system

Abstract

We provide the system. [Solution] A database that stores organizational information and past project data, A generation means that automatically extracts relevant parties from the aforementioned database and sends out invitation emails, A generation AI method that generates a draft proposal based on project information, An interface means for displaying the aforementioned draft and enabling user review and modification, A management system that allows proposals reflecting user modifications to be saved and made available for submission, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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

Summary of the Invention

[0006] "Organizational information" refers to basic data about the structure, departments, and personnel of a company or organization, and is fundamental information for engaging in all activities within the organization.

[0007] "Past project data" refers to the accumulation of information related to projects and matters previously handled by the organization, including records of project objectives, progress, results, and the stakeholders involved.

[0008] A "database" is a system for efficiently storing, managing, and retrieving a structured collection of information, and is used to store organizational information and past project data.

[0009] A "generation mechanism" is a system that automatically extracts necessary data and information based on specified conditions and provides a function for convening relevant parties.

[0010] "Generative AI methods" refer to technologies that utilize machine learning and artificial intelligence techniques to automatically generate proposals from given information.

[0011] An "interface means" is a mechanism that provides a screen or input means for the user to operate the system, enabling them to review and revise proposals.

[0012] "Management measures" refer to the functions that control the entire process of incorporating revisions to a proposal, obtaining final approval, and submitting it to the client. [Brief explanation of the drawing]

[0013] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3]It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Embodiments for Carrying Out the Invention

[0014] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0015] First, the language used in the following description will be explained. <00000​​In the following embodiments, the labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0017] In the following embodiments, the labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0018] In the following embodiments, the labeled storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0019] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor and 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), or Bluetooth (registered trademark), and the like.

[0020] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0024] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0025] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0026] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0027] 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 perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0028] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0031] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0032] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0033] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0034] This invention provides a system that streamlines coordination tasks within an organization by utilizing organizational information and past case data. This system functions between a server, a terminal, and a user.

[0035] The server maintains a database containing organizational information and past project data, efficiently managing necessary information. Using AI generation, it creates drafts based on past proposals and standard templates related to specified projects. This significantly reduces the time required to create proposals. Furthermore, the server analyzes information on stakeholders and, if necessary, automatically recruits suitable stakeholders for the new project.

[0036] The terminal presents the user with project progress and provides an interface for reviewing and revising automatically generated proposal drafts. This interface is designed to allow users to easily review the content and intuitively make necessary revisions.

[0037] Users access the system via a terminal, select a new project, and input project-related requirements and conditions. This allows them to verify that the proposal content meets the project requirements. After the user has reviewed the draft, the system manages the process from final approval to saving and submitting the proposal as an official document.

[0038] As a concrete example, consider a new product development project. In this case, the server analyzes data from similar past projects and generates a proposal that includes technical information and market analysis related to the new product. Users can review this proposal on their terminals and add their opinions on product features and sales strategies. After final approval, the proposal is automatically submitted to the customer. This entire process reduces coordination costs within the organization and enables rapid and efficient work execution.

[0039] The following describes the processing flow.

[0040] Step 1:

[0041] The user logs into the system using their device. After logging in, the dashboard is displayed, offering options to select ongoing projects or start a new project.

[0042] Step 2:

[0043] The user selects a project that requires adjustment. The terminal sends the selection to the server, which retrieves organizational information and past project data related to that project from its database.

[0044] Step 3:

[0045] The server automatically extracts stakeholders related to the project. The server analyzes past project data in the database and lists the departments and personnel involved in the project.

[0046] Step 4:

[0047] The server automatically sends project invitation emails to the identified stakeholders. The emails include an overview of the project and details of their assigned tasks.

[0048] Step 5:

[0049] The AI ​​on the server generates a draft proposal based on project information. The AI ​​creates the content using past success stories and standard templates, and then formats the layout of the proposal.

[0050] Step 6:

[0051] The server sends a draft of the generated proposal to the terminal. The terminal then presents this draft to the user, making it available for review and revision.

[0052] Step 7:

[0053] The user reviews the draft proposal and makes revisions or provides additional feedback as needed. The revisions are sent to the server via the device.

[0054] Step 8:

[0055] The server reflects the user's changes and updates the proposal. The updated proposal is then sent back to the terminal for review.

[0056] Step 9:

[0057] The user will ultimately review and approve the proposal. After approval, the proposal becomes an official document and is stored on the server.

[0058] Step 10:

[0059] The server submits the approved proposal to the customer. Proposals are submitted via an online platform or email, and the user receives a notification that the submission is complete.

[0060] (Example 1)

[0061] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0062] In modern organizations, tasks such as project management and proposal writing require significant time and resources, and inefficient processes are often a major challenge. In particular, efficient information extraction based on past examples, followed by review and revision, is crucial for proposal writing. However, performing these tasks manually is time-consuming and prone to errors. Therefore, there is a need to build a system that streamlines the entire process from information management, generation, review, revision, and submission, enabling tasks to proceed quickly and reliably.

[0063] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0064] In this invention, the server includes an information management means for storing organizational information and past project data, an information generation means for automatically extracting relevant elements from the information management means and transmitting notification information, and an information generation device for generating a draft document based on project-related information. This automates the process from information extraction to proposal creation, content review and revision, and final submission, enabling efficient and error-free work execution.

[0065] "Information management means" refers to devices and methods for storing and efficiently managing organizational information and past case data.

[0066] "Information generation means" refers to a device and method for automatically extracting relevant elements from information management means and generating and transmitting notification information as needed.

[0067] "Information generation device" refers to a device and method for generating draft documents based on project-related information.

[0068] "Display device" refers to a device and method for displaying a draft of a generated document, enabling users to review and modify it.

[0069] "Information management device" refers to a device and method for saving and submitting documents that have been modified by users.

[0070] In this invention, a server, a terminal, and a user work together to build an information processing system.

[0071] The server first prepares a database to store organizational information and past project data. This database can be built using common data management software or cloud storage services. Next, it automates the information generation process using a generative AI model. This model uses natural language processing techniques to generate draft documents related to the project. For example, by inputting a prompt such as, "Please create a draft proposal for a new product development project that includes market analysis and technical information based on similar past cases," it will generate appropriate content.

[0072] The terminal functions as an interface connecting the user and the server. The information displayed on the terminal is designed to be intuitively understandable to the user, allowing them to review and revise draft documents. Ideally, it should be easily operated by the user using a touchscreen display or mouse and keyboard.

[0073] Users can access the system through their terminals to review and modify recorded data and generated documents. This allows them to verify that the final document conforms to the project's objectives before approving it. This entire process streamlines operations and significantly reduces proposal creation time. It also facilitates collaboration across the organization by enabling the rapid dissemination of necessary information to stakeholders.

[0074] In this way, the entire system for carrying out the invention is efficiently and effectively constructed, and a specific form supporting the claims is provided.

[0075] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0076] Step 1:

[0077] The server retrieves organizational information and past project data from the database. Based on the database settings, it searches for relevant data as input and outputs it as structured data. Specifically, it uses SQL queries and API calls to systematically extract the necessary information.

[0078] Step 2:

[0079] The server inputs prompt messages into the generating AI model based on the acquired data. For example, the prompt message used as input might be, "Please create a draft proposal based on similar cases in a new product development project." The generating AI model analyzes the data using natural language processing techniques, creates a draft proposal, and outputs it in text format. Specifically, the process involves inputting prompts into the AI ​​model and receiving responses from the model.

[0080] Step 3:

[0081] The terminal presents the user with a draft proposal received from the server. The input data includes the text of the draft sent from the server, which is then output in a formatted display format. Specifically, the draft is displayed on the screen within the user interface, allowing the user to review its contents.

[0082] Step 4:

[0083] The user reviews the draft presented via the terminal and makes revisions as needed. Input data includes revision instructions and new information from the user. The output is a draft of the new proposal reflecting the revisions. Specifically, the user edits the draft text using a keyboard or touchscreen.

[0084] Step 5:

[0085] The server saves the proposal after it has been modified by the user and makes it ready for submission. It receives the revised proposal as input, updates the database based on it, and prepares it for external transmission. The output is a file containing the final approved proposal stored in the database and automatically sent as needed. The specific actions are data saving after the approval button is pressed and transmission to relevant parties if necessary.

[0086] (Application Example 1)

[0087] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0088] In streamlining production planning and creating proposals, identifying relevant stakeholders and gathering necessary information is time-consuming, leading to increased time and human resource costs. Furthermore, these tasks can cause delays in on-site decision-making and operational inconsistencies, resulting in decreased production efficiency.

[0089] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0090] In this invention, the server includes a storage device for storing organizational information and past event data, a generation means for automatically identifying relevant parties from the storage device and sending notifications, and a generation AI means for generating a draft proposal based on information related to the production plan. This enables effective recruitment of relevant parties in the production plan and rapid proposal creation.

[0091] "Organizational information" refers to structured data about a specific organization, including the roles and responsibilities of its members and contact information.

[0092] "Past event data" refers to recorded data related to activities and projects previously carried out within the organization, and is used as the basis for analyzing and proposing similar events.

[0093] A "storage device" refers to hardware and its management software used to store and retain digital information, and functions as part of a database server.

[0094] "Generation means" refers to devices or software that have the function of identifying relevant parties and automatically issuing notifications based on specific inputs.

[0095] A "production plan" refers to a plan that outlines schedules and procedures designed to improve the efficiency of production processes in the manufacturing industry.

[0096] "Generative AI means" refers to a system that uses artificial intelligence technology to automatically generate drafts of documents and proposals based on specific instructions or data.

[0097] "Display means" refers to an interface that visualizes generated documents and information for the user and enables interaction, and includes monitors and touch devices.

[0098] "Management measures" refer to the mechanisms and procedures for securely storing user-modified proposals and submitting them to relevant parties upon request.

[0099] In this invention, the system is implemented in a form in which a server, a terminal, and a user cooperate to carry it out.

[0100] The server has a storage device that stores organizational information and historical event data, and uses this data to assist in creating production plans. The server is equipped with an AI model as a generation AI means, which generates draft proposals by referring to past success stories and standard templates. This AI model is a natural language generation model such as GPT-3 (registered trademark). As a generation means, the server has a function to automatically identify relevant stakeholders from the storage device and notify them of the progress.

[0101] The terminal provides a means for users to review proposal drafts and make necessary revisions. The displayed draft is designed for interactive review, and smartphones and tablets fulfill this role. This allows users to efficiently provide feedback and revise drafts generated by AI-generated tools to better suit their actual work needs.

[0102] Users access the system via their terminals to review and revise the generated draft proposals. The revised proposals are securely stored by the server's management system and automatically submitted to the relevant departments and end users as needed.

[0103] As a concrete example, when adding a new product line, the system of the present invention can be used to quickly formulate a production plan. For instance, based on a prompt such as, "Please create a proposal for adding a new product line. Required conditions are an additional production capacity of 2,000 units per month, whether existing equipment will be upgraded, and the number of engineers required," the AI ​​model generates a draft, and the information is efficiently shared with relevant parties.

[0104] This series of processes allows organizations to improve productivity and significantly reduce the time required for coordination and response.

[0105] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0106] Step 1:

[0107] The server retrieves organizational information and historical event data from its storage device. This data includes history and information about stakeholders related to production planning. Based on this data, the AI ​​generator uses it as foundational information to produce a draft proposal.

[0108] Step 2:

[0109] The server sends prompt messages to the generative AI model to generate a draft proposal. These prompt messages include information about the expansion requirements and production capacity for the new product line. The generative AI model uses natural language processing based on this information to generate an appropriate draft proposal.

[0110] Step 3:

[0111] The server sends a draft of the generated proposal to the terminal. The terminal displays the draft to the user. This display is done via a smartphone or tablet screen, allowing the user to review the content and provide interactive feedback.

[0112] Step 4:

[0113] Users review the draft proposal using a terminal and make revisions as needed. Revisions are entered through the terminal's interface, and the changes are reflected in the draft. The entered data includes specific production conditions and additional information about stakeholders.

