489] Action: A user accesses the knowledge base module using a terminal and searches for the desired material.

[0490] Output: Historical electronic messages and materials displayed as search results.

[0491] Step 17:

[0492] Users utilize information from the knowledge base to efficiently create new electronic messages and materials.

[0493] Input: Information retrieved from the knowledge base.

[0494] How it works: The device creates new electronic messages and documents based on templates and data acquired by the user, and also utilizes emotional data provided by the emotion engine.

[0495] Output: New electronic messages or materials created.

[0496] (Application example 2)

[0497] 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."

[0498] In modern companies and factories, the process of creating electronic messages and documents is important, but these tasks face the risk of reduced efficiency due to human error and miscommunication. Furthermore, considering the user's emotions when creating messages and documents is an important factor in improving employee motivation and work efficiency, but conventional systems do not adequately address this issue. Therefore, there is a need for an automated communication and document creation system that takes emotions into account.

[0499] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0500] In this invention, the server includes: means for a user to create an electronic message on a terminal; means for the terminal to provide the content of the electronic message to a generative artificial intelligence; means for the server to receive a response from the generative artificial intelligence and automatically optimize the content of the electronic message; means for the server to send the optimized electronic message to an approval workflow module; means for a user to review the electronic message in the approval workflow module and provide approval or feedback; means for the server to adjust the final electronic message based on the feedback; means for the server to send the approved electronic message to a recipient; means for an emotion engine to analyze the user's emotions and provide the emotion data to the generative artificial intelligence; means for generating an optimized electronic message including the emotion data analyzed by the emotion engine; and means for verifying that the optimized electronic message includes emotion information. This enables efficient creation and distribution of high-quality electronic messages and materials that take emotions into consideration.

[0501] An "electronic message" is a digital message sent by electronic means that conveys information primarily in text form.

[0502] A "terminal" is an electronic device operated by a user, such as a PC, smartphone, or tablet.

[0503] "Generative AI" is an AI system that has the ability to generate natural language sentences and content based on input data provided by the user.

[0504] A "server" is a computer system that provides data and services to various computers on a network.

[0505] "Response" refers to the response or output result returned by generative artificial intelligence in response to input data provided by it.

[0506] The "approval workflow module" is a process management system for reviewing created messages and materials and providing approval and feedback.

[0507] "Feedback" refers to any opinions or suggestions provided as a result of a review.

[0508] The "emotion engine" is a system that analyzes the user's emotional state and provides the results as data.

[0509] "Emotion data" is digital data that represents the user's emotional state as analyzed by the emotion engine.

[0510] A "knowledge base" is a database system that stores previously created electronic messages, documents, and related data, and allows them to be searched and referenced.

[0511] An "optimized electronic message" is an electronic message that is generated by a generative artificial intelligence based on user input data and adjusted as necessary to reflect emotional data.

[0512] "Materials" refers to documents, reports, etc. that compile information and data collected for a specific purpose.

[0513] "Overall framework and content requirements" refers to data and information that indicates the main items and requirements needed to create the materials.

[0514] "Automatic generation" refers to the creation of materials based on input data specified by generative artificial intelligence, without manual intervention.

[0515] "Stakeholders" refers to the intended recipients of the messages and materials created.

[0516] This invention is a system for supporting the creation of emotion-sensitive electronic messages and materials, allowing users to efficiently generate and optimize electronic messages and materials using a terminal and provide emotion-based feedback.

[0517] System configuration

[0518] Hardware and Software

[0519] 1. Terminal: An electronic device that a user operates. Examples include computers, smartphones, and tablets.

[0520] 2. Server: A computer system that provides data and services to devices on a network. It processes and stores data, runs AI models, and manages workflow modules.

[0521] 3. Generative AI: Utilizing OpenAI (registered trademark) APIs (e.g., text-davinci-003), etc., it generates optimized messages and materials based on input data provided by the user.

[0522] 4. Emotion engine: A system that analyzes the user's emotional state and provides it as data. For example, a sentiment analysis tool that uses NLP technology.

[0523] 5. Approval Workflow Module: A process management system that manages user reviews and feedback.

[0524] Processing flow details

[0525] Create an electronic message

[0526] 1. User input: The user inputs a draft of an electronic message at a terminal, for example, "Please start the next step."

[0527] 2. Emotion Analysis: The emotion engine analyzes the user's current emotional state and provides the emotion data to the generative AI. For example, the emotion engine analyzes that the user is "tired."

[0528] 3. AI optimization: Generative AI optimizes messages based on input drafts and sentiment data. For example, "Thank you for your hard work today. Please begin the next step."

[0529] 4. Approval Workflow: The optimized electronic message is sent to the approval workflow module, where users review the content and provide approval or feedback.

[0530] 5. Incorporating feedback: The server uses the feedback to adjust the final electronic message using generative artificial intelligence.

[0531] 6. Send: The server sends the final approved electronic message to the recipient.

[0532] Creating materials

[0533] 1. User input: The user starts creating a new document on the terminal and enters the outline and content requirements of the document. For example, they enter sales data and progress status to create a "monthly report."

[0534] 2. Emotion analysis: The emotion engine analyzes the user's emotional state and provides the results to the generative AI.

[0535] 3. AI generation: Generative AI automatically generates documents using the input data and sentiment data provided. For example, a report containing a "sales data section" and a "project progress section" is generated.

[0536] 4. Approval Workflow: The generated materials are sent to the approval workflow module, where users review the content and provide approval or feedback.

[0537] 5. Incorporating feedback: The server uses the feedback to adjust the final material by reusing the generative AI.

[0538] 6. Distribution: Finally, the server distributes the approved materials to the relevant parties.

[0539] Prompt Sentence Examples

[0540] A typical example of a prompt provided to a generative AI model is as follows:

[0541] Instructions: Begin the following steps:

[0542] Emotions: Feeling tired

[0543] The system enables the efficient creation and delivery of high-quality, emotionally sensitive electronic messages and materials.

[0544] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0545] Step 1:

[0546] Input: A user enters a draft of an electronic message at a terminal.

[0547] Operation: A user uses a terminal to input a draft of an electronic message such as "Please begin the next step."

[0548] Output: The draft content is displayed on the terminal.

[0549] Step 2:

[0550] Input: Draft content and user sentiment data.

[0551] Operation: The emotion engine analyzes the user's emotions along with the draft content entered on the device. For example, the emotion data that the user is "feeling tired" is analyzed.

[0552] Output: The analyzed emotion data and draft content are provided to the generative AI.

[0553] Step 3:

[0554] Input: Sentiment analysis results and draft content.

[0555] How it works: The server sends the analysis results and a prompt for the draft content to the generative AI. The generative AI then optimizes the electronic message based on this. For example, it might be optimized to say something like, "Thank you for your hard work today. Please begin the next step."

[0556] Output: The optimized electronic message is returned to the server.

[0557] Step 4:

[0558] Input: Optimized electronic message.

[0559] Action: The server sends this optimized electronic message to the approval workflow module.

[0560] Output: The optimized message is displayed in the approval workflow module.

[0561] Step 5:

[0562] Input: Optimized electronic message.

[0563] How it works: A user uses the approval workflow module to review the optimized electronic message and provide approval or feedback as needed, for example, by providing feedback such as "Please clarify the details."

[0564] Output: The approval or feedback is sent to the server.

[0565] Step 6:

[0566] Input: Approval or feedback.

[0567] How it works: Based on the feedback, the server re-uses the generative AI to adjust the electronic message, e.g., regenerating it with more detail, such as "Please start the next step at 2 PM."

[0568] Output: The final regenerated electronic message is stored on the server.

[0569] Step 7:

[0570] Input: The final approved electronic message.

[0571] Action: The server sends the final approved electronic message to the recipient.

[0572] Output: The recipient receives the final electronic message and sees its contents.

[0573] This series of processes allows for the efficient creation and transmission of high-quality, emotionally sensitive electronic messages.

[0574] 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.

[0575] 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.

[0576] 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.

[0577] [Second embodiment]

[0578] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0579] 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.

[0580] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0581] The 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.

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

[0583] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0584] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0585] Fig. 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.

[0586] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0587] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0588] In the 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.

[0589] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0590] This invention relates to a system that automates the creation and approval process of electronic messages (emails) and documents, and utilizes a knowledge base. This system realizes efficient communication by linking together the components of the server, terminal, and user.

[0591] Mail Processing Embodiment

[0592] Creating and optimizing electronic messages

[0593] The user begins to compose an electronic message on the terminal. For example, if the user wants to send an email inviting a meeting, he or she inputs the content "Schedule a meeting for next Tuesday" into the terminal. This input is provided to the generative artificial intelligence.

[0594] The server receives the response from the generative AI and automatically optimizes its content. The generative AI generates an optimized electronic message saying, "We've scheduled a meeting for next Tuesday at 10:00 AM. Please respond if you can attend." The server then sends this optimized electronic message to the approval workflow module.

[0595] Approval and feedback

[0596] The user reviews the optimized electronic message in the approval workflow module and provides feedback as needed. For example, if the user provides feedback to add a meeting location, the server adds "Conference Room A" to the email based on the feedback.

[0597] Finally, the server sends the approved electronic message to the recipient, effectively sending unified, organized email.

[0598] Form of document creation

[0599] Document creation and automatic generation

[0600] The user starts creating a new document on their device. For example, when creating a "monthly report," the user inputs this month's sales data and progress on major projects into the device. This input information is provided to the generative AI.

[0601] The server receives the response from the generative AI and automatically generates the document. For example, the generative AI creates a report that includes a "sales data section" and a "project progress section." The server then sends this automatically generated document to the approval workflow module.

[0602] Approval and feedback on materials

[0603] The user reviews the document in the approval workflow module and provides feedback. For example, if a user requests that a sales data graph be added to the document, the server reflects that feedback and generates the final document with the graph added.

[0604] Finally, the server distributes the approved materials to the relevant parties, thereby enabling uniform, high-quality materials to be created and distributed efficiently.

[0605] Knowledge base operations

[0606] Accumulation and utilization of knowledge

[0607] The server stores approved electronic messages and materials in a knowledge base module, allowing users to efficiently search and reference past knowledge. When users create new electronic messages or materials by utilizing existing knowledge, they can work quickly and efficiently.

[0608] For example, users can search and refer to past "project progress reports" to quickly find templates and reference information needed to create new reports, significantly reducing time and effort.

[0609] The system of the present invention is expected to significantly improve the efficiency of the process of creating and approving electronic messages and materials, and by making effective use of knowledge, to improve the speed and quality of business communications.

[0610] The processing flow will be explained below.

[0611] Specific processing flow for email processing

[0612] Step 1:

[0613] A user drafts an electronic message on a terminal, specifically, inputs an invitation email for a meeting into the terminal.

[0614] Step 2:

[0615] The device provides the content of the electronic message to the generative artificial intelligence, specifically, sending the phrase "Schedule a meeting for next Tuesday" to the generative artificial intelligence.

[0616] Step 3:

[0617] The server receives a response from the generative artificial intelligence, which generates an optimized electronic message based on the input phrase.

[0618] Step 4:

[0619] The server reviews and formats the optimized electronic message received from the generative AI, improving grammar and expression to say, "We've scheduled a meeting for next Tuesday at 10 a.m. Please reply if you can attend."

[0620] Step 5:

[0621] The server sends the optimized electronic message to the approval workflow module, where the email is added to a queue for review.

[0622] Step 6:

[0623] The user reviews the electronic message at the terminal through an approval workflow module and provides approval or feedback, for example, prompting the addition of a meeting location.

[0624] Step 7:

[0625] The server receives the user's feedback and uses generative AI to reflect the feedback. Specifically, it adds "Meeting location: Meeting Room A."

[0626] Step 8:

[0627] The server then sends the final approved electronic message to the recipient, resulting in a high-quality, approved email.

[0628] Specific process flow for creating materials

[0629] Step 1:

[0630] The user starts creating a new document on the device. Specifically, he opens a new document called "Monthly Report."

[0631] Step 2:

[0632] The device provides the generative AI with the requirements for creating documents. Specifically, it inputs "this month's sales data" and "progress of major projects."

[0633] Step 3:

[0634] The server receives the response from the generative AI, which generates materials with the appropriate sections based on the provided requirements.

[0635] Step 4:

[0636] The server formats the data received from the generative AI, generating a report that includes a sales data section and a project progress section.

[0637] Step 5:

[0638] The server sends the generated material to the approval workflow module, where it is added to a queue for review.

[0639] Step 6:

[0640] The user reviews the document through the approval workflow module on the terminal and provides approval or feedback, for example, requesting the addition of a sales data graph.

[0641] Step 7:

[0642] The server receives the user's feedback and uses generative AI to reflect the feedback. Specifically, it adds a graph of sales data.

[0643] Step 8:

[0644] The server distributes the final approved materials to the relevant parties, thereby ensuring that approved, high-quality materials are distributed.

[0645] Specific process flow for knowledge base operation

[0646] Step 1:

[0647] The server stores the approved electronic messages and materials in a knowledge base module, which stores the information for future reference.

[0648] Step 2:

[0649] The user searches for and references the necessary information from the knowledge base on the terminal. Specifically, the user searches for past "project progress reports."

[0650] Step 3:

[0651] Users can use information retrieved from the knowledge base to efficiently create new electronic messages or documents, for example, creating new reports using templates from the past.

[0652] Through these processing steps, the system efficiently automates the creation, approval, and delivery of electronic messages and materials, and also effectively leverages a knowledge base.

[0653] Example 1

[0654] 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."

[0655] The traditional process of creating and approving electronic messages and documents was often done manually, resulting in inefficiency and a decline in the quality of communication. Furthermore, reusing past data required manual searching and referencing, which was time-consuming and labor-intensive.

[0656] 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.

[0657] In this invention, the server includes: a means for a user to create an electronic message on a terminal; a means for the terminal to provide the content of the electronic message to a generative AI model; a means for the server to receive a response from the generative AI model and automatically optimize the content of the electronic message; a means for the server to send the optimized electronic message to an approval workflow module; a means for a user to review the electronic message in the approval workflow module and provide approval or feedback; a means for the server to adjust the final electronic message based on the feedback; a means for the server to send the approved electronic message to a recipient; and a means for the server to store the approved electronic message in a knowledge base module. This streamlines the creation and approval process of electronic messages and materials, enabling advanced communication that efficiently utilizes knowledge.

[0658] "User" means any person or entity that operates the System and creates, reviews, provides feedback on, and approves electronic messages and materials.

[0659] A "terminal" is an electronic device such as a computer, smartphone, or tablet that is operated by a user.

[0660] "Electronic Message" means a message sent or received by email or other means of electronic communication.

[0661] A "generative AI model" is an artificial intelligence model that generates text data based on prompts entered by a user.

[0662] A "server" is a machine that communicates with devices over a network to provide data to a generative AI model and receive results.

[0663] A "response" is the reply text that a generative AI model generates in response to a prompt.

[0664] "Optimization" refers to converting responses received from a generative AI model into a suitable format for electronic messages or documents.

[0665] An "approval workflow module" is a software module for accepting confirmation, feedback, and approval from users.

[0666] "Feedback" means any instruction for correction or improvement given by a user to a generated electronic message or material.

[0667] "Knowledge Base Module" means a software module for storing approved electronic messages and materials for future retrieval and reuse.

[0668] The present invention is a system for automating the creation and approval process of electronic messages and documents and utilizing a knowledge base. This system is composed of a server, terminals, a generative AI model, and various software modules. Each component and its operation are described in detail below.

[0669] Creating and optimizing electronic messages

[0670] A user uses a device to compose an electronic message. For example, when creating a meeting invitation email, the user types something like "Schedule a meeting for next Tuesday." This input is formatted by the device as a prompt and provided to a generative AI model (e.g., GPT-4).

[0671] The server receives the response from the generative AI model, analyzes its content, and generates an optimized message, such as "We've scheduled a meeting for next Tuesday at 10:00 AM. Please reply if you can attend."

[0672] The server then sends the generated optimization message to the approval workflow module, where the user can review the message and provide feedback if necessary, for example, to add "Conference Room A."

[0673] The server adjusts the final electronic message based on the feedback and sends the approved message to the recipient, a process that ensures a uniform message is sent efficiently.

[0674] Prompt Sentence Examples

[0675] Set up a meeting for next Tuesday

[0676] Document creation and automatic generation

[0677] The user starts creating a new document using the device. For example, to create a "monthly report," the user inputs this month's sales data and the progress of major projects into the device. This input information is provided to the generative AI model as a prompt.

[0678] The server receives the response from the generative AI model and automatically generates the document. For example, a report containing a "sales data section" and a "project progress section" is generated. The server sends this report to the approval workflow module and notifies the user.

[0679] The user reviews the document in the approval workflow module and provides feedback as needed, such as "add a chart of sales data."

[0680] The server then distributes the final revised documents to the relevant parties, ensuring efficient distribution of high-quality, uniform documents.

[0681] Prompt Sentence Examples

[0682] Enter this month's sales data and progress on major projects

[0683] Knowledge base operations

[0684] The server stores approved electronic messages and materials in a knowledge base module, allowing users to access the knowledge base from their terminals and search and refer to past electronic messages and materials.

[0685] Users can efficiently create new electronic messages and documents by utilizing referenced knowledge. For example, by referencing a past "Project Progress Report" and using it as a template for a new report, users can significantly reduce time and effort.

[0686] This system is expected to significantly streamline the process of creating and approving electronic messages and documents, and by making effective use of knowledge, to result in faster, higher-quality business communications.

[0687] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0688] Creating and optimizing electronic messages

[0689] Step 1:

[0690] A user begins composing an electronic message on a terminal, for example, typing "Schedule a meeting for next Tuesday." The input data is formatted as a prompt sentence.

[0691] Step 2:

[0692] The device provides the prompt text entered by the user to the generative AI model. Specifically, it sends the prompt text "Schedule a meeting for next Tuesday" to the generative AI model.

[0693] Step 3:

[0694] The server receives a response from the generative AI model. For example, the generative AI model generates an optimized message such as, "We've scheduled a meeting for next Tuesday at 10:00 AM. Please reply if you can attend." This response is then analyzed to obtain the optimized message.

[0695] Step 4:

[0696] The server sends the optimized electronic message to the approval workflow module, specifically, stores the generated optimized message in a database of the approval workflow module, and notifies the user.

