system

Abstract

Provide a system. A method for collecting official documents; A method for analyzing collected official documents using natural language processing technology and identifying management issues; A means for automatically generating proposal materials based on the analysis results; means for transmitting the generated proposal material to a user terminal; A system including:

Description

[Technical Field]

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

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

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

[0004] Salespeople spend a large portion of their normal working hours preparing materials and gathering information, leaving them with limited time to negotiate with customers and close specific deals. This reduces sales efficiency and hinders performance improvement. Therefore, there is a need for a system that allows salespeople to devote more time to negotiating with customers and closing deals. [Means for solving the problem]

[0005] This invention is a system including means for collecting official documents, means for analyzing the collected official documents using natural language processing technology and identifying management issues, means for automatically generating proposal materials based on the analysis results, and means for transmitting the generated proposal materials to a user terminal. The system further includes database means for storing the official documents, and means for retrieving the official documents from the database and providing them to the analysis means. By including means on the user terminal for displaying and enabling editing of the proposal materials, sales representatives can use the automatically generated proposal materials to efficiently advance business negotiations with customers.

[0006] "Official documents" are documents that contain financial information and management policies that a company makes public, such as investor reports and company overview documents.

[0007] "Means of collection" refers to a process or device that automatically acquires and stores multiple official documents.

[0008] "Natural language processing technology" refers to algorithms and technologies that enable computers to understand, interpret, and generate natural human language.

[0009] "Means of analysis" refers to the process or device that uses natural language processing techniques to analyze official documents and extract useful information and patterns based on context.

[0010] "Management issues" refer to the problems and challenges that companies face in areas such as profitability, market competition, and operational efficiency.

[0011] "Means of identification" refers to the process or device for extracting important information from analyzed official documents and identifying management issues.

[0012] "Proposal materials" refers to documents or presentations that contain solutions or proposals for solving identified business issues.

[0013] "Means for automatic generation" refers to a process or device that automatically assembles proposal materials based on the analysis results.

[0014] "User terminal" refers to electronic devices such as computers and tablets used by sales representatives.

[0015] "Database means" refers to a system that organizes and stores official documents and analysis results, and keeps them available as needed. [Brief explanation of the drawings]

[0016] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12]FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0017] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

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

[0019] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0020] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0021] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0022] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0023] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0024] [First embodiment]

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

[0026] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0027] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0028] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0029] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0031] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0033] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0034] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0035] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0036] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0037] This invention provides a system that automatically generates necessary proposal materials so that sales representatives can concentrate on negotiating with customers. Below, we will explain how this system is implemented by the server, terminals, and users.

[0038] 1. System Overview

[0039] This system collects official documents, analyzes their contents to identify management issues, and automatically generates proposal materials and sends them to the user's device, eliminating the need for users (salespeople) to create documents and allowing them to focus on negotiating with customers and closing deals.

[0040] 2. Program Processing

[0041] 2.1 Collecting official documentation

[0042] The server collects official documents, such as the latest investor relations materials and financial reports, from the official websites and public databases of multiple companies based on a pre-defined list. This collection process is performed automatically on a regular basis, and the collected documents are stored in a database.

[0043] 2.2 Analysis of the official documentation

[0044] The server analyzes the collected official documents using natural language processing technology. For example, it uses a natural language processing library to extract keywords and phrases from the text. This allows the server to identify the business challenges each company faces (such as "declining revenue" or "difficulty entering the market").

[0045] 2.3 Automatic generation of proposal materials

[0046] Based on the analysis results, the server automatically generates proposal materials using templates. These proposal materials include solutions to the identified business issues and the selling points of the services and products to be offered. For example, they could be "Proposing a new marketing strategy to address declining revenues" or "Introducing services to support market entry."

[0047] 2.4 Submitting and Displaying Proposals

[0048] The server transmits the generated proposal material to the user's terminal.

[0049] The terminal displays the received proposal materials to the user, who can then review the materials and make edits or additions as necessary.

[0050] 3. Specific Examples

[0051] 3.1 Company A's IR materials

[0052] The server collects the latest IR documents from the official website of Company A. The documents contain information about a "recent decline in revenue."

[0053] 3.2 Analysis of the data

[0054] A natural language processing algorithm on the server identifies the keyword "revenue decline" and records it as a management issue.

[0055] 3.3 Proposal Creation

[0056] The server automatically incorporates specific approaches to solving declining revenue (for example, cost reduction programs or methods for developing new markets) into the proposal materials.

[0057] 3.4 Sending materials and preparing for negotiations

[0058] The server sends the generated proposal materials to the user's terminal,

[0059] The terminal displays the material to the user.

[0060] 3.5 Conducting business negotiations

[0061] The user begins negotiations with the customer (Company A) based on the proposal materials viewed on the device. Because the proposal materials show specific solutions, the user can proceed with the negotiations efficiently.

[0062] In this way, the system automates the entire process, from collecting official documents to analyzing them, generating proposal materials, and sending them to user devices, allowing sales representatives to devote more time to customer service.

[0063] The processing flow will be explained below.

[0064] Step 1:

[0065] The server retrieves a pre-configured list of companies, which includes URLs of the target companies' official websites or public databases.

[0066] Step 2:

[0067] Based on the company list, the server automatically downloads the latest official documents (e.g., investor relations materials and financial reports) from each company's official website or public database.

[0068] Step 3:

[0069] The server stores the downloaded official documents in a database, along with metadata such as the company name, document name, and download date and time.

[0070] Step 4:

[0071] The server initializes natural language processing (NLP) algorithms, which include text analysis and keyword extraction.

[0072] Step 5:

[0073] The server retrieves official documents from the database and passes them to an NLP algorithm, which analyzes the documents and extracts important keywords and phrases, thereby identifying business challenges for the company.

[0074] Step 6:

[0075] The server identifies business issues based on keywords extracted by the NLP algorithm, such as specific problems like "declining revenue" or "need to enter new markets."

[0076] Step 7:

[0077] The server selects a proposal template corresponding to the identified business problem, the template containing a proposal for solving the particular problem.

[0078] Step 8:

[0079] The server then embeds specific proposal content based on the analysis results into the proposal template and automatically generates proposal materials, which include solutions and selling points for the company's services.

[0080] Step 9:

[0081] The server transmits the generated proposal materials to the user's terminal using a secure communication method.

[0082] Step 10:

[0083] The terminal displays the received proposal materials to the user, who then checks the materials and makes edits or additions as necessary.

[0084] Step 11:

[0085] Users can prepare for business negotiations with customers based on the proposal materials they have edited, which allows them to proceed with business negotiations more efficiently.

[0086] Step 12:

[0087] The user starts negotiations with the customer and proposes specific solutions to the problem using proposal materials. After the negotiations, the user focuses on closing the deal and finalizing the contract.

[0088] Example 1

[0089] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0090] The present invention aims to automate and streamline the time-consuming process of creating proposal materials so that sales representatives can focus on negotiating with customers. Another objective is to effectively analyze collected official documents, identify business issues, and quickly and accurately generate corresponding proposal materials.

[0091] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0092] In this invention, the server includes means for collecting official documents, means for analyzing the collected official documents using natural language processing technology and identifying business issues, means for automatically generating proposal materials based on the analysis results, means for transmitting the generated proposal materials to a terminal, and database means for storing the collected official documents. This allows sales representatives to concentrate on negotiating with customers without spending much time on creating proposal materials.

[0093] "Official documents" are information documents that companies or organizations make public, such as investor relations materials and financial reports.

[0094] "Natural language processing technology" is a technology for analyzing, understanding, and generating natural language used by humans, and is used to extract keywords and phrases from text.

[0095] "Business challenges" are management and operational problems and difficulties faced by a company or organization, including declining revenue and difficulties in entering a market.

[0096] A "proposal" is a document that summarizes solutions and proposals for business issues faced by a company or organization.

[0097] The "means of collection" is the mechanism by which the server obtains official documents from the Internet or public databases.

[0098] The "means of analysis" refers to a system that uses natural language processing technology to analyze official documents collected by the server and identify business issues.

[0099] "Means for automatic generation" refers to a mechanism in which the server automatically creates proposal materials in accordance with a template based on the analysis results.

[0100] The "transmission means" is a mechanism by which the server delivers the generated proposal materials to the user's terminal.

[0101] "Database means for storage" refers to a database system for efficiently storing and managing collected official documents and generated materials.

[0102] A "terminal" is an information device that a user uses to receive and view proposal materials, and includes a personal computer, tablet, etc.

[0103] This invention provides a system that automatically generates necessary proposal materials so that sales representatives can concentrate on negotiating with customers. Below, we will explain in detail how the server, terminals, and users implement this system.

[0104] 1. System Configuration

[0105] The system includes the following major components:

[0106] Server: Collects and analyzes official documents, automatically generates and sends proposal materials.

[0107] Terminal: Receives, displays, and edits proposal materials.

[0108] Database: Stores collected official documents and generated proposal materials.

[0109] 2. Hardware and Software Used

[0110] Server hardware: A computer equipped with a high-performance processor and large memory capacity. For example, a server machine equipped with an NVIDIA GPU.

[0111] Software: Python programming language, requests library, natural language processing library spaCy, template engine Jinja2, library for sending emails smtplib.

[0112] 3. System Processing

[0113] The server collects official documents such as investor relations materials and financial reports from companies' official websites and public databases based on a pre-configured list, for example, by using the Python requests library to retrieve data from the Internet.

[0114] The collected official documents are stored in a database on the server, which then analyzes them using natural language processing technology, using the spaCy library to extract important keywords and phrases from the text and identify the company's business challenges.

[0115] Based on the analysis results, the server uses templates to automatically generate proposal materials. The template engine Jinja2 is used to create materials that include solutions to the company's challenges and the selling points of the service. The generated proposal materials are sent by email from the server to the user's device. The library used for sending emails is smtplib.

[0116] The terminal displays the received proposal documents and allows the user to review and edit them. For example, the user can open the received email, check the contents of the documents, and make supplementary input or corrections as necessary.

[0117] 4. Specific Examples

[0118] Company A's case

[0119] 1. The server collects the latest IR materials from the official website of Company A. The materials contain information about a "recent decline in revenue."

[0120] 2. The server's natural language processing algorithm identifies the keyword "revenue decline" and records it as a business issue.

[0121] 3. The server incorporates approaches to solving the declining revenue (e.g., new marketing strategies) into the proposal.

[0122] 4. The server sends the generated proposal materials to the terminal and displays the materials on the terminal.

[0123] Prompt Sentence Examples

[0124] "Collect the latest IR materials for Company A and generate a proposal to resolve the decline in revenue."

[0125] In this way, the server and terminals work together to automate the process of creating proposal materials, allowing sales representatives to focus on negotiating with customers.

[0126] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0127] System program processing flow

[0128] Step 1: Gather official documents

[0129] The server collects official documents such as investor relations materials and financial reports from companies' official websites and public databases based on a pre-configured list.

[0130] Input: Company website URL list

[0131] How it works: Uses Python's requests library to retrieve HTML data from each URL.

[0132] Output: A list of collected text data from official documents

[0133] Step 2: Save official documents

[0134] The server stores the collected official documents in a database.

[0135] Input: Text data from collected official documents

[0136] What it does: Query an SQL database for stored official documents.

[0137] Output: Official documents stored in a database

[0138] Step 3: Analyzing the official documentation

[0139] The server analyzes the collected official documents using natural language processing technology.

[0140] Input: Official documents stored in a database

[0141] What it does: It uses the spaCy library to parse text and extract keywords and phrases.

[0142] Output: A list of extracted keywords and phrases

[0143] Step 4: Identifying business issues

[0144] The server identifies business issues facing the company based on the analysis results.

[0145] Input: A list of extracted keywords or phrases

[0146] How it works: Uses a rules engine to match keyword lists to specific patterns and extract business issues.

[0147] Output: List of identified business issues

[0148] Step 5: Automatically generate proposal materials

[0149] The server automatically generates proposal materials using templates based on the identified management issues.

[0150] Input: List of identified business issues

[0151] How it works: Uses the Jinja2 template engine to generate proposals that detail solutions and services that address business challenges.

[0152] Output: Generated proposal document

[0153] Step 6: Submit your proposal

[0154] The server transmits the generated proposal material to the user's terminal.

[0155] Input: Generated proposal document

[0156] What it does: Uses the smtplib library to send the proposal to the user's email address.

[0157] Output: Proposal sent to the user's device

[0158] Step 7: View and edit your proposal

[0159] The terminal displays the received proposal material to the user, who can then edit it as necessary.

[0160] Input: Proposal sent to the user's device

[0161] How it works: The proposal document opens in the email client, and the user can edit and add to it.

[0162] Output: Proposal document reviewed and edited by the user

[0163] (Application example 1)

[0164] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0165] Ensuring efficient and safe operations is crucial in modern manufacturing. However, analyzing various official documents and maintenance reports requires a great deal of time and effort, making it difficult to quickly implement appropriate countermeasures. Furthermore, identifying manufacturing process and safety issues and generating specific proposals for improvement is also cumbersome, placing a significant burden on personnel. A solution to these issues and improving the efficiency and safety of manufacturing is needed.

[0166] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0167] In this invention, the server includes means for collecting official documents, means for analyzing the collected official documents using natural language processing technology and identifying issues, means for automatically generating proposal materials based on the analysis results, means for transmitting the generated proposal materials to a user terminal, means for collecting factory maintenance reports and official reports and identifying issues related to the manufacturing process, efficiency, and safety, means for incorporating specific solutions to the identified issues into the proposal materials, and means for displaying the proposal materials on a display installed in a factory robot. This enables personnel to efficiently identify issues and quickly implement solutions.

[0168] "Official documents" are documents such as reports, financial reports, and maintenance reports officially issued by companies or organizations.

[0169] "Collection methods" are techniques and methods for periodically obtaining official documents from multiple sources and storing them in a database.

[0170] "Natural language processing technology" is a technology that allows machines to understand and analyze text data, and can perform tasks such as extracting keywords and identifying relationships.

[0171] "Means of analysis" refers to techniques and methods for analyzing collected official documents using natural language processing technology and extracting specific issues or problems.

[0172] "Means for automatically generating proposal materials" refers to techniques or methods for automatically creating proposal materials in a standard format based on the analysis results.

[0173] A "user terminal" is a device such as a communication device or computer used by a user, and is a device that receives and displays proposal materials.

[0174] "Transmission means" refers to the technology or method for transferring data from the server to the user terminal.

[0175] A "maintenance report" is a document that records the results of regular maintenance inspections of factories and equipment.

[0176] "Manufacturing process" means the process and procedures for producing products in a factory.

[0177] "Efficiency" is a concept that refers to the optimal use of resources in the manufacturing process and the resulting high productivity.

[0178] "Safety" refers to the state of minimizing risks in the manufacturing environment and processes and preventing injuries and accidents.

[0179] A "problem" refers to a problem or challenge that requires a solution under specific circumstances or conditions.

[0180] A "solution" is a specific method or means for overcoming a particular problem.

[0181] A "factory robot" is an automated machine designed to perform work within a factory, and is a device equipped with a display for displaying proposal materials.

[0182] This invention is a system that aims to improve the efficiency and safety of manufacturing processes by having a server collect and analyze official documents and provide proposal materials to the user's factory robots. Specific embodiments for implementing this system are described below.

[0183] 1. System Overview

[0184] This system collects official documents, analyzes their contents to identify issues related to manufacturing processes, efficiency, and safety, and automatically generates proposal materials and sends them to the user's terminal.This system saves the user (factory personnel) the trouble of creating documents and enables them to quickly take efficient countermeasures to the identified issues.

[0185] 2. Hardware and Software

[0186] The system uses the following hardware and software:

[0187] Server: Collects data, analyzes it, and generates proposal materials.

[0188] Factory robot: Equipped with a display that displays the generated proposal materials.

[0189] Python: Used to implement the entire program.

[0190] Requests: Used to collect data on web pages.

[0191] BeautifulSoup: Used to extract text from HTML.

[0192] Transformers: Used to run natural language processing models (e.g., HuggingFace's Transformers library).