[0114] Step 5:

[0115] Proposals modified on the terminal are saved by the server's management system. The server performs final approval and automatically submits the completed proposal to the relevant departments and end users. After all submissions are complete, the server generates a process log and stores it for future reference as needed.

[0116] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0117] This invention combines a system that streamlines internal coordination using organizational information and past project data with an emotion engine that recognizes user emotions. This system operates between a server, a terminal, and a user, enabling sophisticated adjustments that take user emotions into account during the proposal creation process.

[0118] The server stores organizational information and past project data in a database, allowing for quick retrieval of project-related information. It also utilizes generative AI to create draft proposals. Crucially, the server's built-in emotion engine analyzes the user's emotional state and customizes the proposal's style and content accordingly. This ensures the proposal is presented in a more acceptable format.

[0119] The terminal serves as an interface for providing information to the user, presenting them with draft proposals and requests for revisions. Furthermore, the terminal has a function to visually display emotion recognition results, allowing users to easily check their own emotional state. This information is useful when reviewing and revising proposals.

[0120] Users access the system and select projects and review and revise proposals via their terminals. After the proposal content is appropriately adjusted, the user gives final approval, and the proposal is saved by the server and prepared for submission. The emotion engine accumulates user feedback, enabling more refined emotional responses in future proposal creation processes.

[0121] For example, considering the proposal process for a new project, if the user's emotions are positive, the server will generate a proposal that emphasizes proactiveness. On the other hand, if negative emotions are detected, the proposal will focus on solutions to problems, making it more convincing to the user. This leads to smoother communication within the organization and more effective coordination.

[0122] The following describes the processing flow.

[0123] Step 1:

[0124] The user logs into the system using their device. After logging in, the emotion engine analyzes the user's emotional state and records their current emotions.

[0125] Step 2:

[0126] The user selects a project from their device. The device sends the selected information to the server, which retrieves relevant organizational information and past project data from its database.

[0127] Step 3:

[0128] Based on the data acquired by the server, relevant stakeholders are automatically selected. The server then sends an invitation email to the selected stakeholders and shares an overview of the project.

[0129] Step 4:

[0130] The server uses a generative AI based on project information to create a draft proposal. The emotion engine adjusts the style and content of the draft according to the user's emotional state.

[0131] Step 5:

[0132] The server sends a draft proposal it has created to the terminal. The terminal visually displays this draft and the user's emotional state, allowing the user to review it.

[0133] Step 6:

[0134] Users review the proposal draft and input revisions and feedback on their devices as needed. They then fine-tune the content and wording based on the sentiment recognition results.

[0135] Step 7:

[0136] The terminal sends the changes to the server. The server reflects these changes and updates the proposal.

[0137] Step 8:

[0138] The user performs a final review and approves the proposal. The terminal sends this information to the server, which then saves the proposal.

[0139] Step 9:

[0140] The server automatically submits approved proposals to the customer and sends a notification to the user upon completion of submission.

[0141] (Example 2)

[0142] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0143] In today's business environment, the processes of internal coordination and proposal writing are complex and require considerable time and effort. Misunderstandings and conflicts of opinion frequently occur during this process, hindering smooth project progress. Furthermore, coordination that takes into account the emotional state of users is difficult, and communication is often insufficient, especially when reconciling diverse opinions. This often reduces the likelihood of proposals being accepted, hindering efficient work execution.

[0144] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0145] In this invention, the server includes an information storage means for storing organizational information and past project data, a generation means for automatically extracting and communicating with relevant parties, a generation model means for generating proposals based on project-related information, and an emotion analysis means for analyzing the user's emotional state and customizing the writing style and content. This enables flexible proposal creation that takes user emotions into account and efficient internal coordination.

[0146] "Information storage means" refers to devices and systems that allow for the long-term storage and rapid access of organizational information and past case data.

[0147] "Generation means" refers to a device or program that has the function of automatically extracting relevant parties from information storage means and conducting necessary communications.

[0148] The "generative modeling method" is an artificial intelligence-based process that utilizes project-related information and past case studies to create a draft proposal.

[0149] "Emotional analysis tools" refer to devices or software that detect and analyze a user's emotional state and then appropriately customize the writing style and content based on the results.

[0150] "Interface means" refers to devices or programs that provide an operating environment that allows users to review and revise draft proposals.

[0151] A "management tool" refers to a device or system that has the function of saving proposals that reflect user modifications and preparing them for submission.

[0152] This invention is a system aimed at efficient proposal creation and internal coordination that takes user emotions into consideration. First, the server uses information storage means to store organizational information and past project data in a database. This database is constructed, for example, by utilizing a business database management system.

[0153] The server uses a generation mechanism to automatically extract relevant stakeholders from the information storage mechanism and sends the information to the stakeholders via email or other communication methods. Possible communication tools include general email systems and internal chat tools.

[0154] As a generative model, the server uses a generative AI model to generate a draft of the project proposal. This AI model utilizes past case data and templates and employs an AI model known as a text generation engine (for example, a model using natural language processing technology).

[0155] The terminal presents the generated proposal draft to the user via an interface. The terminal also incorporates emotion analysis capabilities, for example, using a camera and emotion recognition software (e.g., an open-source emotion recognition library) to analyze the user's facial expressions and tone of voice and understand the user's emotional state. Based on these results, the server dynamically customizes the style and content of the proposal.

[0156] Furthermore, users can review and, if necessary, revise their proposals through their terminals. After the user has completed revisions and final approval has been granted, the server will save the proposal using its management system and manage it in a state where it can be submitted.

[0157] As a concrete example, in the process of proposing a new project, if the user's emotions are positive, the server can generate a proposal that emphasizes proactiveness. An example of a prompt to input into the generation AI model would be, "Please create a project proposal. The user's emotional state is positive. Please make the content emphasize a proactive stance." In this way, the present invention enables proposal creation and coordination within an organization in an effective and emotionally responsive manner.

[0158] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0159] Step 1:

[0160] The server uses information storage means to store organizational information and historical project data in a database. This is done by importing data from the management system. Organizational metadata and project history data are used as input, and storing this in the database in a structured format makes it readily accessible in later steps.

[0161] Step 2:

[0162] The terminal activates an emotion analysis system to capture the user's emotions. This analysis involves real-time data capture using a camera and microphone. The input consists of the user's facial expressions and voice data, which are analyzed by emotion analysis software. This results in an output indicating an emotional state, such as positive or negative, which is then sent to the server.

[0163] Step 3:

[0164] The server uses a generation mechanism to automatically extract relevant parties from the information storage mechanism and communicates with them regarding the need to create a proposal. Specifically, it performs calculations to select appropriate parties based on past project performance data and automatically sends emails to their contact information. The emails contain background information on the project and guidelines for creating the proposal.

[0165] Step 4:

[0166] The server utilizes a generative model to generate a draft project proposal. Basic project information and the user's emotional state are used as input. By providing prompts to the generative AI model, a draft proposal with adjusted style and content is output. This draft is customized to be easily accepted by the user.

[0167] Step 5:

[0168] The terminal presents the user with a draft proposal through an interface. The user uses this interface to review the proposal and provide revision instructions. The user's input, including specific feedback and revision requests, is converted into a system-readable format and sent to the server. This allows for revisions tailored to the user's specific needs.

[0169] Step 6:

[0170] The user reviews the final revised proposal and gives their approval. The terminal notifies the server of this approval step. The approved proposal is then saved by the server's management system and prepared for automatic transmission to the client or relevant parties as needed. This completes the cycle from proposal creation to submission.

[0171] (Application Example 2)

[0172] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0173] Traditional proposal creation systems often fail to adequately consider emotions during internal coordination and customer interaction, resulting in proposals being less likely to be accepted. Furthermore, it's difficult to grasp emotional shifts in real time during customer interactions and respond accordingly. This leads to situations where proposals and customer service don't fully meet customer expectations and needs.

[0174] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0175] In this invention, the server includes data storage means for storing organizational information and past case data, generation AI means for generating draft proposals based on project-related information, and emotion analysis means for analyzing the user's emotional state and customizing the proposal to match the writing style and content. This makes it possible to generate proposals that respond to the user's emotions and to adjust customer service styles in physical stores to suit the emotions of the customers.

[0176] "Organizational information" refers to data about the structure, roles, and membership of companies and organizations.

[0177] "Past project data" refers to data that includes all records related to projects and tasks that have been processed to date.

[0178] A "data storage system" is a mechanism for storing information and data long-term and making it available for retrieval as needed.

[0179] "Generative means" refers to a tool or method that has the function of generating or processing information and providing the results to another system or human being.

[0180] "Generative AI means" refers to methods that utilize artificial intelligence technology to automatically create information and documents according to specified goals and conditions.

[0181] An "interface means" is a point of contact or method that enables data exchange and manipulation between a user and a system.

[0182] "Emotional analysis tools" are technologies that analyze and identify the emotional state of users and customers, enabling responses based on that analysis.

[0183] A "management mechanism" is a function that oversees the processing and flow of information within a system and efficiently achieves the requested results.

[0184] To realize this application, the system operates in conjunction with multiple hardware and software components. First, the server centrally manages organizational information and past project data using a database. This data is used as reference information necessary for creating proposals relevant to the user. The server also has the ability to automatically generate draft proposals based on project information, utilizing generational AI.

[0185] As a means of emotion analysis, an emotion recognition engine using machine learning libraries such as TENSORFLOW® is used to analyze the emotional state of users and customers in real time. As a result, the content and style of suggested documents are optimized according to the recipient's emotions. In particular, in physical stores, customer service staff can wear smart glasses and display real-time advice on a screen to help them adopt an appropriate customer service style based on the customer's facial expressions.

[0186] The terminal functions as an interface for the user, displaying the draft proposal and facilitating revisions and approvals. Sentiment analysis results are presented as visual information, allowing the user to adjust the proposal content while reflecting their own emotions.

[0187] For example, if a customer's emotions become negative during a conversation, the emotion analysis tool can suggest solutions that focus on addressing the problem, which can then be reflected in the proposal or customer service style. Furthermore, as an example of a prompt, the system can instruct the AI ​​model to "suggest questions to help the customer relax when their expression becomes cloudy," prompting it to respond accordingly. This system allows proposals and customer service to be delivered in a way that more closely reflects the user's intentions and is more readily accepted.

[0188] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0189] Step 1:

[0190] The server retrieves organizational information and past project data from the database. The input is key information related to the project within the database, and the output is corresponding detailed data. The server uses this data to build the foundation for proposal creation.

[0191] Step 2:

[0192] The server uses a generation AI to automatically generate a draft proposal based on project information. The input is the detailed data obtained in Step 1, and the output is an initial draft proposal. The server utilizes a generation AI model to generate the draft while considering successful cases and templates.

[0193] Step 3:

[0194] The server analyzes the user's emotional state using emotion analysis tools. The input is real-time facial expression data and feedback obtained from the user, and the output is the analyzed emotional state. The server processes the data through an emotion recognition model such as TensorFlow to identify the emotional state.

[0195] Step 4:

[0196] The terminal displays a draft proposal to the user, along with the sentiment analysis results. The inputs are the proposal draft from step 2 and the sentiment state from step 3, while the output is a visual representation on the screen. The terminal provides an intuitive interface to facilitate user review and modification.

[0197] Step 5:

[0198] The user reviews the proposal based on the displayed information and makes revisions as needed. Input consists of the visual information from step 4 and the user's judgment; output is the revised proposal. The proposal is adjusted based on the user's actions, and these revisions are reflected in the system.

[0199] Step 6:

[0200] The server customizes and saves the proposal based on the sentiment analysis results. The inputs are the revised proposal from step 5 and the sentiment state from step 3, and the output is the customized proposal. The server saves the proposal and ensures it is ready for submission.

[0201] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0202] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0203] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0204] [Second Embodiment]

[0205] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0206] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0207] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0208] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0209] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0210] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0211] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0212] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0213] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0214] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0215] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0216] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. 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".