[0697] Step 5:

[0698] The user checks the message in the approval workflow module and provides feedback if necessary. For example, the user enters feedback such as adding "Conference Room A" and submits it to the approval workflow module.

[0699] Step 6:

[0700] The server receives feedback from the approval workflow module and adjusts the final electronic message, for example, adding "Conference Room A" based on the feedback, and completes the final message.

[0701] Step 7:

[0702] The server sends the approved electronic message to the recipient, sends the final electronic message based on the recipient list, and notifies the user.

[0703] Step 8:

[0704] The server stores the approved electronic message in the knowledge base module, and accumulates the final message data in the knowledge base database.

[0705] Document creation and automatic generation

[0706] Step 1:

[0707] The user starts creating a document on the terminal. For example, to create a "monthly report," the user inputs this month's sales data and the progress of major projects.

[0708] Step 2:

[0709] The device generates the input information as a prompt and provides it to the generative AI model. Specifically, the prompt "Enter this month's sales data and progress on major projects" is sent to the generative AI model.

[0710] Step 3:

[0711] The server receives a response from the generative AI model. The generated document contains a "sales data section" and a "project progress section." This response is analyzed to obtain the automatically generated document.

[0712] Step 4:

[0713] The server sends the automatically generated material to the approval workflow module, stores the generated material in the database of the approval workflow module, and notifies the user.

[0714] Step 5:

[0715] The user reviews the material in the approval workflow module and provides feedback. For example, the user inputs an instruction to "add a graph of sales data" and submits it to the approval workflow module.

[0716] Step 6:

[0717] The server receives feedback from the approval workflow module and adjusts the final document, adding graphs based on the feedback and completing the final document.

[0718] Step 7:

[0719] The server distributes the approved final documents to the parties concerned, sends the final documents based on the parties list, and notifies the users.

[0720] Step 8:

[0721] The server stores the approved materials in the knowledge base module, and accumulates the final material data in the knowledge base database.

[0722] Knowledge base operations

[0723] Step 1:

[0724] The server stores approved electronic messages and materials in a knowledge base module, organizing and centralizing the data.

[0725] Step 2:

[0726] Users can access the knowledge base from their devices to search and refer to past electronic messages and documents, quickly finding the information they need.

[0727] Step 3:

[0728] Users can leverage knowledge to efficiently create new electronic messages and materials, and save time by leveraging existing templates and reference information.

[0729] Through these steps, the system streamlines the creation and approval process of electronic messages and documents, and makes effective use of knowledge.

[0730] (Application example 1)

[0731] 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."

[0732] In the traditional manufacturing industry, the creation and approval process of work instructions was often performed manually, resulting in time-consuming and labor-intensive tasks. Furthermore, incorporating feedback required significant manual operations, increasing the likelihood of errors and delays. This often resulted in inconsistent quality of the generated instructions. Furthermore, past instructions and documents were not efficiently stored and searched in a knowledge base, resulting in insufficient utilization of past knowledge. To address these issues, the present invention provides a system that automates the creation and approval process of electronic messages and documents, enabling efficient utilization of knowledge.

[0733] 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.

[0734] In this invention, the server includes: a means for a user to create an electronic message on a terminal; a means for the terminal to provide the content of the electronic message to a generative artificial intelligence; a means for the server to receive a response from the generative artificial intelligence and automatically optimize the content of the electronic message; a means for transmitting the electronic message to an approval workflow module; a means for a user to review and approve or provide feedback in the approval workflow module; a means for adjusting the final electronic message based on the feedback; a means for transmitting the approved electronic message to a recipient; a means for receiving a response from the generative artificial intelligence to generate an automated work instruction and generating an optimized work instruction; a means for a user to review and approve or provide feedback in the approval workflow module; a means for adjusting the final work instruction based on the feedback and storing the optimized work instruction in a knowledge base. This reduces errors and delays through automated processes and enables efficient work by utilizing past knowledge while maintaining consistent quality of electronic messages and work instructions.

[0735] A "terminal" is a device that a user operates to create, edit, and input electronic messages, documents, and the like.

[0736] "Generative AI" is an AI system that generates optimized electronic messages and materials based on user input.

[0737] A "server" is a central processing unit that receives responses from generative artificial intelligence and performs various data processing and transmission.

[0738] "Approval Workflow Module" means a software module that manages the process of providing approval or feedback on the content of an electronic message or document.

[0739] "Feedback" refers to additional information or requests for corrections provided by a user through the approval workflow module.

[0740] A "knowledge base" is a database that stores and manages information such as past electronic messages and documents, allowing users to efficiently search and reference them.

[0741] A "work instruction manual" is a document that details the specific work content and procedures for manufacturing robots and workers.

[0742] A "prompt sentence" is an input sentence for obtaining a specific output from a generative artificial intelligence.

[0743] This invention is a system that automates the creation and approval process of electronic messages and documents, utilizing generative artificial intelligence and a knowledge base. This system operates in cooperation with a user terminal, a server that acts as a central processing unit, and an approval workflow module.

[0744] Mail Processing Embodiment

[0745] Creating and optimizing electronic messages

[0746] A user begins to compose an electronic message on a terminal. For example, when the user wants to send an invitation email for a meeting, the user inputs the content "Schedule a meeting for next Tuesday" into the terminal. This input is provided to the generative AI. The server receives a response from the generative AI and automatically optimizes the content. The generative AI generates an optimized electronic message saying "We have scheduled a meeting for next Tuesday at 10:00 AM. Please reply if you can attend." The server then sends this optimized electronic message to the approval workflow module.

[0747] Approval and feedback

[0748] The user reviews the optimized electronic message in the approval workflow module and provides feedback as needed. For example, if the user provides feedback to add a meeting location, the server adds "Conference Room A" to the email based on that feedback. Finally, the server sends the approved electronic message to the recipient, thereby efficiently sending a well-organized email.

[0749] Form of document creation

[0750] Document creation and automatic generation

[0751] A user starts creating a new document on a terminal. For example, when creating a "monthly report," the user inputs this month's sales data and the progress of major projects into the terminal. This input information is provided to the generative AI. The server receives a response from the generative AI and automatically generates the document. For example, the generative AI creates a report that includes a "sales data section" and a "project progress section." The server sends this automatically generated document to an approval workflow module.

[0752] Approval and feedback on materials

[0753] Users review documents and provide feedback in the approval workflow module. For example, if a user requests that a sales data graph be added to the document, the server incorporates that feedback and generates the final document with the graph added. Finally, the server distributes the approved document to the relevant parties. This ensures that consistent, high-quality documents are created and distributed efficiently.

[0754] Generating work instructions for factory robots

[0755] Create and approve work orders

[0756] The user starts creating a work instruction on a terminal. For example, if the work instruction for a manufacturing robot is to "assemble parts A and B," this content is provided to the generative AI. The server receives the response from the generative AI and generates an optimized work instruction. This is then sent to the approval workflow module.

[0757] Users review the instructions in the approval workflow module and provide approval or feedback. If the user provides feedback, the server adjusts and generates the final work instructions based on that feedback. This final work instruction is saved in the knowledge base. This allows for efficient work with fewer errors by utilizing past knowledge.

[0758] As a specific example, if the user inputs "Today's manufactured product will be the assembly of parts A and B. 10 parts A and 20 parts B will be used. Quality checks will be performed," the generated work instruction will be "Today's manufactured product will be the assembly of parts A (10 pieces) and parts B (20 pieces). Please be sure to perform quality checks."

[0759] Example prompt sentence:

[0760] Create a work instruction for your manufacturing robot:

[0761] Today's production involves assembling parts A and B. 10 parts A and 20 parts B are used. Quality check is performed.

[0762] Prepared instructions:

[0763] The present invention automates the process of creating electronic messages, documents, and even work instructions for factory robots, and ensures consistent quality, enabling efficient work with fewer errors and delays.

[0764] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0765] Step 1:

[0766] The user begins composing an electronic message on a terminal. The user types something like "Schedule a meeting for next Tuesday." This input is provided to the generative artificial intelligence as a prompt.

[0767] Input: Message content created by the user

[0768] Output: Prompt sentence to be provided to the generative AI

[0769] Step 2:

[0770] The server sends the prompt to the generative AI and receives a response, which generates an optimized electronic message based on the input: "We've scheduled a meeting for next Tuesday at 10:00 AM. Please reply if you can attend."

[0771] Input: prompt statement

[0772] Output: Optimized electronic message

[0773] Step 3:

[0774] The server sends the optimized electronic message to an approval workflow module, where the electronic message awaits approval or feedback.

[0775] Input: Optimized electronic message

[0776] Output: Waiting for confirmation in the approval workflow module

[0777] Step 4:

[0778] A user reviews the electronic message through the approval workflow module and provides feedback if necessary, for example, "Please add conference room A."

[0779] Input: Feedback

[0780] Output: Feedback content

[0781] Step 5:

[0782] The server adjusts the electronic message based on the feedback, specifically generating a final electronic message with "Conference Room A" added.

[0783] Input: Feedback

[0784] Output: Final corrected electronic message

[0785] Step 6:

[0786] The server sends the final approved electronic message to the recipient, which delivers the completed electronic message to the recipient.

[0787] Input: Final electronic message

[0788] Output: Email sent to recipient

[0789] Step 7:

[0790] The user starts creating a new document on the device. For example, to create a "monthly report," the user inputs this month's sales data and the progress of major projects into the device. This input is provided to the generative AI.

[0791] Input: Original data for document creation

[0792] Output: Prompt sentence to be provided to the generative AI

[0793] Step 8:

[0794] The server receives the response from the generative AI and automatically generates the document, which then creates a report that includes sections on sales data and project progress.

[0795] Input: prompt statement

[0796] Output: Generated material

[0797] Step 9:

[0798] The server sends the generated material to an approval workflow module, where it awaits approval or feedback.

[0799] Input: Generated material

[0800] Output: Waiting for confirmation in the approval workflow module

[0801] Step 10:

[0802] Users review the materials through an approval workflow module and provide feedback, such as "Please add a chart of the sales data."

[0803] Input: Feedback

[0804] Output: Feedback content

[0805] Step 11:

[0806] The server adjusts the materials based on the feedback, specifically generating the final materials with the sales data graph added.

[0807] Input: Feedback

[0808] Output: Final revised document

[0809] Step 12:

[0810] The server distributes the final approved documents to the relevant parties, ensuring that consistent, high-quality documents are delivered to the relevant parties.

[0811] Input: Final material

[0812] Output: Materials distributed to stakeholders

[0813] Step 13:

[0814] The user begins creating work instructions for a manufacturing robot on a terminal. For example, they input, "Today's production task is to assemble parts A and B." This input is provided to the generative artificial intelligence.

[0815] Input: Work instruction source data

[0816] Output: Prompt sentence to be provided to the generative AI

[0817] Step 14:

[0818] The server receives the response from the generative AI and generates an optimized work instruction. The generative AI generates the following: "Today's production involves assembling parts A (10 units) and B (20 units). Please be sure to perform a quality check."

[0819] Input: prompt statement

[0820] Output: Optimized work instructions

[0821] Step 15:

[0822] The server sends the optimized work order to an approval workflow module, where the work order awaits approval or feedback.

[0823] Input: Optimized Work Instruction

[0824] Output: Waiting for confirmation in the approval workflow module

[0825] Step 16:

[0826] A user reviews the work order in the approval workflow module and provides feedback, such as "Please also add a quality check item."

[0827] Input: Feedback

[0828] Output: Feedback content

[0829] Step 17:

[0830] The server adjusts the work instructions based on the feedback, specifically generating the final work instructions with the "quality check items" added.

[0831] Input: Feedback

[0832] Output: Final revised work instructions

[0833] Step 18:

[0834] The server saves the final approved work order in the knowledge base, which accumulates historical data for future work.

[0835] Input: Final Work Order

[0836] Output: Work instructions stored in the knowledge base

[0837] These steps automate the creation and approval process for electronic messages, documents, and work instructions, facilitating effective knowledge utilization.

[0838] 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.

[0839] This invention combines a knowledge-based system that automates the creation and approval process of electronic messages (emails) and documents with an emotion engine. This system, which involves the collaboration of server, terminal, and user components, realizes efficient and emotion-sensitive communication.

[0840] Mail Processing Embodiment

[0841] Creating and optimizing electronic messages

[0842] A user creates a draft of an electronic message on a terminal. For example, if the user wants to send an invitation email to a meeting, the user inputs the content "Schedule a meeting for next Tuesday" into the terminal.

[0843] The terminal provides the contents of the electronic message to the generative AI, and the emotion engine analyzes the user's emotions. This input data and emotion data are provided to the generative AI.

[0844] The server receives the response from the generative AI and automatically optimizes its content. For example, the generative AI generates an optimized message such as, "We have scheduled a meeting for next Tuesday at 10:00 AM. Please reply if you can attend." The user's emotions, analyzed by the emotion engine, are also reflected in this message.

[0845] Approval and feedback

[0846] The server sends the optimized electronic message to the approval workflow module, which adds it to a queue for review.

[0847] The user reviews the electronic message at the terminal through an approval workflow module and provides approval or feedback, for example, prompting the addition of a meeting location.

[0848] The server receives the user's feedback and uses regenerative artificial intelligence to reflect the feedback. At this time, the emotion engine also analyzes the user's emotions and adds appropriate emotional expressions. Specifically, it adds "Meeting location: Meeting Room A."

[0849] Finally, the server sends the approved electronic message to the recipient, resulting in efficient delivery of high-quality, sensitive emails.

[0850] Form of document creation

[0851] Document creation and automatic generation

[0852] The user starts creating a new document on the terminal. For example, when creating a "monthly report," the user inputs this month's sales data and progress on major projects into the terminal.

[0853] The device provides the requirements for creating documents to the generative AI. The emotion engine also analyzes the user's emotions. This data and emotional information are provided to the generative AI.

[0854] The server receives the response from the generative AI and automatically generates the materials. Specifically, it creates a report that includes a "sales data section" and a "project progress section." At this time, expressions based on the emotions analyzed by the emotion engine are applied.

[0855] Approval and feedback on materials

[0856] The server sends the generated document to the approval workflow module, where it is added to a queue for review.

[0857] The user reviews the document through the approval workflow module on the terminal and provides approval or feedback, for example, requesting the addition of a sales data graph.

[0858] The server receives user feedback and uses regenerative artificial intelligence to reflect the feedback. The emotion engine analyzes the user's emotions and adds appropriate emotional expressions to the document. Specifically, it adds a graph of sales data.

[0859] Finally, the server distributes the approved materials to the relevant parties. High-quality materials that take emotional impact into consideration are efficiently created and distributed.

[0860] Knowledge base operation embodiment

[0861] Accumulation and utilization of knowledge

[0862] The server stores the approved electronic messages and materials in a knowledge base module, which stores the information for future reference. Emotion data analyzed by the emotion engine is also stored in the knowledge base.

[0863] The user searches for and references the information they need from the knowledge base on their device. For example, they search for a past "project progress report." At this time, the emotion engine uses the emotion data in the knowledge base to make emotion-based recommendations.

[0864] Users can efficiently create new electronic messages or documents by utilizing information obtained from the knowledge base. For example, they can create new reports using past templates. In this case, too, they can utilize the emotional information provided by the emotion engine.

[0865] Through these processing steps, the system efficiently automates the creation, approval, and transmission of electronic messages and materials, and also utilizes an emotion engine to realize emotion-sensitive communication.In addition, by utilizing a knowledge base, it is possible to effectively utilize past experience and information.

[0866] The processing flow will be explained below.

[0867] Specific processing flow for email processing

[0868] Step 1:

[0869] A user drafts an electronic message on a terminal, specifically, inputs an invitation email for a meeting into the terminal.

[0870] Step 2:

[0871] The device provides the content of the electronic message to the generative artificial intelligence, specifically, sending the phrase "Schedule a meeting for next Tuesday" to the generative artificial intelligence.

[0872] Step 3:

[0873] The emotion engine recognizes and analyzes the user's emotions. For example, if the user is feeling stressed, the emotion engine will analyze the information.

[0874] Step 4:

[0875] The server receives a response from the generative artificial intelligence, which generates an optimized electronic message based on the input phrase.

[0876] Step 5:

[0877] The server acquires the user's emotion data from the emotion engine and reflects it in an optimized electronic message, for example, adding a phrase such as "This meeting is very important and we look forward to your participation."

[0878] Step 6:

[0879] The server sends the optimized electronic message to the approval workflow module, where the email is added to a queue for review.

[0880] Step 7:

[0881] The user reviews the electronic message at the terminal through an approval workflow module and provides approval or feedback, for example, prompting the addition of a meeting location.

[0882] Step 8:

[0883] The server receives the user's feedback and uses generative AI to reflect the feedback. Specifically, it adds "Meeting location: Meeting Room A."

[0884] Step 9:

[0885] The server uses an emotion engine to double-check the final electronic message and reinforce any necessary emotional expressions.

[0886] Step 10:

[0887] The server then sends the final approved electronic message to the recipient, ensuring that the email is sent with high quality, emotional sensitivity, and approval.

[0888] Specific process flow for creating materials

[0889] Step 1:

[0890] The user starts creating a new document on the device. Specifically, he opens a new document called "Monthly Report."

[0891] Step 2:

[0892] The device provides the generative AI with the requirements for creating documents. Specifically, it inputs "this month's sales data" and "progress of major projects."

[0893] Step 3:

[0894] The emotion engine recognizes and analyzes the user's emotion, for example, if the user is happy, the information will be analyzed by the emotion engine.

[0895] Step 4:

[0896] The server receives the response from the generative AI, which generates materials with the appropriate sections based on the provided requirements.

[0897] Step 5:

[0898] The server retrieves the user's emotional data from the emotion engine and reflects it in the document. Specifically, it adds a phrase such as, "This monthly report shows very good results."

[0899] Step 6:

[0900] The server sends the generated material to the approval workflow module, where it is added to a queue for review.

[0901] Step 7:

[0902] The user reviews the document through the approval workflow module on the terminal and provides approval or feedback, for example, requesting the addition of a sales data graph.

[0903] Step 8:

[0904] The server receives the user's feedback and uses generative AI to reflect the feedback, for example adding a graph of sales data.