[0193] 3. Program Processing

[0194] 3.1 Collecting official documentation

[0195] The server automatically and periodically collects official documents (e.g., maintenance reports) from multiple official websites and public databases based on a pre-configured list and stores them in a database.

[0196] 3.2 Document Parsing

[0197] The server then analyzes the collected official documents using natural language processing techniques to extract key keywords and phrases, using HuggingFace's Transformers library, to identify issues related to manufacturing processes, efficiency, and safety.

[0198] 3.3 Automatic generation of proposal materials

[0199] Based on the analysis results, the server automatically generates proposal materials using templates. The proposal materials include solutions to the identified issues and their effects. For example, the proposals might include "application of a new process control system" or "regular maintenance and the introduction of high-performance sensors."

[0200] 3.4 Submitting and Displaying Proposals

[0201] The server sends the generated proposal materials to the user terminal (factory robot), which displays the received proposal materials on its display, allowing the user (factory staff) to check them and take appropriate measures as necessary.

[0202] 4. Specific Examples

[0203] For example, the server collects the latest maintenance reports from the factory's official website and uses analysis technology to identify mentions of "declining efficiency" or "safety issues." The server then generates a proposal document containing solutions such as "proposing a new process control system to address the efficiency decline" or "introducing high-performance sensors to address safety issues." The generated proposal document is then displayed on the factory robot's display for the user to review.

[0204] An example of a prompt sentence to be input into a generative AI model is, "Please extract issues and solutions from the factory report below and generate a proposal document."

[0205] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0206] Step 1:

[0207] Collecting official documentation

[0208] Input: A list of official website URLs pre-configured by the server.

[0209] How it works: The server periodically collects official documents (e.g., maintenance reports and financial reports) from multiple official websites based on a configured list of URLs. This process involves issuing HTTP requests using the Requests library to retrieve the contents of the web pages.

[0210] Data processing: The acquired HTML data is analyzed using the BeautifulSoup library and text data is extracted.

[0211] Output: A text data list of the official documentation.

[0212] Step 2:

[0213] Analysis of the official documentation

[0214] Input: A text data list of official documents collected in Step 1.

[0215] How it works: The server analyzes the collected official documents using natural language processing techniques, specifically using the Transformers library to extract keywords and important phrases from the text data.

[0216] Data Computing: Natural language processing models analyze text and identify issues (e.g., inefficiencies, safety issues, etc.) contained in each document.

[0217] Output: A list of identified issues.

[0218] Step 3:

[0219] Automatic generation of proposal materials

[0220] Input: The list of issues identified in step 2.

[0221] How it works: The server uses templates to automatically generate proposals containing solutions to identified issues, such as proposing a new process control system to address "decreased efficiency" or introducing high-performance sensors to address "safety issues."

[0222] Data processing: Combine the issue list and solution template to generate concrete proposal materials.

[0223] Output: The generated proposal (e.g. PDF, HTML format).

[0224] Step 4:

[0225] Submitting and viewing proposals

[0226] Input: The proposal generated in Step 3.

[0227] Operation: The server sends the generated proposal materials to the user terminal (factory robot). The factory robot displays the received proposal materials on its built-in display.

[0228] Data operations: file transfer and display.

[0229] Output: Proposal materials displayed on the user's terminal.

[0230] Step 5:

[0231] User verification and action taken

[0232] Input: Proposal materials displayed on the user's terminal.

[0233] Operation: The user (factory staff) checks the proposal documents displayed on the factory robot's display and takes appropriate measures as necessary. Based on the proposal documents, the user adjusts the manufacturing process and implements safety measures.

[0234] Output: Efficient manufacturing processes and improved safety.

[0235] The above are the specific processing steps of this system. By performing detailed operations for each step, it is possible to improve the efficiency and ensure safety of the manufacturing process.

[0236] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0237] This invention provides a system that automatically generates necessary proposal materials so that salespeople can concentrate on negotiating with customers, and furthermore, recognizes the user's emotions and adjusts the proposal content accordingly. Below, we will explain how this system is implemented by the server, terminal, and user.

[0238] 1. System Overview

[0239] This system collects official documents, analyzes their contents to identify management issues, and automatically generates proposal materials and sends them to the user's device. Furthermore, by incorporating an emotion engine that recognizes the user's (sales representative's) emotions, the system has the ability to adjust proposal materials according to the user's emotional state. This saves users the trouble of creating documents, allowing them to focus on negotiating with customers and closing deals.

[0240] 2. Program Processing

[0241] 2.1 Collecting official documentation

[0242] The server retrieves a pre-configured list of companies, which includes URLs of the target companies' official websites or public databases.

[0243] Based on the company list, the server automatically downloads the latest official documents (e.g., investor relations materials and financial reports) from each company's official website or public database.

[0244] The server stores the downloaded official documents in a database, along with metadata such as the company name, document name, and download date and time.

[0245] 2.2 Analysis of the official documentation

[0246] The server initializes natural language processing (NLP) algorithms, which include text analysis and keyword extraction.

[0247] The server retrieves official documents from the database and passes them to an NLP algorithm, which analyzes the documents and extracts key keywords and phrases, identifying business challenges for the company (e.g., "declining revenue" or "need to enter new markets").

[0248] 2.3 Automatic generation of proposal materials

[0249] Based on the analysis results, the server automatically generates proposal materials using templates. These proposal materials include solutions to the identified business issues and the selling points of the services and products to be offered. For example, they could be "Proposing a new marketing strategy to address declining revenues" or "Introducing services to support market entry."

[0250] 2.4 Submitting proposal materials and operation of the emotion engine

[0251] The server transmits the generated proposal materials to the user's terminal using a secure communication method.

[0252] The terminal displays the received proposal materials to the user and activates the emotion engine, which analyzes the user's facial expressions and tone of voice while reviewing the proposal materials to determine the user's emotional state.

[0253] 2.5 Adjustment of proposal materials

[0254] The device's emotion engine analyzes the user's emotions and automatically adjusts the content of the proposal based on the results. For example, if the user looks anxious, it will add additional reassuring information to the proposal.

[0255] 2.6 Preparation and implementation of business negotiations

[0256] The user can review the proposal materials adjusted by the emotion engine and make further edits or additions as necessary.

[0257] Users can prepare and efficiently conduct business negotiations with customers based on the proposal materials. At this point, the materials reflect the user's emotions and are optimized, improving the success rate of the negotiations.

[0258] 3. Specific Examples

[0259] 3.1 Analysis of Company A's IR materials and generation of proposal materials

[0260] The server collects the latest IR documents from the official website of Company A. The documents contain information about a "recent decline in revenue."

[0261] A natural language processing algorithm on the server identifies the keyword "revenue decline" and records it as a management issue.

[0262] The server automatically incorporates specific approaches to solving declining revenue (for example, cost reduction programs or methods for developing new markets) into the proposal materials.

[0263] 3.2 Data transmission and user emotion recognition

[0264] The server transmits the generated proposal materials to the user's terminal.

[0265] The terminal displays the document to the user, and the emotion engine recognizes the user's emotions. The emotion engine may detect that the user has an anxious expression while checking the document.

[0266] 3.3 Adjusting materials and preparing for business meetings

[0267] The device's emotion engine detects the user's anxiety and automatically inserts additional supporting information into the proposal materials to increase the user's sense of security.

[0268] The user prepares and conducts business negotiations with the client (Company A) based on the adjusted proposal materials. By using materials optimized by the emotion engine, the negotiations proceed more effectively and the probability of successfully closing the contract increases.

[0269] In this way, the system automates a series of processes, from collecting official documents to analyzing them, generating proposal materials, and adjusting them using an emotion engine, allowing sales representatives to devote more time to customer service and increasing the success rate of sales negotiations.

[0270] The processing flow will be explained below.

[0271] Step 1:

[0272] The server retrieves a pre-configured list of companies, which includes URLs of the target companies' official websites or public databases.

[0273] Step 2:

[0274] Based on the company list, the server automatically downloads the latest official documents (e.g., investor relations materials and financial reports) from each company's official website or public database.

[0275] Step 3:

[0276] The server stores the downloaded official documents in a database, along with metadata such as the company name, document name, and download date and time.

[0277] Step 4:

[0278] The server initializes natural language processing (NLP) algorithms, which include text analysis and keyword extraction.

[0279] Step 5:

[0280] The server retrieves official documents from the database and passes them to an NLP algorithm, which analyzes the documents and extracts key keywords and phrases, identifying business challenges for the company (e.g., "declining revenue" or "need to enter new markets").

[0281] Step 6:

[0282] The server identifies business challenges based on keywords extracted by the NLP algorithm, such as specific problems like "declining revenue" or "barriers to market entry."

[0283] Step 7:

[0284] The server selects a proposal template corresponding to the identified business problem, the template containing a proposal for solving the particular problem.

[0285] Step 8:

[0286] The server then embeds specific proposal content based on the analysis results into the proposal template and automatically generates proposal materials, which include solutions and selling points for the company's services.

[0287] Step 9:

[0288] The server transmits the generated proposal materials to the user's terminal using a secure communication method.

[0289] Step 10:

[0290] The device displays the received proposal materials to the user and activates the emotion engine, which analyzes the user's facial expressions and tone of voice while reviewing the proposal materials to determine the user's emotional state.

[0291] Step 11:

[0292] The device's emotion engine analyzes the user's emotions and automatically adjusts the content of the proposal based on the results. For example, if the user looks anxious, it will add additional reassuring information to the proposal.

[0293] Step 12:

[0294] The user can review the proposal materials adjusted by the emotion engine and make further edits or additions as necessary.

[0295] Step 13:

[0296] Users can use the proposal documents to prepare and efficiently conduct business negotiations with customers. At this point, the documents reflect the user's emotions and are optimized, improving the success rate of the negotiations.

[0297] Step 14:

[0298] The user starts negotiations with the customer and proposes specific solutions to the problem using proposal materials. After the negotiations, the user focuses on closing the deal and finalizing the contract.

[0299] Example 2

[0300] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0301] In order to allow sales representatives to concentrate on business negotiations with customers, it is necessary to improve the efficiency of the creation of proposal materials and the accuracy of proposals. Another challenge is to improve the success rate of sales negotiations by enabling the adjustment of proposal materials according to the user's emotional state.

[0302] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0303] In this invention, the server includes means for collecting company information, means for analyzing the collected company information using natural language processing technology and identifying management issues, means for automatically generating proposal materials based on the analysis results, means for transmitting the generated proposal materials to a user terminal, and means for recognizing the user's emotions and adjusting the proposal materials. This automates the creation of proposal materials and optimizes the materials according to the user's emotions, thereby reducing the burden on the user during business negotiations and improving the success rate.

[0304] "Corporate information" refers to official documents such as financial reports and investor relations materials collected from a company's official website or public databases.

[0305] "Natural language processing technology" refers to technology that enables computers to understand and analyze human language, and in particular technology that extracts keywords and analyzes text.

[0306] "Business challenges" are operational problems or obstacles facing a company, such as declining revenue or the need to enter new markets.

[0307] A "proposal document" is a document generated based on the analysis results, which describes solutions to identified business issues and the selling points of the services and products to be offered.

[0308] A "user terminal" is a device that receives proposal materials sent from the server and allows the user to check and operate them, and includes a PC, tablet, etc.

[0309] "Means for recognizing emotions" refers to technology that determines a user's emotional state by analyzing their facial expressions, tone of voice, etc., and includes, for example, facial recognition technology and voice analysis technology.

[0310] "Data storage" refers to a storage device or database for storing collected company information and generated proposal materials.

[0311] This invention provides a system that automatically generates necessary proposal materials so that salespeople can concentrate on negotiating with customers, and furthermore, recognizes the user's emotions and adjusts the proposal content accordingly. Below, we will explain how this system is implemented by the server, terminal, and user.

[0312] System Overview

[0313] This system collects corporate information, analyzes the content to identify management issues, and automatically generates proposal materials and sends them to the user's device. Furthermore, by incorporating an emotion engine that recognizes the user's (salesperson's) emotions, the system has the ability to adjust proposal materials according to the user's emotional state. This saves users the trouble of creating materials, allowing them to focus on negotiating and closing deals with customers.

[0314] Hardware and software used

[0315] This system uses the following hardware and software:

[0316] Server: Collects and analyzes data, generates and sends proposal materials.

[0317] Terminal: Displays proposal materials and performs emotion recognition using an emotion engine.

[0318] Natural language processing algorithm: GOOGLE TENSOR® FLOW® is used.

[0319] Proposal generation tool: Uses Microsoft(R) Word API.

[0320] Emotion Engine: Uses Microsoft Azure® Cognitive Services.

[0321] Specific processing

[0322] 1. Collection of company information

[0323] The server retrieves a pre-defined list of companies, which includes the URLs of the target companies' official websites and public databases. Based on the list, the server automatically downloads the latest official documents from each company's official website or public database. For example, it retrieves PDF files from websites using HTTP requests. The downloaded official documents are stored in data storage, along with metadata such as the company name, document name, and download date and time.

[0324] Specific working example:

[0325] The server collects the latest IR documents from the official website of Company A. The documents contain information about a "recent decline in revenue."

[0326] 2. Analysis of company information

[0327] The server initializes TensorFlow, retrieves the official document from data storage, and passes it to the NLP algorithm, which analyzes the document and extracts important keywords and phrases. For example, the keyword "revenue decline" is extracted.

[0328] Specific working example:

[0329] TensorFlow identifies the keyword "revenue decline" from Company A's IR materials.

[0330] 3. Automatic generation of proposal materials

[0331] Based on the analysis results, the server uses Microsoft Word API to automatically generate a proposal document using a template, which includes solutions to the identified business issues and the selling points of the services and products to be offered.

[0332] Specific working example:

[0333] The server automatically incorporates specific approaches (for example, cost reduction programs and methods for developing new markets) for solving Company A's declining profits into the proposal materials.

[0334] Example prompt sentence:

[0335] "Generate proposal materials to address revenue declines based on Company A's IR materials"

[0336] 4. Sending proposal materials and running the emotion engine

[0337] The server sends the generated proposal materials to the user's device. SSL / TLS is used as a secure communication method for transmission. The device displays the received proposal materials to the user and uses Azure Cognitive Services to start the emotion engine. The emotion engine analyzes facial expressions and tone of voice while the user is reviewing the proposal materials to determine the user's emotional state.

[0338] Specific working example:

[0339] The server sends the generated proposal materials to the user's device, where Azure Cognitive Services, the device's emotion engine, recognizes the user's emotions.

[0340] 5. Adjusting the proposal materials

[0341] The device's emotion engine analyzes the user's emotions and automatically adjusts the content of the proposal based on the results. For example, if the user looks anxious, it will add additional information to the proposal to make them feel more reassured.

[0342] Specific working example:

[0343] The device's emotion engine detects the user's anxiety and automatically inserts supplementary information into the proposal materials to provide reassurance.

[0344] 6. Preparation and implementation of business negotiations

[0345] The user can review the proposal materials adjusted by the emotion engine and make further edits or additions as necessary. Based on the proposal materials, the user can prepare and efficiently proceed with business negotiations with customers. Because the materials are optimized to reflect the user's emotions, the success rate of business negotiations increases.

[0346] Specific working example:

[0347] Based on the adjusted proposal materials, the user prepares and conducts a business negotiation with Company A. By using materials optimized by the emotion engine, the business negotiation can be conducted effectively, increasing the probability of successfully closing the contract.

[0348] In this way, by automating a series of processes, from collecting corporate information to analyzing it, generating proposal materials, and adjusting them using an emotion engine, the system allows sales representatives to devote more time to customer service and increases the success rate of sales negotiations.

[0349] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0350] Step 1: Gather company information

[0351] The server takes as input a list of companies, which includes URLs of their official websites and public databases.

[0352] Input: Company list (including URL)

[0353] The server automatically downloads the latest official documents from each company's official website or public database based on the company list using HTTP requests.

[0354] Specific operation: Download IR materials (example_IR.pdf) from Company A's official website (https: / / example.com)

[0355] Output: Downloaded official documentation

[0356] Step 2: Save your company information

[0357] The server saves the downloaded official documents in data storage, including metadata such as the company name, document name, and download date and time.