[0217] This invention provides a system that streamlines coordination tasks within an organization by utilizing organizational information and past case data. This system functions between a server, a terminal, and a user.

[0218] The server maintains a database containing organizational information and past project data, efficiently managing necessary information. Using AI generation, it creates drafts based on past proposals and standard templates related to specified projects. This significantly reduces the time required to create proposals. Furthermore, the server analyzes information on stakeholders and, if necessary, automatically recruits suitable stakeholders for the new project.

[0219] The terminal presents the user with project progress and provides an interface for reviewing and revising automatically generated proposal drafts. This interface is designed to allow users to easily review the content and intuitively make necessary revisions.

[0220] Users access the system via a terminal, select a new project, and input project-related requirements and conditions. This allows them to verify that the proposal content meets the project requirements. After the user has reviewed the draft, the system manages the process from final approval to saving and submitting the proposal as an official document.

[0221] As a concrete example, consider a new product development project. In this case, the server analyzes data from similar past projects and generates a proposal that includes technical information and market analysis related to the new product. Users can review this proposal on their terminals and add their opinions on product features and sales strategies. After final approval, the proposal is automatically submitted to the customer. This entire process reduces coordination costs within the organization and enables rapid and efficient work execution.

[0222] The following describes the processing flow.

[0223] Step 1:

[0224] The user logs into the system using their device. After logging in, the dashboard is displayed, offering options to select ongoing projects or start a new project.

[0225] Step 2:

[0226] The user selects a project that requires adjustment. The terminal sends the selection to the server, which retrieves organizational information and past project data related to that project from its database.

[0227] Step 3:

[0228] The server automatically extracts stakeholders related to the project. The server analyzes past project data in the database and lists the departments and personnel involved in the project.

[0229] Step 4:

[0230] The server automatically sends project invitation emails to the identified stakeholders. The emails include an overview of the project and details of their assigned tasks.

[0231] Step 5:

[0232] The AI ​​on the server generates a draft proposal based on project information. The AI ​​creates the content using past success stories and standard templates, and then formats the layout of the proposal.

[0233] Step 6:

[0234] The server sends a draft of the generated proposal to the terminal. The terminal then presents this draft to the user, making it available for review and revision.

[0235] Step 7:

[0236] The user reviews the draft proposal and makes revisions or provides additional feedback as needed. The revisions are sent to the server via the device.

[0237] Step 8:

[0238] The server reflects the user's changes and updates the proposal. The updated proposal is then sent back to the terminal for review.

[0239] Step 9:

[0240] The user will ultimately review and approve the proposal. After approval, the proposal becomes an official document and is stored on the server.

[0241] Step 10:

[0242] The server submits the approved proposal to the customer. Proposals are submitted via an online platform or email, and the user receives a notification that the submission is complete.

[0243] (Example 1)

[0244] Next, we will describe Example 1. 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."

[0245] In modern organizations, tasks such as project management and proposal writing require significant time and resources, and inefficient processes are often a major challenge. In particular, efficient information extraction based on past examples, followed by review and revision, is crucial for proposal writing. However, performing these tasks manually is time-consuming and prone to errors. Therefore, there is a need to build a system that streamlines the entire process from information management, generation, review, revision, and submission, enabling tasks to proceed quickly and reliably.

[0246] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0247] In this invention, the server includes an information management means for storing organizational information and past project data, an information generation means for automatically extracting relevant elements from the information management means and transmitting notification information, and an information generation device for generating a draft document based on project-related information. This automates the process from information extraction to proposal creation, content review and revision, and final submission, enabling efficient and error-free work execution.

[0248] "Information management means" refers to devices and methods for storing and efficiently managing organizational information and past case data.

[0249] "Information generation means" refers to a device and method for automatically extracting relevant elements from information management means and generating and transmitting notification information as needed.

[0250] "Information generation device" refers to a device and method for generating draft documents based on project-related information.

[0251] "Display device" refers to a device and method for displaying a draft of a generated document, enabling users to review and modify it.

[0252] "Information management device" refers to a device and method for saving and submitting documents that have been modified by users.

[0253] In this invention, a server, a terminal, and a user work together to build an information processing system.

[0254] The server first prepares a database to store organizational information and past project data. This database can be built using common data management software or cloud storage services. Next, it automates the information generation process using a generative AI model. This model uses natural language processing techniques to generate draft documents related to the project. For example, by inputting a prompt such as, "Please create a draft proposal for a new product development project that includes market analysis and technical information based on similar past cases," it will generate appropriate content.

[0255] The terminal functions as an interface connecting the user and the server. The information displayed on the terminal is designed to be intuitively understandable to the user, allowing them to review and revise draft documents. Ideally, it should be easily operated by the user using a touchscreen display or mouse and keyboard.

[0256] Users can access the system through their terminals to review and modify recorded data and generated documents. This allows them to verify that the final document conforms to the project's objectives before approving it. This entire process streamlines operations and significantly reduces proposal creation time. It also facilitates collaboration across the organization by enabling the rapid dissemination of necessary information to stakeholders.

[0257] In this way, the entire system for carrying out the invention is efficiently and effectively constructed, and a specific form supporting the claims is provided.

[0258] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0259] Step 1:

[0260] The server retrieves organizational information and past project data from the database. Based on the database settings, it searches for relevant data as input and outputs it as structured data. Specifically, it uses SQL queries and API calls to systematically extract the necessary information.

[0261] Step 2:

[0262] The server inputs prompt messages into the generating AI model based on the acquired data. For example, the prompt message used as input might be, "Please create a draft proposal based on similar cases in a new product development project." The generating AI model analyzes the data using natural language processing techniques, creates a draft proposal, and outputs it in text format. Specifically, the process involves inputting prompts into the AI ​​model and receiving responses from the model.

[0263] Step 3:

[0264] The terminal presents the user with a draft proposal received from the server. The input data includes the text of the draft sent from the server, which is then output in a formatted display format. Specifically, the draft is displayed on the screen within the user interface, allowing the user to review its contents.

[0265] Step 4:

[0266] The user reviews the draft presented via the terminal and makes revisions as needed. Input data includes revision instructions and new information from the user. The output is a draft of the new proposal reflecting the revisions. Specifically, the user edits the draft text using a keyboard or touchscreen.

[0267] Step 5:

[0268] The server saves the proposal after it has been modified by the user and makes it ready for submission. It receives the revised proposal as input, updates the database based on it, and prepares it for external transmission. The output is a file containing the final approved proposal stored in the database and automatically sent as needed. The specific actions are data saving after the approval button is pressed and transmission to relevant parties if necessary.

[0269] (Application Example 1)

[0270] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0271] In streamlining production planning and creating proposals, identifying relevant stakeholders and gathering necessary information is time-consuming, leading to increased time and human resource costs. Furthermore, these tasks can cause delays in on-site decision-making and operational inconsistencies, resulting in decreased production efficiency.

[0272] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0273] In this invention, the server includes a storage device for storing organizational information and past event data, a generation means for automatically identifying relevant parties from the storage device and sending notifications, and a generation AI means for generating a draft proposal based on information related to the production plan. This enables effective recruitment of relevant parties in the production plan and rapid proposal creation.

[0274] "Organizational information" refers to structured data about a specific organization, including the roles and responsibilities of its members and contact information.

[0275] "Past event data" refers to recorded data related to activities and projects previously carried out within the organization, and is used as the basis for analyzing and proposing similar events.

[0276] A "storage device" refers to hardware and its management software used to store and retain digital information, and functions as part of a database server.

[0277] "Generation means" refers to devices or software that have the function of identifying relevant parties and automatically issuing notifications based on specific inputs.

[0278] A "production plan" refers to a plan that outlines schedules and procedures designed to improve the efficiency of production processes in the manufacturing industry.

[0279] "Generative AI means" refers to a system that uses artificial intelligence technology to automatically generate drafts of documents and proposals based on specific instructions or data.

[0280] The "display means" is an interface for visualizing the generated documents and information to the user and enabling interaction, and includes monitors and touch devices.

[0281] The "management means" refers to the mechanism and procedures for safely storing the proposal documents modified by the user and submitting them to the relevant parties according to the request.

[0282] In this invention, a form is taken in which a server, a terminal, and a user cooperate to implement the system.

[0283] The server has a storage device for storing organizational information and past event data, and supports the creation of production plans based on these data. An AI model is installed on the server as generation AI means, and a draft of the proposal is generated by referring to past successful cases and standard templates. As this AI model, for example, a natural language generation model such as GPT-3 is used. The server, as generation means, has a function of automatically identifying relevant parties from the storage device and notifying the progress status.

[0284] The terminal provides display means for the user to check the draft of the proposal and make necessary corrections. The displayed draft is designed to be confirmed interactively, and devices such as smartphones and tablets play this role. As a result, the user can efficiently provide feedback and modify the draft generated by the generation AI means to be in line with the actual business.

[0285] The user accesses the system through the terminal, checks and modifies the generated draft proposal. The modified proposal is safely stored by the management means of the server and automatically submitted to the relevant departments and end-users as necessary.

[0286] As a specific example, when a new product line is added, by using the system of the present invention, a production plan can be quickly formulated. For example, based on a prompt sentence such as "Please create a proposal for adding a new product line. The necessary conditions are an additional production capacity of 2,000 units per month, whether there is an upgrade of existing equipment, and the number of required engineers.", the AI model generates a draft, and information is efficiently shared with relevant personnel.

[0287] Through this series of processes, the organization can improve productivity and significantly reduce the time required for adjustments and responses.

[0288] The flow of the specific process in Application Example 1 will be described with reference to FIG. 12.

[0289] Step 1:

[0290] The server acquires organizational information and past event data from the storage device. This data includes history related to production plans and information on relevant personnel. Based on this data, the generation AI means uses it as basic information for generating a draft of the proposal.

[0291] Step 2:

[0292] The server sends a prompt sentence to the generation AI model to generate a draft of the proposal. The prompt sentence includes information on the conditions for adding a new product line and production capacity. The generation AI model performs natural language processing based on this information and generates an appropriate draft of the proposal.

[0293] Step 3:

[0294] The server sends the generated draft of the proposal to the terminal. The terminal displays the draft to the user. This display means is carried out through the screen of a smartphone or a tablet, enabling the user to check the content and provide interactive feedback.

[0295] Step 4:

[0296] Users review the draft proposal using a terminal and make revisions as needed. Revisions are entered through the terminal's interface, and the changes are reflected in the draft. The entered data includes specific production conditions and additional information about stakeholders.

[0297] Step 5:

[0298] Proposals modified on the terminal are saved by the server's management system. The server performs final approval and automatically submits the completed proposal to the relevant departments and end users. After all submissions are complete, the server generates a process log and stores it for future reference as needed.

[0299] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0300] This invention combines a system that streamlines internal coordination using organizational information and past project data with an emotion engine that recognizes user emotions. This system operates between a server, a terminal, and a user, enabling sophisticated adjustments that take user emotions into account during the proposal creation process.

[0301] The server stores organizational information and past project data in a database, allowing for quick retrieval of project-related information. It also utilizes generative AI to create draft proposals. Crucially, the server's built-in emotion engine analyzes the user's emotional state and customizes the proposal's style and content accordingly. This ensures the proposal is presented in a more acceptable format.

[0302] The terminal is responsible for the interface to provide information to the user, and presents the draft of the proposal and the user's requests for corrections. Furthermore, the terminal has a function to visually display the emotion recognition results, enabling the user to easily confirm their own emotional state. This information is useful for the confirmation and correction of the proposal.

[0303] The user accesses the system and selects a project, checks and corrects the proposal through the terminal. After the content of the proposal is appropriately adjusted, the user gives final approval, and the proposal is prepared for storage and submission by the server. The emotion engine accumulates the user's feedback, enabling more refined emotional responses in future proposal creation processes.