[0905] Step 9:

[0906] The server uses an emotion engine to double-check the final material and reinforce any necessary emotional expressions.

[0907] Step 10:

[0908] The server distributes the final approved materials to the relevant parties, ensuring that approved, high-quality, and emotionally sensitive materials are delivered.

[0909] Specific process flow for knowledge base operation

[0910] Step 1:

[0911] The server stores the approved electronic messages and materials in a knowledge base module, which stores the information for future reference.

[0912] Step 2:

[0913] The emotion engine stores the emotional data contained in these approved electronic messages and materials in a knowledge base, allowing for emotion-based search and referencing.

[0914] Step 3:

[0915] The user searches for and references the necessary information from the knowledge base on the terminal. For example, the user searches for a past "project progress report."

[0916] Step 4:

[0917] Users can use information retrieved from the knowledge base to efficiently create new electronic messages or documents, for example, creating new reports using templates from the past.

[0918] Step 5:

[0919] The server updates the knowledge base with newly approved electronic messages and materials and their associated emotional data.

[0920] Step 6:

[0921] An emotion engine analyzes the information in the knowledge base and makes emotion-based recommendations, such as recommending the best email template for a particular situation or emotion.

[0922] Through these processing steps, the system efficiently automates the creation, approval, and transmission of electronic messages and materials, and utilizes an emotion engine to ensure emotion-sensitive communication.It also utilizes a knowledge base to effectively utilize past experience and information.

[0923] Example 2

[0924] 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."

[0925] The traditional process of creating electronic messages and materials requires many manual operations, resulting in inefficiencies and human error. It is also difficult to communicate with recipients in a way that takes their emotions into account, which can lead to inappropriate messaging and a lack of consideration for the recipient. Furthermore, there is no established method for effectively utilizing past data and knowledge, which limits the reusability of materials and messages. A system that can solve these issues is needed.

[0926] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0927] In this invention, the server includes: a means for a user to create an electronic message on a terminal; a means for the terminal to provide the content of the electronic message to a generative AI model; a means for an emotion engine to analyze the user's emotions and provide the data to the generative AI model; a means for the server to receive a response from the generative AI model and automatically optimize the content of the electronic message; a means for the server to send the optimized electronic message to an approval workflow module; a means for a user to review the electronic message in the approval workflow module and provide approval or feedback; a means for the server to receive the feedback and adjust the final electronic message using a regenerative AI; and a means for the server to send the approved electronic message to a recipient. This allows for efficient automation of the creation, approval, and sending of electronic messages and materials, and further enables emotion-conscious communication using an emotion engine.

[0928] An "electronic message" is a communication message in digital form for a user to send through a terminal.

[0929] A "generative AI model" is a model that uses artificial intelligence to generate and optimize text data.

[0930] An "emotion engine" is a system that analyzes emotions from user input data and provides that emotion data.

[0931] "Terminal" means a user-operated device and hardware for creating, editing, and transmitting electronic messages and materials.

[0932] "Server" means a central computer that receives responses from the generative AI model and automates the processing of electronic messages and materials.

[0933] An "approval workflow module" is a system for reviewing user-created electronic messages and materials and providing approval or feedback.

[0934] "Feedback" means any corrections or additional instructions or suggestions provided by a User to an Electronic Message or Material.

[0935] "Knowledge Base Module" means a system for storing approved electronic messages and materials and allowing users to search and reference historical data.

[0936] "Optimization" is the process of improving the content of electronic messages and materials provided by generative AI models, tailoring them to be efficient and emotionally sensitive.

[0937] "Reconciliation" is the process of modifying electronic messages and materials to reflect user feedback.

[0938] A "prompt" is text data and instructions input to a generative AI model to obtain a specific output.

[0939] The present invention is a system that automates the creation and approval process of electronic messages and documents, and realizes emotion-conscious communication by combining it with an emotion engine. This system operates in cooperation with the user, terminal, and server components. Specific embodiments of the present invention are described below.

[0940] Creating and optimizing electronic messages

[0941] A user drafts an electronic message, such as a meeting invitation email, on a device. For example, the user types, "Schedule a meeting for next Tuesday." The device provides this input to a generative AI (e.g., GPT-3) in the form of a prompt. At the same time, an emotion engine (e.g., IBM Watson) analyzes the user's emotions and sends this data to the generative AI.

[0942] The server receives the response from the generative AI and automatically optimizes its content. For example, it receives an optimized message such as, "We've scheduled a meeting for next Tuesday at 10:00 AM. Please reply if you can attend." This message also appropriately reflects the results of user sentiment analysis.

[0943] Approval and feedback

[0944] The server sends the optimized electronic message to the approval workflow module and adds it to a queue for review. The user reviews the electronic message through the approval workflow module at the terminal and provides approval or feedback, such as requesting the addition of a meeting location.

[0945] The server receives the user's feedback and incorporates it using the generative AI. The generative AI and emotion engine run again to add a new element to the message, specifically "Meeting location: Meeting Room A." Finally, the server sends the approved electronic message to the recipient.

[0946] Form of document creation

[0947] A user creates a new document, such as a monthly report, on a terminal. The user inputs this month's sales data and progress on major projects into the terminal. The terminal provides the data necessary to create the document and the results of sentiment analysis to the generative AI, and the server receives the generated document. For example, a report is generated that includes a "sales data section" and a "project progress section."

[0948] Similarly, the server sends the generated material to the approval workflow module, receives user feedback and regenerates it, and finally, the server distributes the approved material to the relevant parties.

[0949] Knowledge base operations

[0950] The server stores approved electronic messages and materials in a knowledge base module, which stores information for future reference. Emotional data analyzed by the emotion engine is also stored in the knowledge base. Users can search and reference the knowledge base from their devices and use past information to efficiently create new electronic messages and materials. Recommendations based on emotions are also made.

[0951] Examples of prompt statements

[0952] 1. Generate a meeting invitation email

[0953] An electronic message typed "Schedule a meeting for next Tuesday." User sentiment: Polite and cooperative.

[0954] 2. Preparation of monthly reports

[0955] Data entered: "This month's sales data: $1 million" and "Major project progress: 80% complete." User emotion: Pride and accomplishment.

[0956] As described above, the present invention is a system that efficiently automates a series of processes from creating, approving, and sending electronic messages and materials, and realizes communication that takes emotions into consideration.

[0957] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0958] Step 1:

[0959] A user drafts an electronic message at a terminal.

[0960] Type: A user types "Schedule a meeting for next Tuesday" into a terminal.

[0961] What it does: A user creates and saves a draft using the text input field on their device.

[0962] Output: The created draft of the electronic message is saved in the terminal.

[0963] Step 2:

[0964] The device provides a draft electronic message to the generative AI model.

[0965] Input: The created draft of the electronic message.

[0966] How it works: The device sends the draft content to the generative AI model via API. At the same time, the emotion engine parses the emotion data from the user's input and sends this emotion data to the generative AI model as well.

[0967] Output: The draft content and sentiment data are fed into the generative AI model.

[0968] Step 3:

[0969] The server receives the response from the generative AI model and automatically optimizes the content of the electronic message.

[0970] Input: Optimized message and sentiment data from the generative AI model.

[0971] Action: The server receives the generated AI model's response and checks the generated optimization message.

[0972] Output: The optimized electronic message is stored on the server.

[0973] Step 4:

[0974] The server sends the optimized electronic message to the approval workflow module.

[0975] Input: Optimized electronic message.

[0976] What happens: The server sends the optimized message to the approval workflow module and adds it to the queue for review.

[0977] Output: A message is added to the Approval Workflow module.

[0978] Step 5:

[0979] A user reviews the electronic message in the approval workflow module and provides approval or feedback.

[0980] Input: Electronic messages added to the Approval Workflow module.

[0981] Action: The user accesses the approval workflow module on their device, checks the electronic message, and optionally enters feedback, such as "Please add a meeting location."

[0982] Output: The approval or feedback is saved in the Approval Workflow module.

[0983] Step 6:

[0984] The server receives user feedback and uses regenerative artificial intelligence to adjust the final electronic message.

[0985] Input: User feedback.

[0986] Action: The server calls the generative AI model again based on the feedback, and uses the emotion engine to generate a new message that reflects the feedback. "Meeting location: Meeting Room A" is added.

[0987] Output: The final reconciled electronic message.

[0988] Step 7:

[0989] The server sends the approved electronic message to the recipient.

[0990] Input: The final reconciled electronic message.

[0991] What happens: The server sends the final message to the email server for delivery to the appropriate recipients.

[0992] Output: The recipient receives the optimized electronic message.

[0993] Step 8:

[0994] The user starts creating a new document on the device.

[0995] Input: The user enters sales data and project progress into the terminal.

[0996] Action: The user uses the input fields on the device to enter the required data and begin creating a document.

[0997] Output: The created draft document is saved on the device.

[0998] Step 9:

[0999] The device provides the generative AI model with the data necessary to create the document.

[1000] Input: Draft of the material created.

[1001] How it works: The device uses an API to send the draft and sentiment data to the generative AI model.

[1002] Output: The outline of the material and content requirements are provided to the generative AI model.

[1003] Step 10:

[1004] The server receives the response from the generative AI model and automatically generates the materials.

[1005] Input: Generated material from a generative AI model.

[1006] How it works: The server receives the response from the generative AI model and automatically generates the final version of the material.

[1007] Output: The automatically generated materials are saved on the server.

[1008] Step 11:

[1009] The server sends the generated materials to an approval workflow module.

[1010] Input: Auto-generated materials.

[1011] What happens: The server sends the document to the approval workflow module and adds it to the queue for review.

[1012] Output: The document is added to the approval workflow module.

[1013] Step 12:

[1014] The user reviews the material in the approval workflow module and provides approval or feedback.

[1015] Input: Materials added to the Approval Workflow module.

[1016] Action: The user reviews the material on their device and provides feedback, such as adding graphs, if necessary.

[1017] Output: The approval or feedback is saved in the Approval Workflow module.

[1018] Step 13:

[1019] The server receives user feedback and uses regenerative artificial intelligence to adjust the final material.

[1020] Input: User feedback.

[1021] Action: The server invokes the regenerative AI model and generates materials that reflect the feedback. A sales data graph is added.

[1022] Output: Final, adjusted materials.

[1023] Step 14:

[1024] The server distributes the approved materials to the relevant parties.

[1025] Input: Final adjusted materials.

[1026] How it works: The server distributes the materials to interested parties via email or shared file services.

[1027] Output: Stakeholders receive the final materials.

[1028] Step 15:

[1029] The server stores the approved electronic messages and materials in a knowledge base module.

[1030] Input: Authorized electronic messages and materials.

[1031] How it works: The server stores these data in a knowledge base module, along with the emotion data.

[1032] Output: Stored electronic messages and materials and emotional data.

[1033] Step 16:

[1034] A user searches and references past electronic messages and documents from the knowledge base.

[1035] Input: The user's search query.

[1036] Action: A user accesses the knowledge base module using a terminal and searches for the desired material.

[1037] Output: Historical electronic messages and materials displayed as search results.

[1038] Step 17:

[1039] Users utilize information from the knowledge base to efficiently create new electronic messages and materials.

[1040] Input: Information retrieved from the knowledge base.

[1041] How it works: The device creates new electronic messages and documents based on templates and data acquired by the user, and also utilizes emotional data provided by the emotion engine.

[1042] Output: New electronic messages or materials created.

[1043] (Application example 2)

[1044] 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."

[1045] In modern companies and factories, the process of creating electronic messages and documents is important, but these tasks face the risk of reduced efficiency due to human error and miscommunication. Furthermore, considering the user's emotions when creating messages and documents is an important factor in improving employee motivation and work efficiency, but conventional systems do not adequately address this issue. Therefore, there is a need for an automated communication and document creation system that takes emotions into account.

[1046] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1047] In this invention, the server includes: means for a user to create an electronic message on a terminal; means for the terminal to provide the content of the electronic message to a generative artificial intelligence; means for the server to receive a response from the generative artificial intelligence and automatically optimize the content of the electronic message; means for the server to send the optimized electronic message to an approval workflow module; means for a user to review the electronic message in the approval workflow module and provide approval or feedback; means for the server to adjust the final electronic message based on the feedback; means for the server to send the approved electronic message to a recipient; means for an emotion engine to analyze the user's emotions and provide the emotion data to the generative artificial intelligence; means for generating an optimized electronic message including the emotion data analyzed by the emotion engine; and means for verifying that the optimized electronic message includes emotion information. This enables efficient creation and distribution of high-quality electronic messages and materials that take emotions into consideration.

[1048] An "electronic message" is a digital message sent by electronic means that conveys information primarily in text form.

[1049] A "terminal" is an electronic device operated by a user, such as a PC, smartphone, or tablet.

[1050] "Generative AI" is an AI system that has the ability to generate natural language sentences and content based on input data provided by the user.

[1051] A "server" is a computer system that provides data and services to various computers on a network.

[1052] "Response" refers to the response or output result returned by generative artificial intelligence in response to input data provided by it.

[1053] The "approval workflow module" is a process management system for reviewing created messages and materials and providing approval and feedback.

[1054] "Feedback" refers to any opinions or suggestions provided as a result of a review.

[1055] The "emotion engine" is a system that analyzes the user's emotional state and provides the results as data.

[1056] "Emotion data" is digital data that represents the user's emotional state as analyzed by the emotion engine.

[1057] A "knowledge base" is a database system that stores previously created electronic messages, documents, and related data, and allows them to be searched and referenced.

[1058] An "optimized electronic message" is an electronic message that is generated by a generative artificial intelligence based on user input data and adjusted as necessary to reflect emotional data.

[1059] "Materials" refers to documents, reports, etc. that compile information and data collected for a specific purpose.

[1060] "Overall framework and content requirements" refers to data and information that indicates the main items and requirements needed to create the materials.

[1061] "Automatic generation" refers to the creation of materials based on input data specified by generative artificial intelligence, without manual intervention.

[1062] "Stakeholders" refers to the intended recipients of the messages and materials created.

[1063] This invention is a system for supporting the creation of emotion-sensitive electronic messages and materials, allowing users to efficiently generate and optimize electronic messages and materials using a terminal and provide emotion-based feedback.

[1064] System configuration

[1065] Hardware and Software

[1066] 1. Terminal: An electronic device that a user operates. Examples include computers, smartphones, and tablets.

[1067] 2. Server: A computer system that provides data and services to devices on a network. It processes and stores data, runs AI models, and manages workflow modules.

[1068] 3. Generative AI: Utilizing OpenAI APIs (e.g., text-davinci-003), etc., it generates optimized messages and materials based on input data provided by the user.

[1069] 4. Emotion engine: A system that analyzes the user's emotional state and provides it as data. For example, a sentiment analysis tool that uses NLP technology.

[1070] 5. Approval Workflow Module: A process management system that manages user reviews and feedback.

[1071] Processing flow details

[1072] Create an electronic message

[1073] 1. User input: The user inputs a draft of an electronic message at a terminal, for example, "Please start the next step."

[1074] 2. Emotion Analysis: The emotion engine analyzes the user's current emotional state and provides the emotion data to the generative AI. For example, the emotion engine analyzes that the user is "tired."

[1075] 3. AI optimization: Generative AI optimizes messages based on input drafts and sentiment data. For example, "Thank you for your hard work today. Please begin the next step."

[1076] 4. Approval Workflow: The optimized electronic message is sent to the approval workflow module, where users review the content and provide approval or feedback.

[1077] 5. Incorporating feedback: The server uses the feedback to adjust the final electronic message using generative artificial intelligence.

[1078] 6. Send: The server sends the final approved electronic message to the recipient.

[1079] Creating materials

[1080] 1. User input: The user starts creating a new document on the terminal and enters the outline and content requirements of the document. For example, they enter sales data and progress status to create a "monthly report."

[1081] 2. Emotion analysis: The emotion engine analyzes the user's emotional state and provides the results to the generative AI.

[1082] 3. AI generation: Generative AI automatically generates documents using the input data and sentiment data provided. For example, a report containing a "sales data section" and a "project progress section" is generated.

[1083] 4. Approval Workflow: The generated materials are sent to the approval workflow module, where users review the content and provide approval or feedback.

[1084] 5. Incorporating feedback: The server uses the feedback to adjust the final material by reusing the generative AI.

[1085] 6. Distribution: Finally, the server distributes the approved materials to the relevant parties.

[1086] Prompt Sentence Examples

[1087] A typical example of a prompt provided to a generative AI model is as follows:

[1088] Instructions: Begin the following steps:

[1089] Emotions: Feeling tired

[1090] The system enables the efficient creation and delivery of high-quality, emotionally sensitive electronic messages and materials.

[1091] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1092] Step 1:

[1093] Input: A user enters a draft of an electronic message at a terminal.

[1094] Operation: A user uses a terminal to input a draft of an electronic message such as "Please begin the next step."

[1095] Output: The draft content is displayed on the terminal.

[1096] Step 2:

[1097] Input: Draft content and user sentiment data.

[1098] Operation: The emotion engine analyzes the user's emotions along with the draft content entered on the device. For example, the emotion data that the user is "feeling tired" is analyzed.

[1099] Output: The analyzed emotion data and draft content are provided to the generative AI.

[1100] Step 3:

[1101] Input: Sentiment analysis results and draft content.

[1102] How it works: The server sends the analysis results and a prompt for the draft content to the generative AI. The generative AI then optimizes the electronic message based on this. For example, it might be optimized to say something like, "Thank you for your hard work today. Please begin the next step."

[1103] Output: The optimized electronic message is returned to the server.

[1104] Step 4:

[1105] Input: Optimized electronic message.

[1106] Action: The server sends this optimized electronic message to the approval workflow module.

[1107] Output: The optimized message is displayed in the approval workflow module.

[1108] Step 5:

[1109] Input: Optimized electronic message.

[1110] How it works: A user uses the approval workflow module to review the optimized electronic message and provide approval or feedback as needed, for example, by providing feedback such as "Please clarify the details."

[1111] Output: The approval or feedback is sent to the server.

[1112] Step 6:

[1113] Input: Approval or feedback.

[1114] How it works: Based on the feedback, the server re-uses the generative AI to adjust the electronic message, e.g., regenerating it with more detail, such as "Please start the next step at 2 PM."

[1115] Output: The final regenerated electronic message is stored on the server.