[0358] Input: Downloaded official documentation

[0359] Data processing: Save the document in the data storage along with the company name, document name, and download date and time.

[0360] Specific operation: Save a document with metadata such as Company A, example_IR.pdf, 2023-10-01

[0361] Output: Official documents stored in data storage

[0362] Step 3: Analyze company information

[0363] The server retrieves official documents from data storage and passes them to a natural language processing (NLP) algorithm, which uses TensorFlow for analysis.

[0364] Input: Official documents stored in data storage

[0365] The server uses NLP algorithms to analyze the document and extract important keywords and phrases.

[0366] Specific behavior: TensorFlow extracts "Diminishing Revenue" from example_IR.pdf

[0367] Output: Extracted keywords and phrases

[0368] Step 4: Automatically generate proposal materials

[0369] Based on the analysis results, the server automatically generates proposal materials using templates using the Microsoft Word API.

[0370] Input: Extracted keywords or phrases (e.g., "revenue decline")

[0371] Data processing: Describe solutions to the management issues identified in the proposal materials and the selling points of the services and products to be offered.

[0372] Specific action: Include a marketing strategy for "revenue decline" in the proposal materials

[0373] Output: Auto-generated proposal

[0374] Step 5: Submit your proposal

[0375] The server sends the generated proposal materials to the user's device, securely using SSL / TLS.

[0376] Input: Auto-generated proposal

[0377] Data calculation: Proposal materials are sent to the user terminal.

[0378] Specific operation: Proposal materials are sent to the user's device using SSL / TLS

[0379] Output: Proposal materials sent to the user's device

[0380] Step 6: Activating the Emotion Engine

[0381] The device displays the received proposal to the user and activates the emotion engine, analyzing the user's facial expressions and tone of voice using Azure Cognitive Services.

[0382] Input: Proposal document being reviewed by the user

[0383] Data calculation: The emotion engine analyzes the user's facial expressions and tone of voice to determine their emotional state.

[0384] Specific operation: Launch Azure Cognitive Services to read the user's facial expression

[0385] Output: Parsed user's emotional state

[0386] Step 7: Adjust your proposal

[0387] The device's emotion engine analyzes the user's emotions and automatically adjusts the content of the proposal materials based on the results.

[0388] Input: Parsed emotional state of the user

[0389] Data processing: If the user looks anxious, add supplementary information to the proposal materials to make them feel more reassured.

[0390] Specific behavior: Automatically insert supplemental information into proposal materials

[0391] Output: Tailored proposal

[0392] Step 8: Prepare and conduct the deal

[0393] The user can then review the proposal documents adjusted by the emotion engine, and make further edits or additions as necessary. Based on the proposal documents, the user can prepare and efficiently conduct business negotiations with customers.

[0394] Input: Adjusted proposal

[0395] Data Computing: Prepare for customer meetings with tailored proposals.

[0396] Specific action: Add or correct the proposal document

[0397] Output: Deal preparation and execution completed

[0398] Through these specific processing steps, the system enables salespeople to efficiently create proposal materials and increase the success rate of business negotiations with customers.

[0399] (Application example 2)

[0400] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0401] In today's virtual stores, customers often lack the proper information to select products, which can lead to a loss of motivation to purchase. Furthermore, it is difficult to understand customer emotions and provide optimal product recommendations in real time, ultimately hindering the improvement of the customer experience. There is a need for a method to solve these problems and provide customers with a more personalized shopping experience.

[0402] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0403] In this invention, the server includes means for collecting official data, means for analyzing the collected official data using natural language processing technology to identify management issues, and means for automatically generating proposal materials based on the analysis results, thereby enabling means for transmitting the generated proposal materials to an end-user terminal, activating an emotion engine for analyzing the end-user's emotional state, and means for adjusting the content of the proposal materials in real time based on the analysis results of the emotion engine.

[0404] "Official data" refers to written information such as reports, financial documents, and news releases that are officially made public by companies and organizations.

[0405] "Natural language processing technology" is a technology that allows computers to understand, analyze, and generate human language, and is used to analyze text data and extract keywords.

[0406] "Management issues" refer to problems and challenges that companies and organizations face that need to be solved, such as declining performance, entering new markets, and cost reduction.

[0407] A "proposal document" is a document automatically generated based on collected and analyzed data, which includes solutions and selling points.

[0408] "End-user terminal" refers to the device ultimately used by the user, including smartphones, tablets, and PCs.

[0409] An "emotion engine" refers to software or functionality that analyzes a user's facial expressions and tone of voice to determine their emotional state.

[0410] "Real-time adjustment" refers to the process of instantly analyzing the user's emotional state and instantly changing or modifying the content of the proposal materials based on the results.

[0411] "Means of collection" refers to methods or devices for automatically obtaining and storing the official data in question.

[0412] "Analysis means" refers to a set of processing techniques and algorithms used to process the data obtained and identify important information and issues.

[0413] This invention provides a system that automatically generates necessary proposal materials so that sales representatives can concentrate on negotiating with customers, and furthermore, adjusts the proposal content by recognizing the user's emotions. This system collects official data, analyzes its contents to identify management issues, automatically generates proposal materials, and sends them to the end user's terminal. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it has the function of adjusting the proposal materials according to the user's emotional state. Below, we will explain how this system is implemented by the server, terminal, and user.

[0414] 1. System Overview

[0415] This system collects "official data" such as reports and financial documents officially released by companies and organizations, and analyzes them using "natural language processing technology." It identifies "management issues" from the analysis results and automatically generates "proposal materials" based on those. The proposal materials are sent to the "end-user device," which then activates an emotion engine to analyze the user's emotions and adjusts the content of the proposal materials in real time based on the results.

[0416] 2. Program Processing

[0417] 1. Collection Method:

[0418] The server retrieves the URLs of official data from a pre-defined list of companies, then automatically downloads the latest official data from official websites and public databases, and stores the collected data in a database along with metadata such as company name, document name, and download date and time.

[0419] 2. Analysis method:

[0420] The server initializes a natural language processing engine (e.g., Google® Cloud Natural Language API). By retrieving official data from the database and passing it to the analysis engine, important keywords and phrases are extracted and the company's management issues are identified.

[0421] 3. Automatic generation of proposal materials:

[0422] Based on the analysis results, a proposal document is automatically generated using a template, which includes solutions to the identified business issues and the selling points of the services and products to be offered.

[0423] 4. Submit your proposal:

[0424] The generated proposal material is transmitted to the end user terminal via a secure communication means.

[0425] 5. Emotion Engine in Action:

[0426] The end user's device displays the proposal documents and activates an emotion engine (e.g., Microsoft Azure Emotion API). The emotion engine analyzes the user's facial expressions and tone of voice while reviewing the documents to determine the user's emotional state.

[0427] 6. Adjustment of proposal materials:

[0428] The emotion engine on the end-user device analyzes the user's emotions and automatically adjusts the content of the proposal in real time based on the results. For example, if the user looks anxious, it will add supplemental information to the proposal to make them feel reassured.

[0429] 3. Specific Examples

[0430] Product "Smartwatch" proposal

[0431] 1. Collection of official data:

[0432] When a customer views a "smartwatch," product information is collected and recorded in a database.

[0433] 2. Automatic generation of proposal materials:

[0434] The server automatically generates a proposal document based on the collected information, including information such as tracking function, battery life, and waterproof performance.

[0435] 3. Emotion recognition:

[0436] The end-user device activates an emotion engine that analyzes the customer's facial expressions and voice as they review the materials. If the customer looks surprised, the system emphasizes "special discounts" and "limited-time offers." Conversely, if the customer looks anxious, the system adds more detailed information about the return policy and product warranty.

[0437] Prompt Sentence Examples

[0438] "Generate a proposal for a smartwatch."

[0439] Features: Tracking, battery life, waterproof

[0440] Additional Information: User Reviews, Special Offers, Return Policy

[0441] Customer facial expression and voice data: Surprise, anxiety

[0442] Proposal adjustments: surprise (emphasis on special discounts), anxiety (details of return policy)

[0443] In this way, the system automates a series of processes, from collecting official data to analyzing it, generating proposal materials, and adjusting them using an emotion engine, allowing end users to enjoy a personalized purchasing experience.

[0444] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0445] Step 1:

[0446] The server retrieves official data URLs from a pre-defined list of companies. This list includes URLs of the target companies' official websites and public databases. The input is the company list, and the output is the retrieved URLs. Based on this, the server automatically downloads official data (reports, financial documents, etc.) from the official websites and databases and stores them in a database.

[0447] Step 2:

[0448] The server retrieves the stored official data and initializes a natural language processing engine (e.g., Google Cloud Natural Language API). The official data is input, and analysis results (important keywords and phrases) are obtained as output. The server uses this analysis engine to extract important keywords and phrases from the official data and identify the company's management issues.

[0449] Step 3:

[0450] The server uses templates based on the analysis results of natural language processing to automatically generate proposal materials. The analysis results are input, and proposal materials are obtained as output. These proposal materials include solutions to identified business issues and the selling points of the products and services offered. For example, they can be embodied as "proposing a new marketing strategy" in response to "declining revenue."

[0451] Step 4:

[0452] The server transmits the generated proposal materials to the end user terminal via a secure communication means. The proposal materials are input, and the proposal materials transmitted to the end user terminal are obtained as output.

[0453] Step 5:

[0454] The device displays the received proposal documents and launches an emotion engine (e.g., Microsoft Azure Emotion API). The proposal documents are input, and the end user's emotional data is obtained as output. The emotion engine analyzes the end user's facial expressions and tone of voice when reviewing the proposal documents to identify their emotional state.

[0455] Step 6:

[0456] The device adjusts the content of the proposal materials in real time based on the results of analysis by the emotion engine. The input is emotion data and the proposal materials, and the output is the adjusted proposal materials. For example, if the user shows an anxious expression, additional information that gives a sense of security is inserted into the proposal materials.

[0457] Step 7:

[0458] The user checks the adjusted proposal document and makes further edits or additions as necessary. The adjusted proposal document is the input, and the final proposal document is the output. The user uses this as a basis to prepare and proceed with business negotiations with the customer.

[0459] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0460] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0461] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0462] [Second embodiment]

[0463] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0464] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0465] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0466] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0467] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0468] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0469] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0470] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0471] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0472] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0473] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0474] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0475] This invention provides a system that automatically generates necessary proposal materials so that sales representatives can concentrate on negotiating with customers. Below, we will explain how this system is implemented by the server, terminals, and users.

[0476] 1. System Overview

[0477] This system collects official documents, analyzes their contents to identify management issues, and automatically generates proposal materials and sends them to the user's device, eliminating the need for users (salespeople) to create documents and allowing them to focus on negotiating with customers and closing deals.

[0478] 2. Program Processing

[0479] 2.1 Collecting official documentation

[0480] The server collects official documents, such as the latest investor relations materials and financial reports, from the official websites and public databases of multiple companies based on a pre-defined list. This collection process is performed automatically on a regular basis, and the collected documents are stored in a database.

[0481] 2.2 Analysis of the official documentation

[0482] The server analyzes the collected official documents using natural language processing technology. For example, it uses a natural language processing library to extract keywords and phrases from the text. This allows the server to identify the business challenges each company faces (such as "declining revenue" or "difficulty entering the market").

[0483] 2.3 Automatic generation of proposal materials

[0484] Based on the analysis results, the server automatically generates proposal materials using templates. These proposal materials include solutions to the identified business issues and the selling points of the services and products to be offered. For example, they could be "Proposing a new marketing strategy to address declining revenues" or "Introducing services to support market entry."

[0485] 2.4 Submitting and Displaying Proposals

[0486] The server transmits the generated proposal material to the user's terminal.

[0487] The terminal displays the received proposal materials to the user, who can then review the materials and make edits or additions as necessary.

[0488] 3. Specific Examples

[0489] 3.1 Company A's IR materials

[0490] The server collects the latest IR documents from the official website of Company A. The documents contain information about a "recent decline in revenue."

[0491] 3.2 Analysis of the data

[0492] A natural language processing algorithm on the server identifies the keyword "revenue decline" and records it as a management issue.

[0493] 3.3 Proposal Creation

[0494] The server automatically incorporates specific approaches to solving declining revenue (for example, cost reduction programs or methods for developing new markets) into the proposal materials.

[0495] 3.4 Sending materials and preparing for negotiations

[0496] The server sends the generated proposal materials to the user's terminal,

[0497] The terminal displays the material to the user.

[0498] 3.5 Conducting business negotiations

[0499] The user begins negotiations with the customer (Company A) based on the proposal materials viewed on the device. Because the proposal materials show specific solutions, the user can proceed with the negotiations efficiently.

[0500] In this way, the system automates the entire process, from collecting official documents to analyzing them, generating proposal materials, and sending them to user devices, allowing sales representatives to devote more time to customer service.

[0501] The processing flow will be explained below.

[0502] Step 1:

[0503] The server retrieves a pre-configured list of companies, which includes URLs of the target companies' official websites or public databases.

[0504] Step 2:

[0505] Based on the company list, the server automatically downloads the latest official documents (e.g., investor relations materials and financial reports) from each company's official website or public database.

[0506] Step 3:

[0507] The server stores the downloaded official documents in a database, along with metadata such as the company name, document name, and download date and time.

[0508] Step 4:

[0509] The server initializes natural language processing (NLP) algorithms, which include text analysis and keyword extraction.

[0510] Step 5:

[0511] The server retrieves official documents from the database and passes them to an NLP algorithm, which analyzes the documents and extracts important keywords and phrases, thereby identifying business challenges for the company.

[0512] Step 6:

[0513] The server identifies business issues based on keywords extracted by the NLP algorithm, such as specific problems like "declining revenue" or "need to enter new markets."

[0514] Step 7:

[0515] The server selects a proposal template corresponding to the identified business problem, the template containing a proposal for solving the particular problem.

[0516] Step 8:

[0517] The server then embeds specific proposal content based on the analysis results into the proposal template and automatically generates proposal materials, which include solutions and selling points for the company's services.

[0518] Step 9:

[0519] The server transmits the generated proposal materials to the user's terminal using a secure communication method.

[0520] Step 10:

[0521] The terminal displays the received proposal materials to the user, who then checks the materials and makes edits or additions as necessary.

[0522] Step 11:

[0523] Users can prepare for business negotiations with customers based on the proposal materials they have edited, which allows them to proceed with business negotiations more efficiently.

[0524] Step 12:

[0525] The user starts negotiations with the customer and proposes specific solutions to the problem using proposal materials. After the negotiations, the user focuses on closing the deal and finalizing the contract.

[0526] Example 1

[0527] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0528] The present invention aims to automate and streamline the time-consuming process of creating proposal materials so that sales representatives can focus on negotiating with customers. Another objective is to effectively analyze collected official documents, identify business issues, and quickly and accurately generate corresponding proposal materials.

[0529] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0530] In this invention, the server includes means for collecting official documents, means for analyzing the collected official documents using natural language processing technology and identifying business issues, means for automatically generating proposal materials based on the analysis results, means for transmitting the generated proposal materials to a terminal, and database means for storing the collected official documents. This allows sales representatives to concentrate on negotiating with customers without spending much time on creating proposal materials.

[0531] "Official documents" are information documents that companies or organizations make public, such as investor relations materials and financial reports.

[0532] "Natural language processing technology" is a technology for analyzing, understanding, and generating natural language used by humans, and is used to extract keywords and phrases from text.

[0533] "Business challenges" are management and operational problems and difficulties faced by a company or organization, including declining revenue and difficulties in entering a market.

[0534] A "proposal" is a document that summarizes solutions and proposals for business issues faced by a company or organization.

[0535] The "means of collection" is the mechanism by which the server obtains official documents from the Internet or public databases.

[0536] The "means of analysis" refers to a system that uses natural language processing technology to analyze official documents collected by the server and identify business issues.

[0537] "Means for automatic generation" refers to a mechanism in which the server automatically creates proposal materials in accordance with a template based on the analysis results.

[0538] The "transmission means" is a mechanism by which the server delivers the generated proposal materials to the user's terminal.

[0539] "Database means for storage" refers to a database system for efficiently storing and managing collected official documents and generated materials.