[0304] For example, considering the proposal process for a new project, if the user's emotion is positive, the server generates a proposal that emphasizes initiative. On the other hand, if a negative emotion is detected, the proposal focuses on solutions to problems, making the content more convincing to the user. This smooths communication within the organization and enables more effective adjustment work.

[0305] The following explains the processing flow.

[0306] Step 1:

[0307] The user logs in to the system using the terminal. After logging in, the emotion engine analyzes the user's emotional state and records the current emotion.

[0308] Step 2:

[0309] The user selects a project from the terminal. The terminal sends the selected information to the server, and the server retrieves the relevant organizational information and past case data from the database.

[0310] Step 3: <--0000978-->

[0311] Based on the data acquired by the server, relevant stakeholders are automatically selected. The server then sends an invitation email to the selected stakeholders and shares an overview of the project.

[0312] Step 4:

[0313] The server uses a generative AI based on project information to create a draft proposal. The emotion engine adjusts the style and content of the draft according to the user's emotional state.

[0314] Step 5:

[0315] The server sends a draft proposal it has created to the terminal. The terminal visually displays this draft and the user's emotional state, allowing the user to review it.

[0316] Step 6:

[0317] Users review the proposal draft and input revisions and feedback on their devices as needed. They then fine-tune the content and wording based on the sentiment recognition results.

[0318] Step 7:

[0319] The terminal sends the changes to the server. The server reflects these changes and updates the proposal.

[0320] Step 8:

[0321] The user performs a final review and approves the proposal. The terminal sends this information to the server, which then saves the proposal.

[0322] Step 9:

[0323] The server automatically submits approved proposals to the customer and sends a notification to the user upon completion of submission.

[0324] (Example 2)

[0325] Next, we will describe Example 2. 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".

[0326] In today's business environment, the processes of internal coordination and proposal writing are complex and require considerable time and effort. Misunderstandings and conflicts of opinion frequently occur during this process, hindering smooth project progress. Furthermore, coordination that takes into account the emotional state of users is difficult, and communication is often insufficient, especially when reconciling diverse opinions. This often reduces the likelihood of proposals being accepted, hindering efficient work execution.

[0327] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0328] In this invention, the server includes an information storage means for storing organizational information and past project data, a generation means for automatically extracting and communicating with relevant parties, a generation model means for generating proposals based on project-related information, and an emotion analysis means for analyzing the user's emotional state and customizing the writing style and content. This enables flexible proposal creation that takes user emotions into account and efficient internal coordination.

[0329] "Information storage means" refers to devices and systems that allow for the long-term storage and rapid access of organizational information and past case data.

[0330] "Generation means" refers to a device or program that has the function of automatically extracting relevant parties from information storage means and conducting necessary communications.

[0331] The "generative modeling method" is an artificial intelligence-based process that utilizes project-related information and past case studies to create a draft proposal.

[0332] "Emotional analysis tools" refer to devices or software that detect and analyze a user's emotional state and then appropriately customize the writing style and content based on the results.

[0333] "Interface means" refers to devices or programs that provide an operating environment that allows users to review and revise draft proposals.

[0334] A "management tool" refers to a device or system that has the function of saving proposals that reflect user modifications and preparing them for submission.

[0335] This invention is a system aimed at efficient proposal creation and internal coordination that takes user emotions into consideration. First, the server uses information storage means to store organizational information and past project data in a database. This database is constructed, for example, by utilizing a business database management system.

[0336] The server uses a generation mechanism to automatically extract relevant stakeholders from the information storage mechanism and sends the information to the stakeholders via email or other communication methods. Possible communication tools include general email systems and internal chat tools.

[0337] As a generative model, the server uses a generative AI model to generate a draft of the project proposal. This AI model utilizes past case data and templates and employs an AI model known as a text generation engine (for example, a model using natural language processing technology).

[0338] The terminal presents the generated proposal draft to the user via an interface. The terminal also incorporates emotion analysis capabilities, for example, using a camera and emotion recognition software (e.g., an open-source emotion recognition library) to analyze the user's facial expressions and tone of voice and understand the user's emotional state. Based on these results, the server dynamically customizes the style and content of the proposal.

[0339] Furthermore, users can review and, if necessary, revise their proposals through their terminals. After the user has completed revisions and final approval has been granted, the server will save the proposal using its management system and manage it in a state where it can be submitted.

[0340] As a concrete example, in the process of proposing a new project, if the user's emotions are positive, the server can generate a proposal that emphasizes proactiveness. An example of a prompt to input into the generation AI model would be, "Please create a project proposal. The user's emotional state is positive. Please make the content emphasize a proactive stance." In this way, the present invention enables proposal creation and coordination within an organization in an effective and emotionally responsive manner.

[0341] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0342] Step 1:

[0343] The server uses information storage means to store organizational information and historical project data in a database. This is done by importing data from the management system. Organizational metadata and project history data are used as input, and storing this in the database in a structured format makes it readily accessible in later steps.

[0344] Step 2:

[0345] The terminal activates an emotion analysis system to capture the user's emotions. This analysis involves real-time data capture using a camera and microphone. The input consists of the user's facial expressions and voice data, which are analyzed by emotion analysis software. This results in an output indicating an emotional state, such as positive or negative, which is then sent to the server.

[0346] Step 3:

[0347] The server uses a generation mechanism to automatically extract relevant parties from the information storage mechanism and communicates with them regarding the need to create a proposal. Specifically, it performs calculations to select appropriate parties based on past project performance data and automatically sends emails to their contact information. The emails contain background information on the project and guidelines for creating the proposal.

[0348] Step 4:

[0349] The server utilizes a generative model to generate a draft project proposal. Basic project information and the user's emotional state are used as input. By providing prompts to the generative AI model, a draft proposal with adjusted style and content is output. This draft is customized to be easily accepted by the user.

[0350] Step 5:

[0351] The terminal presents the user with a draft proposal through an interface. The user uses this interface to review the proposal and provide revision instructions. The user's input, including specific feedback and revision requests, is converted into a system-readable format and sent to the server. This allows for revisions tailored to the user's specific needs.

[0352] Step 6:

[0353] The user reviews the final revised proposal and gives their approval. The terminal notifies the server of this approval step. The approved proposal is then saved by the server's management system and prepared for automatic transmission to the client or relevant parties as needed. This completes the cycle from proposal creation to submission.

[0354] (Application Example 2)

[0355] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0356] Traditional proposal creation systems often fail to adequately consider emotions during internal coordination and customer interaction, resulting in proposals being less likely to be accepted. Furthermore, it's difficult to grasp emotional shifts in real time during customer interactions and respond accordingly. This leads to situations where proposals and customer service don't fully meet customer expectations and needs.

[0357] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0358] In this invention, the server includes data storage means for storing organizational information and past case data, generation AI means for generating draft proposals based on project-related information, and emotion analysis means for analyzing the user's emotional state and customizing the proposal to match the writing style and content. This makes it possible to generate proposals that respond to the user's emotions and to adjust customer service styles in physical stores to suit the emotions of the customers.

[0359] "Organizational information" refers to data about the structure, roles, and membership of companies and organizations.

[0360] "Past project data" refers to data that includes all records related to projects and tasks that have been processed to date.

[0361] A "data storage system" is a mechanism for storing information and data long-term and making it available for retrieval as needed.

[0362] "Generative means" refers to a tool or method that has the function of generating or processing information and providing the results to another system or human being.

[0363] "Generative AI means" refers to methods that utilize artificial intelligence technology to automatically create information and documents according to specified goals and conditions.

[0364] An "interface means" is a point of contact or method that enables data exchange and manipulation between a user and a system.

[0365] "Emotional analysis tools" are technologies that analyze and identify the emotional state of users and customers, enabling responses based on that analysis.

[0366] A "management mechanism" is a function that oversees the processing and flow of information within a system and efficiently achieves the requested results.

[0367] To realize this application, the system operates in conjunction with multiple hardware and software components. First, the server centrally manages organizational information and past project data using a database. This data is used as reference information necessary for creating proposals relevant to the user. The server also has the ability to automatically generate draft proposals based on project information, utilizing generational AI.

[0368] As a means of sentiment analysis, an emotion recognition engine using machine learning libraries such as TensorFlow is used to analyze the emotional state of users and customers in real time. As a result, the content and style of suggested documents are optimized according to the recipient's emotions. In particular, in physical stores, sales staff can wear smart glasses and display real-time advice on a screen to help them adopt an appropriate sales style based on the customer's facial expressions.

[0369] The terminal functions as an interface for the user, displaying the draft proposal and facilitating revisions and approvals. Sentiment analysis results are presented as visual information, allowing the user to adjust the proposal content while reflecting their own emotions.

[0370] For example, if a customer's emotions become negative during a conversation, the emotion analysis tool can suggest solutions that focus on addressing the problem, which can then be reflected in the proposal or customer service style. Furthermore, as an example of a prompt, the system can instruct the AI ​​model to "suggest questions to help the customer relax when their expression becomes cloudy," prompting it to respond accordingly. This system allows proposals and customer service to be delivered in a way that more closely reflects the user's intentions and is more readily accepted.

[0371] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0372] Step 1:

[0373] The server retrieves organizational information and past project data from the database. The input is key information related to the project within the database, and the output is corresponding detailed data. The server uses this data to build the foundation for proposal creation.

[0374] Step 2:

[0375] The server uses a generation AI to automatically generate a draft proposal based on project information. The input is the detailed data obtained in Step 1, and the output is an initial draft proposal. The server utilizes a generation AI model to generate the draft while considering successful cases and templates.

[0376] Step 3:

[0377] The server analyzes the user's emotional state using emotion analysis tools. The input is real-time facial expression data and feedback obtained from the user, and the output is the analyzed emotional state. The server processes the data through an emotion recognition model such as TensorFlow to identify the emotional state.

[0378] Step 4:

[0379] The terminal displays a draft proposal to the user, along with the sentiment analysis results. The inputs are the proposal draft from step 2 and the sentiment state from step 3, while the output is a visual representation on the screen. The terminal provides an intuitive interface to facilitate user review and modification.

[0380] Step 5:

[0381] The user reviews the proposal based on the displayed information and makes revisions as needed. Input consists of the visual information from step 4 and the user's judgment; output is the revised proposal. The proposal is adjusted based on the user's actions, and these revisions are reflected in the system.

[0382] Step 6:

[0383] The server customizes and saves the proposal based on the sentiment analysis results. The inputs are the revised proposal from step 5 and the sentiment state from step 3, and the output is the customized proposal. The server saves the proposal and ensures it is ready for submission.

[0384] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0385] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0386] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0387] [Third Embodiment]

[0388] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0389] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0390] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0391] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0392] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0393] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0394] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0395] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0396] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0397] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0398] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0399] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0400] This invention provides a system that streamlines coordination tasks within an organization by utilizing organizational information and past case data. This system functions between a server, a terminal, and a user.

[0401] The server maintains a database containing organizational information and past project data, efficiently managing necessary information. Using AI generation, it creates drafts based on past proposals and standard templates related to specified projects. This significantly reduces the time required to create proposals. Furthermore, the server analyzes information on stakeholders and, if necessary, automatically recruits suitable stakeholders for the new project.

[0402] The terminal presents the user with project progress and provides an interface for reviewing and revising automatically generated proposal drafts. This interface is designed to allow users to easily review the content and intuitively make necessary revisions.

[0403] Users access the system via a terminal, select a new project, and input project-related requirements and conditions. This allows them to verify that the proposal content meets the project requirements. After the user has reviewed the draft, the system manages the process from final approval to saving and submitting the proposal as an official document.

[0404] As a concrete example, consider a new product development project. In this case, the server analyzes data from similar past projects and generates a proposal that includes technical information and market analysis related to the new product. Users can review this proposal on their terminals and add their opinions on product features and sales strategies. After final approval, the proposal is automatically submitted to the customer. This entire process reduces coordination costs within the organization and enables rapid and efficient work execution.

[0405] The following describes the processing flow.

[0406] Step 1:

[0407] The user logs into the system using their device. After logging in, the dashboard is displayed, offering options to select ongoing projects or start a new project.