[1116] Step 7:

[1117] Input: The final approved electronic message.

[1118] Action: The server sends the final approved electronic message to the recipient.

[1119] Output: The recipient receives the final electronic message and sees its contents.

[1120] This series of processes allows for the efficient creation and transmission of high-quality, emotionally sensitive electronic messages.

[1121] 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.

[1122] 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.

[1123] 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.

[1124] [Third embodiment]

[1125] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1126] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1127] 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).

[1128] 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.

[1129] 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.

[1130] 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).

[1131] 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.

[1132] 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.

[1133] 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.

[1134] 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.

[1135] 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.

[1136] 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."

[1137] This invention relates to a system that automates the creation and approval process of electronic messages (emails) and documents, and utilizes a knowledge base. This system realizes efficient communication by linking together the components of the server, terminal, and user.

[1138] Mail Processing Embodiment

[1139] Creating and optimizing electronic messages

[1140] The user begins to compose an electronic message on the terminal. For example, if the user wants to send an email inviting a meeting, he or she inputs the content "Schedule a meeting for next Tuesday" into the terminal. This input is provided to the generative artificial intelligence.

[1141] The server receives the response from the generative AI and automatically optimizes its content. The generative AI generates an optimized electronic message saying, "We've scheduled a meeting for next Tuesday at 10:00 AM. Please respond if you can attend." The server then sends this optimized electronic message to the approval workflow module.

[1142] Approval and feedback

[1143] The user reviews the optimized electronic message in the approval workflow module and provides feedback as needed. For example, if the user provides feedback to add a meeting location, the server adds "Conference Room A" to the email based on the feedback.

[1144] Finally, the server sends the approved electronic message to the recipient, effectively sending unified, organized email.

[1145] Form of document creation

[1146] Document creation and automatic generation

[1147] The user starts creating a new document on their device. For example, when creating a "monthly report," the user inputs this month's sales data and progress on major projects into the device. This input information is provided to the generative AI.

[1148] The server receives the response from the generative AI and automatically generates the document. For example, the generative AI creates a report that includes a "sales data section" and a "project progress section." The server then sends this automatically generated document to the approval workflow module.

[1149] Approval and feedback on materials

[1150] The user reviews the document in the approval workflow module and provides feedback. For example, if a user requests that a sales data graph be added to the document, the server reflects that feedback and generates the final document with the graph added.

[1151] Finally, the server distributes the approved materials to the relevant parties, thereby enabling uniform, high-quality materials to be created and distributed efficiently.

[1152] Knowledge base operations

[1153] Accumulation and utilization of knowledge

[1154] The server stores approved electronic messages and materials in a knowledge base module, allowing users to efficiently search and reference past knowledge. When users create new electronic messages or materials by utilizing existing knowledge, they can work quickly and efficiently.

[1155] For example, users can search and refer to past "project progress reports" to quickly find templates and reference information needed to create new reports, significantly reducing time and effort.

[1156] The system of the present invention is expected to significantly improve the efficiency of the process of creating and approving electronic messages and materials, and by making effective use of knowledge, to improve the speed and quality of business communications.

[1157] The processing flow will be explained below.

[1158] Specific processing flow for email processing

[1159] Step 1:

[1160] A user drafts an electronic message on a terminal, specifically, inputs an invitation email for a meeting into the terminal.

[1161] Step 2:

[1162] The device provides the content of the electronic message to the generative artificial intelligence, specifically, sending the phrase "Schedule a meeting for next Tuesday" to the generative artificial intelligence.

[1163] Step 3:

[1164] The server receives a response from the generative artificial intelligence, which generates an optimized electronic message based on the input phrase.

[1165] Step 4:

[1166] The server reviews and formats the optimized electronic message received from the generative AI, improving grammar and expression to say, "We've scheduled a meeting for next Tuesday at 10 a.m. Please reply if you can attend."

[1167] Step 5:

[1168] The server sends the optimized electronic message to the approval workflow module, where the email is added to a queue for review.

[1169] Step 6:

[1170] The user reviews the electronic message at the terminal through an approval workflow module and provides approval or feedback, for example, prompting the addition of a meeting location.

[1171] Step 7:

[1172] The server receives the user's feedback and uses generative AI to reflect the feedback. Specifically, it adds "Meeting location: Meeting Room A."

[1173] Step 8:

[1174] The server then sends the final approved electronic message to the recipient, resulting in a high-quality, approved email.

[1175] Specific process flow for creating materials

[1176] Step 1:

[1177] The user starts creating a new document on the device. Specifically, he opens a new document called "Monthly Report."

[1178] Step 2:

[1179] The device provides the generative AI with the requirements for creating documents. Specifically, it inputs "this month's sales data" and "progress of major projects."

[1180] Step 3:

[1181] The server receives the response from the generative AI, which generates materials with the appropriate sections based on the provided requirements.

[1182] Step 4:

[1183] The server formats the data received from the generative AI, generating a report that includes a sales data section and a project progress section.

[1184] Step 5:

[1185] The server sends the generated material to the approval workflow module, where it is added to a queue for review.

[1186] Step 6:

[1187] The user reviews the document through the approval workflow module on the terminal and provides approval or feedback, for example, requesting the addition of a sales data graph.

[1188] Step 7:

[1189] The server receives the user's feedback and uses generative AI to reflect the feedback. Specifically, it adds a graph of sales data.

[1190] Step 8:

[1191] The server distributes the final approved materials to the relevant parties, thereby ensuring that approved, high-quality materials are distributed.

[1192] Specific process flow for knowledge base operation

[1193] Step 1:

[1194] The server stores the approved electronic messages and materials in a knowledge base module, which stores the information for future reference.

[1195] Step 2:

[1196] The user searches for and references the necessary information from the knowledge base on the terminal. Specifically, the user searches for past "project progress reports."

[1197] Step 3:

[1198] Users can use information retrieved from the knowledge base to efficiently create new electronic messages or documents, for example, creating new reports using templates from the past.

[1199] Through these processing steps, the system efficiently automates the creation, approval, and delivery of electronic messages and materials, and also effectively leverages a knowledge base.

[1200] Example 1

[1201] 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."

[1202] The traditional process of creating and approving electronic messages and documents was often done manually, resulting in inefficiency and a decline in the quality of communication. Furthermore, reusing past data required manual searching and referencing, which was time-consuming and labor-intensive.

[1203] 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.

[1204] In this invention, the server includes: a means for a user to create an electronic message on a terminal; a means for the terminal to provide the content of the electronic message to a generative AI model; a means for the server to receive a response from the generative AI model and automatically optimize the content of the electronic message; a means for the server to send the optimized electronic message to an approval workflow module; a means for a user to review the electronic message in the approval workflow module and provide approval or feedback; a means for the server to adjust the final electronic message based on the feedback; a means for the server to send the approved electronic message to a recipient; and a means for the server to store the approved electronic message in a knowledge base module. This streamlines the creation and approval process of electronic messages and materials, enabling advanced communication that efficiently utilizes knowledge.

[1205] "User" means any person or entity that operates the System and creates, reviews, provides feedback on, and approves electronic messages and materials.

[1206] A "terminal" is an electronic device such as a computer, smartphone, or tablet that is operated by a user.

[1207] "Electronic Message" means a message sent or received by email or other means of electronic communication.

[1208] A "generative AI model" is an artificial intelligence model that generates text data based on prompts entered by a user.

[1209] A "server" is a machine that communicates with devices over a network to provide data to a generative AI model and receive results.

[1210] A "response" is the reply text that a generative AI model generates in response to a prompt.

[1211] "Optimization" refers to converting responses received from a generative AI model into a suitable format for electronic messages or documents.

[1212] An "approval workflow module" is a software module for accepting confirmation, feedback, and approval from users.

[1213] "Feedback" means any instruction for correction or improvement given by a user to a generated electronic message or material.

[1214] "Knowledge Base Module" means a software module for storing approved electronic messages and materials for future retrieval and reuse.

[1215] The present invention is a system for automating the creation and approval process of electronic messages and documents and utilizing a knowledge base. This system is composed of a server, terminals, a generative AI model, and various software modules. Each component and its operation are described in detail below.

[1216] Creating and optimizing electronic messages

[1217] A user uses a device to compose an electronic message. For example, when creating a meeting invitation email, the user types something like "Schedule a meeting for next Tuesday." This input is formatted by the device as a prompt and provided to a generative AI model (e.g., GPT-4).

[1218] The server receives the response from the generative AI model, analyzes its content, and generates an optimized message, such as "We've scheduled a meeting for next Tuesday at 10:00 AM. Please reply if you can attend."

[1219] The server then sends the generated optimization message to the approval workflow module, where the user can review the message and provide feedback if necessary, for example, to add "Conference Room A."

[1220] The server adjusts the final electronic message based on the feedback and sends the approved message to the recipient, a process that ensures a uniform message is sent efficiently.

[1221] Prompt Sentence Examples

[1222] Set up a meeting for next Tuesday

[1223] Document creation and automatic generation

[1224] The user starts creating a new document using the device. For example, to create a "monthly report," the user inputs this month's sales data and the progress of major projects into the device. This input information is provided to the generative AI model as a prompt.

[1225] The server receives the response from the generative AI model and automatically generates the document. For example, a report containing a "sales data section" and a "project progress section" is generated. The server sends this report to the approval workflow module and notifies the user.

[1226] The user reviews the document in the approval workflow module and provides feedback as needed, such as "add a chart of sales data."

[1227] The server then distributes the final revised documents to the relevant parties, ensuring efficient distribution of high-quality, uniform documents.

[1228] Prompt Sentence Examples

[1229] Enter this month's sales data and progress on major projects

[1230] Knowledge base operations

[1231] The server stores approved electronic messages and materials in a knowledge base module, allowing users to access the knowledge base from their terminals and search and refer to past electronic messages and materials.

[1232] Users can efficiently create new electronic messages and documents by utilizing referenced knowledge. For example, by referencing a past "Project Progress Report" and using it as a template for a new report, users can significantly reduce time and effort.

[1233] This system is expected to significantly streamline the process of creating and approving electronic messages and documents, and by making effective use of knowledge, to result in faster, higher-quality business communications.

[1234] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1235] Creating and optimizing electronic messages

[1236] Step 1:

[1237] A user begins composing an electronic message on a terminal, for example, typing "Schedule a meeting for next Tuesday." The input data is formatted as a prompt sentence.

[1238] Step 2:

[1239] The device provides the prompt text entered by the user to the generative AI model. Specifically, it sends the prompt text "Schedule a meeting for next Tuesday" to the generative AI model.

[1240] Step 3:

[1241] The server receives a response from the generative AI model. For example, the generative AI model generates an optimized message such as, "We've scheduled a meeting for next Tuesday at 10:00 AM. Please reply if you can attend." This response is then analyzed to obtain the optimized message.

[1242] Step 4:

[1243] The server sends the optimized electronic message to the approval workflow module, specifically, stores the generated optimized message in a database of the approval workflow module, and notifies the user.

[1244] Step 5:

[1245] The user checks the message in the approval workflow module and provides feedback if necessary. For example, the user enters feedback such as adding "Conference Room A" and submits it to the approval workflow module.

[1246] Step 6:

[1247] The server receives feedback from the approval workflow module and adjusts the final electronic message, for example, adding "Conference Room A" based on the feedback, and completes the final message.

[1248] Step 7:

[1249] The server sends the approved electronic message to the recipient, sends the final electronic message based on the recipient list, and notifies the user.

[1250] Step 8:

[1251] The server stores the approved electronic message in the knowledge base module, and accumulates the final message data in the knowledge base database.

[1252] Document creation and automatic generation

[1253] Step 1:

[1254] The user starts creating a document on the terminal. For example, to create a "monthly report," the user inputs this month's sales data and the progress of major projects.

[1255] Step 2:

[1256] The device generates the input information as a prompt and provides it to the generative AI model. Specifically, the prompt "Enter this month's sales data and progress on major projects" is sent to the generative AI model.

[1257] Step 3:

[1258] The server receives a response from the generative AI model. The generated document contains a "sales data section" and a "project progress section." This response is analyzed to obtain the automatically generated document.

[1259] Step 4:

[1260] The server sends the automatically generated material to the approval workflow module, stores the generated material in the database of the approval workflow module, and notifies the user.

[1261] Step 5:

[1262] The user reviews the material in the approval workflow module and provides feedback. For example, the user inputs an instruction to "add a graph of sales data" and submits it to the approval workflow module.

[1263] Step 6:

[1264] The server receives feedback from the approval workflow module and adjusts the final document, adding graphs based on the feedback and completing the final document.

[1265] Step 7:

[1266] The server distributes the approved final documents to the parties concerned, sends the final documents based on the parties list, and notifies the users.

[1267] Step 8:

[1268] The server stores the approved materials in the knowledge base module, and accumulates the final material data in the knowledge base database.

[1269] Knowledge base operations

[1270] Step 1:

[1271] The server stores approved electronic messages and materials in a knowledge base module, organizing and centralizing the data.

[1272] Step 2:

[1273] Users can access the knowledge base from their devices to search and refer to past electronic messages and documents, quickly finding the information they need.

[1274] Step 3:

[1275] Users can leverage knowledge to efficiently create new electronic messages and materials, and save time by leveraging existing templates and reference information.

[1276] Through these steps, the system streamlines the creation and approval process of electronic messages and documents, and makes effective use of knowledge.

[1277] (Application example 1)

[1278] 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."

[1279] In the traditional manufacturing industry, the creation and approval process of work instructions was often performed manually, resulting in time-consuming and labor-intensive tasks. Furthermore, incorporating feedback required significant manual operations, increasing the likelihood of errors and delays. This often resulted in inconsistent quality of the generated instructions. Furthermore, past instructions and documents were not efficiently stored and searched in a knowledge base, resulting in insufficient utilization of past knowledge. To address these issues, the present invention provides a system that automates the creation and approval process of electronic messages and documents, enabling efficient utilization of knowledge.

[1280] 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.

[1281] In this invention, the server includes: a means for a user to create an electronic message on a terminal; a means for the terminal to provide the content of the electronic message to a generative artificial intelligence; a means for the server to receive a response from the generative artificial intelligence and automatically optimize the content of the electronic message; a means for transmitting the electronic message to an approval workflow module; a means for a user to review and approve or provide feedback in the approval workflow module; a means for adjusting the final electronic message based on the feedback; a means for transmitting the approved electronic message to a recipient; a means for receiving a response from the generative artificial intelligence to generate an automated work instruction and generating an optimized work instruction; a means for a user to review and approve or provide feedback in the approval workflow module; a means for adjusting the final work instruction based on the feedback and storing the optimized work instruction in a knowledge base. This reduces errors and delays through automated processes and enables efficient work by utilizing past knowledge while maintaining consistent quality of electronic messages and work instructions.

[1282] A "terminal" is a device that a user operates to create, edit, and input electronic messages, documents, and the like.

[1283] "Generative AI" is an AI system that generates optimized electronic messages and materials based on user input.

[1284] A "server" is a central processing unit that receives responses from generative artificial intelligence and performs various data processing and transmission.

[1285] "Approval Workflow Module" means a software module that manages the process of providing approval or feedback on the content of an electronic message or document.

[1286] "Feedback" refers to additional information or requests for corrections provided by a user through the approval workflow module.

[1287] A "knowledge base" is a database that stores and manages information such as past electronic messages and documents, allowing users to efficiently search and reference them.

[1288] A "work instruction manual" is a document that details the specific work content and procedures for manufacturing robots and workers.

[1289] A "prompt sentence" is an input sentence for obtaining a specific output from a generative artificial intelligence.

[1290] This invention is a system that automates the creation and approval process of electronic messages and documents, utilizing generative artificial intelligence and a knowledge base. This system operates in cooperation with a user terminal, a server that acts as a central processing unit, and an approval workflow module.

[1291] Mail Processing Embodiment

[1292] Creating and optimizing electronic messages

[1293] A user begins to compose an electronic message on a terminal. For example, when the user wants to send an invitation email for a meeting, the user inputs the content "Schedule a meeting for next Tuesday" into the terminal. This input is provided to the generative AI. The server receives a response from the generative AI and automatically optimizes the content. The generative AI generates an optimized electronic message saying "We have scheduled a meeting for next Tuesday at 10:00 AM. Please reply if you can attend." The server then sends this optimized electronic message to the approval workflow module.

[1294] Approval and feedback

[1295] The user reviews the optimized electronic message in the approval workflow module and provides feedback as needed. For example, if the user provides feedback to add a meeting location, the server adds "Conference Room A" to the email based on that feedback. Finally, the server sends the approved electronic message to the recipient, thereby efficiently sending a well-organized email.

[1296] Form of document creation

[1297] Document creation and automatic generation

[1298] A user starts creating a new document on a terminal. For example, when creating a "monthly report," the user inputs this month's sales data and the progress of major projects into the terminal. This input information is provided to the generative AI. The server receives a response from the generative AI and automatically generates the document. For example, the generative AI creates a report that includes a "sales data section" and a "project progress section." The server sends this automatically generated document to an approval workflow module.

[1299] Approval and feedback on materials

[1300] Users review documents and provide feedback in the approval workflow module. For example, if a user requests that a sales data graph be added to the document, the server incorporates that feedback and generates the final document with the graph added. Finally, the server distributes the approved document to the relevant parties. This ensures that consistent, high-quality documents are created and distributed efficiently.

[1301] Generating work instructions for factory robots

[1302] Create and approve work orders

[1303] The user starts creating a work instruction on a terminal. For example, if the work instruction for a manufacturing robot is to "assemble parts A and B," this content is provided to the generative AI. The server receives the response from the generative AI and generates an optimized work instruction. This is then sent to the approval workflow module.

[1304] Users review the instructions in the approval workflow module and provide approval or feedback. If the user provides feedback, the server adjusts and generates the final work instructions based on that feedback. This final work instruction is saved in the knowledge base. This allows for efficient work with fewer errors by utilizing past knowledge.

[1305] As a specific example, if the user inputs "Today's manufactured product will be the assembly of parts A and B. 10 parts A and 20 parts B will be used. Quality checks will be performed," the generated work instruction will be "Today's manufactured product will be the assembly of parts A (10 pieces) and parts B (20 pieces). Please be sure to perform quality checks."