[0540] A "terminal" is an information device that a user uses to receive and view proposal materials, and includes a personal computer, tablet, etc.

[0541] This invention provides a system that automatically generates necessary proposal materials so that sales representatives can concentrate on negotiating with customers. Below, we will explain in detail how the server, terminals, and users implement this system.

[0542] 1. System Configuration

[0543] The system includes the following major components:

[0544] Server: Collects and analyzes official documents, automatically generates and sends proposal materials.

[0545] Terminal: Receives, displays, and edits proposal materials.

[0546] Database: Stores collected official documents and generated proposal materials.

[0547] 2. Hardware and Software Used

[0548] Server hardware: A computer equipped with a high-performance processor and large memory capacity. For example, a server machine equipped with an NVIDIA GPU.

[0549] Software: Python programming language, requests library, natural language processing library spaCy, template engine Jinja2, library for sending emails smtplib.

[0550] 3. System Processing

[0551] The server collects official documents such as investor relations materials and financial reports from companies' official websites and public databases based on a pre-configured list, for example, by using the Python requests library to retrieve data from the Internet.

[0552] The collected official documents are stored in a database on the server, which then analyzes them using natural language processing technology, using the spaCy library to extract important keywords and phrases from the text and identify the company's business challenges.

[0553] Based on the analysis results, the server uses templates to automatically generate proposal materials. The template engine Jinja2 is used to create materials that include solutions to the company's challenges and the selling points of the service. The generated proposal materials are sent by email from the server to the user's device. The library used for sending emails is smtplib.

[0554] The terminal displays the received proposal documents and allows the user to review and edit them. For example, the user can open the received email, check the contents of the documents, and make supplementary input or corrections as necessary.

[0555] 4. Specific Examples

[0556] Company A's case

[0557] 1. The server collects the latest IR materials from the official website of Company A. The materials contain information about a "recent decline in revenue."

[0558] 2. The server's natural language processing algorithm identifies the keyword "revenue decline" and records it as a business issue.

[0559] 3. The server incorporates approaches to solving the declining revenue (e.g., new marketing strategies) into the proposal.

[0560] 4. The server sends the generated proposal materials to the terminal and displays the materials on the terminal.

[0561] Prompt Sentence Examples

[0562] "Collect the latest IR materials for Company A and generate a proposal to resolve the decline in revenue."

[0563] In this way, the server and terminals work together to automate the process of creating proposal materials, allowing sales representatives to focus on negotiating with customers.

[0564] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0565] System program processing flow

[0566] Step 1: Gather official documents

[0567] The server collects official documents such as investor relations materials and financial reports from companies' official websites and public databases based on a pre-configured list.

[0568] Input: Company website URL list

[0569] How it works: Uses Python's requests library to retrieve HTML data from each URL.

[0570] Output: A list of collected text data from official documents

[0571] Step 2: Save official documents

[0572] The server stores the collected official documents in a database.

[0573] Input: Text data from collected official documents

[0574] What it does: Query an SQL database for stored official documents.

[0575] Output: Official documents stored in a database

[0576] Step 3: Analyzing the official documentation

[0577] The server analyzes the collected official documents using natural language processing technology.

[0578] Input: Official documents stored in a database

[0579] What it does: It uses the spaCy library to parse text and extract keywords and phrases.

[0580] Output: A list of extracted keywords and phrases

[0581] Step 4: Identifying business issues

[0582] The server identifies business issues facing the company based on the analysis results.

[0583] Input: A list of extracted keywords or phrases

[0584] How it works: Uses a rules engine to match keyword lists to specific patterns and extract business issues.

[0585] Output: List of identified business issues

[0586] Step 5: Automatically generate proposal materials

[0587] The server automatically generates proposal materials using templates based on the identified management issues.

[0588] Input: List of identified business issues

[0589] How it works: Uses the Jinja2 template engine to generate proposals that detail solutions and services that address business challenges.

[0590] Output: Generated proposal document

[0591] Step 6: Submit your proposal

[0592] The server transmits the generated proposal material to the user's terminal.

[0593] Input: Generated proposal document

[0594] What it does: Uses the smtplib library to send the proposal to the user's email address.

[0595] Output: Proposal sent to the user's device

[0596] Step 7: View and edit your proposal

[0597] The terminal displays the received proposal material to the user, who can then edit it as necessary.

[0598] Input: Proposal sent to the user's device

[0599] How it works: The proposal document opens in the email client, and the user can edit and add to it.

[0600] Output: Proposal document reviewed and edited by the user

[0601] (Application example 1)

[0602] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0603] Ensuring efficient and safe operations is crucial in modern manufacturing. However, analyzing various official documents and maintenance reports requires a great deal of time and effort, making it difficult to quickly implement appropriate countermeasures. Furthermore, identifying manufacturing process and safety issues and generating specific proposals for improvement is also cumbersome, placing a significant burden on personnel. A solution to these issues and improving the efficiency and safety of manufacturing is needed.

[0604] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0605] In this invention, the server includes means for collecting official documents, means for analyzing the collected official documents using natural language processing technology and identifying issues, means for automatically generating proposal materials based on the analysis results, means for transmitting the generated proposal materials to a user terminal, means for collecting factory maintenance reports and official reports and identifying issues related to the manufacturing process, efficiency, and safety, means for incorporating specific solutions to the identified issues into the proposal materials, and means for displaying the proposal materials on a display installed in a factory robot. This enables personnel to efficiently identify issues and quickly implement solutions.

[0606] "Official documents" are documents such as reports, financial reports, and maintenance reports officially issued by companies or organizations.

[0607] "Collection methods" are techniques and methods for periodically obtaining official documents from multiple sources and storing them in a database.

[0608] "Natural language processing technology" is a technology that allows machines to understand and analyze text data, and can perform tasks such as extracting keywords and identifying relationships.

[0609] "Means of analysis" refers to techniques and methods for analyzing collected official documents using natural language processing technology and extracting specific issues or problems.

[0610] "Means for automatically generating proposal materials" refers to techniques or methods for automatically creating proposal materials in a standard format based on the analysis results.

[0611] A "user terminal" is a device such as a communication device or computer used by a user, and is a device that receives and displays proposal materials.

[0612] "Transmission means" refers to the technology or method for transferring data from the server to the user terminal.

[0613] A "maintenance report" is a document that records the results of regular maintenance inspections of factories and equipment.

[0614] "Manufacturing process" means the process and procedures for producing products in a factory.

[0615] "Efficiency" is a concept that refers to the optimal use of resources in the manufacturing process and the resulting high productivity.

[0616] "Safety" refers to the state of minimizing risks in the manufacturing environment and processes and preventing injuries and accidents.

[0617] A "problem" refers to a problem or challenge that requires a solution under specific circumstances or conditions.

[0618] A "solution" is a specific method or means for overcoming a particular problem.

[0619] A "factory robot" is an automated machine designed to perform work within a factory, and is a device equipped with a display for displaying proposal materials.

[0620] This invention is a system that aims to improve the efficiency and safety of manufacturing processes by having a server collect and analyze official documents and provide proposal materials to the user's factory robots. Specific embodiments for implementing this system are described below.

[0621] 1. System Overview

[0622] This system collects official documents, analyzes their contents to identify issues related to manufacturing processes, efficiency, and safety, and automatically generates proposal materials and sends them to the user's terminal.This system saves the user (factory personnel) the trouble of creating documents and enables them to quickly take efficient countermeasures to the identified issues.

[0623] 2. Hardware and Software

[0624] The system uses the following hardware and software:

[0625] Server: Collects data, analyzes it, and generates proposal materials.

[0626] Factory robot: Equipped with a display that displays the generated proposal materials.

[0627] Python: Used to implement the entire program.

[0628] Requests: Used to collect data on web pages.

[0629] BeautifulSoup: Used to extract text from HTML.

[0630] Transformers: Used to run natural language processing models (e.g., HuggingFace's Transformers library).

[0631] 3. Program Processing

[0632] 3.1 Collecting official documentation

[0633] The server automatically and periodically collects official documents (e.g., maintenance reports) from multiple official websites and public databases based on a pre-configured list and stores them in a database.

[0634] 3.2 Document Parsing

[0635] The server then analyzes the collected official documents using natural language processing techniques to extract key keywords and phrases, using HuggingFace's Transformers library, to identify issues related to manufacturing processes, efficiency, and safety.

[0636] 3.3 Automatic generation of proposal materials

[0637] Based on the analysis results, the server automatically generates proposal materials using templates. The proposal materials include solutions to the identified issues and their effects. For example, the proposals might include "application of a new process control system" or "regular maintenance and the introduction of high-performance sensors."

[0638] 3.4 Submitting and Displaying Proposals

[0639] The server sends the generated proposal materials to the user terminal (factory robot), which displays the received proposal materials on its display, allowing the user (factory staff) to check them and take appropriate measures as necessary.

[0640] 4. Specific Examples

[0641] For example, the server collects the latest maintenance reports from the factory's official website and uses analysis technology to identify mentions of "declining efficiency" or "safety issues." The server then generates a proposal document containing solutions such as "proposing a new process control system to address the efficiency decline" or "introducing high-performance sensors to address safety issues." The generated proposal document is then displayed on the factory robot's display for the user to review.

[0642] An example of a prompt sentence to be input into a generative AI model is, "Please extract issues and solutions from the factory report below and generate a proposal document."

[0643] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0644] Step 1:

[0645] Collecting official documentation

[0646] Input: A list of official website URLs pre-configured by the server.

[0647] How it works: The server periodically collects official documents (e.g., maintenance reports and financial reports) from multiple official websites based on a configured list of URLs. This process involves issuing HTTP requests using the Requests library to retrieve the contents of the web pages.

[0648] Data processing: The acquired HTML data is analyzed using the BeautifulSoup library and text data is extracted.

[0649] Output: A text data list of the official documentation.

[0650] Step 2:

[0651] Analysis of the official documentation

[0652] Input: A text data list of official documents collected in Step 1.

[0653] How it works: The server analyzes the collected official documents using natural language processing techniques, specifically using the Transformers library to extract keywords and important phrases from the text data.

[0654] Data Computing: Natural language processing models analyze text and identify issues (e.g., inefficiencies, safety issues, etc.) contained in each document.

[0655] Output: A list of identified issues.

[0656] Step 3:

[0657] Automatic generation of proposal materials

[0658] Input: The list of issues identified in step 2.

[0659] How it works: The server uses templates to automatically generate proposals containing solutions to identified issues, such as proposing a new process control system to address "decreased efficiency" or introducing high-performance sensors to address "safety issues."

[0660] Data processing: Combine the issue list and solution template to generate concrete proposal materials.

[0661] Output: The generated proposal (e.g. PDF, HTML format).

[0662] Step 4:

[0663] Submitting and viewing proposals

[0664] Input: The proposal generated in Step 3.

[0665] Operation: The server sends the generated proposal materials to the user terminal (factory robot). The factory robot displays the received proposal materials on its built-in display.

[0666] Data operations: file transfer and display.

[0667] Output: Proposal materials displayed on the user's terminal.

[0668] Step 5:

[0669] User verification and action taken

[0670] Input: Proposal materials displayed on the user's terminal.

[0671] Operation: The user (factory staff) checks the proposal documents displayed on the factory robot's display and takes appropriate measures as necessary. Based on the proposal documents, the user adjusts the manufacturing process and implements safety measures.

[0672] Output: Efficient manufacturing processes and improved safety.

[0673] The above are the specific processing steps of this system. By performing detailed operations for each step, it is possible to improve the efficiency and ensure safety of the manufacturing process.

[0674] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0675] This invention provides a system that automatically generates necessary proposal materials so that salespeople can concentrate on negotiating with customers, and furthermore, recognizes the user's emotions and adjusts the proposal content accordingly. Below, we will explain how this system is implemented by the server, terminal, and user.

[0676] 1. System Overview

[0677] This system collects official documents, analyzes their contents to identify management issues, and automatically generates proposal materials and sends them to the user's device. Furthermore, by incorporating an emotion engine that recognizes the user's (sales representative's) emotions, the system has the ability to adjust proposal materials according to the user's emotional state. This saves users the trouble of creating documents, allowing them to focus on negotiating with customers and closing deals.

[0678] 2. Program Processing

[0679] 2.1 Collecting official documentation

[0680] The server retrieves a pre-configured list of companies, which includes URLs of the target companies' official websites or public databases.

[0681] Based on the company list, the server automatically downloads the latest official documents (e.g., investor relations materials and financial reports) from each company's official website or public database.

[0682] The server stores the downloaded official documents in a database, along with metadata such as the company name, document name, and download date and time.

[0683] 2.2 Analysis of the official documentation

[0684] The server initializes natural language processing (NLP) algorithms, which include text analysis and keyword extraction.

[0685] The server retrieves official documents from the database and passes them to an NLP algorithm, which analyzes the documents and extracts key keywords and phrases, identifying business challenges for the company (e.g., "declining revenue" or "need to enter new markets").

[0686] 2.3 Automatic generation of proposal materials

[0687] Based on the analysis results, the server automatically generates proposal materials using templates. These proposal materials include solutions to the identified business issues and the selling points of the services and products to be offered. For example, they could be "Proposing a new marketing strategy to address declining revenues" or "Introducing services to support market entry."

[0688] 2.4 Submitting proposal materials and operation of the emotion engine

[0689] The server transmits the generated proposal materials to the user's terminal using a secure communication method.

[0690] The terminal displays the received proposal materials to the user and activates the emotion engine, which analyzes the user's facial expressions and tone of voice while reviewing the proposal materials to determine the user's emotional state.

[0691] 2.5 Adjustment of proposal materials

[0692] The device's emotion engine analyzes the user's emotions and automatically adjusts the content of the proposal based on the results. For example, if the user looks anxious, it will add additional reassuring information to the proposal.

[0693] 2.6 Preparation and implementation of business negotiations

[0694] The user can review the proposal materials adjusted by the emotion engine and make further edits or additions as necessary.

[0695] Users can prepare and efficiently conduct business negotiations with customers based on the proposal materials. At this point, the materials reflect the user's emotions and are optimized, improving the success rate of the negotiations.

[0696] 3. Specific Examples

[0697] 3.1 Analysis of Company A's IR materials and generation of proposal materials

[0698] The server collects the latest IR documents from the official website of Company A. The documents contain information about a "recent decline in revenue."

[0699] A natural language processing algorithm on the server identifies the keyword "revenue decline" and records it as a management issue.

[0700] The server automatically incorporates specific approaches to solving declining revenue (for example, cost reduction programs or methods for developing new markets) into the proposal materials.

[0701] 3.2 Data transmission and user emotion recognition

[0702] The server transmits the generated proposal materials to the user's terminal.

[0703] The terminal displays the document to the user, and the emotion engine recognizes the user's emotions. The emotion engine may detect that the user has an anxious expression while checking the document.

[0704] 3.3 Adjusting materials and preparing for business meetings

[0705] The device's emotion engine detects the user's anxiety and automatically inserts additional supporting information into the proposal materials to increase the user's sense of security.

[0706] The user prepares and conducts business negotiations with the client (Company A) based on the adjusted proposal materials. By using materials optimized by the emotion engine, the negotiations proceed more effectively and the probability of successfully closing the contract increases.

[0707] In this way, the system automates a series of processes, from collecting official documents to analyzing them, generating proposal materials, and adjusting them using an emotion engine, allowing sales representatives to devote more time to customer service and increasing the success rate of sales negotiations.

[0708] The processing flow will be explained below.

[0709] Step 1:

[0710] The server retrieves a pre-configured list of companies, which includes URLs of the target companies' official websites or public databases.

[0711] Step 2:

[0712] Based on the company list, the server automatically downloads the latest official documents (e.g., investor relations materials and financial reports) from each company's official website or public database.

[0713] Step 3:

[0714] The server stores the downloaded official documents in a database, along with metadata such as the company name, document name, and download date and time.

[0715] Step 4:

[0716] The server initializes natural language processing (NLP) algorithms, which include text analysis and keyword extraction.