[0408] Step 2:

[0409] The user selects a project that requires adjustment. The terminal sends the selection to the server, which retrieves organizational information and past project data related to that project from its database.

[0410] Step 3:

[0411] The server automatically extracts stakeholders related to the project. The server analyzes past project data in the database and lists the departments and personnel involved in the project.

[0412] Step 4:

[0413] The server automatically sends project invitation emails to the identified stakeholders. The emails include an overview of the project and details of their assigned tasks.

[0414] Step 5:

[0415] The AI ​​on the server generates a draft proposal based on project information. The AI ​​creates the content using past success stories and standard templates, and then formats the layout of the proposal.

[0416] Step 6:

[0417] The server sends a draft of the generated proposal to the terminal. The terminal then presents this draft to the user, making it available for review and revision.

[0418] Step 7:

[0419] The user reviews the draft proposal and makes revisions or provides additional feedback as needed. The revisions are sent to the server via the device.

[0420] Step 8:

[0421] The server reflects the user's changes and updates the proposal. The updated proposal is then sent back to the terminal for review.

[0422] Step 9:

[0423] The user will ultimately review and approve the proposal. After approval, the proposal becomes an official document and is stored on the server.

[0424] Step 10:

[0425] The server submits the approved proposal to the customer. Proposals are submitted via an online platform or email, and the user receives a notification that the submission is complete.

[0426] (Example 1)

[0427] Next, we will describe Example 1. 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."

[0428] In modern organizations, tasks such as project management and proposal writing require significant time and resources, and inefficient processes are often a major challenge. In particular, efficient information extraction based on past examples, followed by review and revision, is crucial for proposal writing. However, performing these tasks manually is time-consuming and prone to errors. Therefore, there is a need to build a system that streamlines the entire process from information management, generation, review, revision, and submission, enabling tasks to proceed quickly and reliably.

[0429] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0430] In this invention, the server includes an information management means for storing organizational information and past project data, an information generation means for automatically extracting relevant elements from the information management means and transmitting notification information, and an information generation device for generating a draft document based on project-related information. This automates the process from information extraction to proposal creation, content review and revision, and final submission, enabling efficient and error-free work execution.

[0431] "Information management means" refers to devices and methods for storing and efficiently managing organizational information and past case data.

[0432] "Information generation means" refers to a device and method for automatically extracting relevant elements from information management means and generating and transmitting notification information as needed.

[0433] "Information generation device" refers to a device and method for generating draft documents based on project-related information.

[0434] "Display device" refers to a device and method for displaying a draft of a generated document, enabling users to review and modify it.

[0435] "Information management device" refers to a device and method for saving and submitting documents that have been modified by users.

[0436] In this invention, a server, a terminal, and a user work together to build an information processing system.

[0437] The server first prepares a database to store organizational information and past project data. This database can be built using common data management software or cloud storage services. Next, it automates the information generation process using a generative AI model. This model uses natural language processing techniques to generate draft documents related to the project. For example, by inputting a prompt such as, "Please create a draft proposal for a new product development project that includes market analysis and technical information based on similar past cases," it will generate appropriate content.

[0438] The terminal functions as an interface connecting the user and the server. The information displayed on the terminal is designed to be intuitively understandable to the user, allowing them to review and revise draft documents. Ideally, it should be easily operated by the user using a touchscreen display or mouse and keyboard.

[0439] Users can access the system through their terminals to review and modify recorded data and generated documents. This allows them to verify that the final document conforms to the project's objectives before approving it. This entire process streamlines operations and significantly reduces proposal creation time. It also facilitates collaboration across the organization by enabling the rapid dissemination of necessary information to stakeholders.

[0440] In this way, the entire system for carrying out the invention is efficiently and effectively constructed, and a specific form supporting the claims is provided.

[0441] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0442] Step 1:

[0443] The server retrieves organizational information and past project data from the database. Based on the database settings, it searches for relevant data as input and outputs it as structured data. Specifically, it uses SQL queries and API calls to systematically extract the necessary information.

[0444] Step 2:

[0445] The server inputs prompt messages into the generating AI model based on the acquired data. For example, the prompt message used as input might be, "Please create a draft proposal based on similar cases in a new product development project." The generating AI model analyzes the data using natural language processing techniques, creates a draft proposal, and outputs it in text format. Specifically, the process involves inputting prompts into the AI ​​model and receiving responses from the model.

[0446] Step 3:

[0447] The terminal presents the user with a draft proposal received from the server. The input data includes the text of the draft sent from the server, which is then output in a formatted display format. Specifically, the draft is displayed on the screen within the user interface, allowing the user to review its contents.

[0448] Step 4:

[0449] The user reviews the draft presented via the terminal and makes revisions as needed. Input data includes revision instructions and new information from the user. The output is a draft of the new proposal reflecting the revisions. Specifically, the user edits the draft text using a keyboard or touchscreen.

[0450] Step 5:

[0451] The server saves the proposal after it has been modified by the user and makes it ready for submission. It receives the revised proposal as input, updates the database based on it, and prepares it for external transmission. The output is a file containing the final approved proposal stored in the database and automatically sent as needed. The specific actions are data saving after the approval button is pressed and transmission to relevant parties if necessary.

[0452] (Application Example 1)

[0453] Next, we will explain Application Example 1. In the following explanation, 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."

[0454] In streamlining production planning and creating proposals, identifying relevant stakeholders and gathering necessary information is time-consuming, leading to increased time and human resource costs. Furthermore, these tasks can cause delays in on-site decision-making and operational inconsistencies, resulting in decreased production efficiency.

[0455] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0456] In this invention, the server includes a storage device for storing organizational information and past event data, a generation means for automatically identifying relevant parties from the storage device and sending notifications, and a generation AI means for generating a draft proposal based on information related to the production plan. This enables effective recruitment of relevant parties in the production plan and rapid proposal creation.

[0457] "Organizational information" refers to structured data about a specific organization, including the roles and responsibilities of its members and contact information.

[0458] "Past event data" refers to recorded data related to activities and projects previously carried out within the organization, and is used as the basis for analyzing and proposing similar events.

[0459] A "storage device" refers to hardware and its management software used to store and retain digital information, and functions as part of a database server.

[0460] "Generation means" refers to devices or software that have the function of identifying relevant parties and automatically issuing notifications based on specific inputs.

[0461] A "production plan" refers to a plan that outlines schedules and procedures designed to improve the efficiency of production processes in the manufacturing industry.

[0462] "Generative AI means" refers to a system that uses artificial intelligence technology to automatically generate drafts of documents and proposals based on specific instructions or data.

[0463] "Display means" refers to an interface that visualizes generated documents and information for the user and enables interaction, and includes monitors and touch devices.

[0464] "Management measures" refer to the mechanisms and procedures for securely storing user-modified proposals and submitting them to relevant parties upon request.

[0465] In this invention, the system is implemented in a form in which a server, a terminal, and a user cooperate to carry it out.

[0466] The server has a storage device that stores organizational information and historical event data, and uses this data to assist in creating production plans. The server is equipped with an AI model as a generation AI means, which generates draft proposals based on past success stories and standard templates. This AI model utilizes a natural language generation model such as GPT-3. As a generation means, the server has a function to automatically identify relevant stakeholders from the storage device and notify them of the progress.

[0467] The terminal provides a means for users to review proposal drafts and make necessary revisions. The displayed draft is designed for interactive review, and smartphones and tablets fulfill this role. This allows users to efficiently provide feedback and revise drafts generated by AI-generated tools to better suit their actual work needs.

[0468] Users access the system via their terminals to review and revise the generated draft proposals. The revised proposals are securely stored by the server's management system and automatically submitted to the relevant departments and end users as needed.

[0469] As a concrete example, when adding a new product line, the system of the present invention can be used to quickly formulate a production plan. For instance, based on a prompt such as, "Please create a proposal for adding a new product line. Required conditions are an additional production capacity of 2,000 units per month, whether existing equipment will be upgraded, and the number of engineers required," the AI ​​model generates a draft, and the information is efficiently shared with relevant parties.

[0470] This series of processes allows organizations to improve productivity and significantly reduce the time required for coordination and response.

[0471] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0472] Step 1:

[0473] The server retrieves organizational information and historical event data from its storage device. This data includes history and information about stakeholders related to production planning. Based on this data, the AI ​​generator uses it as foundational information to produce a draft proposal.

[0474] Step 2:

[0475] The server sends prompt messages to the generative AI model to generate a draft proposal. These prompt messages include information about the expansion requirements and production capacity for the new product line. The generative AI model uses natural language processing based on this information to generate an appropriate draft proposal.

[0476] Step 3:

[0477] The server sends a draft of the generated proposal to the terminal. The terminal displays the draft to the user. This display is done via a smartphone or tablet screen, allowing the user to review the content and provide interactive feedback.

[0478] Step 4:

[0479] Users review the draft proposal using a terminal and make revisions as needed. Revisions are entered through the terminal's interface, and the changes are reflected in the draft. The entered data includes specific production conditions and additional information about stakeholders.

[0480] Step 5:

[0481] Proposals modified on the terminal are saved by the server's management system. The server performs final approval and automatically submits the completed proposal to the relevant departments and end users. After all submissions are complete, the server generates a process log and stores it for future reference as needed.

[0482] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0483] This invention combines a system that streamlines internal coordination using organizational information and past project data with an emotion engine that recognizes user emotions. This system operates between a server, a terminal, and a user, enabling sophisticated adjustments that take user emotions into account during the proposal creation process.

[0484] The server stores organizational information and past project data in a database, allowing for quick retrieval of project-related information. It also utilizes generative AI to create draft proposals. Crucially, the server's built-in emotion engine analyzes the user's emotional state and customizes the proposal's style and content accordingly. This ensures the proposal is presented in a more acceptable format.

[0485] The terminal serves as an interface for providing information to the user, presenting them with draft proposals and requests for revisions. Furthermore, the terminal has a function to visually display emotion recognition results, allowing users to easily check their own emotional state. This information is useful when reviewing and revising proposals.

[0486] Users access the system and select projects and review and revise proposals via their terminals. After the proposal content is appropriately adjusted, the user gives final approval, and the proposal is saved by the server and prepared for submission. The emotion engine accumulates user feedback, enabling more refined emotional responses in future proposal creation processes.

[0487] For example, considering the proposal process for a new project, if the user's emotions are positive, the server will generate a proposal that emphasizes proactiveness. On the other hand, if negative emotions are detected, the proposal will focus on solutions to problems, making it more convincing to the user. This leads to smoother communication within the organization and more effective coordination.

[0488] The following describes the processing flow.

[0489] Step 1:

[0490] The user logs into the system using their device. After logging in, the emotion engine analyzes the user's emotional state and records their current emotions.

[0491] Step 2:

[0492] The user selects a project from their device. The device sends the selected information to the server, which retrieves relevant organizational information and past project data from its database.

[0493] Step 3:

[0494] Based on the data acquired by the server, relevant stakeholders are automatically selected. The server then sends an invitation email to the selected stakeholders and shares an overview of the project.

[0495] Step 4:

[0496] The server uses a generative AI based on project information to create a draft proposal. The emotion engine adjusts the style and content of the draft according to the user's emotional state.

[0497] Step 5:

[0498] The server sends a draft proposal it has created to the terminal. The terminal visually displays this draft and the user's emotional state, allowing the user to review it.

[0499] Step 6:

[0500] Users review the proposal draft and input revisions and feedback on their devices as needed. They then fine-tune the content and wording based on the sentiment recognition results.

[0501] Step 7:

[0502] The terminal sends the changes to the server. The server reflects these changes and updates the proposal.

[0503] Step 8:

[0504] The user performs a final review and approves the proposal. The terminal sends this information to the server, which then saves the proposal.

[0505] Step 9:

[0506] The server automatically submits approved proposals to the customer and sends a notification to the user upon completion of submission.

[0507] (Example 2)

[0508] Next, we will describe Example 2. 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."