[1306] Example prompt sentence:

[1307] Create a work instruction for your manufacturing robot:

[1308] Today's production involves assembling parts A and B. 10 parts A and 20 parts B are used. Quality check is performed.

[1309] Prepared instructions:

[1310] The present invention automates the process of creating electronic messages, documents, and even work instructions for factory robots, and ensures consistent quality, enabling efficient work with fewer errors and delays.

[1311] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1312] Step 1:

[1313] The user begins composing an electronic message on a terminal. The user types something like "Schedule a meeting for next Tuesday." This input is provided to the generative artificial intelligence as a prompt.

[1314] Input: Message content created by the user

[1315] Output: Prompt sentence to be provided to the generative AI

[1316] Step 2:

[1317] The server sends the prompt to the generative AI and receives a response, which generates an optimized electronic message based on the input: "We've scheduled a meeting for next Tuesday at 10:00 AM. Please reply if you can attend."

[1318] Input: prompt statement

[1319] Output: Optimized electronic message

[1320] Step 3:

[1321] The server sends the optimized electronic message to an approval workflow module, where the electronic message awaits approval or feedback.

[1322] Input: Optimized electronic message

[1323] Output: Waiting for confirmation in the approval workflow module

[1324] Step 4:

[1325] A user reviews the electronic message through the approval workflow module and provides feedback if necessary, for example, "Please add conference room A."

[1326] Input: Feedback

[1327] Output: Feedback content

[1328] Step 5:

[1329] The server adjusts the electronic message based on the feedback, specifically generating a final electronic message with "Conference Room A" added.

[1330] Input: Feedback

[1331] Output: Final corrected electronic message

[1332] Step 6:

[1333] The server sends the final approved electronic message to the recipient, which delivers the completed electronic message to the recipient.

[1334] Input: Final electronic message

[1335] Output: Email sent to recipient

[1336] Step 7:

[1337] The user starts creating a new document on the device. For example, to create a "monthly report," the user inputs this month's sales data and the progress of major projects into the device. This input is provided to the generative AI.

[1338] Input: Original data for document creation

[1339] Output: Prompt sentence to be provided to the generative AI

[1340] Step 8:

[1341] The server receives the response from the generative AI and automatically generates the document, which then creates a report that includes sections on sales data and project progress.

[1342] Input: prompt statement

[1343] Output: Generated material

[1344] Step 9:

[1345] The server sends the generated material to an approval workflow module, where it awaits approval or feedback.

[1346] Input: Generated material

[1347] Output: Waiting for confirmation in the approval workflow module

[1348] Step 10:

[1349] Users review the materials through an approval workflow module and provide feedback, such as "Please add a chart of the sales data."

[1350] Input: Feedback

[1351] Output: Feedback content

[1352] Step 11:

[1353] The server adjusts the materials based on the feedback, specifically generating the final materials with the sales data graph added.

[1354] Input: Feedback

[1355] Output: Final revised document

[1356] Step 12:

[1357] The server distributes the final approved documents to the relevant parties, ensuring that consistent, high-quality documents are delivered to the relevant parties.

[1358] Input: Final material

[1359] Output: Materials distributed to stakeholders

[1360] Step 13:

[1361] The user begins creating work instructions for a manufacturing robot on a terminal. For example, they input, "Today's production task is to assemble parts A and B." This input is provided to the generative artificial intelligence.

[1362] Input: Work instruction source data

[1363] Output: Prompt sentence to be provided to the generative AI

[1364] Step 14:

[1365] The server receives the response from the generative AI and generates an optimized work instruction. The generative AI generates the following: "Today's production involves assembling parts A (10 units) and B (20 units). Please be sure to perform a quality check."

[1366] Input: prompt statement

[1367] Output: Optimized work instructions

[1368] Step 15:

[1369] The server sends the optimized work order to an approval workflow module, where the work order awaits approval or feedback.

[1370] Input: Optimized Work Instruction

[1371] Output: Waiting for confirmation in the approval workflow module

[1372] Step 16:

[1373] A user reviews the work order in the approval workflow module and provides feedback, such as "Please also add a quality check item."

[1374] Input: Feedback

[1375] Output: Feedback content

[1376] Step 17:

[1377] The server adjusts the work instructions based on the feedback, specifically generating the final work instructions with the "quality check items" added.

[1378] Input: Feedback

[1379] Output: Final revised work instructions

[1380] Step 18:

[1381] The server saves the final approved work order in the knowledge base, which accumulates historical data for future work.

[1382] Input: Final Work Order

[1383] Output: Work instructions stored in the knowledge base

[1384] These steps automate the creation and approval process for electronic messages, documents, and work instructions, facilitating effective knowledge utilization.

[1385] 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.

[1386] This invention combines a knowledge-based system that automates the creation and approval process of electronic messages (emails) and documents with an emotion engine. This system, which involves the collaboration of server, terminal, and user components, realizes efficient and emotion-sensitive communication.

[1387] Mail Processing Embodiment

[1388] Creating and optimizing electronic messages

[1389] A user creates a draft of an electronic message on a terminal. For example, if the user wants to send an invitation email to a meeting, the user inputs the content "Schedule a meeting for next Tuesday" into the terminal.

[1390] The terminal provides the contents of the electronic message to the generative AI, and the emotion engine analyzes the user's emotions. This input data and emotion data are provided to the generative AI.

[1391] The server receives the response from the generative AI and automatically optimizes its content. For example, the generative AI generates an optimized message such as, "We have scheduled a meeting for next Tuesday at 10:00 AM. Please reply if you can attend." The user's emotions, analyzed by the emotion engine, are also reflected in this message.

[1392] Approval and feedback

[1393] The server sends the optimized electronic message to the approval workflow module, which adds it to a queue for review.

[1394] The user reviews the electronic message at the terminal through an approval workflow module and provides approval or feedback, for example, prompting the addition of a meeting location.

[1395] The server receives the user's feedback and uses regenerative artificial intelligence to reflect the feedback. At this time, the emotion engine also analyzes the user's emotions and adds appropriate emotional expressions. Specifically, it adds "Meeting location: Meeting Room A."

[1396] Finally, the server sends the approved electronic message to the recipient, resulting in efficient delivery of high-quality, sensitive emails.

[1397] Form of document creation

[1398] Document creation and automatic generation

[1399] The user starts creating a new document on the terminal. For example, when creating a "monthly report," the user inputs this month's sales data and progress on major projects into the terminal.

[1400] The device provides the requirements for creating documents to the generative AI. The emotion engine also analyzes the user's emotions. This data and emotional information are provided to the generative AI.

[1401] The server receives the response from the generative AI and automatically generates the materials. Specifically, it creates a report that includes a "sales data section" and a "project progress section." At this time, expressions based on the emotions analyzed by the emotion engine are applied.

[1402] Approval and feedback on materials

[1403] The server sends the generated document to the approval workflow module, where it is added to a queue for review.

[1404] The user reviews the document through the approval workflow module on the terminal and provides approval or feedback, for example, requesting the addition of a sales data graph.

[1405] The server receives user feedback and uses regenerative artificial intelligence to reflect the feedback. The emotion engine analyzes the user's emotions and adds appropriate emotional expressions to the document. Specifically, it adds a graph of sales data.

[1406] Finally, the server distributes the approved materials to the relevant parties. High-quality materials that take emotional impact into consideration are efficiently created and distributed.

[1407] Knowledge base operation embodiment

[1408] Accumulation and utilization of knowledge

[1409] The server stores the approved electronic messages and materials in a knowledge base module, which stores the information for future reference. Emotion data analyzed by the emotion engine is also stored in the knowledge base.

[1410] The user searches for and references the information they need from the knowledge base on their device. For example, they search for a past "project progress report." At this time, the emotion engine uses the emotion data in the knowledge base to make emotion-based recommendations.

[1411] Users can efficiently create new electronic messages or documents by utilizing information obtained from the knowledge base. For example, they can create new reports using past templates. In this case, too, they can utilize the emotional information provided by the emotion engine.

[1412] Through these processing steps, the system efficiently automates the creation, approval, and transmission of electronic messages and materials, and also utilizes an emotion engine to realize emotion-sensitive communication.In addition, by utilizing a knowledge base, it is possible to effectively utilize past experience and information.

[1413] The processing flow will be explained below.

[1414] Specific processing flow for email processing

[1415] Step 1:

[1416] A user drafts an electronic message on a terminal, specifically, inputs an invitation email for a meeting into the terminal.

[1417] Step 2:

[1418] The device provides the content of the electronic message to the generative artificial intelligence, specifically, sending the phrase "Schedule a meeting for next Tuesday" to the generative artificial intelligence.

[1419] Step 3:

[1420] The emotion engine recognizes and analyzes the user's emotions. For example, if the user is feeling stressed, the emotion engine will analyze the information.

[1421] Step 4:

[1422] The server receives a response from the generative artificial intelligence, which generates an optimized electronic message based on the input phrase.

[1423] Step 5:

[1424] The server acquires the user's emotion data from the emotion engine and reflects it in an optimized electronic message, for example, adding a phrase such as "This meeting is very important and we look forward to your participation."

[1425] Step 6:

[1426] The server sends the optimized electronic message to the approval workflow module, where the email is added to a queue for review.

[1427] Step 7:

[1428] The user reviews the electronic message at the terminal through an approval workflow module and provides approval or feedback, for example, prompting the addition of a meeting location.

[1429] Step 8:

[1430] The server receives the user's feedback and uses generative AI to reflect the feedback. Specifically, it adds "Meeting location: Meeting Room A."

[1431] Step 9:

[1432] The server uses an emotion engine to double-check the final electronic message and reinforce any necessary emotional expressions.

[1433] Step 10:

[1434] The server then sends the final approved electronic message to the recipient, ensuring that the email is sent with high quality, emotional sensitivity, and approval.

[1435] Specific process flow for creating materials

[1436] Step 1:

[1437] The user starts creating a new document on the device. Specifically, he opens a new document called "Monthly Report."

[1438] Step 2:

[1439] The device provides the generative AI with the requirements for creating documents. Specifically, it inputs "this month's sales data" and "progress of major projects."

[1440] Step 3:

[1441] The emotion engine recognizes and analyzes the user's emotion, for example, if the user is happy, the information will be analyzed by the emotion engine.

[1442] Step 4:

[1443] The server receives the response from the generative AI, which generates materials with the appropriate sections based on the provided requirements.

[1444] Step 5:

[1445] The server retrieves the user's emotional data from the emotion engine and reflects it in the document. Specifically, it adds a phrase such as, "This monthly report shows very good results."

[1446] Step 6:

[1447] The server sends the generated material to the approval workflow module, where it is added to a queue for review.

[1448] Step 7:

[1449] The user reviews the document through the approval workflow module on the terminal and provides approval or feedback, for example, requesting the addition of a sales data graph.

[1450] Step 8:

[1451] The server receives the user's feedback and uses generative AI to reflect the feedback, for example adding a graph of sales data.

[1452] Step 9:

[1453] The server uses an emotion engine to double-check the final material and reinforce any necessary emotional expressions.

[1454] Step 10:

[1455] The server distributes the final approved materials to the relevant parties, ensuring that approved, high-quality, and emotionally sensitive materials are delivered.

[1456] Specific process flow for knowledge base operation

[1457] Step 1:

[1458] The server stores the approved electronic messages and materials in a knowledge base module, which stores the information for future reference.

[1459] Step 2:

[1460] The emotion engine stores the emotional data contained in these approved electronic messages and materials in a knowledge base, allowing for emotion-based search and referencing.

[1461] Step 3:

[1462] The user searches for and references the necessary information from the knowledge base on the terminal. For example, the user searches for a past "project progress report."

[1463] Step 4:

[1464] Users can use information retrieved from the knowledge base to efficiently create new electronic messages or documents, for example, creating new reports using templates from the past.

[1465] Step 5:

[1466] The server updates the knowledge base with newly approved electronic messages and materials and their associated emotional data.

[1467] Step 6:

[1468] An emotion engine analyzes the information in the knowledge base and makes emotion-based recommendations, such as recommending the best email template for a particular situation or emotion.

[1469] Through these processing steps, the system efficiently automates the creation, approval, and transmission of electronic messages and materials, and utilizes an emotion engine to ensure emotion-sensitive communication.It also utilizes a knowledge base to effectively utilize past experience and information.

[1470] Example 2

[1471] 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."

[1472] The traditional process of creating electronic messages and materials requires many manual operations, resulting in inefficiencies and human error. It is also difficult to communicate with recipients in a way that takes their emotions into account, which can lead to inappropriate messaging and a lack of consideration for the recipient. Furthermore, there is no established method for effectively utilizing past data and knowledge, which limits the reusability of materials and messages. A system that can solve these issues is needed.

[1473] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1474] In this invention, the server includes: a means for a user to create an electronic message on a terminal; a means for the terminal to provide the content of the electronic message to a generative AI model; a means for an emotion engine to analyze the user's emotions and provide the data to the generative AI model; a means for the server to receive a response from the generative AI model and automatically optimize the content of the electronic message; a means for the server to send the optimized electronic message to an approval workflow module; a means for a user to review the electronic message in the approval workflow module and provide approval or feedback; a means for the server to receive the feedback and adjust the final electronic message using a regenerative AI; and a means for the server to send the approved electronic message to a recipient. This allows for efficient automation of the creation, approval, and sending of electronic messages and materials, and further enables emotion-conscious communication using an emotion engine.

[1475] An "electronic message" is a communication message in digital form for a user to send through a terminal.

[1476] A "generative AI model" is a model that uses artificial intelligence to generate and optimize text data.

[1477] An "emotion engine" is a system that analyzes emotions from user input data and provides that emotion data.

[1478] "Terminal" means a user-operated device and hardware for creating, editing, and transmitting electronic messages and materials.

[1479] "Server" means a central computer that receives responses from the generative AI model and automates the processing of electronic messages and materials.

[1480] An "approval workflow module" is a system for reviewing user-created electronic messages and materials and providing approval or feedback.

[1481] "Feedback" means any corrections or additional instructions or suggestions provided by a User to an Electronic Message or Material.

[1482] "Knowledge Base Module" means a system for storing approved electronic messages and materials and allowing users to search and reference historical data.

[1483] "Optimization" is the process of improving the content of electronic messages and materials provided by generative AI models, tailoring them to be efficient and emotionally sensitive.

[1484] "Reconciliation" is the process of modifying electronic messages and materials to reflect user feedback.

[1485] A "prompt" is text data and instructions input to a generative AI model to obtain a specific output.

[1486] The present invention is a system that automates the creation and approval process of electronic messages and documents, and realizes emotion-conscious communication by combining it with an emotion engine. This system operates in cooperation with the user, terminal, and server components. Specific embodiments of the present invention are described below.

[1487] Creating and optimizing electronic messages

[1488] A user drafts an electronic message, such as a meeting invitation email, on a device. For example, the user types, "Schedule a meeting for next Tuesday." The device provides this input to a generative AI (e.g., GPT-3) in the form of a prompt. At the same time, an emotion engine (e.g., IBM Watson) analyzes the user's emotions and sends this data to the generative AI.

[1489] The server receives the response from the generative AI and automatically optimizes its content. For example, it receives an optimized message such as, "We've scheduled a meeting for next Tuesday at 10:00 AM. Please reply if you can attend." This message also appropriately reflects the results of user sentiment analysis.

[1490] Approval and feedback

[1491] The server sends the optimized electronic message to the approval workflow module and adds it to a queue for review. The user reviews the electronic message through the approval workflow module at the terminal and provides approval or feedback, such as requesting the addition of a meeting location.

[1492] The server receives the user's feedback and incorporates it using the generative AI. The generative AI and emotion engine run again to add a new element to the message, specifically "Meeting location: Meeting Room A." Finally, the server sends the approved electronic message to the recipient.

[1493] Form of document creation

[1494] A user creates a new document, such as a monthly report, on a terminal. The user inputs this month's sales data and progress on major projects into the terminal. The terminal provides the data necessary to create the document and the results of sentiment analysis to the generative AI, and the server receives the generated document. For example, a report is generated that includes a "sales data section" and a "project progress section."

[1495] Similarly, the server sends the generated material to the approval workflow module, receives user feedback and regenerates it, and finally, the server distributes the approved material to the relevant parties.

[1496] Knowledge base operations

[1497] The server stores approved electronic messages and materials in a knowledge base module, which stores information for future reference. Emotional data analyzed by the emotion engine is also stored in the knowledge base. Users can search and reference the knowledge base from their devices and use past information to efficiently create new electronic messages and materials. Recommendations based on emotions are also made.

[1498] Examples of prompt statements

[1499] 1. Generate a meeting invitation email

[1500] An electronic message typed "Schedule a meeting for next Tuesday." User sentiment: Polite and cooperative.

[1501] 2. Preparation of monthly reports

[1502] Data entered: "This month's sales data: $1 million" and "Major project progress: 80% complete." User emotion: Pride and accomplishment.

[1503] As described above, the present invention is a system that efficiently automates a series of processes from creating, approving, and sending electronic messages and materials, and realizes communication that takes emotions into consideration.

[1504] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1505] Step 1:

[1506] A user drafts an electronic message at a terminal.

[1507] Type: A user types "Schedule a meeting for next Tuesday" into a terminal.

[1508] What it does: A user creates and saves a draft using the text input field on their device.

[1509] Output: The created draft of the electronic message is saved in the terminal.

[1510] Step 2:

[1511] The device provides a draft electronic message to the generative AI model.

[1512] Input: The created draft of the electronic message.

[1513] How it works: The device sends the draft content to the generative AI model via API. At the same time, the emotion engine parses the emotion data from the user's input and sends this emotion data to the generative AI model as well.

[1514] Output: The draft content and sentiment data are fed into the generative AI model.

[1515] Step 3:

[1516] The server receives the response from the generative AI model and automatically optimizes the content of the electronic message.

[1517] Input: Optimized message and sentiment data from the generative AI model.

[1518] Action: The server receives the generated AI model's response and checks the generated optimization message.

[1519] Output: The optimized electronic message is stored on the server.

[1520] Step 4:

[1521] The server sends the optimized electronic message to the approval workflow module.

[1522] Input: Optimized electronic message.

[1523] What happens: The server sends the optimized message to the approval workflow module and adds it to the queue for review.