[0717] Step 5:

[0718] The server retrieves official documents from the database and passes them to an NLP algorithm, which analyzes the documents and extracts key keywords and phrases, identifying business challenges for the company (e.g., "declining revenue" or "need to enter new markets").

[0719] Step 6:

[0720] The server identifies business challenges based on keywords extracted by the NLP algorithm, such as specific problems like "declining revenue" or "barriers to market entry."

[0721] Step 7:

[0722] The server selects a proposal template corresponding to the identified business problem, the template containing a proposal for solving the particular problem.

[0723] Step 8:

[0724] The server then embeds specific proposal content based on the analysis results into the proposal template and automatically generates proposal materials, which include solutions and selling points for the company's services.

[0725] Step 9:

[0726] The server transmits the generated proposal materials to the user's terminal using a secure communication method.

[0727] Step 10:

[0728] The device displays the received proposal materials to the user and activates the emotion engine, which analyzes the user's facial expressions and tone of voice while reviewing the proposal materials to determine the user's emotional state.

[0729] Step 11:

[0730] The device's emotion engine analyzes the user's emotions and automatically adjusts the content of the proposal based on the results. For example, if the user looks anxious, it will add additional reassuring information to the proposal.

[0731] Step 12:

[0732] The user can review the proposal materials adjusted by the emotion engine and make further edits or additions as necessary.

[0733] Step 13:

[0734] Users can use the proposal documents to prepare and efficiently conduct business negotiations with customers. At this point, the documents reflect the user's emotions and are optimized, improving the success rate of the negotiations.

[0735] Step 14:

[0736] The user starts negotiations with the customer and proposes specific solutions to the problem using proposal materials. After the negotiations, the user focuses on closing the deal and finalizing the contract.

[0737] Example 2

[0738] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0739] In order to allow sales representatives to concentrate on business negotiations with customers, it is necessary to improve the efficiency of the creation of proposal materials and the accuracy of proposals. Another challenge is to improve the success rate of sales negotiations by enabling the adjustment of proposal materials according to the user's emotional state.

[0740] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0741] In this invention, the server includes means for collecting company information, means for analyzing the collected company information using natural language processing technology and identifying management issues, means for automatically generating proposal materials based on the analysis results, means for transmitting the generated proposal materials to a user terminal, and means for recognizing the user's emotions and adjusting the proposal materials. This automates the creation of proposal materials and optimizes the materials according to the user's emotions, thereby reducing the burden on the user during business negotiations and improving the success rate.

[0742] "Corporate information" refers to official documents such as financial reports and investor relations materials collected from a company's official website or public databases.

[0743] "Natural language processing technology" refers to technology that enables computers to understand and analyze human language, and in particular technology that extracts keywords and analyzes text.

[0744] "Business challenges" are operational problems or obstacles facing a company, such as declining revenue or the need to enter new markets.

[0745] A "proposal document" is a document generated based on the analysis results, which describes solutions to identified business issues and the selling points of the services and products to be offered.

[0746] A "user terminal" is a device that receives proposal materials sent from the server and allows the user to check and operate them, and includes a PC, tablet, etc.

[0747] "Means for recognizing emotions" refers to technology that determines a user's emotional state by analyzing their facial expressions, tone of voice, etc., and includes, for example, facial recognition technology and voice analysis technology.

[0748] "Data storage" refers to a storage device or database for storing collected company information and generated proposal materials.

[0749] This invention provides a system that automatically generates necessary proposal materials so that salespeople can concentrate on negotiating with customers, and furthermore, recognizes the user's emotions and adjusts the proposal content accordingly. Below, we will explain how this system is implemented by the server, terminal, and user.

[0750] System Overview

[0751] This system collects corporate information, analyzes the content to identify management issues, and automatically generates proposal materials and sends them to the user's device. Furthermore, by incorporating an emotion engine that recognizes the user's (salesperson's) emotions, the system has the ability to adjust proposal materials according to the user's emotional state. This saves users the trouble of creating materials, allowing them to focus on negotiating and closing deals with customers.

[0752] Hardware and software used

[0753] This system uses the following hardware and software:

[0754] Server: Collects and analyzes data, generates and sends proposal materials.

[0755] Terminal: Displays proposal materials and performs emotion recognition using an emotion engine.

[0756] Natural language processing algorithm: Uses Google TensorFlow.

[0757] Proposal generation tool: Uses the Microsoft Word API.

[0758] Sentiment Engine: Uses Microsoft Azure Cognitive Services.

[0759] Specific processing

[0760] 1. Collection of company information

[0761] The server retrieves a pre-defined list of companies, which includes the URLs of the target companies' official websites and public databases. Based on the list, the server automatically downloads the latest official documents from each company's official website or public database. For example, it retrieves PDF files from websites using HTTP requests. The downloaded official documents are stored in data storage, along with metadata such as the company name, document name, and download date and time.

[0762] Specific working example:

[0763] The server collects the latest IR documents from the official website of Company A. The documents contain information about a "recent decline in revenue."

[0764] 2. Analysis of company information

[0765] The server initializes TensorFlow, retrieves the official document from data storage, and passes it to the NLP algorithm, which analyzes the document and extracts important keywords and phrases. For example, the keyword "revenue decline" is extracted.

[0766] Specific working example:

[0767] TensorFlow identifies the keyword "revenue decline" from Company A's IR materials.

[0768] 3. Automatic generation of proposal materials

[0769] Based on the analysis results, the server uses Microsoft Word API to automatically generate a proposal document using a template, which includes solutions to the identified business issues and the selling points of the services and products to be offered.

[0770] Specific working example:

[0771] The server automatically incorporates specific approaches (for example, cost reduction programs and methods for developing new markets) for solving Company A's declining profits into the proposal materials.

[0772] Example prompt sentence:

[0773] "Generate proposal materials to address revenue declines based on Company A's IR materials"

[0774] 4. Sending proposal materials and running the emotion engine

[0775] The server sends the generated proposal materials to the user's device. SSL / TLS is used as a secure communication method for transmission. The device displays the received proposal materials to the user and uses Azure Cognitive Services to start the emotion engine. The emotion engine analyzes facial expressions and tone of voice while the user is reviewing the proposal materials to determine the user's emotional state.

[0776] Specific working example:

[0777] The server sends the generated proposal materials to the user's device, where Azure Cognitive Services, the device's emotion engine, recognizes the user's emotions.

[0778] 5. Adjusting the proposal materials

[0779] The device's emotion engine analyzes the user's emotions and automatically adjusts the content of the proposal based on the results. For example, if the user looks anxious, it will add additional information to the proposal to make them feel more reassured.

[0780] Specific working example:

[0781] The device's emotion engine detects the user's anxiety and automatically inserts supplementary information into the proposal materials to provide reassurance.

[0782] 6. Preparation and implementation of business negotiations

[0783] The user can review the proposal materials adjusted by the emotion engine and make further edits or additions as necessary. Based on the proposal materials, the user can prepare and efficiently proceed with business negotiations with customers. Because the materials are optimized to reflect the user's emotions, the success rate of business negotiations increases.

[0784] Specific working example:

[0785] Based on the adjusted proposal materials, the user prepares and conducts a business negotiation with Company A. By using materials optimized by the emotion engine, the business negotiation can be conducted effectively, increasing the probability of successfully closing the contract.

[0786] In this way, by automating a series of processes, from collecting corporate information to analyzing it, generating proposal materials, and adjusting them using an emotion engine, the system allows sales representatives to devote more time to customer service and increases the success rate of sales negotiations.

[0787] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0788] Step 1: Gather company information

[0789] The server takes as input a list of companies, which includes URLs of their official websites and public databases.

[0790] Input: Company list (including URL)

[0791] The server automatically downloads the latest official documents from each company's official website or public database based on the company list using HTTP requests.

[0792] Specific operation: Download IR materials (example_IR.pdf) from Company A's official website (https: / / example.com)

[0793] Output: Downloaded official documentation

[0794] Step 2: Save your company information

[0795] The server saves the downloaded official documents in data storage, including metadata such as the company name, document name, and download date and time.

[0796] Input: Downloaded official documentation

[0797] Data processing: Save the document in the data storage along with the company name, document name, and download date and time.

[0798] Specific operation: Save a document with metadata such as Company A, example_IR.pdf, 2023-10-01

[0799] Output: Official documents stored in data storage

[0800] Step 3: Analyze company information

[0801] The server retrieves official documents from data storage and passes them to a natural language processing (NLP) algorithm, which uses TensorFlow for analysis.

[0802] Input: Official documents stored in data storage

[0803] The server uses NLP algorithms to analyze the document and extract important keywords and phrases.

[0804] Specific behavior: TensorFlow extracts "Diminishing Revenue" from example_IR.pdf

[0805] Output: Extracted keywords and phrases

[0806] Step 4: Automatically generate proposal materials

[0807] Based on the analysis results, the server automatically generates proposal materials using templates using the Microsoft Word API.

[0808] Input: Extracted keywords or phrases (e.g., "revenue decline")

[0809] Data processing: Describe solutions to the management issues identified in the proposal materials and the selling points of the services and products to be offered.

[0810] Specific action: Include a marketing strategy for "revenue decline" in the proposal materials

[0811] Output: Auto-generated proposal

[0812] Step 5: Submit your proposal

[0813] The server sends the generated proposal materials to the user's device, securely using SSL / TLS.

[0814] Input: Auto-generated proposal

[0815] Data calculation: Proposal materials are sent to the user terminal.

[0816] Specific operation: Proposal materials are sent to the user's device using SSL / TLS

[0817] Output: Proposal materials sent to the user's device

[0818] Step 6: Activating the Emotion Engine

[0819] The device displays the received proposal to the user and activates the emotion engine, analyzing the user's facial expressions and tone of voice using Azure Cognitive Services.

[0820] Input: Proposal document being reviewed by the user

[0821] Data calculation: The emotion engine analyzes the user's facial expressions and tone of voice to determine their emotional state.

[0822] Specific operation: Launch Azure Cognitive Services to read the user's facial expression

[0823] Output: Parsed user's emotional state

[0824] Step 7: Adjust your proposal

[0825] The device's emotion engine analyzes the user's emotions and automatically adjusts the content of the proposal materials based on the results.

[0826] Input: Parsed emotional state of the user

[0827] Data processing: If the user looks anxious, add supplementary information to the proposal materials to make them feel more reassured.

[0828] Specific behavior: Automatically insert supplemental information into proposal materials

[0829] Output: Tailored proposal

[0830] Step 8: Prepare and conduct the deal

[0831] The user can then review the proposal documents adjusted by the emotion engine, and make further edits or additions as necessary. Based on the proposal documents, the user can prepare and efficiently conduct business negotiations with customers.

[0832] Input: Adjusted proposal

[0833] Data Computing: Prepare for customer meetings with tailored proposals.

[0834] Specific action: Add or correct the proposal document

[0835] Output: Deal preparation and execution completed

[0836] Through these specific processing steps, the system enables salespeople to efficiently create proposal materials and increase the success rate of business negotiations with customers.

[0837] (Application example 2)

[0838] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0839] In today's virtual stores, customers often lack the proper information to select products, which can lead to a loss of motivation to purchase. Furthermore, it is difficult to understand customer emotions and provide optimal product recommendations in real time, ultimately hindering the improvement of the customer experience. There is a need for a method to solve these problems and provide customers with a more personalized shopping experience.

[0840] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0841] In this invention, the server includes means for collecting official data, means for analyzing the collected official data using natural language processing technology to identify management issues, and means for automatically generating proposal materials based on the analysis results, thereby enabling means for transmitting the generated proposal materials to an end-user terminal, activating an emotion engine for analyzing the end-user's emotional state, and means for adjusting the content of the proposal materials in real time based on the analysis results of the emotion engine.

[0842] "Official data" refers to written information such as reports, financial documents, and news releases that are officially made public by companies and organizations.

[0843] "Natural language processing technology" is a technology that allows computers to understand, analyze, and generate human language, and is used to analyze text data and extract keywords.

[0844] "Management issues" refer to problems and challenges that companies and organizations face that need to be solved, such as declining performance, entering new markets, and cost reduction.

[0845] A "proposal document" is a document automatically generated based on collected and analyzed data, which includes solutions and selling points.

[0846] "End-user terminal" refers to the device ultimately used by the user, including smartphones, tablets, and PCs.

[0847] An "emotion engine" refers to software or functionality that analyzes a user's facial expressions and tone of voice to determine their emotional state.

[0848] "Real-time adjustment" refers to the process of instantly analyzing the user's emotional state and instantly changing or modifying the content of the proposal materials based on the results.

[0849] "Means of collection" refers to methods or devices for automatically obtaining and storing the official data in question.

[0850] "Analysis means" refers to a set of processing techniques and algorithms used to process the data obtained and identify important information and issues.

[0851] This invention provides a system that automatically generates necessary proposal materials so that sales representatives can concentrate on negotiating with customers, and furthermore, adjusts the proposal content by recognizing the user's emotions. This system collects official data, analyzes its contents to identify management issues, automatically generates proposal materials, and sends them to the end user's terminal. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it has the function of adjusting the proposal materials according to the user's emotional state. Below, we will explain how this system is implemented by the server, terminal, and user.

[0852] 1. System Overview

[0853] This system collects "official data" such as reports and financial documents officially released by companies and organizations, and analyzes them using "natural language processing technology." It identifies "management issues" from the analysis results and automatically generates "proposal materials" based on those. The proposal materials are sent to the "end-user device," which then activates an emotion engine to analyze the user's emotions and adjusts the content of the proposal materials in real time based on the results.

[0854] 2. Program Processing

[0855] 1. Collection Method:

[0856] The server retrieves the URLs of official data from a pre-defined list of companies, then automatically downloads the latest official data from official websites and public databases, and stores the collected data in a database along with metadata such as company name, document name, and download date and time.

[0857] 2. Analysis method:

[0858] The server initializes a natural language processing engine (e.g., Google Cloud Natural Language API). By retrieving official data from the database and passing it to the analysis engine, important keywords and phrases are extracted and the company's management issues are identified.

[0859] 3. Automatic generation of proposal materials:

[0860] Based on the analysis results, a proposal document is automatically generated using a template, which includes solutions to the identified business issues and the selling points of the services and products to be offered.

[0861] 4. Submit your proposal:

[0862] The generated proposal material is transmitted to the end user terminal via a secure communication means.

[0863] 5. Emotion Engine in Action:

[0864] The end user's device displays the proposal documents and activates an emotion engine (e.g., Microsoft Azure Emotion API). The emotion engine analyzes the user's facial expressions and tone of voice while reviewing the documents to determine the user's emotional state.

[0865] 6. Adjustment of proposal materials:

[0866] The emotion engine on the end-user device analyzes the user's emotions and automatically adjusts the content of the proposal in real time based on the results. For example, if the user looks anxious, it will add supplemental information to the proposal to make them feel reassured.

[0867] 3. Specific Examples

[0868] Product "Smartwatch" proposal

[0869] 1. Collection of official data:

[0870] When a customer views a "smartwatch," product information is collected and recorded in a database.

[0871] 2. Automatic generation of proposal materials:

[0872] The server automatically generates a proposal document based on the collected information, including information such as tracking function, battery life, and waterproof performance.

[0873] 3. Emotion recognition:

[0874] The end-user device activates an emotion engine that analyzes the customer's facial expressions and voice as they review the materials. If the customer looks surprised, the system emphasizes "special discounts" and "limited-time offers." Conversely, if the customer looks anxious, the system adds more detailed information about the return policy and product warranty.

[0875] Prompt Sentence Examples

[0876] "Generate a proposal for a smartwatch."

[0877] Features: Tracking, battery life, waterproof

[0878] Additional Information: User Reviews, Special Offers, Return Policy

[0879] Customer facial expression and voice data: Surprise, anxiety

[0880] Proposal adjustments: surprise (emphasis on special discounts), anxiety (details of return policy)

[0881] In this way, the system automates a series of processes, from collecting official data to analyzing it, generating proposal materials, and adjusting them using an emotion engine, allowing end users to enjoy a personalized purchasing experience.

[0882] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0883] Step 1:

[0884] The server retrieves official data URLs from a pre-defined list of companies. This list includes URLs of the target companies' official websites and public databases. The input is the company list, and the output is the retrieved URLs. Based on this, the server automatically downloads official data (reports, financial documents, etc.) from the official websites and databases and stores them in a database.