[0509] In today's business environment, the processes of internal coordination and proposal writing are complex and require considerable time and effort. Misunderstandings and conflicts of opinion frequently occur during this process, hindering smooth project progress. Furthermore, coordination that takes into account the emotional state of users is difficult, and communication is often insufficient, especially when reconciling diverse opinions. This often reduces the likelihood of proposals being accepted, hindering efficient work execution.

[0510] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0511] In this invention, the server includes an information storage means for storing organizational information and past project data, a generation means for automatically extracting and communicating with relevant parties, a generation model means for generating proposals based on project-related information, and an emotion analysis means for analyzing the user's emotional state and customizing the writing style and content. This enables flexible proposal creation that takes user emotions into account and efficient internal coordination.

[0512] "Information storage means" refers to devices and systems that allow for the long-term storage and rapid access of organizational information and past case data.

[0513] "Generation means" refers to a device or program that has the function of automatically extracting relevant parties from information storage means and conducting necessary communications.

[0514] The "generative modeling method" is an artificial intelligence-based process that utilizes project-related information and past case studies to create a draft proposal.

[0515] "Emotional analysis tools" refer to devices or software that detect and analyze a user's emotional state and then appropriately customize the writing style and content based on the results.

[0516] "Interface means" refers to devices or programs that provide an operating environment that allows users to review and revise draft proposals.

[0517] A "management tool" refers to a device or system that has the function of saving proposals that reflect user modifications and preparing them for submission.

[0518] This invention is a system aimed at efficient proposal creation and internal coordination that takes user emotions into consideration. First, the server uses information storage means to store organizational information and past project data in a database. This database is constructed, for example, by utilizing a business database management system.

[0519] The server uses a generation mechanism to automatically extract relevant stakeholders from the information storage mechanism and sends the information to the stakeholders via email or other communication methods. Possible communication tools include general email systems and internal chat tools.

[0520] As a generative model, the server uses a generative AI model to generate a draft of the project proposal. This AI model utilizes past case data and templates and employs an AI model known as a text generation engine (for example, a model using natural language processing technology).

[0521] The terminal presents the generated proposal draft to the user via an interface. The terminal also incorporates emotion analysis capabilities, for example, using a camera and emotion recognition software (e.g., an open-source emotion recognition library) to analyze the user's facial expressions and tone of voice and understand the user's emotional state. Based on these results, the server dynamically customizes the style and content of the proposal.

[0522] Furthermore, users can review and, if necessary, revise their proposals through their terminals. After the user has completed revisions and final approval has been granted, the server will save the proposal using its management system and manage it in a state where it can be submitted.

[0523] As a concrete example, in the process of proposing a new project, if the user's emotions are positive, the server can generate a proposal that emphasizes proactiveness. An example of a prompt to input into the generation AI model would be, "Please create a project proposal. The user's emotional state is positive. Please make the content emphasize a proactive stance." In this way, the present invention enables proposal creation and coordination within an organization in an effective and emotionally responsive manner.

[0524] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0525] Step 1:

[0526] The server uses information storage means to store organizational information and historical project data in a database. This is done by importing data from the management system. Organizational metadata and project history data are used as input, and storing this in the database in a structured format makes it readily accessible in later steps.

[0527] Step 2:

[0528] The terminal activates an emotion analysis system to capture the user's emotions. This analysis involves real-time data capture using a camera and microphone. The input consists of the user's facial expressions and voice data, which are analyzed by emotion analysis software. This results in an output indicating an emotional state, such as positive or negative, which is then sent to the server.

[0529] Step 3:

[0530] The server uses a generation mechanism to automatically extract relevant parties from the information storage mechanism and communicates with them regarding the need to create a proposal. Specifically, it performs calculations to select appropriate parties based on past project performance data and automatically sends emails to their contact information. The emails contain background information on the project and guidelines for creating the proposal.

[0531] Step 4:

[0532] The server utilizes a generative model to generate a draft project proposal. Basic project information and the user's emotional state are used as input. By providing prompts to the generative AI model, a draft proposal with adjusted style and content is output. This draft is customized to be easily accepted by the user.

[0533] Step 5:

[0534] The terminal presents the user with a draft proposal through an interface. The user uses this interface to review the proposal and provide revision instructions. The user's input, including specific feedback and revision requests, is converted into a system-readable format and sent to the server. This allows for revisions tailored to the user's specific needs.

[0535] Step 6:

[0536] The user reviews the final revised proposal and gives their approval. The terminal notifies the server of this approval step. The approved proposal is then saved by the server's management system and prepared for automatic transmission to the client or relevant parties as needed. This completes the cycle from proposal creation to submission.

[0537] (Application Example 2)

[0538] Next, we will explain application example 2. In the following explanation, 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."

[0539] Traditional proposal creation systems often fail to adequately consider emotions during internal coordination and customer interaction, resulting in proposals being less likely to be accepted. Furthermore, it's difficult to grasp emotional shifts in real time during customer interactions and respond accordingly. This leads to situations where proposals and customer service don't fully meet customer expectations and needs.

[0540] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0541] In this invention, the server includes data storage means for storing organizational information and past case data, generation AI means for generating draft proposals based on project-related information, and emotion analysis means for analyzing the user's emotional state and customizing the proposal to match the writing style and content. This makes it possible to generate proposals that respond to the user's emotions and to adjust customer service styles in physical stores to suit the emotions of the customers.

[0542] "Organizational information" refers to data about the structure, roles, and membership of companies and organizations.

[0543] "Past project data" refers to data that includes all records related to projects and tasks that have been processed to date.

[0544] A "data storage system" is a mechanism for storing information and data long-term and making it available for retrieval as needed.

[0545] "Generative means" refers to a tool or method that has the function of generating or processing information and providing the results to another system or human being.

[0546] "Generative AI means" refers to methods that utilize artificial intelligence technology to automatically create information and documents according to specified goals and conditions.

[0547] An "interface means" is a point of contact or method that enables data exchange and manipulation between a user and a system.

[0548] "Emotional analysis tools" are technologies that analyze and identify the emotional state of users and customers, enabling responses based on that analysis.

[0549] A "management mechanism" is a function that oversees the processing and flow of information within a system and efficiently achieves the requested results.

[0550] To realize this application, the system operates in conjunction with multiple hardware and software components. First, the server centrally manages organizational information and past project data using a database. This data is used as reference information necessary for creating proposals relevant to the user. The server also has the ability to automatically generate draft proposals based on project information, utilizing generational AI.

[0551] As a means of sentiment analysis, an emotion recognition engine using machine learning libraries such as TensorFlow is used to analyze the emotional state of users and customers in real time. As a result, the content and style of suggested documents are optimized according to the recipient's emotions. In particular, in physical stores, sales staff can wear smart glasses and display real-time advice on a screen to help them adopt an appropriate sales style based on the customer's facial expressions.

[0552] The terminal functions as an interface for the user, displaying the draft proposal and facilitating revisions and approvals. Sentiment analysis results are presented as visual information, allowing the user to adjust the proposal content while reflecting their own emotions.

[0553] For example, if a customer's emotions become negative during a conversation, the emotion analysis tool can suggest solutions that focus on addressing the problem, which can then be reflected in the proposal or customer service style. Furthermore, as an example of a prompt, the system can instruct the AI ​​model to "suggest questions to help the customer relax when their expression becomes cloudy," prompting it to respond accordingly. This system allows proposals and customer service to be delivered in a way that more closely reflects the user's intentions and is more readily accepted.

[0554] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0555] Step 1:

[0556] The server retrieves organizational information and past project data from the database. The input is key information related to the project within the database, and the output is corresponding detailed data. The server uses this data to build the foundation for proposal creation.

[0557] Step 2:

[0558] The server uses a generation AI to automatically generate a draft proposal based on project information. The input is the detailed data obtained in Step 1, and the output is an initial draft proposal. The server utilizes a generation AI model to generate the draft while considering successful cases and templates.

[0559] Step 3:

[0560] The server analyzes the user's emotional state using emotion analysis tools. The input is real-time facial expression data and feedback obtained from the user, and the output is the analyzed emotional state. The server processes the data through an emotion recognition model such as TensorFlow to identify the emotional state.

[0561] Step 4:

[0562] The terminal displays a draft proposal to the user, along with the sentiment analysis results. The inputs are the proposal draft from step 2 and the sentiment state from step 3, while the output is a visual representation on the screen. The terminal provides an intuitive interface to facilitate user review and modification.

[0563] Step 5:

[0564] The user reviews the proposal based on the displayed information and makes revisions as needed. Input consists of the visual information from step 4 and the user's judgment; output is the revised proposal. The proposal is adjusted based on the user's actions, and these revisions are reflected in the system.

[0565] Step 6:

[0566] The server customizes and saves the proposal based on the sentiment analysis results. The inputs are the revised proposal from step 5 and the sentiment state from step 3, and the output is the customized proposal. The server saves the proposal and ensures it is ready for submission.

[0567] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0568] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0569] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0570] [Fourth Embodiment]

[0571] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0572] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0573] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0574] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0575] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0576] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0577] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0578] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0579] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0580] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0581] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0582] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0583] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0584] This invention provides a system that streamlines coordination tasks within an organization by utilizing organizational information and past case data. This system functions between a server, a terminal, and a user.

[0585] The server maintains a database containing organizational information and past project data, efficiently managing necessary information. Using AI generation, it creates drafts based on past proposals and standard templates related to specified projects. This significantly reduces the time required to create proposals. Furthermore, the server analyzes information on stakeholders and, if necessary, automatically recruits suitable stakeholders for the new project.

[0586] The terminal presents the user with project progress and provides an interface for reviewing and revising automatically generated proposal drafts. This interface is designed to allow users to easily review the content and intuitively make necessary revisions.

[0587] Users access the system via a terminal, select a new project, and input project-related requirements and conditions. This allows them to verify that the proposal content meets the project requirements. After the user has reviewed the draft, the system manages the process from final approval to saving and submitting the proposal as an official document.

[0588] As a concrete example, consider a new product development project. In this case, the server analyzes data from similar past projects and generates a proposal that includes technical information and market analysis related to the new product. Users can review this proposal on their terminals and add their opinions on product features and sales strategies. After final approval, the proposal is automatically submitted to the customer. This entire process reduces coordination costs within the organization and enables rapid and efficient work execution.

[0589] The following describes the processing flow.

[0590] Step 1:

[0591] The user logs into the system using their device. After logging in, the dashboard is displayed, offering options to select ongoing projects or start a new project.

[0592] Step 2:

[0593] The user selects a project that requires adjustment. The terminal sends the selection to the server, which retrieves organizational information and past project data related to that project from its database.

[0594] Step 3:

[0595] The server automatically extracts stakeholders related to the project. The server analyzes past project data in the database and lists the departments and personnel involved in the project.

[0596] Step 4:

[0597] The server automatically sends project invitation emails to the identified stakeholders. The emails include an overview of the project and details of their assigned tasks.

[0598] Step 5:

[0599] The AI ​​on the server generates a draft proposal based on project information. The AI ​​creates the content using past success stories and standard templates, and then formats the layout of the proposal.

[0600] Step 6:

[0601] The server sends a draft of the generated proposal to the terminal. The terminal then presents this draft to the user, making it available for review and revision.

[0602] Step 7:

[0603] The user reviews the draft proposal and makes revisions or provides additional feedback as needed. The revisions are sent to the server via the device.

[0604] Step 8:

[0605] The server reflects the user's changes and updates the proposal. The updated proposal is then sent back to the terminal for review.

[0606] Step 9:

[0607] The user will ultimately review and approve the proposal. After approval, the proposal becomes an official document and is stored on the server.

[0608] Step 10:

[0609] The server submits the approved proposal to the customer. Proposals are submitted via an online platform or email, and the user receives a notification that the submission is complete.