[1524] Output: A message is added to the Approval Workflow module.

[1525] Step 5:

[1526] A user reviews the electronic message in the approval workflow module and provides approval or feedback.

[1527] Input: Electronic messages added to the Approval Workflow module.

[1528] Action: The user accesses the approval workflow module on their device, checks the electronic message, and optionally enters feedback, such as "Please add a meeting location."

[1529] Output: The approval or feedback is saved in the Approval Workflow module.

[1530] Step 6:

[1531] The server receives user feedback and uses regenerative artificial intelligence to adjust the final electronic message.

[1532] Input: User feedback.

[1533] Action: The server calls the generative AI model again based on the feedback, and uses the emotion engine to generate a new message that reflects the feedback. "Meeting location: Meeting Room A" is added.

[1534] Output: The final reconciled electronic message.

[1535] Step 7:

[1536] The server sends the approved electronic message to the recipient.

[1537] Input: The final reconciled electronic message.

[1538] What happens: The server sends the final message to the email server for delivery to the appropriate recipients.

[1539] Output: The recipient receives the optimized electronic message.

[1540] Step 8:

[1541] The user starts creating a new document on the device.

[1542] Input: The user enters sales data and project progress into the terminal.

[1543] Action: The user uses the input fields on the device to enter the required data and begin creating a document.

[1544] Output: The created draft document is saved on the device.

[1545] Step 9:

[1546] The device provides the generative AI model with the data necessary to create the document.

[1547] Input: Draft of the material created.

[1548] How it works: The device uses an API to send the draft and sentiment data to the generative AI model.

[1549] Output: The outline of the material and content requirements are provided to the generative AI model.

[1550] Step 10:

[1551] The server receives the response from the generative AI model and automatically generates the materials.

[1552] Input: Generated material from a generative AI model.

[1553] How it works: The server receives the response from the generative AI model and automatically generates the final version of the material.

[1554] Output: The automatically generated materials are saved on the server.

[1555] Step 11:

[1556] The server sends the generated materials to an approval workflow module.

[1557] Input: Auto-generated materials.

[1558] What happens: The server sends the document to the approval workflow module and adds it to the queue for review.

[1559] Output: The document is added to the approval workflow module.

[1560] Step 12:

[1561] The user reviews the material in the approval workflow module and provides approval or feedback.

[1562] Input: Materials added to the Approval Workflow module.

[1563] Action: The user reviews the material on their device and provides feedback, such as adding graphs, if necessary.

[1564] Output: The approval or feedback is saved in the Approval Workflow module.

[1565] Step 13:

[1566] The server receives user feedback and uses regenerative artificial intelligence to adjust the final material.

[1567] Input: User feedback.

[1568] Action: The server invokes the regenerative AI model and generates materials that reflect the feedback. A sales data graph is added.

[1569] Output: Final, adjusted materials.

[1570] Step 14:

[1571] The server distributes the approved materials to the relevant parties.

[1572] Input: Final adjusted materials.

[1573] How it works: The server distributes the materials to interested parties via email or shared file services.

[1574] Output: Stakeholders receive the final materials.

[1575] Step 15:

[1576] The server stores the approved electronic messages and materials in a knowledge base module.

[1577] Input: Authorized electronic messages and materials.

[1578] How it works: The server stores these data in a knowledge base module, along with the emotion data.

[1579] Output: Stored electronic messages and materials and emotional data.

[1580] Step 16:

[1581] A user searches and references past electronic messages and documents from the knowledge base.

[1582] Input: The user's search query.

[1583] Action: A user accesses the knowledge base module using a terminal and searches for the desired material.

[1584] Output: Historical electronic messages and materials displayed as search results.

[1585] Step 17:

[1586] Users utilize information from the knowledge base to efficiently create new electronic messages and materials.

[1587] Input: Information retrieved from the knowledge base.

[1588] How it works: The device creates new electronic messages and documents based on templates and data acquired by the user, and also utilizes emotional data provided by the emotion engine.

[1589] Output: New electronic messages or materials created.

[1590] (Application example 2)

[1591] 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."

[1592] In modern companies and factories, the process of creating electronic messages and documents is important, but these tasks face the risk of reduced efficiency due to human error and miscommunication. Furthermore, considering the user's emotions when creating messages and documents is an important factor in improving employee motivation and work efficiency, but conventional systems do not adequately address this issue. Therefore, there is a need for an automated communication and document creation system that takes emotions into account.

[1593] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1594] In this invention, the server includes: means for a user to create an electronic message on a terminal; means for the terminal to provide the content of the electronic message to a generative artificial intelligence; means for the server to receive a response from the generative artificial intelligence and automatically optimize the content of the electronic message; means for the server to send the optimized electronic message to an approval workflow module; means for a user to review the electronic message in the approval workflow module and provide approval or feedback; means for the server to adjust the final electronic message based on the feedback; means for the server to send the approved electronic message to a recipient; means for an emotion engine to analyze the user's emotions and provide the emotion data to the generative artificial intelligence; means for generating an optimized electronic message including the emotion data analyzed by the emotion engine; and means for verifying that the optimized electronic message includes emotion information. This enables efficient creation and distribution of high-quality electronic messages and materials that take emotions into consideration.

[1595] An "electronic message" is a digital message sent by electronic means that conveys information primarily in text form.

[1596] A "terminal" is an electronic device operated by a user, such as a PC, smartphone, or tablet.

[1597] "Generative AI" is an AI system that has the ability to generate natural language sentences and content based on input data provided by the user.

[1598] A "server" is a computer system that provides data and services to various computers on a network.

[1599] "Response" refers to the response or output result returned by generative artificial intelligence in response to input data provided by it.

[1600] The "approval workflow module" is a process management system for reviewing created messages and materials and providing approval and feedback.

[1601] "Feedback" refers to any opinions or suggestions provided as a result of a review.

[1602] The "emotion engine" is a system that analyzes the user's emotional state and provides the results as data.

[1603] "Emotion data" is digital data that represents the user's emotional state as analyzed by the emotion engine.

[1604] A "knowledge base" is a database system that stores previously created electronic messages, documents, and related data, and allows them to be searched and referenced.

[1605] An "optimized electronic message" is an electronic message that is generated by a generative artificial intelligence based on user input data and adjusted as necessary to reflect emotional data.

[1606] "Materials" refers to documents, reports, etc. that compile information and data collected for a specific purpose.

[1607] "Overall framework and content requirements" refers to data and information that indicates the main items and requirements needed to create the materials.

[1608] "Automatic generation" refers to the creation of materials based on input data specified by generative artificial intelligence, without manual intervention.

[1609] "Stakeholders" refers to the intended recipients of the messages and materials created.

[1610] This invention is a system for supporting the creation of emotion-sensitive electronic messages and materials, allowing users to efficiently generate and optimize electronic messages and materials using a terminal and provide emotion-based feedback.

[1611] System configuration

[1612] Hardware and Software

[1613] 1. Terminal: An electronic device that a user operates. Examples include computers, smartphones, and tablets.

[1614] 2. Server: A computer system that provides data and services to devices on a network. It processes and stores data, runs AI models, and manages workflow modules.

[1615] 3. Generative AI: Utilizing OpenAI APIs (e.g., text-davinci-003), etc., it generates optimized messages and materials based on input data provided by the user.

[1616] 4. Emotion engine: A system that analyzes the user's emotional state and provides it as data. For example, a sentiment analysis tool that uses NLP technology.

[1617] 5. Approval Workflow Module: A process management system that manages user reviews and feedback.

[1618] Processing flow details

[1619] Create an electronic message

[1620] 1. User input: The user inputs a draft of an electronic message at a terminal, for example, "Please start the next step."

[1621] 2. Emotion Analysis: The emotion engine analyzes the user's current emotional state and provides the emotion data to the generative AI. For example, the emotion engine analyzes that the user is "tired."

[1622] 3. AI optimization: Generative AI optimizes messages based on input drafts and sentiment data. For example, "Thank you for your hard work today. Please begin the next step."

[1623] 4. Approval Workflow: The optimized electronic message is sent to the approval workflow module, where users review the content and provide approval or feedback.

[1624] 5. Incorporating feedback: The server uses the feedback to adjust the final electronic message using generative artificial intelligence.

[1625] 6. Send: The server sends the final approved electronic message to the recipient.

[1626] Creating materials

[1627] 1. User input: The user starts creating a new document on the terminal and enters the outline and content requirements of the document. For example, they enter sales data and progress status to create a "monthly report."

[1628] 2. Emotion analysis: The emotion engine analyzes the user's emotional state and provides the results to the generative AI.

[1629] 3. AI generation: Generative AI automatically generates documents using the input data and sentiment data provided. For example, a report containing a "sales data section" and a "project progress section" is generated.

[1630] 4. Approval Workflow: The generated materials are sent to the approval workflow module, where users review the content and provide approval or feedback.

[1631] 5. Incorporating feedback: The server uses the feedback to adjust the final material by reusing the generative AI.

[1632] 6. Distribution: Finally, the server distributes the approved materials to the relevant parties.

[1633] Prompt Sentence Examples

[1634] A typical example of a prompt provided to a generative AI model is as follows:

[1635] Instructions: Begin the following steps:

[1636] Emotions: Feeling tired

[1637] The system enables the efficient creation and delivery of high-quality, emotionally sensitive electronic messages and materials.

[1638] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1639] Step 1:

[1640] Input: A user enters a draft of an electronic message at a terminal.

[1641] Operation: A user uses a terminal to input a draft of an electronic message such as "Please begin the next step."

[1642] Output: The draft content is displayed on the terminal.

[1643] Step 2:

[1644] Input: Draft content and user sentiment data.

[1645] Operation: The emotion engine analyzes the user's emotions along with the draft content entered on the device. For example, the emotion data that the user is "feeling tired" is analyzed.

[1646] Output: The analyzed emotion data and draft content are provided to the generative AI.

[1647] Step 3:

[1648] Input: Sentiment analysis results and draft content.

[1649] How it works: The server sends the analysis results and a prompt for the draft content to the generative AI. The generative AI then optimizes the electronic message based on this. For example, it might be optimized to say something like, "Thank you for your hard work today. Please begin the next step."

[1650] Output: The optimized electronic message is returned to the server.

[1651] Step 4:

[1652] Input: Optimized electronic message.

[1653] Action: The server sends this optimized electronic message to the approval workflow module.

[1654] Output: The optimized message is displayed in the approval workflow module.

[1655] Step 5:

[1656] Input: Optimized electronic message.

[1657] How it works: A user uses the approval workflow module to review the optimized electronic message and provide approval or feedback as needed, for example, by providing feedback such as "Please clarify the details."

[1658] Output: The approval or feedback is sent to the server.

[1659] Step 6:

[1660] Input: Approval or feedback.

[1661] How it works: Based on the feedback, the server re-uses the generative AI to adjust the electronic message, e.g., regenerating it with more detail, such as "Please start the next step at 2 PM."

[1662] Output: The final regenerated electronic message is stored on the server.

[1663] Step 7:

[1664] Input: The final approved electronic message.

[1665] Action: The server sends the final approved electronic message to the recipient.

[1666] Output: The recipient receives the final electronic message and sees its contents.

[1667] This series of processes allows for the efficient creation and transmission of high-quality, emotionally sensitive electronic messages.

[1668] 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.

[1669] 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.

[1670] 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.

[1671] [Fourth embodiment]

[1672] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1673] 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.

[1674] 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).

[1675] 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.

[1676] 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.

[1677] 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).

[1678] 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.

[1679] 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.

[1680] 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.

[1681] 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.

[1682] 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.

[1683] 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.

[1684] 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."

[1685] This invention relates to a system that automates the creation and approval process of electronic messages (emails) and documents, and utilizes a knowledge base. This system realizes efficient communication by linking together the components of the server, terminal, and user.

[1686] Mail Processing Embodiment

[1687] Creating and optimizing electronic messages

[1688] The user begins to compose an electronic message on the terminal. For example, if the user wants to send an email inviting a meeting, he or she inputs the content "Schedule a meeting for next Tuesday" into the terminal. This input is provided to the generative artificial intelligence.

[1689] The server receives the response from the generative AI and automatically optimizes its content. The generative AI generates an optimized electronic message saying, "We've scheduled a meeting for next Tuesday at 10:00 AM. Please respond if you can attend." The server then sends this optimized electronic message to the approval workflow module.

[1690] Approval and feedback

[1691] The user reviews the optimized electronic message in the approval workflow module and provides feedback as needed. For example, if the user provides feedback to add a meeting location, the server adds "Conference Room A" to the email based on the feedback.

[1692] Finally, the server sends the approved electronic message to the recipient, effectively sending unified, organized email.

[1693] Form of document creation

[1694] Document creation and automatic generation

[1695] The user starts creating a new document on their device. For example, when creating a "monthly report," the user inputs this month's sales data and progress on major projects into the device. This input information is provided to the generative AI.

[1696] The server receives the response from the generative AI and automatically generates the document. For example, the generative AI creates a report that includes a "sales data section" and a "project progress section." The server then sends this automatically generated document to the approval workflow module.

[1697] Approval and feedback on materials

[1698] The user reviews the document in the approval workflow module and provides feedback. For example, if a user requests that a sales data graph be added to the document, the server reflects that feedback and generates the final document with the graph added.

[1699] Finally, the server distributes the approved materials to the relevant parties, thereby enabling uniform, high-quality materials to be created and distributed efficiently.

[1700] Knowledge base operations

[1701] Accumulation and utilization of knowledge

[1702] The server stores approved electronic messages and materials in a knowledge base module, allowing users to efficiently search and reference past knowledge. When users create new electronic messages or materials by utilizing existing knowledge, they can work quickly and efficiently.

[1703] For example, users can search and refer to past "project progress reports" to quickly find templates and reference information needed to create new reports, significantly reducing time and effort.

[1704] The system of the present invention is expected to significantly improve the efficiency of the process of creating and approving electronic messages and materials, and by making effective use of knowledge, to improve the speed and quality of business communications.

[1705] The processing flow will be explained below.

[1706] Specific processing flow for email processing

[1707] Step 1:

[1708] A user drafts an electronic message on a terminal, specifically, inputs an invitation email for a meeting into the terminal.

[1709] Step 2:

[1710] The device provides the content of the electronic message to the generative artificial intelligence, specifically, sending the phrase "Schedule a meeting for next Tuesday" to the generative artificial intelligence.

[1711] Step 3:

[1712] The server receives a response from the generative artificial intelligence, which generates an optimized electronic message based on the input phrase.

[1713] Step 4:

[1714] The server reviews and formats the optimized electronic message received from the generative AI, improving grammar and expression to say, "We've scheduled a meeting for next Tuesday at 10 a.m. Please reply if you can attend."

[1715] Step 5:

[1716] The server sends the optimized electronic message to the approval workflow module, where the email is added to a queue for review.

[1717] Step 6:

[1718] The user reviews the electronic message at the terminal through an approval workflow module and provides approval or feedback, for example, prompting the addition of a meeting location.

[1719] Step 7:

[1720] The server receives the user's feedback and uses generative AI to reflect the feedback. Specifically, it adds "Meeting location: Meeting Room A."

[1721] Step 8:

[1722] The server then sends the final approved electronic message to the recipient, resulting in a high-quality, approved email.

[1723] Specific process flow for creating materials

[1724] Step 1:

[1725] The user starts creating a new document on the device. Specifically, he opens a new document called "Monthly Report."

[1726] Step 2:

[1727] The device provides the generative AI with the requirements for creating documents. Specifically, it inputs "this month's sales data" and "progress of major projects."

[1728] Step 3:

[1729] The server receives the response from the generative AI, which generates materials with the appropriate sections based on the provided requirements.

[1730] Step 4:

[1731] The server formats the data received from the generative AI, generating a report that includes a sales data section and a project progress section.

[1732] Step 5:

[1733] The server sends the generated material to the approval workflow module, where it is added to a queue for review.

[1734] Step 6:

[1735] The user reviews the document through the approval workflow module on the terminal and provides approval or feedback, for example, requesting the addition of a sales data graph.

[1736] Step 7:

[1737] The server receives the user's feedback and uses generative AI to reflect the feedback. Specifically, it adds a graph of sales data.

[1738] Step 8:

[1739] The server distributes the final approved materials to the relevant parties, thereby ensuring that approved, high-quality materials are distributed.

[1740] Specific process flow for knowledge base operation

[1741] Step 1:

[1742] The server stores the approved electronic messages and materials in a knowledge base module, which stores the information for future reference.

[1743] Step 2:

[1744] The user searches for and references the necessary information from the knowledge base on the terminal. Specifically, the user searches for past "project progress reports."

[1745] Step 3:

[1746] Users can use information retrieved from the knowledge base to efficiently create new electronic messages or documents, for example, creating new reports using templates from the past.

[1747] Through these processing steps, the system efficiently automates the creation, approval, and delivery of electronic messages and materials, and also effectively leverages a knowledge base.

[1748] Example 1

[1749] 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."

[1750] The traditional process of creating and approving electronic messages and documents was often done manually, resulting in inefficiency and a decline in the quality of communication. Furthermore, reusing past data required manual searching and referencing, which was time-consuming and labor-intensive.

[1751] 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.

[1752] In this invention, the server includes: a means for a user to create an electronic message on a terminal; a means for the terminal to provide the content of the electronic message to a generative AI model; a means for the server to receive a response from the generative AI model and automatically optimize the content of the electronic message; a means for the server to send the optimized electronic message to an approval workflow module; a means for a user to review the electronic message in the approval workflow module and provide approval or feedback; a means for the server to adjust the final electronic message based on the feedback; a means for the server to send the approved electronic message to a recipient; and a means for the server to store the approved electronic message in a knowledge base module. This streamlines the creation and approval process of electronic messages and materials, enabling advanced communication that efficiently utilizes knowledge.

[1753] "User" means any person or entity that operates the System and creates, reviews, provides feedback on, and approves electronic messages and materials.

[1754] A "terminal" is an electronic device such as a computer, smartphone, or tablet that is operated by a user.

[1755] "Electronic Message" means a message sent or received by email or other means of electronic communication.

[1756] A "generative AI model" is an artificial intelligence model that generates text data based on prompts entered by a user.

[1757] A "server" is a machine that communicates with devices over a network to provide data to a generative AI model and receive results.