[0885] Step 2:

[0886] The server retrieves the stored official data and initializes a natural language processing engine (e.g., Google Cloud Natural Language API). The official data is input, and analysis results (important keywords and phrases) are obtained as output. The server uses this analysis engine to extract important keywords and phrases from the official data and identify the company's management issues.

[0887] Step 3:

[0888] The server uses templates based on the analysis results of natural language processing to automatically generate proposal materials. The analysis results are input, and proposal materials are obtained as output. These proposal materials include solutions to identified business issues and the selling points of the products and services offered. For example, they can be embodied as "proposing a new marketing strategy" in response to "declining revenue."

[0889] Step 4:

[0890] The server transmits the generated proposal materials to the end user terminal via a secure communication means. The proposal materials are input, and the proposal materials transmitted to the end user terminal are obtained as output.

[0891] Step 5:

[0892] The device displays the received proposal documents and launches an emotion engine (e.g., Microsoft Azure Emotion API). The proposal documents are input, and the end user's emotional data is obtained as output. The emotion engine analyzes the end user's facial expressions and tone of voice when reviewing the proposal documents to identify their emotional state.

[0893] Step 6:

[0894] The device adjusts the content of the proposal materials in real time based on the results of analysis by the emotion engine. The input is emotion data and the proposal materials, and the output is the adjusted proposal materials. For example, if the user shows an anxious expression, additional information that gives a sense of security is inserted into the proposal materials.

[0895] Step 7:

[0896] The user checks the adjusted proposal document and makes further edits or additions as necessary. The adjusted proposal document is the input, and the final proposal document is the output. The user uses this as a basis to prepare and proceed with business negotiations with the customer.

[0897] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0898] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0899] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0900] [Third embodiment]

[0901] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0902] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0903] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0904] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0905] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0906] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0907] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0908] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0909] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0910] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0911] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0912] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0913] This invention provides a system that automatically generates necessary proposal materials so that sales representatives can concentrate on negotiating with customers. Below, we will explain how this system is implemented by the server, terminals, and users.

[0914] 1. System Overview

[0915] This system collects official documents, analyzes their contents to identify management issues, and automatically generates proposal materials and sends them to the user's device, eliminating the need for users (salespeople) to create documents and allowing them to focus on negotiating with customers and closing deals.

[0916] 2. Program Processing

[0917] 2.1 Collecting official documentation

[0918] The server collects official documents, such as the latest investor relations materials and financial reports, from the official websites and public databases of multiple companies based on a pre-defined list. This collection process is performed automatically on a regular basis, and the collected documents are stored in a database.

[0919] 2.2 Analysis of the official documentation

[0920] The server analyzes the collected official documents using natural language processing technology. For example, it uses a natural language processing library to extract keywords and phrases from the text. This allows the server to identify the business challenges each company faces (such as "declining revenue" or "difficulty entering the market").

[0921] 2.3 Automatic generation of proposal materials

[0922] Based on the analysis results, the server automatically generates proposal materials using templates. These proposal materials include solutions to the identified business issues and the selling points of the services and products to be offered. For example, they could be "Proposing a new marketing strategy to address declining revenues" or "Introducing services to support market entry."

[0923] 2.4 Submitting and Displaying Proposals

[0924] The server transmits the generated proposal material to the user's terminal.

[0925] The terminal displays the received proposal materials to the user, who can then review the materials and make edits or additions as necessary.

[0926] 3. Specific Examples

[0927] 3.1 Company A's IR materials

[0928] The server collects the latest IR documents from the official website of Company A. The documents contain information about a "recent decline in revenue."

[0929] 3.2 Analysis of the data

[0930] A natural language processing algorithm on the server identifies the keyword "revenue decline" and records it as a management issue.

[0931] 3.3 Proposal Creation

[0932] The server automatically incorporates specific approaches to solving declining revenue (for example, cost reduction programs or methods for developing new markets) into the proposal materials.

[0933] 3.4 Sending materials and preparing for negotiations

[0934] The server sends the generated proposal materials to the user's terminal,

[0935] The terminal displays the material to the user.

[0936] 3.5 Conducting business negotiations

[0937] The user begins negotiations with the customer (Company A) based on the proposal materials viewed on the device. Because the proposal materials show specific solutions, the user can proceed with the negotiations efficiently.

[0938] In this way, the system automates the entire process, from collecting official documents to analyzing them, generating proposal materials, and sending them to user devices, allowing sales representatives to devote more time to customer service.

[0939] The processing flow will be explained below.

[0940] Step 1:

[0941] The server retrieves a pre-configured list of companies, which includes URLs of the target companies' official websites or public databases.

[0942] Step 2:

[0943] Based on the company list, the server automatically downloads the latest official documents (e.g., investor relations materials and financial reports) from each company's official website or public database.

[0944] Step 3:

[0945] The server stores the downloaded official documents in a database, along with metadata such as the company name, document name, and download date and time.

[0946] Step 4:

[0947] The server initializes natural language processing (NLP) algorithms, which include text analysis and keyword extraction.

[0948] Step 5:

[0949] The server retrieves official documents from the database and passes them to an NLP algorithm, which analyzes the documents and extracts important keywords and phrases, thereby identifying business challenges for the company.

[0950] Step 6:

[0951] The server identifies business issues based on keywords extracted by the NLP algorithm, such as specific problems like "declining revenue" or "need to enter new markets."

[0952] Step 7:

[0953] The server selects a proposal template corresponding to the identified business problem, the template containing a proposal for solving the particular problem.

[0954] Step 8:

[0955] The server then embeds specific proposal content based on the analysis results into the proposal template and automatically generates proposal materials, which include solutions and selling points for the company's services.

[0956] Step 9:

[0957] The server transmits the generated proposal materials to the user's terminal using a secure communication method.

[0958] Step 10:

[0959] The terminal displays the received proposal materials to the user, who then checks the materials and makes edits or additions as necessary.

[0960] Step 11:

[0961] Users can prepare for business negotiations with customers based on the proposal materials they have edited, which allows them to proceed with business negotiations more efficiently.

[0962] Step 12:

[0963] The user starts negotiations with the customer and proposes specific solutions to the problem using proposal materials. After the negotiations, the user focuses on closing the deal and finalizing the contract.

[0964] Example 1

[0965] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0966] The present invention aims to automate and streamline the time-consuming process of creating proposal materials so that sales representatives can focus on negotiating with customers. Another objective is to effectively analyze collected official documents, identify business issues, and quickly and accurately generate corresponding proposal materials.

[0967] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0968] In this invention, the server includes means for collecting official documents, means for analyzing the collected official documents using natural language processing technology and identifying business issues, means for automatically generating proposal materials based on the analysis results, means for transmitting the generated proposal materials to a terminal, and database means for storing the collected official documents. This allows sales representatives to concentrate on negotiating with customers without spending much time on creating proposal materials.

[0969] "Official documents" are information documents that companies or organizations make public, such as investor relations materials and financial reports.

[0970] "Natural language processing technology" is a technology for analyzing, understanding, and generating natural language used by humans, and is used to extract keywords and phrases from text.

[0971] "Business challenges" are management and operational problems and difficulties faced by a company or organization, including declining revenue and difficulties in entering a market.

[0972] A "proposal" is a document that summarizes solutions and proposals for business issues faced by a company or organization.

[0973] The "means of collection" is the mechanism by which the server obtains official documents from the Internet or public databases.

[0974] The "means of analysis" refers to a system that uses natural language processing technology to analyze official documents collected by the server and identify business issues.

[0975] "Means for automatic generation" refers to a mechanism in which the server automatically creates proposal materials in accordance with a template based on the analysis results.

[0976] The "transmission means" is a mechanism by which the server delivers the generated proposal materials to the user's terminal.

[0977] "Database means for storage" refers to a database system for efficiently storing and managing collected official documents and generated materials.

[0978] A "terminal" is an information device that a user uses to receive and view proposal materials, and includes a personal computer, tablet, etc.

[0979] This invention provides a system that automatically generates necessary proposal materials so that sales representatives can concentrate on negotiating with customers. Below, we will explain in detail how the server, terminals, and users implement this system.

[0980] 1. System Configuration

[0981] The system includes the following major components:

[0982] Server: Collects and analyzes official documents, automatically generates and sends proposal materials.

[0983] Terminal: Receives, displays, and edits proposal materials.

[0984] Database: Stores collected official documents and generated proposal materials.

[0985] 2. Hardware and Software Used

[0986] Server hardware: A computer equipped with a high-performance processor and large memory capacity. For example, a server machine equipped with an NVIDIA GPU.

[0987] Software: Python programming language, requests library, natural language processing library spaCy, template engine Jinja2, library for sending emails smtplib.

[0988] 3. System Processing

[0989] The server collects official documents such as investor relations materials and financial reports from companies' official websites and public databases based on a pre-configured list, for example, by using the Python requests library to retrieve data from the Internet.

[0990] The collected official documents are stored in a database on the server, which then analyzes them using natural language processing technology, using the spaCy library to extract important keywords and phrases from the text and identify the company's business challenges.

[0991] Based on the analysis results, the server uses templates to automatically generate proposal materials. The template engine Jinja2 is used to create materials that include solutions to the company's challenges and the selling points of the service. The generated proposal materials are sent by email from the server to the user's device. The library used for sending emails is smtplib.

[0992] The terminal displays the received proposal documents and allows the user to review and edit them. For example, the user can open the received email, check the contents of the documents, and make supplementary input or corrections as necessary.

[0993] 4. Specific Examples

[0994] Company A's case

[0995] 1. The server collects the latest IR materials from the official website of Company A. The materials contain information about a "recent decline in revenue."

[0996] 2. The server's natural language processing algorithm identifies the keyword "revenue decline" and records it as a business issue.

[0997] 3. The server incorporates approaches to solving the declining revenue (e.g., new marketing strategies) into the proposal.

[0998] 4. The server sends the generated proposal materials to the terminal and displays the materials on the terminal.

[0999] Prompt Sentence Examples

[1000] "Collect the latest IR materials for Company A and generate a proposal to resolve the decline in revenue."

[1001] In this way, the server and terminals work together to automate the process of creating proposal materials, allowing sales representatives to focus on negotiating with customers.

[1002] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1003] System program processing flow

[1004] Step 1: Gather official documents

[1005] The server collects official documents such as investor relations materials and financial reports from companies' official websites and public databases based on a pre-configured list.

[1006] Input: Company website URL list

[1007] How it works: Uses Python's requests library to retrieve HTML data from each URL.

[1008] Output: A list of collected text data from official documents

[1009] Step 2: Save official documents

[1010] The server stores the collected official documents in a database.

[1011] Input: Text data from collected official documents

[1012] What it does: Query an SQL database for stored official documents.

[1013] Output: Official documents stored in a database

[1014] Step 3: Analyzing the official documentation

[1015] The server analyzes the collected official documents using natural language processing technology.

[1016] Input: Official documents stored in a database

[1017] What it does: It uses the spaCy library to parse text and extract keywords and phrases.

[1018] Output: A list of extracted keywords and phrases

[1019] Step 4: Identifying business issues

[1020] The server identifies business issues facing the company based on the analysis results.

[1021] Input: A list of extracted keywords or phrases

[1022] How it works: Uses a rules engine to match keyword lists to specific patterns and extract business issues.

[1023] Output: List of identified business issues

[1024] Step 5: Automatically generate proposal materials

[1025] The server automatically generates proposal materials using templates based on the identified management issues.

[1026] Input: List of identified business issues

[1027] How it works: Uses the Jinja2 template engine to generate proposals that detail solutions and services that address business challenges.

[1028] Output: Generated proposal document

[1029] Step 6: Submit your proposal

[1030] The server transmits the generated proposal material to the user's terminal.

[1031] Input: Generated proposal document

[1032] What it does: Uses the smtplib library to send the proposal to the user's email address.

[1033] Output: Proposal sent to the user's device

[1034] Step 7: View and edit your proposal

[1035] The terminal displays the received proposal material to the user, who can then edit it as necessary.

[1036] Input: Proposal sent to the user's device

[1037] How it works: The proposal document opens in the email client, and the user can edit and add to it.

[1038] Output: Proposal document reviewed and edited by the user

[1039] (Application example 1)

[1040] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1041] Ensuring efficient and safe operations is crucial in modern manufacturing. However, analyzing various official documents and maintenance reports requires a great deal of time and effort, making it difficult to quickly implement appropriate countermeasures. Furthermore, identifying manufacturing process and safety issues and generating specific proposals for improvement is also cumbersome, placing a significant burden on personnel. A solution to these issues and improving the efficiency and safety of manufacturing is needed.

[1042] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1043] In this invention, the server includes means for collecting official documents, means for analyzing the collected official documents using natural language processing technology and identifying issues, means for automatically generating proposal materials based on the analysis results, means for transmitting the generated proposal materials to a user terminal, means for collecting factory maintenance reports and official reports and identifying issues related to the manufacturing process, efficiency, and safety, means for incorporating specific solutions to the identified issues into the proposal materials, and means for displaying the proposal materials on a display installed in a factory robot. This enables personnel to efficiently identify issues and quickly implement solutions.

[1044] "Official documents" are documents such as reports, financial reports, and maintenance reports officially issued by companies or organizations.

[1045] "Collection methods" are techniques and methods for periodically obtaining official documents from multiple sources and storing them in a database.

[1046] "Natural language processing technology" is a technology that allows machines to understand and analyze text data, and can perform tasks such as extracting keywords and identifying relationships.

[1047] "Means of analysis" refers to techniques and methods for analyzing collected official documents using natural language processing technology and extracting specific issues or problems.

[1048] "Means for automatically generating proposal materials" refers to techniques or methods for automatically creating proposal materials in a standard format based on the analysis results.

[1049] A "user terminal" is a device such as a communication device or computer used by a user, and is a device that receives and displays proposal materials.

[1050] "Transmission means" refers to the technology or method for transferring data from the server to the user terminal.

[1051] A "maintenance report" is a document that records the results of regular maintenance inspections of factories and equipment.

[1052] "Manufacturing process" means the process and procedures for producing products in a factory.

[1053] "Efficiency" is a concept that refers to the optimal use of resources in the manufacturing process and the resulting high productivity.

[1054] "Safety" refers to the state of minimizing risks in the manufacturing environment and processes and preventing injuries and accidents.

[1055] A "problem" refers to a problem or challenge that requires a solution under specific circumstances or conditions.

[1056] A "solution" is a specific method or means for overcoming a particular problem.

[1057] A "factory robot" is an automated machine designed to perform work within a factory, and is a device equipped with a display for displaying proposal materials.

[1058] This invention is a system that aims to improve the efficiency and safety of manufacturing processes by having a server collect and analyze official documents and provide proposal materials to the user's factory robots. Specific embodiments for implementing this system are described below.

[1059] 1. System Overview

[1060] This system collects official documents, analyzes their contents to identify issues related to manufacturing processes, efficiency, and safety, and automatically generates proposal materials and sends them to the user's terminal.This system saves the user (factory personnel) the trouble of creating documents and enables them to quickly take efficient countermeasures to the identified issues.

[1061] 2. Hardware and Software

[1062] The system uses the following hardware and software:

[1063] Server: Collects data, analyzes it, and generates proposal materials.

[1064] Factory robot: Equipped with a display that displays the generated proposal materials.

[1065] Python: Used to implement the entire program.

[1066] Requests: Used to collect data on web pages.

[1067] BeautifulSoup: Used to extract text from HTML.

[1068] Transformers: Used to run natural language processing models (e.g., HuggingFace's Transformers library).

[1069] 3. Program Processing

[1070] 3.1 Collecting official documentation

[1071] The server automatically and periodically collects official documents (e.g., maintenance reports) from multiple official websites and public databases based on a pre-configured list and stores them in a database.

[1072] 3.2 Document Parsing

[1073] The server then analyzes the collected official documents using natural language processing techniques to extract key keywords and phrases, using HuggingFace's Transformers library, to identify issues related to manufacturing processes, efficiency, and safety.