[0610] (Example 1)

[0611] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0612] In modern organizations, tasks such as project management and proposal writing require significant time and resources, and inefficient processes are often a major challenge. In particular, efficient information extraction based on past examples, followed by review and revision, is crucial for proposal writing. However, performing these tasks manually is time-consuming and prone to errors. Therefore, there is a need to build a system that streamlines the entire process from information management, generation, review, revision, and submission, enabling tasks to proceed quickly and reliably.

[0613] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0614] In this invention, the server includes an information management means for storing organizational information and past project data, an information generation means for automatically extracting relevant elements from the information management means and transmitting notification information, and an information generation device for generating a draft document based on project-related information. This automates the process from information extraction to proposal creation, content review and revision, and final submission, enabling efficient and error-free work execution.

[0615] "Information management means" refers to devices and methods for storing and efficiently managing organizational information and past case data.

[0616] "Information generation means" refers to a device and method for automatically extracting relevant elements from information management means and generating and transmitting notification information as needed.

[0617] "Information generation device" refers to a device and method for generating draft documents based on project-related information.

[0618] "Display device" refers to a device and method for displaying a draft of a generated document, enabling users to review and modify it.

[0619] "Information management device" refers to a device and method for saving and submitting documents that have been modified by users.

[0620] In this invention, a server, a terminal, and a user work together to build an information processing system.

[0621] The server first prepares a database to store organizational information and past project data. This database can be built using common data management software or cloud storage services. Next, it automates the information generation process using a generative AI model. This model uses natural language processing techniques to generate draft documents related to the project. For example, by inputting a prompt such as, "Please create a draft proposal for a new product development project that includes market analysis and technical information based on similar past cases," it will generate appropriate content.

[0622] The terminal functions as an interface connecting the user and the server. The information displayed on the terminal is designed to be intuitively understandable to the user, allowing them to review and revise draft documents. Ideally, it should be easily operated by the user using a touchscreen display or mouse and keyboard.

[0623] Users can access the system through their terminals to review and modify recorded data and generated documents. This allows them to verify that the final document conforms to the project's objectives before approving it. This entire process streamlines operations and significantly reduces proposal creation time. It also facilitates collaboration across the organization by enabling the rapid dissemination of necessary information to stakeholders.

[0624] In this way, the entire system for carrying out the invention is efficiently and effectively constructed, and a specific form supporting the claims is provided.

[0625] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0626] Step 1:

[0627] The server retrieves organizational information and past project data from the database. Based on the database settings, it searches for relevant data as input and outputs it as structured data. Specifically, it uses SQL queries and API calls to systematically extract the necessary information.

[0628] Step 2:

[0629] The server inputs prompt messages into the generating AI model based on the acquired data. For example, the prompt message used as input might be, "Please create a draft proposal based on similar cases in a new product development project." The generating AI model analyzes the data using natural language processing techniques, creates a draft proposal, and outputs it in text format. Specifically, the process involves inputting prompts into the AI ​​model and receiving responses from the model.

[0630] Step 3:

[0631] The terminal presents the user with a draft proposal received from the server. The input data includes the text of the draft sent from the server, which is then output in a formatted display format. Specifically, the draft is displayed on the screen within the user interface, allowing the user to review its contents.

[0632] Step 4:

[0633] The user reviews the draft presented via the terminal and makes revisions as needed. Input data includes revision instructions and new information from the user. The output is a draft of the new proposal reflecting the revisions. Specifically, the user edits the draft text using a keyboard or touchscreen.

[0634] Step 5:

[0635] The server saves the proposal after it has been modified by the user and makes it ready for submission. It receives the revised proposal as input, updates the database based on it, and prepares it for external transmission. The output is a file containing the final approved proposal stored in the database and automatically sent as needed. The specific actions are data saving after the approval button is pressed and transmission to relevant parties if necessary.

[0636] (Application Example 1)

[0637] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0638] In streamlining production planning and creating proposals, identifying relevant stakeholders and gathering necessary information is time-consuming, leading to increased time and human resource costs. Furthermore, these tasks can cause delays in on-site decision-making and operational inconsistencies, resulting in decreased production efficiency.

[0639] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0640] In this invention, the server includes a storage device for storing organizational information and past event data, a generation means for automatically identifying relevant parties from the storage device and sending notifications, and a generation AI means for generating a draft proposal based on information related to the production plan. This enables effective recruitment of relevant parties in the production plan and rapid proposal creation.

[0641] "Organizational information" refers to structured data about a specific organization, including the roles and responsibilities of its members and contact information.

[0642] "Past event data" refers to recorded data related to activities and projects previously carried out within the organization, and is used as the basis for analyzing and proposing similar events.

[0643] A "storage device" refers to hardware and its management software used to store and retain digital information, and functions as part of a database server.

[0644] "Generation means" refers to devices or software that have the function of identifying relevant parties and automatically issuing notifications based on specific inputs.

[0645] A "production plan" refers to a plan that outlines schedules and procedures designed to improve the efficiency of production processes in the manufacturing industry.

[0646] "Generative AI means" refers to a system that uses artificial intelligence technology to automatically generate drafts of documents and proposals based on specific instructions or data.

[0647] "Display means" refers to an interface that visualizes generated documents and information for the user and enables interaction, and includes monitors and touch devices.

[0648] "Management measures" refer to the mechanisms and procedures for securely storing user-modified proposals and submitting them to relevant parties upon request.

[0649] In this invention, the system is implemented in a form in which a server, a terminal, and a user cooperate to carry it out.

[0650] The server has a storage device that stores organizational information and historical event data, and uses this data to assist in creating production plans. The server is equipped with an AI model as a generation AI means, which generates draft proposals based on past success stories and standard templates. This AI model utilizes a natural language generation model such as GPT-3. As a generation means, the server has a function to automatically identify relevant stakeholders from the storage device and notify them of the progress.

[0651] The terminal provides a means for users to review proposal drafts and make necessary revisions. The displayed draft is designed for interactive review, and smartphones and tablets fulfill this role. This allows users to efficiently provide feedback and revise drafts generated by AI-generated tools to better suit their actual work needs.

[0652] Users access the system via their terminals to review and revise the generated draft proposals. The revised proposals are securely stored by the server's management system and automatically submitted to the relevant departments and end users as needed.

[0653] As a concrete example, when adding a new product line, the system of the present invention can be used to quickly formulate a production plan. For instance, based on a prompt such as, "Please create a proposal for adding a new product line. Required conditions are an additional production capacity of 2,000 units per month, whether existing equipment will be upgraded, and the number of engineers required," the AI ​​model generates a draft, and the information is efficiently shared with relevant parties.

[0654] This series of processes allows organizations to improve productivity and significantly reduce the time required for coordination and response.

[0655] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0656] Step 1:

[0657] The server retrieves organizational information and historical event data from its storage device. This data includes history and information about stakeholders related to production planning. Based on this data, the AI ​​generator uses it as foundational information to produce a draft proposal.

[0658] Step 2:

[0659] The server sends prompt messages to the generative AI model to generate a draft proposal. These prompt messages include information about the expansion requirements and production capacity for the new product line. The generative AI model uses natural language processing based on this information to generate an appropriate draft proposal.

[0660] Step 3:

[0661] The server sends a draft of the generated proposal to the terminal. The terminal displays the draft to the user. This display is done via a smartphone or tablet screen, allowing the user to review the content and provide interactive feedback.

[0662] Step 4:

[0663] Users review the draft proposal using a terminal and make revisions as needed. Revisions are entered through the terminal's interface, and the changes are reflected in the draft. The entered data includes specific production conditions and additional information about stakeholders.

[0664] Step 5:

[0665] Proposals modified on the terminal are saved by the server's management system. The server performs final approval and automatically submits the completed proposal to the relevant departments and end users. After all submissions are complete, the server generates a process log and stores it for future reference as needed.

[0666] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0667] This invention combines a system that streamlines internal coordination using organizational information and past project data with an emotion engine that recognizes user emotions. This system operates between a server, a terminal, and a user, enabling sophisticated adjustments that take user emotions into account during the proposal creation process.

[0668] The server stores organizational information and past project data in a database, allowing for quick retrieval of project-related information. It also utilizes generative AI to create draft proposals. Crucially, the server's built-in emotion engine analyzes the user's emotional state and customizes the proposal's style and content accordingly. This ensures the proposal is presented in a more acceptable format.

[0669] The terminal serves as an interface for providing information to the user, presenting them with draft proposals and requests for revisions. Furthermore, the terminal has a function to visually display emotion recognition results, allowing users to easily check their own emotional state. This information is useful when reviewing and revising proposals.

[0670] Users access the system and select projects and review and revise proposals via their terminals. After the proposal content is appropriately adjusted, the user gives final approval, and the proposal is saved by the server and prepared for submission. The emotion engine accumulates user feedback, enabling more refined emotional responses in future proposal creation processes.

[0671] For example, considering the proposal process for a new project, if the user's emotions are positive, the server will generate a proposal that emphasizes proactiveness. On the other hand, if negative emotions are detected, the proposal will focus on solutions to problems, making it more convincing to the user. This leads to smoother communication within the organization and more effective coordination.

[0672] The following describes the processing flow.

[0673] Step 1:

[0674] The user logs into the system using their device. After logging in, the emotion engine analyzes the user's emotional state and records their current emotions.

[0675] Step 2:

[0676] The user selects a project from their device. The device sends the selected information to the server, which retrieves relevant organizational information and past project data from its database.

[0677] Step 3:

[0678] Based on the data acquired by the server, relevant stakeholders are automatically selected. The server then sends an invitation email to the selected stakeholders and shares an overview of the project.

[0679] Step 4:

[0680] The server uses a generative AI based on project information to create a draft proposal. The emotion engine adjusts the style and content of the draft according to the user's emotional state.

[0681] Step 5:

[0682] The server sends a draft proposal it has created to the terminal. The terminal visually displays this draft and the user's emotional state, allowing the user to review it.

[0683] Step 6:

[0684] Users review the proposal draft and input revisions and feedback on their devices as needed. They then fine-tune the content and wording based on the sentiment recognition results.

[0685] Step 7:

[0686] The terminal sends the changes to the server. The server reflects these changes and updates the proposal.

[0687] Step 8:

[0688] The user performs a final review and approves the proposal. The terminal sends this information to the server, which then saves the proposal.

[0689] Step 9:

[0690] The server automatically submits approved proposals to the customer and sends a notification to the user upon completion of submission.

[0691] (Example 2)

[0692] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0693] In today's business environment, the processes of internal coordination and proposal writing are complex and require considerable time and effort. Misunderstandings and conflicts of opinion frequently occur during this process, hindering smooth project progress. Furthermore, coordination that takes into account the emotional state of users is difficult, and communication is often insufficient, especially when reconciling diverse opinions. This often reduces the likelihood of proposals being accepted, hindering efficient work execution.

[0694] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0695] In this invention, the server includes an information storage means for storing organizational information and past project data, a generation means for automatically extracting and communicating with relevant parties, a generation model means for generating proposals based on project-related information, and an emotion analysis means for analyzing the user's emotional state and customizing the writing style and content. This enables flexible proposal creation that takes user emotions into account and efficient internal coordination.

[0696] "Information storage means" refers to devices and systems that allow for the long-term storage and rapid access of organizational information and past case data.

[0697] "Generation means" refers to a device or program that has the function of automatically extracting relevant parties from information storage means and conducting necessary communications.

[0698] The "generative modeling method" is an artificial intelligence-based process that utilizes project-related information and past case studies to create a draft proposal.

[0699] "Emotional analysis tools" refer to devices or software that detect and analyze a user's emotional state and then appropriately customize the writing style and content based on the results.

[0700] "Interface means" refers to devices or programs that provide an operating environment that allows users to review and revise draft proposals.

[0701] A "management tool" refers to a device or system that has the function of saving proposals that reflect user modifications and preparing them for submission.

[0702] This invention is a system aimed at efficient proposal creation and internal coordination that takes user emotions into consideration. First, the server uses information storage means to store organizational information and past project data in a database. This database is constructed, for example, by utilizing a business database management system.