[1758] A "response" is the reply text that a generative AI model generates in response to a prompt.

[1759] "Optimization" refers to converting responses received from a generative AI model into a suitable format for electronic messages or documents.

[1760] An "approval workflow module" is a software module for accepting confirmation, feedback, and approval from users.

[1761] "Feedback" means any instruction for correction or improvement given by a user to a generated electronic message or material.

[1762] "Knowledge Base Module" means a software module for storing approved electronic messages and materials for future retrieval and reuse.

[1763] The present invention is a system for automating the creation and approval process of electronic messages and documents and utilizing a knowledge base. This system is composed of a server, terminals, a generative AI model, and various software modules. Each component and its operation are described in detail below.

[1764] Creating and optimizing electronic messages

[1765] A user uses a device to compose an electronic message. For example, when creating a meeting invitation email, the user types something like "Schedule a meeting for next Tuesday." This input is formatted by the device as a prompt and provided to a generative AI model (e.g., GPT-4).

[1766] The server receives the response from the generative AI model, analyzes its content, and generates an optimized message, such as "We've scheduled a meeting for next Tuesday at 10:00 AM. Please reply if you can attend."

[1767] The server then sends the generated optimization message to the approval workflow module, where the user can review the message and provide feedback if necessary, for example, to add "Conference Room A."

[1768] The server adjusts the final electronic message based on the feedback and sends the approved message to the recipient, a process that ensures a uniform message is sent efficiently.

[1769] Prompt Sentence Examples

[1770] Set up a meeting for next Tuesday

[1771] Document creation and automatic generation

[1772] The user starts creating a new document using the device. For example, to create a "monthly report," the user inputs this month's sales data and the progress of major projects into the device. This input information is provided to the generative AI model as a prompt.

[1773] The server receives the response from the generative AI model and automatically generates the document. For example, a report containing a "sales data section" and a "project progress section" is generated. The server sends this report to the approval workflow module and notifies the user.

[1774] The user reviews the document in the approval workflow module and provides feedback as needed, such as "add a chart of sales data."

[1775] The server then distributes the final revised documents to the relevant parties, ensuring efficient distribution of high-quality, uniform documents.

[1776] Prompt Sentence Examples

[1777] Enter this month's sales data and progress on major projects

[1778] Knowledge base operations

[1779] The server stores approved electronic messages and materials in a knowledge base module, allowing users to access the knowledge base from their terminals and search and refer to past electronic messages and materials.

[1780] Users can efficiently create new electronic messages and documents by utilizing referenced knowledge. For example, by referencing a past "Project Progress Report" and using it as a template for a new report, users can significantly reduce time and effort.

[1781] This system is expected to significantly streamline the process of creating and approving electronic messages and documents, and by making effective use of knowledge, to result in faster, higher-quality business communications.

[1782] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1783] Creating and optimizing electronic messages

[1784] Step 1:

[1785] A user begins composing an electronic message on a terminal, for example, typing "Schedule a meeting for next Tuesday." The input data is formatted as a prompt sentence.

[1786] Step 2:

[1787] The device provides the prompt text entered by the user to the generative AI model. Specifically, it sends the prompt text "Schedule a meeting for next Tuesday" to the generative AI model.

[1788] Step 3:

[1789] The server receives a response from the generative AI model. For example, the generative AI model generates an optimized message such as, "We've scheduled a meeting for next Tuesday at 10:00 AM. Please reply if you can attend." This response is then analyzed to obtain the optimized message.

[1790] Step 4:

[1791] The server sends the optimized electronic message to the approval workflow module, specifically, stores the generated optimized message in a database of the approval workflow module, and notifies the user.

[1792] Step 5:

[1793] The user checks the message in the approval workflow module and provides feedback if necessary. For example, the user enters feedback such as adding "Conference Room A" and submits it to the approval workflow module.

[1794] Step 6:

[1795] The server receives feedback from the approval workflow module and adjusts the final electronic message, for example, adding "Conference Room A" based on the feedback, and completes the final message.

[1796] Step 7:

[1797] The server sends the approved electronic message to the recipient, sends the final electronic message based on the recipient list, and notifies the user.

[1798] Step 8:

[1799] The server stores the approved electronic message in the knowledge base module, and accumulates the final message data in the knowledge base database.

[1800] Document creation and automatic generation

[1801] Step 1:

[1802] The user starts creating a document on the terminal. For example, to create a "monthly report," the user inputs this month's sales data and the progress of major projects.

[1803] Step 2:

[1804] The device generates the input information as a prompt and provides it to the generative AI model. Specifically, the prompt "Enter this month's sales data and progress on major projects" is sent to the generative AI model.

[1805] Step 3:

[1806] The server receives a response from the generative AI model. The generated document contains a "sales data section" and a "project progress section." This response is analyzed to obtain the automatically generated document.

[1807] Step 4:

[1808] The server sends the automatically generated material to the approval workflow module, stores the generated material in the database of the approval workflow module, and notifies the user.

[1809] Step 5:

[1810] The user reviews the material in the approval workflow module and provides feedback. For example, the user inputs an instruction to "add a graph of sales data" and submits it to the approval workflow module.

[1811] Step 6:

[1812] The server receives feedback from the approval workflow module and adjusts the final document, adding graphs based on the feedback and completing the final document.

[1813] Step 7:

[1814] The server distributes the approved final documents to the parties concerned, sends the final documents based on the parties list, and notifies the users.

[1815] Step 8:

[1816] The server stores the approved materials in the knowledge base module, and accumulates the final material data in the knowledge base database.

[1817] Knowledge base operations

[1818] Step 1:

[1819] The server stores approved electronic messages and materials in a knowledge base module, organizing and centralizing the data.

[1820] Step 2:

[1821] Users can access the knowledge base from their devices to search and refer to past electronic messages and documents, quickly finding the information they need.

[1822] Step 3:

[1823] Users can leverage knowledge to efficiently create new electronic messages and materials, and save time by leveraging existing templates and reference information.

[1824] Through these steps, the system streamlines the creation and approval process of electronic messages and documents, and makes effective use of knowledge.

[1825] (Application example 1)

[1826] 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."

[1827] In the traditional manufacturing industry, the creation and approval process of work instructions was often performed manually, resulting in time-consuming and labor-intensive tasks. Furthermore, incorporating feedback required significant manual operations, increasing the likelihood of errors and delays. This often resulted in inconsistent quality of the generated instructions. Furthermore, past instructions and documents were not efficiently stored and searched in a knowledge base, resulting in insufficient utilization of past knowledge. To address these issues, the present invention provides a system that automates the creation and approval process of electronic messages and documents, enabling efficient utilization of knowledge.

[1828] 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.

[1829] In this invention, the server includes: a means for a user to create an electronic message on a terminal; a means for the terminal to provide the content of the electronic message to a generative artificial intelligence; a means for the server to receive a response from the generative artificial intelligence and automatically optimize the content of the electronic message; a means for transmitting the electronic message to an approval workflow module; a means for a user to review and approve or provide feedback in the approval workflow module; a means for adjusting the final electronic message based on the feedback; a means for transmitting the approved electronic message to a recipient; a means for receiving a response from the generative artificial intelligence to generate an automated work instruction and generating an optimized work instruction; a means for a user to review and approve or provide feedback in the approval workflow module; a means for adjusting the final work instruction based on the feedback and storing the optimized work instruction in a knowledge base. This reduces errors and delays through automated processes and enables efficient work by utilizing past knowledge while maintaining consistent quality of electronic messages and work instructions.

[1830] A "terminal" is a device that a user operates to create, edit, and input electronic messages, documents, and the like.

[1831] "Generative AI" is an AI system that generates optimized electronic messages and materials based on user input.

[1832] A "server" is a central processing unit that receives responses from generative artificial intelligence and performs various data processing and transmission.

[1833] "Approval Workflow Module" means a software module that manages the process of providing approval or feedback on the content of an electronic message or document.

[1834] "Feedback" refers to additional information or requests for corrections provided by a user through the approval workflow module.

[1835] A "knowledge base" is a database that stores and manages information such as past electronic messages and documents, allowing users to efficiently search and reference them.

[1836] A "work instruction manual" is a document that details the specific work content and procedures for manufacturing robots and workers.

[1837] A "prompt sentence" is an input sentence for obtaining a specific output from a generative artificial intelligence.

[1838] This invention is a system that automates the creation and approval process of electronic messages and documents, utilizing generative artificial intelligence and a knowledge base. This system operates in cooperation with a user terminal, a server that acts as a central processing unit, and an approval workflow module.

[1839] Mail Processing Embodiment

[1840] Creating and optimizing electronic messages

[1841] A user begins to compose an electronic message on a terminal. For example, when the user wants to send an invitation email for a meeting, the user inputs the content "Schedule a meeting for next Tuesday" into the terminal. This input is provided to the generative AI. The server receives a response from the generative AI and automatically optimizes the content. The generative AI generates an optimized electronic message saying "We have scheduled a meeting for next Tuesday at 10:00 AM. Please reply if you can attend." The server then sends this optimized electronic message to the approval workflow module.

[1842] Approval and feedback

[1843] The user reviews the optimized electronic message in the approval workflow module and provides feedback as needed. For example, if the user provides feedback to add a meeting location, the server adds "Conference Room A" to the email based on that feedback. Finally, the server sends the approved electronic message to the recipient, thereby efficiently sending a well-organized email.

[1844] Form of document creation

[1845] Document creation and automatic generation

[1846] A user starts creating a new document on a terminal. For example, when creating a "monthly report," the user inputs this month's sales data and the progress of major projects into the terminal. This input information is provided to the generative AI. The server receives a response from the generative AI and automatically generates the document. For example, the generative AI creates a report that includes a "sales data section" and a "project progress section." The server sends this automatically generated document to an approval workflow module.

[1847] Approval and feedback on materials

[1848] Users review documents and provide feedback in the approval workflow module. For example, if a user requests that a sales data graph be added to the document, the server incorporates that feedback and generates the final document with the graph added. Finally, the server distributes the approved document to the relevant parties. This ensures that consistent, high-quality documents are created and distributed efficiently.

[1849] Generating work instructions for factory robots

[1850] Create and approve work orders

[1851] The user starts creating a work instruction on a terminal. For example, if the work instruction for a manufacturing robot is to "assemble parts A and B," this content is provided to the generative AI. The server receives the response from the generative AI and generates an optimized work instruction. This is then sent to the approval workflow module.

[1852] Users review the instructions in the approval workflow module and provide approval or feedback. If the user provides feedback, the server adjusts and generates the final work instructions based on that feedback. This final work instruction is saved in the knowledge base. This allows for efficient work with fewer errors by utilizing past knowledge.

[1853] As a specific example, if the user inputs "Today's manufactured product will be the assembly of parts A and B. 10 parts A and 20 parts B will be used. Quality checks will be performed," the generated work instruction will be "Today's manufactured product will be the assembly of parts A (10 pieces) and parts B (20 pieces). Please be sure to perform quality checks."

[1854] Example prompt sentence:

[1855] Create a work instruction for your manufacturing robot:

[1856] Today's production involves assembling parts A and B. 10 parts A and 20 parts B are used. Quality check is performed.

[1857] Prepared instructions:

[1858] The present invention automates the process of creating electronic messages, documents, and even work instructions for factory robots, and ensures consistent quality, enabling efficient work with fewer errors and delays.

[1859] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1860] Step 1:

[1861] The user begins composing an electronic message on a terminal. The user types something like "Schedule a meeting for next Tuesday." This input is provided to the generative artificial intelligence as a prompt.

[1862] Input: Message content created by the user

[1863] Output: Prompt sentence to be provided to the generative AI

[1864] Step 2:

[1865] The server sends the prompt to the generative AI and receives a response, which generates an optimized electronic message based on the input: "We've scheduled a meeting for next Tuesday at 10:00 AM. Please reply if you can attend."

[1866] Input: prompt statement

[1867] Output: Optimized electronic message

[1868] Step 3:

[1869] The server sends the optimized electronic message to an approval workflow module, where the electronic message awaits approval or feedback.

[1870] Input: Optimized electronic message

[1871] Output: Waiting for confirmation in the approval workflow module

[1872] Step 4:

[1873] A user reviews the electronic message through the approval workflow module and provides feedback if necessary, for example, "Please add conference room A."

[1874] Input: Feedback

[1875] Output: Feedback content

[1876] Step 5:

[1877] The server adjusts the electronic message based on the feedback, specifically generating a final electronic message with "Conference Room A" added.

[1878] Input: Feedback

[1879] Output: Final corrected electronic message

[1880] Step 6:

[1881] The server sends the final approved electronic message to the recipient, which delivers the completed electronic message to the recipient.

[1882] Input: Final electronic message

[1883] Output: Email sent to recipient

[1884] Step 7:

[1885] The user starts creating a new document on the device. For example, to create a "monthly report," the user inputs this month's sales data and the progress of major projects into the device. This input is provided to the generative AI.

[1886] Input: Original data for document creation

[1887] Output: Prompt sentence to be provided to the generative AI

[1888] Step 8:

[1889] The server receives the response from the generative AI and automatically generates the document, which then creates a report that includes sections on sales data and project progress.

[1890] Input: prompt statement

[1891] Output: Generated material

[1892] Step 9:

[1893] The server sends the generated material to an approval workflow module, where it awaits approval or feedback.

[1894] Input: Generated material

[1895] Output: Waiting for confirmation in the approval workflow module

[1896] Step 10:

[1897] Users review the materials through an approval workflow module and provide feedback, such as "Please add a chart of the sales data."

[1898] Input: Feedback

[1899] Output: Feedback content

[1900] Step 11:

[1901] The server adjusts the materials based on the feedback, specifically generating the final materials with the sales data graph added.

[1902] Input: Feedback

[1903] Output: Final revised document

[1904] Step 12:

[1905] The server distributes the final approved documents to the relevant parties, ensuring that consistent, high-quality documents are delivered to the relevant parties.

[1906] Input: Final material

[1907] Output: Materials distributed to stakeholders

[1908] Step 13:

[1909] The user begins creating work instructions for a manufacturing robot on a terminal. For example, they input, "Today's production task is to assemble parts A and B." This input is provided to the generative artificial intelligence.

[1910] Input: Work instruction source data

[1911] Output: Prompt sentence to be provided to the generative AI

[1912] Step 14:

[1913] The server receives the response from the generative AI and generates an optimized work instruction. The generative AI generates the following: "Today's production involves assembling parts A (10 units) and B (20 units). Please be sure to perform a quality check."

[1914] Input: prompt statement

[1915] Output: Optimized work instructions

[1916] Step 15:

[1917] The server sends the optimized work order to an approval workflow module, where the work order awaits approval or feedback.

[1918] Input: Optimized Work Instruction

[1919] Output: Waiting for confirmation in the approval workflow module

[1920] Step 16:

[1921] A user reviews the work order in the approval workflow module and provides feedback, such as "Please also add a quality check item."

[1922] Input: Feedback

[1923] Output: Feedback content

[1924] Step 17:

[1925] The server adjusts the work instructions based on the feedback, specifically generating the final work instructions with the "quality check items" added.

[1926] Input: Feedback

[1927] Output: Final revised work instructions

[1928] Step 18:

[1929] The server saves the final approved work order in the knowledge base, which accumulates historical data for future work.

[1930] Input: Final Work Order

[1931] Output: Work instructions stored in the knowledge base

[1932] These steps automate the creation and approval process for electronic messages, documents, and work instructions, facilitating effective knowledge utilization.

[1933] 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.

[1934] This invention combines a knowledge-based system that automates the creation and approval process of electronic messages (emails) and documents with an emotion engine. This system, which involves the collaboration of server, terminal, and user components, realizes efficient and emotion-sensitive communication.

[1935] Mail Processing Embodiment

[1936] Creating and optimizing electronic messages

[1937] A user creates a draft of an electronic message on a terminal. For example, if the user wants to send an invitation email to a meeting, the user inputs the content "Schedule a meeting for next Tuesday" into the terminal.

[1938] The terminal provides the contents of the electronic message to the generative AI, and the emotion engine analyzes the user's emotions. This input data and emotion data are provided to the generative AI.

[1939] The server receives the response from the generative AI and automatically optimizes its content. For example, the generative AI generates an optimized message such as, "We have scheduled a meeting for next Tuesday at 10:00 AM. Please reply if you can attend." The user's emotions, analyzed by the emotion engine, are also reflected in this message.

[1940] Approval and feedback

[1941] The server sends the optimized electronic message to the approval workflow module, which adds it to a queue for review.

[1942] The user reviews the electronic message at the terminal through an approval workflow module and provides approval or feedback, for example, prompting the addition of a meeting location.

[1943] The server receives the user's feedback and uses regenerative artificial intelligence to reflect the feedback. At this time, the emotion engine also analyzes the user's emotions and adds appropriate emotional expressions. Specifically, it adds "Meeting location: Meeting Room A."

[1944] Finally, the server sends the approved electronic message to the recipient, resulting in efficient delivery of high-quality, sensitive emails.

[1945] Form of document creation

[1946] Document creation and automatic generation

[1947] The user starts creating a new document on the terminal. For example, when creating a "monthly report," the user inputs this month's sales data and progress on major projects into the terminal.

[1948] The device provides the requirements for creating documents to the generative AI. The emotion engine also analyzes the user's emotions. This data and emotional information are provided to the generative AI.

[1949] The server receives the response from the generative AI and automatically generates the materials. Specifically, it creates a report that includes a "sales data section" and a "project progress section." At this time, expressions based on the emotions analyzed by the emotion engine are applied.

[1950] Approval and feedback on materials

[1951] The server sends the generated document to the approval workflow module, where it is added to a queue for review.

[1952] The user reviews the document through the approval workflow module on the terminal and provides approval or feedback, for example, requesting the addition of a sales data graph.

[1953] The server receives user feedback and uses regenerative artificial intelligence to reflect the feedback. The emotion engine analyzes the user's emotions and adds appropriate emotional expressions to the document. Specifically, it adds a graph of sales data.

[1954] Finally, the server distributes the approved materials to the relevant parties. High-quality materials that take emotional impact into consideration are efficiently created and distributed.

[1955] Knowledge base operation embodiment

[1956] Accumulation and utilization of knowledge

[1957] The server stores the approved electronic messages and materials in a knowledge base module, which stores the information for future reference. Emotion data analyzed by the emotion engine is also stored in the knowledge base.