[1074] 3.3 Automatic generation of proposal materials

[1075] Based on the analysis results, the server automatically generates proposal materials using templates. The proposal materials include solutions to the identified issues and their effects. For example, the proposals might include "application of a new process control system" or "regular maintenance and the introduction of high-performance sensors."

[1076] 3.4 Submitting and Displaying Proposals

[1077] The server sends the generated proposal materials to the user terminal (factory robot), which displays the received proposal materials on its display, allowing the user (factory staff) to check them and take appropriate measures as necessary.

[1078] 4. Specific Examples

[1079] For example, the server collects the latest maintenance reports from the factory's official website and uses analysis technology to identify mentions of "declining efficiency" or "safety issues." The server then generates a proposal document containing solutions such as "proposing a new process control system to address the efficiency decline" or "introducing high-performance sensors to address safety issues." The generated proposal document is then displayed on the factory robot's display for the user to review.

[1080] An example of a prompt sentence to be input into a generative AI model is, "Please extract issues and solutions from the factory report below and generate a proposal document."

[1081] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1082] Step 1:

[1083] Collecting official documentation

[1084] Input: A list of official website URLs pre-configured by the server.

[1085] How it works: The server periodically collects official documents (e.g., maintenance reports and financial reports) from multiple official websites based on a configured list of URLs. This process involves issuing HTTP requests using the Requests library to retrieve the contents of the web pages.

[1086] Data processing: The acquired HTML data is analyzed using the BeautifulSoup library and text data is extracted.

[1087] Output: A text data list of the official documentation.

[1088] Step 2:

[1089] Analysis of the official documentation

[1090] Input: A text data list of official documents collected in Step 1.

[1091] How it works: The server analyzes the collected official documents using natural language processing techniques, specifically using the Transformers library to extract keywords and important phrases from the text data.

[1092] Data Computing: Natural language processing models analyze text and identify issues (e.g., inefficiencies, safety issues, etc.) contained in each document.

[1093] Output: A list of identified issues.

[1094] Step 3:

[1095] Automatic generation of proposal materials

[1096] Input: The list of issues identified in step 2.

[1097] How it works: The server uses templates to automatically generate proposals containing solutions to identified issues, such as proposing a new process control system to address "decreased efficiency" or introducing high-performance sensors to address "safety issues."

[1098] Data processing: Combine the issue list and solution template to generate concrete proposal materials.

[1099] Output: The generated proposal (e.g. PDF, HTML format).

[1100] Step 4:

[1101] Submitting and viewing proposals

[1102] Input: The proposal generated in Step 3.

[1103] Operation: The server sends the generated proposal materials to the user terminal (factory robot). The factory robot displays the received proposal materials on its built-in display.

[1104] Data operations: file transfer and display.

[1105] Output: Proposal materials displayed on the user's terminal.

[1106] Step 5:

[1107] User verification and action taken

[1108] Input: Proposal materials displayed on the user's terminal.

[1109] Operation: The user (factory staff) checks the proposal documents displayed on the factory robot's display and takes appropriate measures as necessary. Based on the proposal documents, the user adjusts the manufacturing process and implements safety measures.

[1110] Output: Efficient manufacturing processes and improved safety.

[1111] The above are the specific processing steps of this system. By performing detailed operations for each step, it is possible to improve the efficiency and ensure safety of the manufacturing process.

[1112] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1113] This invention provides a system that automatically generates necessary proposal materials so that salespeople can concentrate on negotiating with customers, and furthermore, recognizes the user's emotions and adjusts the proposal content accordingly. Below, we will explain how this system is implemented by the server, terminal, and user.

[1114] 1. System Overview

[1115] This system collects official documents, analyzes their contents to identify management issues, and automatically generates proposal materials and sends them to the user's device. Furthermore, by incorporating an emotion engine that recognizes the user's (sales representative's) emotions, the system has the ability to adjust proposal materials according to the user's emotional state. This saves users the trouble of creating documents, allowing them to focus on negotiating with customers and closing deals.

[1116] 2. Program Processing

[1117] 2.1 Collecting official documentation

[1118] The server retrieves a pre-configured list of companies, which includes URLs of the target companies' official websites or public databases.

[1119] Based on the company list, the server automatically downloads the latest official documents (e.g., investor relations materials and financial reports) from each company's official website or public database.

[1120] The server stores the downloaded official documents in a database, along with metadata such as the company name, document name, and download date and time.

[1121] 2.2 Analysis of the official documentation

[1122] The server initializes natural language processing (NLP) algorithms, which include text analysis and keyword extraction.

[1123] The server retrieves official documents from the database and passes them to an NLP algorithm, which analyzes the documents and extracts key keywords and phrases, identifying business challenges for the company (e.g., "declining revenue" or "need to enter new markets").

[1124] 2.3 Automatic generation of proposal materials

[1125] Based on the analysis results, the server automatically generates proposal materials using templates. These proposal materials include solutions to the identified business issues and the selling points of the services and products to be offered. For example, they could be "Proposing a new marketing strategy to address declining revenues" or "Introducing services to support market entry."

[1126] 2.4 Submitting proposal materials and operation of the emotion engine

[1127] The server transmits the generated proposal materials to the user's terminal using a secure communication method.

[1128] The terminal displays the received proposal materials to the user and activates the emotion engine, which analyzes the user's facial expressions and tone of voice while reviewing the proposal materials to determine the user's emotional state.

[1129] 2.5 Adjustment of proposal materials

[1130] The device's emotion engine analyzes the user's emotions and automatically adjusts the content of the proposal based on the results. For example, if the user looks anxious, it will add additional reassuring information to the proposal.

[1131] 2.6 Preparation and implementation of business negotiations

[1132] The user can review the proposal materials adjusted by the emotion engine and make further edits or additions as necessary.

[1133] Users can prepare and efficiently conduct business negotiations with customers based on the proposal materials. At this point, the materials reflect the user's emotions and are optimized, improving the success rate of the negotiations.

[1134] 3. Specific Examples

[1135] 3.1 Analysis of Company A's IR materials and generation of proposal materials

[1136] The server collects the latest IR documents from the official website of Company A. The documents contain information about a "recent decline in revenue."

[1137] A natural language processing algorithm on the server identifies the keyword "revenue decline" and records it as a management issue.

[1138] The server automatically incorporates specific approaches to solving declining revenue (for example, cost reduction programs or methods for developing new markets) into the proposal materials.

[1139] 3.2 Data transmission and user emotion recognition

[1140] The server transmits the generated proposal materials to the user's terminal.

[1141] The terminal displays the document to the user, and the emotion engine recognizes the user's emotions. The emotion engine may detect that the user has an anxious expression while checking the document.

[1142] 3.3 Adjusting materials and preparing for business meetings

[1143] The device's emotion engine detects the user's anxiety and automatically inserts additional supporting information into the proposal materials to increase the user's sense of security.

[1144] The user prepares and conducts business negotiations with the client (Company A) based on the adjusted proposal materials. By using materials optimized by the emotion engine, the negotiations proceed more effectively and the probability of successfully closing the contract increases.

[1145] In this way, the system automates a series of processes, from collecting official documents to analyzing them, generating proposal materials, and adjusting them using an emotion engine, allowing sales representatives to devote more time to customer service and increasing the success rate of sales negotiations.

[1146] The processing flow will be explained below.

[1147] Step 1:

[1148] The server retrieves a pre-configured list of companies, which includes URLs of the target companies' official websites or public databases.

[1149] Step 2:

[1150] Based on the company list, the server automatically downloads the latest official documents (e.g., investor relations materials and financial reports) from each company's official website or public database.

[1151] Step 3:

[1152] The server stores the downloaded official documents in a database, along with metadata such as the company name, document name, and download date and time.

[1153] Step 4:

[1154] The server initializes natural language processing (NLP) algorithms, which include text analysis and keyword extraction.

[1155] Step 5:

[1156] The server retrieves official documents from the database and passes them to an NLP algorithm, which analyzes the documents and extracts key keywords and phrases, identifying business challenges for the company (e.g., "declining revenue" or "need to enter new markets").

[1157] Step 6:

[1158] The server identifies business challenges based on keywords extracted by the NLP algorithm, such as specific problems like "declining revenue" or "barriers to market entry."

[1159] Step 7:

[1160] The server selects a proposal template corresponding to the identified business problem, the template containing a proposal for solving the particular problem.

[1161] Step 8:

[1162] The server then embeds specific proposal content based on the analysis results into the proposal template and automatically generates proposal materials, which include solutions and selling points for the company's services.

[1163] Step 9:

[1164] The server transmits the generated proposal materials to the user's terminal using a secure communication method.

[1165] Step 10:

[1166] The device displays the received proposal materials to the user and activates the emotion engine, which analyzes the user's facial expressions and tone of voice while reviewing the proposal materials to determine the user's emotional state.

[1167] Step 11:

[1168] The device's emotion engine analyzes the user's emotions and automatically adjusts the content of the proposal based on the results. For example, if the user looks anxious, it will add additional reassuring information to the proposal.

[1169] Step 12:

[1170] The user can review the proposal materials adjusted by the emotion engine and make further edits or additions as necessary.

[1171] Step 13:

[1172] Users can use the proposal documents to prepare and efficiently conduct business negotiations with customers. At this point, the documents reflect the user's emotions and are optimized, improving the success rate of the negotiations.

[1173] Step 14:

[1174] The user starts negotiations with the customer and proposes specific solutions to the problem using proposal materials. After the negotiations, the user focuses on closing the deal and finalizing the contract.

[1175] Example 2

[1176] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1177] In order to allow sales representatives to concentrate on business negotiations with customers, it is necessary to improve the efficiency of the creation of proposal materials and the accuracy of proposals. Another challenge is to improve the success rate of sales negotiations by enabling the adjustment of proposal materials according to the user's emotional state.

[1178] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1179] In this invention, the server includes means for collecting company information, means for analyzing the collected company information using natural language processing technology and identifying management issues, means for automatically generating proposal materials based on the analysis results, means for transmitting the generated proposal materials to a user terminal, and means for recognizing the user's emotions and adjusting the proposal materials. This automates the creation of proposal materials and optimizes the materials according to the user's emotions, thereby reducing the burden on the user during business negotiations and improving the success rate.

[1180] "Corporate information" refers to official documents such as financial reports and investor relations materials collected from a company's official website or public databases.

[1181] "Natural language processing technology" refers to technology that enables computers to understand and analyze human language, and in particular technology that extracts keywords and analyzes text.

[1182] "Business challenges" are operational problems or obstacles facing a company, such as declining revenue or the need to enter new markets.

[1183] A "proposal document" is a document generated based on the analysis results, which describes solutions to identified business issues and the selling points of the services and products to be offered.

[1184] A "user terminal" is a device that receives proposal materials sent from the server and allows the user to check and operate them, and includes a PC, tablet, etc.

[1185] "Means for recognizing emotions" refers to technology that determines a user's emotional state by analyzing their facial expressions, tone of voice, etc., and includes, for example, facial recognition technology and voice analysis technology.

[1186] "Data storage" refers to a storage device or database for storing collected company information and generated proposal materials.

[1187] This invention provides a system that automatically generates necessary proposal materials so that salespeople can concentrate on negotiating with customers, and furthermore, recognizes the user's emotions and adjusts the proposal content accordingly. Below, we will explain how this system is implemented by the server, terminal, and user.

[1188] System Overview

[1189] This system collects corporate information, analyzes the content to identify management issues, and automatically generates proposal materials and sends them to the user's device. Furthermore, by incorporating an emotion engine that recognizes the user's (salesperson's) emotions, the system has the ability to adjust proposal materials according to the user's emotional state. This saves users the trouble of creating materials, allowing them to focus on negotiating and closing deals with customers.

[1190] Hardware and software used

[1191] This system uses the following hardware and software:

[1192] Server: Collects and analyzes data, generates and sends proposal materials.

[1193] Terminal: Displays proposal materials and performs emotion recognition using an emotion engine.

[1194] Natural language processing algorithm: Uses Google TensorFlow.

[1195] Proposal generation tool: Uses the Microsoft Word API.

[1196] Sentiment Engine: Uses Microsoft Azure Cognitive Services.

[1197] Specific processing

[1198] 1. Collection of company information

[1199] The server retrieves a pre-defined list of companies, which includes the URLs of the target companies' official websites and public databases. Based on the list, the server automatically downloads the latest official documents from each company's official website or public database. For example, it retrieves PDF files from websites using HTTP requests. The downloaded official documents are stored in data storage, along with metadata such as the company name, document name, and download date and time.

[1200] Specific working example:

[1201] The server collects the latest IR documents from the official website of Company A. The documents contain information about a "recent decline in revenue."

[1202] 2. Analysis of company information

[1203] The server initializes TensorFlow, retrieves the official document from data storage, and passes it to the NLP algorithm, which analyzes the document and extracts important keywords and phrases. For example, the keyword "revenue decline" is extracted.

[1204] Specific working example:

[1205] TensorFlow identifies the keyword "revenue decline" from Company A's IR materials.

[1206] 3. Automatic generation of proposal materials

[1207] Based on the analysis results, the server uses Microsoft Word API to automatically generate a proposal document using a template, which includes solutions to the identified business issues and the selling points of the services and products to be offered.

[1208] Specific working example:

[1209] The server automatically incorporates specific approaches (for example, cost reduction programs and methods for developing new markets) for solving Company A's declining profits into the proposal materials.

[1210] Example prompt sentence:

[1211] "Generate proposal materials to address revenue declines based on Company A's IR materials"

[1212] 4. Sending proposal materials and running the emotion engine

[1213] The server sends the generated proposal materials to the user's device. SSL / TLS is used as a secure communication method for transmission. The device displays the received proposal materials to the user and uses Azure Cognitive Services to start the emotion engine. The emotion engine analyzes facial expressions and tone of voice while the user is reviewing the proposal materials to determine the user's emotional state.

[1214] Specific working example:

[1215] The server sends the generated proposal materials to the user's device, where Azure Cognitive Services, the device's emotion engine, recognizes the user's emotions.

[1216] 5. Adjusting the proposal materials

[1217] The device's emotion engine analyzes the user's emotions and automatically adjusts the content of the proposal based on the results. For example, if the user looks anxious, it will add additional information to the proposal to make them feel more reassured.

[1218] Specific working example:

[1219] The device's emotion engine detects the user's anxiety and automatically inserts supplementary information into the proposal materials to provide reassurance.

[1220] 6. Preparation and implementation of business negotiations

[1221] The user can review the proposal materials adjusted by the emotion engine and make further edits or additions as necessary. Based on the proposal materials, the user can prepare and efficiently proceed with business negotiations with customers. Because the materials are optimized to reflect the user's emotions, the success rate of business negotiations increases.

[1222] Specific working example:

[1223] Based on the adjusted proposal materials, the user prepares and conducts a business negotiation with Company A. By using materials optimized by the emotion engine, the business negotiation can be conducted effectively, increasing the probability of successfully closing the contract.

[1224] In this way, by automating a series of processes, from collecting corporate information to analyzing it, generating proposal materials, and adjusting them using an emotion engine, the system allows sales representatives to devote more time to customer service and increases the success rate of sales negotiations.

[1225] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1226] Step 1: Gather company information

[1227] The server takes as input a list of companies, which includes URLs of their official websites and public databases.

[1228] Input: Company list (including URL)

[1229] The server automatically downloads the latest official documents from each company's official website or public database based on the company list using HTTP requests.

[1230] Specific operation: Download IR materials (example_IR.pdf) from Company A's official website (https: / / example.com)

[1231] Output: Downloaded official documentation

[1232] Step 2: Save your company information

[1233] The server saves the downloaded official documents in data storage, including metadata such as the company name, document name, and download date and time.

[1234] Input: Downloaded official documentation

[1235] Data processing: Save the document in the data storage along with the company name, document name, and download date and time.

[1236] Specific operation: Save a document with metadata such as Company A, example_IR.pdf, 2023-10-01

[1237] Output: Official documents stored in data storage

[1238] Step 3: Analyze company information

[1239] The server retrieves official documents from data storage and passes them to a natural language processing (NLP) algorithm, which uses TensorFlow for analysis.