[0703] The server uses a generation mechanism to automatically extract relevant stakeholders from the information storage mechanism and sends the information to the stakeholders via email or other communication methods. Possible communication tools include general email systems and internal chat tools.

[0704] As a generative model, the server uses a generative AI model to generate a draft of the project proposal. This AI model utilizes past case data and templates and employs an AI model known as a text generation engine (for example, a model using natural language processing technology).

[0705] The terminal presents the generated proposal draft to the user via an interface. The terminal also incorporates emotion analysis capabilities, for example, using a camera and emotion recognition software (e.g., an open-source emotion recognition library) to analyze the user's facial expressions and tone of voice and understand the user's emotional state. Based on these results, the server dynamically customizes the style and content of the proposal.

[0706] Furthermore, users can review and, if necessary, revise their proposals through their terminals. After the user has completed revisions and final approval has been granted, the server will save the proposal using its management system and manage it in a state where it can be submitted.

[0707] As a concrete example, in the process of proposing a new project, if the user's emotions are positive, the server can generate a proposal that emphasizes proactiveness. An example of a prompt to input into the generation AI model would be, "Please create a project proposal. The user's emotional state is positive. Please make the content emphasize a proactive stance." In this way, the present invention enables proposal creation and coordination within an organization in an effective and emotionally responsive manner.

[0708] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0709] Step 1:

[0710] The server uses information storage means to store organizational information and historical project data in a database. This is done by importing data from the management system. Organizational metadata and project history data are used as input, and storing this in the database in a structured format makes it readily accessible in later steps.

[0711] Step 2:

[0712] The terminal activates an emotion analysis system to capture the user's emotions. This analysis involves real-time data capture using a camera and microphone. The input consists of the user's facial expressions and voice data, which are analyzed by emotion analysis software. This results in an output indicating an emotional state, such as positive or negative, which is then sent to the server.

[0713] Step 3:

[0714] The server uses a generation mechanism to automatically extract relevant parties from the information storage mechanism and communicates with them regarding the need to create a proposal. Specifically, it performs calculations to select appropriate parties based on past project performance data and automatically sends emails to their contact information. The emails contain background information on the project and guidelines for creating the proposal.

[0715] Step 4:

[0716] The server utilizes a generative model to generate a draft project proposal. Basic project information and the user's emotional state are used as input. By providing prompts to the generative AI model, a draft proposal with adjusted style and content is output. This draft is customized to be easily accepted by the user.

[0717] Step 5:

[0718] The terminal presents the user with a draft proposal through an interface. The user uses this interface to review the proposal and provide revision instructions. The user's input, including specific feedback and revision requests, is converted into a system-readable format and sent to the server. This allows for revisions tailored to the user's specific needs.

[0719] Step 6:

[0720] The user reviews the final revised proposal and gives their approval. The terminal notifies the server of this approval step. The approved proposal is then saved by the server's management system and prepared for automatic transmission to the client or relevant parties as needed. This completes the cycle from proposal creation to submission.

[0721] (Application Example 2)

[0722] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0723] Traditional proposal creation systems often fail to adequately consider emotions during internal coordination and customer interaction, resulting in proposals being less likely to be accepted. Furthermore, it's difficult to grasp emotional shifts in real time during customer interactions and respond accordingly. This leads to situations where proposals and customer service don't fully meet customer expectations and needs.

[0724] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0725] In this invention, the server includes data storage means for storing organizational information and past case data, generation AI means for generating draft proposals based on project-related information, and emotion analysis means for analyzing the user's emotional state and customizing the proposal to match the writing style and content. This makes it possible to generate proposals that respond to the user's emotions and to adjust customer service styles in physical stores to suit the emotions of the customers.

[0726] "Organizational information" refers to data about the structure, roles, and membership of companies and organizations.

[0727] "Past project data" refers to data that includes all records related to projects and tasks that have been processed to date.

[0728] A "data storage system" is a mechanism for storing information and data long-term and making it available for retrieval as needed.

[0729] "Generative means" refers to a tool or method that has the function of generating or processing information and providing the results to another system or human being.

[0730] "Generative AI means" refers to methods that utilize artificial intelligence technology to automatically create information and documents according to specified goals and conditions.

[0731] An "interface means" is a point of contact or method that enables data exchange and manipulation between a user and a system.

[0732] "Emotional analysis tools" are technologies that analyze and identify the emotional state of users and customers, enabling responses based on that analysis.

[0733] A "management mechanism" is a function that oversees the processing and flow of information within a system and efficiently achieves the requested results.

[0734] To realize this application, the system operates in conjunction with multiple hardware and software components. First, the server centrally manages organizational information and past project data using a database. This data is used as reference information necessary for creating proposals relevant to the user. The server also has the ability to automatically generate draft proposals based on project information, utilizing generational AI.

[0735] As a means of sentiment analysis, an emotion recognition engine using machine learning libraries such as TensorFlow is used to analyze the emotional state of users and customers in real time. As a result, the content and style of suggested documents are optimized according to the recipient's emotions. In particular, in physical stores, sales staff can wear smart glasses and display real-time advice on a screen to help them adopt an appropriate sales style based on the customer's facial expressions.

[0736] The terminal functions as an interface for the user, displaying the draft proposal and facilitating revisions and approvals. Sentiment analysis results are presented as visual information, allowing the user to adjust the proposal content while reflecting their own emotions.

[0737] For example, if a customer's emotions become negative during a conversation, the emotion analysis tool can suggest solutions that focus on addressing the problem, which can then be reflected in the proposal or customer service style. Furthermore, as an example of a prompt, the system can instruct the AI ​​model to "suggest questions to help the customer relax when their expression becomes cloudy," prompting it to respond accordingly. This system allows proposals and customer service to be delivered in a way that more closely reflects the user's intentions and is more readily accepted.

[0738] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0739] Step 1:

[0740] The server retrieves organizational information and past project data from the database. The input is key information related to the project within the database, and the output is corresponding detailed data. The server uses this data to build the foundation for proposal creation.

[0741] Step 2:

[0742] The server uses a generation AI to automatically generate a draft proposal based on project information. The input is the detailed data obtained in Step 1, and the output is an initial draft proposal. The server utilizes a generation AI model to generate the draft while considering successful cases and templates.

[0743] Step 3:

[0744] The server analyzes the user's emotional state using emotion analysis tools. The input is real-time facial expression data and feedback obtained from the user, and the output is the analyzed emotional state. The server processes the data through an emotion recognition model such as TensorFlow to identify the emotional state.

[0745] Step 4:

[0746] The terminal displays a draft proposal to the user, along with the sentiment analysis results. The inputs are the proposal draft from step 2 and the sentiment state from step 3, while the output is a visual representation on the screen. The terminal provides an intuitive interface to facilitate user review and modification.

[0747] Step 5:

[0748] The user reviews the proposal based on the displayed information and makes revisions as needed. Input consists of the visual information from step 4 and the user's judgment; output is the revised proposal. The proposal is adjusted based on the user's actions, and these revisions are reflected in the system.

[0749] Step 6:

[0750] The server customizes and saves the proposal based on the sentiment analysis results. The inputs are the revised proposal from step 5 and the sentiment state from step 3, and the output is the customized proposal. The server saves the proposal and ensures it is ready for submission.

[0751] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0752] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0753] 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 this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0754] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0755] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0756] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0757] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0758] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0759] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0760] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0761] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0762] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0763] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0764] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0765] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0766] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0767] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0768] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0769] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0770] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0771] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0772] The following is further disclosed regarding the embodiments described above.

[0773] (Claim 1)

[0774] A database that stores organizational information and past project data,

[0775] A generation means that automatically extracts relevant parties from the aforementioned database and sends out invitation emails,

[0776] A generation AI method that generates a draft proposal based on project information,

[0777] An interface means for displaying the aforementioned draft and enabling user review and modification,

[0778] A management system that allows proposals reflecting user modifications to be saved and made available for submission,

[0779] A system that includes this.

[0780] (Claim 2)

[0781] The system according to claim 1, characterized in that the generation AI means generates a proposal document with reference to past success stories and standard templates.

[0782] (Claim 3)

[0783] The system according to claim 1, characterized in that the management means has a function to automatically submit the final approved proposal to the customer.

[0784] "Example 1"

[0785] (Claim 1)

[0786] An information management system for storing organizational information and past project data,

[0787] Information generation means that automatically extracts relevant elements from the aforementioned information management means and transmits notification information,

[0788] An information generation device that generates a draft document based on project-related information,

[0789] A display device that displays the aforementioned draft and allows users to confirm and modify it,

[0790] An information management device that makes documents reflecting user modifications available for storage and submission,

[0791] An information processing system that includes this.

[0792] (Claim 2)

[0793] The aforementioned information generation device is an information processing system that generates documents based on past success stories and standard documents.

[0794] (Claim 3)

[0795] The aforementioned information management device is an information processing system that has the function of automatically submitting final approved documents to others.

[0796] "Application Example 1"

[0797] (Claim 1)

[0798] A storage device for storing organizational information and past event data,

[0799] A generation means that automatically identifies relevant parties from the storage device and sends notifications,

[0800] A generation AI method that generates a draft proposal based on information regarding the production plan,

[0801] A display means that displays the aforementioned draft and allows users to review and modify it,

[0802] A management system that allows proposals reflecting user revisions to be stored and made available for submission,

[0803] A system that includes this.

[0804] (Claim 2)

[0805] The system according to claim 1, characterized in that the generation AI means generates a proposal document with reference to past successful examples and standard templates.

[0806] (Claim 3)

[0807] The system according to claim 1, characterized in that the management means has a function to automatically send the final approved proposal to the relevant departments.

[0808] "Example 2 of combining an emotion engine"

[0809] (Claim 1)

[0810] Information storage means for storing organizational information and past project data,

[0811] A generation means that automatically extracts relevant parties from the information storage means and communicates with them,

[0812] A generative model means for generating a draft proposal based on project information,

[0813] A sentiment analysis means that analyzes the user's emotional state and customizes the writing style and content of the proposal based on the said emotional state,

[0814] An interface means for displaying the aforementioned draft and enabling user review and modification,

[0815] A management system that allows proposals reflecting user modifications to be saved and made available for submission,

[0816] A system that includes this.

[0817] (Claim 2)

[0818] The system according to claim 1, characterized in that the generation model means generates a proposal document with reference to past success stories and standard templates.

[0819] (Claim 3)

[0820] The system according to claim 1, characterized in that the management means has a function to automatically submit the final approved proposal to the customer.

[0821] "Application example 2 when combining with an emotional engine"

[0822] (Claim 1)

[0823] A data storage means for storing organizational information and past project data,

[0824] A generation means that automatically extracts relevant parties from the aforementioned data storage means and transmits convocation information,

[0825] A generation AI method that generates a draft proposal based on project information,

[0826] An interface means that displays the aforementioned draft and allows users to review and modify it,

[0827] A sentiment analysis tool that analyzes the user's emotional state and customizes the proposal document with a corresponding writing style and content,

[0828] A management system that allows proposals reflecting user revisions to be saved and made available for submission,

[0829] A system that includes this.

[0830] (Claim 2)

[0831] The system according to claim 1, characterized in that the generation AI means generates a proposal by referring to past success stories and standard templates, and taking into account the emotional state of the user.

[0832] (Claim 3)

[0833] The system according to claim 1, characterized in that the emotion analysis means has the function of analyzing customer emotions in real time at a physical store and adjusting the customer service style. [Explanation of Symbols]

[0834] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A database that stores organizational information and past project data, A generation means that automatically extracts relevant parties from the aforementioned database and sends out invitation emails, A generation AI method that generates a draft proposal based on project information, An interface means for displaying the aforementioned draft and enabling user review and modification, A management system that allows proposals reflecting user modifications to be saved and made available for submission, A system that includes this.

2. The system according to claim 1, characterized in that the generation AI means generates a proposal document with reference to past success stories and standard templates.

3. The system according to claim 1, characterized in that the management means has a function to automatically submit the final approved proposal to the customer.

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