[1958] The user searches for and references the information they need from the knowledge base on their device. For example, they search for a past "project progress report." At this time, the emotion engine uses the emotion data in the knowledge base to make emotion-based recommendations.

[1959] Users can efficiently create new electronic messages or documents by utilizing information obtained from the knowledge base. For example, they can create new reports using past templates. In this case, too, they can utilize the emotional information provided by the emotion engine.

[1960] Through these processing steps, the system efficiently automates the creation, approval, and transmission of electronic messages and materials, and also utilizes an emotion engine to realize emotion-sensitive communication.In addition, by utilizing a knowledge base, it is possible to effectively utilize past experience and information.

[1961] The processing flow will be explained below.

[1962] Specific processing flow for email processing

[1963] Step 1:

[1964] A user drafts an electronic message on a terminal, specifically, inputs an invitation email for a meeting into the terminal.

[1965] Step 2:

[1966] The device provides the content of the electronic message to the generative artificial intelligence, specifically, sending the phrase "Schedule a meeting for next Tuesday" to the generative artificial intelligence.

[1967] Step 3:

[1968] The emotion engine recognizes and analyzes the user's emotions. For example, if the user is feeling stressed, the emotion engine will analyze the information.

[1969] Step 4:

[1970] The server receives a response from the generative artificial intelligence, which generates an optimized electronic message based on the input phrase.

[1971] Step 5:

[1972] The server acquires the user's emotion data from the emotion engine and reflects it in an optimized electronic message, for example, adding a phrase such as "This meeting is very important and we look forward to your participation."

[1973] Step 6:

[1974] The server sends the optimized electronic message to the approval workflow module, where the email is added to a queue for review.

[1975] Step 7:

[1976] The user reviews the electronic message at the terminal through an approval workflow module and provides approval or feedback, for example, prompting the addition of a meeting location.

[1977] Step 8:

[1978] The server receives the user's feedback and uses generative AI to reflect the feedback. Specifically, it adds "Meeting location: Meeting Room A."

[1979] Step 9:

[1980] The server uses an emotion engine to double-check the final electronic message and reinforce any necessary emotional expressions.

[1981] Step 10:

[1982] The server then sends the final approved electronic message to the recipient, ensuring that the email is sent with high quality, emotional sensitivity, and approval.

[1983] Specific process flow for creating materials

[1984] Step 1:

[1985] The user starts creating a new document on the device. Specifically, he opens a new document called "Monthly Report."

[1986] Step 2:

[1987] The device provides the generative AI with the requirements for creating documents. Specifically, it inputs "this month's sales data" and "progress of major projects."

[1988] Step 3:

[1989] The emotion engine recognizes and analyzes the user's emotion, for example, if the user is happy, the information will be analyzed by the emotion engine.

[1990] Step 4:

[1991] The server receives the response from the generative AI, which generates materials with the appropriate sections based on the provided requirements.

[1992] Step 5:

[1993] The server retrieves the user's emotional data from the emotion engine and reflects it in the document. Specifically, it adds a phrase such as, "This monthly report shows very good results."

[1994] Step 6:

[1995] The server sends the generated material to the approval workflow module, where it is added to a queue for review.

[1996] Step 7:

[1997] The user reviews the document through the approval workflow module on the terminal and provides approval or feedback, for example, requesting the addition of a sales data graph.

[1998] Step 8:

[1999] The server receives the user's feedback and uses generative AI to reflect the feedback, for example adding a graph of sales data.

[2000] Step 9:

[2001] The server uses an emotion engine to double-check the final material and reinforce any necessary emotional expressions.

[2002] Step 10:

[2003] The server distributes the final approved materials to the relevant parties, ensuring that approved, high-quality, and emotionally sensitive materials are delivered.

[2004] Specific process flow for knowledge base operation

[2005] Step 1:

[2006] The server stores the approved electronic messages and materials in a knowledge base module, which stores the information for future reference.

[2007] Step 2:

[2008] The emotion engine stores the emotional data contained in these approved electronic messages and materials in a knowledge base, allowing for emotion-based search and referencing.

[2009] Step 3:

[2010] The user searches for and references the necessary information from the knowledge base on the terminal. For example, the user searches for a past "project progress report."

[2011] Step 4:

[2012] Users can use information retrieved from the knowledge base to efficiently create new electronic messages or documents, for example, creating new reports using templates from the past.

[2013] Step 5:

[2014] The server updates the knowledge base with newly approved electronic messages and materials and their associated emotional data.

[2015] Step 6:

[2016] An emotion engine analyzes the information in the knowledge base and makes emotion-based recommendations, such as recommending the best email template for a particular situation or emotion.

[2017] Through these processing steps, the system efficiently automates the creation, approval, and transmission of electronic messages and materials, and utilizes an emotion engine to ensure emotion-sensitive communication.It also utilizes a knowledge base to effectively utilize past experience and information.

[2018] Example 2

[2019] 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."

[2020] The traditional process of creating electronic messages and materials requires many manual operations, resulting in inefficiencies and human error. It is also difficult to communicate with recipients in a way that takes their emotions into account, which can lead to inappropriate messaging and a lack of consideration for the recipient. Furthermore, there is no established method for effectively utilizing past data and knowledge, which limits the reusability of materials and messages. A system that can solve these issues is needed.

[2021] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[2022] In this invention, the server includes: a means for a user to create an electronic message on a terminal; a means for the terminal to provide the content of the electronic message to a generative AI model; a means for an emotion engine to analyze the user's emotions and provide the data to the generative AI model; a means for the server to receive a response from the generative AI model and automatically optimize the content of the electronic message; a means for the server to send the optimized electronic message to an approval workflow module; a means for a user to review the electronic message in the approval workflow module and provide approval or feedback; a means for the server to receive the feedback and adjust the final electronic message using a regenerative AI; and a means for the server to send the approved electronic message to a recipient. This allows for efficient automation of the creation, approval, and sending of electronic messages and materials, and further enables emotion-conscious communication using an emotion engine.

[2023] An "electronic message" is a communication message in digital form for a user to send through a terminal.

[2024] A "generative AI model" is a model that uses artificial intelligence to generate and optimize text data.

[2025] An "emotion engine" is a system that analyzes emotions from user input data and provides that emotion data.

[2026] "Terminal" means a user-operated device and hardware for creating, editing, and transmitting electronic messages and materials.

[2027] "Server" means a central computer that receives responses from the generative AI model and automates the processing of electronic messages and materials.

[2028] An "approval workflow module" is a system for reviewing user-created electronic messages and materials and providing approval or feedback.

[2029] "Feedback" means any corrections or additional instructions or suggestions provided by a User to an Electronic Message or Material.

[2030] "Knowledge Base Module" means a system for storing approved electronic messages and materials and allowing users to search and reference historical data.

[2031] "Optimization" is the process of improving the content of electronic messages and materials provided by generative AI models, tailoring them to be efficient and emotionally sensitive.

[2032] "Reconciliation" is the process of modifying electronic messages and materials to reflect user feedback.

[2033] A "prompt" is text data and instructions input to a generative AI model to obtain a specific output.

[2034] The present invention is a system that automates the creation and approval process of electronic messages and documents, and realizes emotion-conscious communication by combining it with an emotion engine. This system operates in cooperation with the user, terminal, and server components. Specific embodiments of the present invention are described below.

[2035] Creating and optimizing electronic messages

[2036] A user drafts an electronic message, such as a meeting invitation email, on a device. For example, the user types, "Schedule a meeting for next Tuesday." The device provides this input to a generative AI (e.g., GPT-3) in the form of a prompt. At the same time, an emotion engine (e.g., IBM Watson) analyzes the user's emotions and sends this data to the generative AI.

[2037] The server receives the response from the generative AI and automatically optimizes its content. For example, it receives an optimized message such as, "We've scheduled a meeting for next Tuesday at 10:00 AM. Please reply if you can attend." This message also appropriately reflects the results of user sentiment analysis.

[2038] Approval and feedback

[2039] The server sends the optimized electronic message to the approval workflow module and adds it to a queue for review. The user reviews the electronic message through the approval workflow module at the terminal and provides approval or feedback, such as requesting the addition of a meeting location.

[2040] The server receives the user's feedback and incorporates it using the generative AI. The generative AI and emotion engine run again to add a new element to the message, specifically "Meeting location: Meeting Room A." Finally, the server sends the approved electronic message to the recipient.

[2041] Form of document creation

[2042] A user creates a new document, such as a monthly report, on a terminal. The user inputs this month's sales data and progress on major projects into the terminal. The terminal provides the data necessary to create the document and the results of sentiment analysis to the generative AI, and the server receives the generated document. For example, a report is generated that includes a "sales data section" and a "project progress section."

[2043] Similarly, the server sends the generated material to the approval workflow module, receives user feedback and regenerates it, and finally, the server distributes the approved material to the relevant parties.

[2044] Knowledge base operations

[2045] The server stores approved electronic messages and materials in a knowledge base module, which stores information for future reference. Emotional data analyzed by the emotion engine is also stored in the knowledge base. Users can search and reference the knowledge base from their devices and use past information to efficiently create new electronic messages and materials. Recommendations based on emotions are also made.

[2046] Examples of prompt statements

[2047] 1. Generate a meeting invitation email

[2048] An electronic message typed "Schedule a meeting for next Tuesday." User sentiment: Polite and cooperative.

[2049] 2. Preparation of monthly reports

[2050] Data entered: "This month's sales data: $1 million" and "Major project progress: 80% complete." User emotion: Pride and accomplishment.

[2051] As described above, the present invention is a system that efficiently automates a series of processes from creating, approving, and sending electronic messages and materials, and realizes communication that takes emotions into consideration.

[2052] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2053] Step 1:

[2054] A user drafts an electronic message at a terminal.

[2055] Type: A user types "Schedule a meeting for next Tuesday" into a terminal.

[2056] What it does: A user creates and saves a draft using the text input field on their device.

[2057] Output: The created draft of the electronic message is saved in the terminal.

[2058] Step 2:

[2059] The device provides a draft electronic message to the generative AI model.

[2060] Input: The created draft of the electronic message.

[2061] How it works: The device sends the draft content to the generative AI model via API. At the same time, the emotion engine parses the emotion data from the user's input and sends this emotion data to the generative AI model as well.

[2062] Output: The draft content and sentiment data are fed into the generative AI model.

[2063] Step 3:

[2064] The server receives the response from the generative AI model and automatically optimizes the content of the electronic message.

[2065] Input: Optimized message and sentiment data from the generative AI model.

[2066] Action: The server receives the generated AI model's response and checks the generated optimization message.

[2067] Output: The optimized electronic message is stored on the server.

[2068] Step 4:

[2069] The server sends the optimized electronic message to the approval workflow module.

[2070] Input: Optimized electronic message.

[2071] What happens: The server sends the optimized message to the approval workflow module and adds it to the queue for review.

[2072] Output: A message is added to the Approval Workflow module.

[2073] Step 5:

[2074] A user reviews the electronic message in the approval workflow module and provides approval or feedback.

[2075] Input: Electronic messages added to the Approval Workflow module.

[2076] Action: The user accesses the approval workflow module on their device, checks the electronic message, and optionally enters feedback, such as "Please add a meeting location."

[2077] Output: The approval or feedback is saved in the Approval Workflow module.

[2078] Step 6:

[2079] The server receives user feedback and uses regenerative artificial intelligence to adjust the final electronic message.

[2080] Input: User feedback.

[2081] Action: The server calls the generative AI model again based on the feedback, and uses the emotion engine to generate a new message that reflects the feedback. "Meeting location: Meeting Room A" is added.

[2082] Output: The final reconciled electronic message.

[2083] Step 7:

[2084] The server sends the approved electronic message to the recipient.

[2085] Input: The final reconciled electronic message.

[2086] What happens: The server sends the final message to the email server for delivery to the appropriate recipients.

[2087] Output: The recipient receives the optimized electronic message.

[2088] Step 8:

[2089] The user starts creating a new document on the device.

[2090] Input: The user enters sales data and project progress into the terminal.

[2091] Action: The user uses the input fields on the device to enter the required data and begin creating a document.

[2092] Output: The created draft document is saved on the device.

[2093] Step 9:

[2094] The device provides the generative AI model with the data necessary to create the document.

[2095] Input: Draft of the material created.

[2096] How it works: The device uses an API to send the draft and sentiment data to the generative AI model.

[2097] Output: The outline of the material and content requirements are provided to the generative AI model.

[2098] Step 10:

[2099] The server receives the response from the generative AI model and automatically generates the materials.

[2100] Input: Generated material from a generative AI model.

[2101] How it works: The server receives the response from the generative AI model and automatically generates the final version of the material.

[2102] Output: The automatically generated materials are saved on the server.

[2103] Step 11:

[2104] The server sends the generated materials to an approval workflow module.

[2105] Input: Auto-generated materials.

[2106] What happens: The server sends the document to the approval workflow module and adds it to the queue for review.

[2107] Output: The document is added to the approval workflow module.

[2108] Step 12:

[2109] The user reviews the material in the approval workflow module and provides approval or feedback.

[2110] Input: Materials added to the Approval Workflow module.

[2111] Action: The user reviews the material on their device and provides feedback, such as adding graphs, if necessary.

[2112] Output: The approval or feedback is saved in the Approval Workflow module.

[2113] Step 13:

[2114] The server receives user feedback and uses regenerative artificial intelligence to adjust the final material.

[2115] Input: User feedback.

[2116] Action: The server invokes the regenerative AI model and generates materials that reflect the feedback. A sales data graph is added.

[2117] Output: Final, adjusted materials.

[2118] Step 14:

[2119] The server distributes the approved materials to the relevant parties.

[2120] Input: Final adjusted materials.

[2121] How it works: The server distributes the materials to interested parties via email or shared file services.

[2122] Output: Stakeholders receive the final materials.

[2123] Step 15:

[2124] The server stores the approved electronic messages and materials in a knowledge base module.

[2125] Input: Authorized electronic messages and materials.

[2126] How it works: The server stores these data in a knowledge base module, along with the emotion data.

[2127] Output: Stored electronic messages and materials and emotional data.

[2128] Step 16:

[2129] A user searches and references past electronic messages and documents from the knowledge base.

[2130] Input: The user's search query.

[2131] Action: A user accesses the knowledge base module using a terminal and searches for the desired material.

[2132] Output: Historical electronic messages and materials displayed as search results.

[2133] Step 17:

[2134] Users utilize information from the knowledge base to efficiently create new electronic messages and materials.

[2135] Input: Information retrieved from the knowledge base.

[2136] How it works: The device creates new electronic messages and documents based on templates and data acquired by the user, and also utilizes emotional data provided by the emotion engine.

[2137] Output: New electronic messages or materials created.

[2138] (Application example 2)

[2139] 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."

[2140] In modern companies and factories, the process of creating electronic messages and documents is important, but these tasks face the risk of reduced efficiency due to human error and miscommunication. Furthermore, considering the user's emotions when creating messages and documents is an important factor in improving employee motivation and work efficiency, but conventional systems do not adequately address this issue. Therefore, there is a need for an automated communication and document creation system that takes emotions into account.

[2141] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[2142] In this invention, the server includes: means for a user to create an electronic message on a terminal; means for the terminal to provide the content of the electronic message to a generative artificial intelligence; means for the server to receive a response from the generative artificial intelligence and automatically optimize the content of the electronic message; means for the server to send the optimized electronic message to an approval workflow module; means for a user to review the electronic message in the approval workflow module and provide approval or feedback; means for the server to adjust the final electronic message based on the feedback; means for the server to send the approved electronic message to a recipient; means for an emotion engine to analyze the user's emotions and provide the emotion data to the generative artificial intelligence; means for generating an optimized electronic message including the emotion data analyzed by the emotion engine; and means for verifying that the optimized electronic message includes emotion information. This enables efficient creation and distribution of high-quality electronic messages and materials that take emotions into consideration.

[2143] An "electronic message" is a digital message sent by electronic means that conveys information primarily in text form.

[2144] A "terminal" is an electronic device operated by a user, such as a PC, smartphone, or tablet.

[2145] "Generative AI" is an AI system that has the ability to generate natural language sentences and content based on input data provided by the user.

[2146] A "server" is a computer system that provides data and services to various computers on a network.

[2147] "Response" refers to the response or output result returned by generative artificial intelligence in response to input data provided by it.

[2148] The "approval workflow module" is a process management system for reviewing created messages and materials and providing approval and feedback.

[2149] "Feedback" refers to any opinions or suggestions provided as a result of a review.

[2150] The "emotion engine" is a system that analyzes the user's emotional state and provides the results as data.

[2151] "Emotion data" is digital data that represents the user's emotional state as analyzed by the emotion engine.

[2152] A "knowledge base" is a database system that stores previously created electronic messages, documents, and related data, and allows them to be searched and referenced.

[2153] An "optimized electronic message" is an electronic message that is generated by a generative artificial intelligence based on user input data and adjusted as necessary to reflect emotional data.

[2154] "Materials" refers to documents, reports, etc. that compile information and data collected for a specific p...

Claims

1. means for a user to compose an electronic message at a terminal; means for the terminal to provide the content of the electronic message to a generative artificial intelligence; a server receiving a response from the generative artificial intelligence and automatically optimizing the content of the electronic message; means for the server to send the optimized electronic message to the approval workflow module; means for a user to review said electronic message in said approval workflow module and provide approval or feedback; means for the server to adjust the final electronic message based on said feedback; means for the server to send the authorized electronic message to the recipient; A system including:

2. a means for a user to initiate creation of a document on the terminal; A means for the terminal to provide the generative artificial intelligence with a request for the outline and content of the materials; A server receives a response from the generative artificial intelligence and automatically generates materials; A means for the server to send the generated material to an approval workflow module; a means for users to review the materials and provide approval or feedback in an approval workflow module; means for the server to adjust the final material based on said feedback; a means by which the server distributes the approved materials to interested parties; The system of claim 1 , comprising:

3. means for the server to store authorized electronic messages and materials in a knowledge base module; means for a user to search and reference past electronic messages and materials from said knowledge base; a means for users to utilize said knowledge to efficiently create new electronic messages and materials; The system of claim 1 , comprising:

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