[1240] Input: Official documents stored in data storage

[1241] The server uses NLP algorithms to analyze the document and extract important keywords and phrases.

[1242] Specific behavior: TensorFlow extracts "Diminishing Revenue" from example_IR.pdf

[1243] Output: Extracted keywords and phrases

[1244] Step 4: Automatically generate proposal materials

[1245] Based on the analysis results, the server automatically generates proposal materials using templates using the Microsoft Word API.

[1246] Input: Extracted keywords or phrases (e.g., "revenue decline")

[1247] Data processing: Describe solutions to the management issues identified in the proposal materials and the selling points of the services and products to be offered.

[1248] Specific action: Include a marketing strategy for "revenue decline" in the proposal materials

[1249] Output: Auto-generated proposal

[1250] Step 5: Submit your proposal

[1251] The server sends the generated proposal materials to the user's device, securely using SSL / TLS.

[1252] Input: Auto-generated proposal

[1253] Data calculation: Proposal materials are sent to the user terminal.

[1254] Specific operation: Proposal materials are sent to the user's device using SSL / TLS

[1255] Output: Proposal materials sent to the user's device

[1256] Step 6: Activating the Emotion Engine

[1257] The device displays the received proposal to the user and activates the emotion engine, analyzing the user's facial expressions and tone of voice using Azure Cognitive Services.

[1258] Input: Proposal document being reviewed by the user

[1259] Data calculation: The emotion engine analyzes the user's facial expressions and tone of voice to determine their emotional state.

[1260] Specific operation: Launch Azure Cognitive Services to read the user's facial expression

[1261] Output: Parsed user's emotional state

[1262] Step 7: Adjust your proposal

[1263] The device's emotion engine analyzes the user's emotions and automatically adjusts the content of the proposal materials based on the results.

[1264] Input: Parsed emotional state of the user

[1265] Data processing: If the user looks anxious, add supplementary information to the proposal materials to make them feel more reassured.

[1266] Specific behavior: Automatically insert supplemental information into proposal materials

[1267] Output: Tailored proposal

[1268] Step 8: Prepare and conduct the deal

[1269] The user can then review the proposal documents adjusted by the emotion engine, and make further edits or additions as necessary. Based on the proposal documents, the user can prepare and efficiently conduct business negotiations with customers.

[1270] Input: Adjusted proposal

[1271] Data Computing: Prepare for customer meetings with tailored proposals.

[1272] Specific action: Add or correct the proposal document

[1273] Output: Deal preparation and execution completed

[1274] Through these specific processing steps, the system enables salespeople to efficiently create proposal materials and increase the success rate of business negotiations with customers.

[1275] (Application example 2)

[1276] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1277] In today's virtual stores, customers often lack the proper information to select products, which can lead to a loss of motivation to purchase. Furthermore, it is difficult to understand customer emotions and provide optimal product recommendations in real time, ultimately hindering the improvement of the customer experience. There is a need for a method to solve these problems and provide customers with a more personalized shopping experience.

[1278] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1279] In this invention, the server includes means for collecting official data, means for analyzing the collected official data using natural language processing technology to identify management issues, and means for automatically generating proposal materials based on the analysis results, thereby enabling means for transmitting the generated proposal materials to an end-user terminal, activating an emotion engine for analyzing the end-user's emotional state, and means for adjusting the content of the proposal materials in real time based on the analysis results of the emotion engine.

[1280] "Official data" refers to written information such as reports, financial documents, and news releases that are officially made public by companies and organizations.

[1281] "Natural language processing technology" is a technology that allows computers to understand, analyze, and generate human language, and is used to analyze text data and extract keywords.

[1282] "Management issues" refer to problems and challenges that companies and organizations face that need to be solved, such as declining performance, entering new markets, and cost reduction.

[1283] A "proposal document" is a document automatically generated based on collected and analyzed data, which includes solutions and selling points.

[1284] "End-user terminal" refers to the device ultimately used by the user, including smartphones, tablets, and PCs.

[1285] An "emotion engine" refers to software or functionality that analyzes a user's facial expressions and tone of voice to determine their emotional state.

[1286] "Real-time adjustment" refers to the process of instantly analyzing the user's emotional state and instantly changing or modifying the content of the proposal materials based on the results.

[1287] "Means of collection" refers to methods or devices for automatically obtaining and storing the official data in question.

[1288] "Analysis means" refers to a set of processing techniques and algorithms used to process the data obtained and identify important information and issues.

[1289] This invention provides a system that automatically generates necessary proposal materials so that sales representatives can concentrate on negotiating with customers, and furthermore, adjusts the proposal content by recognizing the user's emotions. This system collects official data, analyzes its contents to identify management issues, automatically generates proposal materials, and sends them to the end user's terminal. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it has the function of adjusting the proposal materials according to the user's emotional state. Below, we will explain how this system is implemented by the server, terminal, and user.

[1290] 1. System Overview

[1291] This system collects "official data" such as reports and financial documents officially released by companies and organizations, and analyzes them using "natural language processing technology." It identifies "management issues" from the analysis results and automatically generates "proposal materials" based on those. The proposal materials are sent to the "end-user device," which then activates an emotion engine to analyze the user's emotions and adjusts the content of the proposal materials in real time based on the results.

[1292] 2. Program Processing

[1293] 1. Collection Method:

[1294] The server retrieves the URLs of official data from a pre-defined list of companies, then automatically downloads the latest official data from official websites and public databases, and stores the collected data in a database along with metadata such as company name, document name, and download date and time.

[1295] 2. Analysis method:

[1296] The server initializes a natural language processing engine (e.g., Google Cloud Natural Language API). By retrieving official data from the database and passing it to the analysis engine, important keywords and phrases are extracted and the company's management issues are identified.

[1297] 3. Automatic generation of proposal materials:

[1298] Based on the analysis results, a proposal document is automatically generated using a template, which includes solutions to the identified business issues and the selling points of the services and products to be offered.

[1299] 4. Submit your proposal:

[1300] The generated proposal material is transmitted to the end user terminal via a secure communication means.

[1301] 5. Emotion Engine in Action:

[1302] The end user's device displays the proposal documents and activates an emotion engine (e.g., Microsoft Azure Emotion API). The emotion engine analyzes the user's facial expressions and tone of voice while reviewing the documents to determine the user's emotional state.

[1303] 6. Adjustment of proposal materials:

[1304] The emotion engine on the end-user device analyzes the user's emotions and automatically adjusts the content of the proposal in real time based on the results. For example, if the user looks anxious, it will add supplemental information to the proposal to make them feel reassured.

[1305] 3. Specific Examples

[1306] Product "Smartwatch" proposal

[1307] 1. Collection of official data:

[1308] When a customer views a "smartwatch," product information is collected and recorded in a database.

[1309] 2. Automatic generation of proposal materials:

[1310] The server automatically generates a proposal document based on the collected information, including information such as tracking function, battery life, and waterproof performance.

[1311] 3. Emotion recognition:

[1312] The end-user device activates an emotion engine that analyzes the customer's facial expressions and voice as they review the materials. If the customer looks surprised, the system emphasizes "special discounts" and "limited-time offers." Conversely, if the customer looks anxious, the system adds more detailed information about the return policy and product warranty.

[1313] Prompt Sentence Examples

[1314] "Generate a proposal for a smartwatch."

[1315] Features: Tracking, battery life, waterproof

[1316] Additional Information: User Reviews, Special Offers, Return Policy

[1317] Customer facial expression and voice data: Surprise, anxiety

[1318] Proposal adjustments: surprise (emphasis on special discounts), anxiety (details of return policy)

[1319] In this way, the system automates a series of processes, from collecting official data to analyzing it, generating proposal materials, and adjusting them using an emotion engine, allowing end users to enjoy a personalized purchasing experience.

[1320] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1321] Step 1:

[1322] The server retrieves official data URLs from a pre-defined list of companies. This list includes URLs of the target companies' official websites and public databases. The input is the company list, and the output is the retrieved URLs. Based on this, the server automatically downloads official data (reports, financial documents, etc.) from the official websites and databases and stores them in a database.

[1323] Step 2:

[1324] The server retrieves the stored official data and initializes a natural language processing engine (e.g., Google Cloud Natural Language API). The official data is input, and analysis results (important keywords and phrases) are obtained as output. The server uses this analysis engine to extract important keywords and phrases from the official data and identify the company's management issues.

[1325] Step 3:

[1326] The server uses templates based on the analysis results of natural language processing to automatically generate proposal materials. The analysis results are input, and proposal materials are obtained as output. These proposal materials include solutions to identified business issues and the selling points of the products and services offered. For example, they can be embodied as "proposing a new marketing strategy" in response to "declining revenue."

[1327] Step 4:

[1328] The server transmits the generated proposal materials to the end user terminal via a secure communication means. The proposal materials are input, and the proposal materials transmitted to the end user terminal are obtained as output.

[1329] Step 5:

[1330] The device displays the received proposal documents and launches an emotion engine (e.g., Microsoft Azure Emotion API). The proposal documents are input, and the end user's emotional data is obtained as output. The emotion engine analyzes the end user's facial expressions and tone of voice when reviewing the proposal documents to identify their emotional state.

[1331] Step 6:

[1332] The device adjusts the content of the proposal materials in real time based on the results of analysis by the emotion engine. The input is emotion data and the proposal materials, and the output is the adjusted proposal materials. For example, if the user shows an anxious expression, additional information that gives a sense of security is inserted into the proposal materials.

[1333] Step 7:

[1334] The user checks the adjusted proposal document and makes further edits or additions as necessary. The adjusted proposal document is the input, and the final proposal document is the output. The user uses this as a basis to prepare and proceed with business negotiations with the customer.

[1335] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1336] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1337] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1338] [Fourth embodiment]

[1339] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1340] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1341] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1342] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1343] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1344] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1345] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1346] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1347] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1348] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1349] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1350] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1351] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1352] This invention provides a system that automatically generates necessary proposal materials so that sales representatives can concentrate on negotiating with customers. Below, we will explain how this system is implemented by the server, terminals, and users.

[1353] 1. System Overview

[1354] This system collects official documents, analyzes their contents to identify management issues, and automatically generates proposal materials and sends them to the user's device, eliminating the need for users (salespeople) to create documents and allowing them to focus on negotiating with customers and closing deals.

[1355] 2. Program Processing

[1356] 2.1 Collecting official documentation

[1357] The server collects official documents, such as the latest investor relations materials and financial reports, from the official websites and public databases of multiple companies based on a pre-defined list. This collection process is performed automatically on a regular basis, and the collected documents are stored in a database.

[1358] 2.2 Analysis of the official documentation

[1359] The server analyzes the collected official documents using natural language processing technology. For example, it uses a natural language processing library to extract keywords and phrases from the text. This allows the server to identify the business challenges each company faces (such as "declining revenue" or "difficulty entering the market").

[1360] 2.3 Automatic generation of proposal materials

[1361] Based on the analysis results, the server automatically generates proposal materials using templates. These proposal materials include solutions to the identified business issues and the selling points of the services and products to be offered. For example, they could be "Proposing a new marketing strategy to address declining revenues" or "Introducing services to support market entry."

[1362] 2.4 Submitting and Displaying Proposals

[1363] The server transmits the generated proposal material to the user's terminal.

[1364] The terminal displays the received proposal materials to the user, who can then review the materials and make edits or additions as necessary.

[1365] 3. Specific Examples

[1366] 3.1 Company A's IR materials

[1367] The server collects the latest IR documents from the official website of Company A. The documents contain information about a "recent decline in revenue."

[1368] 3.2 Analysis of the data

[1369] A natural language processing algorithm on the server identifies the keyword "revenue decline" and records it as a management issue.

[1370] 3.3 Proposal Creation

[1371] The server automatically incorporates specific approaches to solving declining revenue (for example, cost reduction programs or methods for developing new markets) into the proposal materials.

[1372] 3.4 Sending materials and preparing for negotiations

[1373] The server sends the generated proposal materials to the user's terminal,

[1374] The terminal displays the material to the user.

[1375] 3.5 Conducting business negotiations

[1376] The user begins negotiations with the customer (Company A) based on the proposal materials viewed on the device. Because the proposal materials show specific solutions, the user can proceed with the negotiations efficiently.

[1377] In this way, the system automates the entire process, from collecting official documents to analyzing them, generating proposal materials, and sending them to user devices, allowing sales representatives to devote more time to customer service.

[1378] The processing flow will be explained below.

[1379] Step 1:

[1380] The server retrieves a pre-configured list of companies, which includes URLs of the target companies' official websites or public databases.

[1381] Step 2:

[1382] Based on the company list, the server automatically downloads the latest official documents (e.g., investor relations materials and financial reports) from each company's official website or public database.

[1383] Step 3:

[1384] The server stores the downloaded official documents in a database, along with metadata such as the company name, document name, and download date and time.

[1385] Step 4:

[1386] The server initializes natural language processing (NLP) algorithms, which include text analysis and keyword extraction.

[1387] Step 5:

[1388] The server retrieves official documents from the database and passes them to an NLP algorithm, which analyzes the documents and extracts important keywords and phrases, thereby identifying business challenges for the company.

[1389] Step 6:

[1390] The server identifies business issues based on keywords extracted by the NLP algorithm, such as specific problems like "declining revenue" or "need to enter new markets."

[1391] Step 7:

[1392] The server selects a proposal template corresponding to the identified business problem, the template containing a proposal for solving the particular problem.

[1393] Step 8:

[1394] The server then embeds specific proposal content based on the analysis results into the proposal template and automatically generates proposal materials, which include solutions and selling points for the company's services.

[1395] Step 9:

[1396] The server transmits the generated proposal materials to the user's terminal using a secure communication method.

[1397] Step 10:

[1398] The terminal displays the received proposal materials to the user, who then checks the materials and makes edits or additions as necessary.

[1399] Step 11:

[1400] Users can prepare for business negotiations with customers based on the proposal materials they have edited, which allows them to proceed with business negotiations more efficiently.

[1401] Step 12:

[1402] The user starts negotiations with the customer and proposes specific solutions to the problem using proposal materials. After the negotiations, the user focuses on closing the deal and finalizing the contract.

[1403] Example 1

[1404] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1405] The present invention aims to automate and streamline the time-consuming process of creating proposal materials so that sales representatives can focus on negotiating with customers. Another objective is to effectively analyze collected official documents, identify business issues, and quickly and accurately generate corresponding proposal materials.

[1406] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1407] In this invention, the server includes means for collecting official documents, means for analyzing the collected official documents using natural language processing technology and identifying business issues, means for automatically generating proposal materials based on the analysis results, means for transmitting the generated proposal materials to a terminal, and database means for storing the collected official documents. This allows sales representatives to concentrate on negotiating with customers without spending much time on creating proposal materials.

[1408] "Official documents" are information documents that companies or organizations make public, such as investor relations materials and financial reports.

[1409] "Natural language processing technology" is a technology for analyzing, understanding, and generating natural language used by humans, and is used to extract keywords and phrases from text.

[1410] "Business challenges" are management and operational problems and difficulties faced by a company or organization, including declining revenue and difficulties in entering a market.

[1411] A "proposal" is a document that summarizes solutions and proposals for business issues faced by a company or organization.

[1412] The "means of collection" is the mechanism by which the server obtains official documents from the Internet or public databases.

[1413] The "means of analysis" refers to a system that uses natural language processing technology to analyze official documents collected by the server and identify business issues.

[1414] "Means for automatic generation" refers to a mechanism in which the server automatically creates proposal materials in accordance with a template based on the analysis results.

[1415] The "transmission means" is a mechanism by which the server delivers the generated proposal materials to the user's terminal.

[1416] "Database means for storage" refers to a database system for efficiently storing and managing collected official documents and generated materials.

[1417] A "terminal" is an information device that a user uses to receive and view proposal materials, and includes a personal computer, tablet, etc.

[1418] This invention provides a system that automatically generates necessary proposal materials so that sales representatives can concentrate on negotiating with customers. Below, we will explain in detail how the server, terminals, and users implement this system.

[1419] 1. System Configuration

[1420] The system includes the following major components:

[1421] Server: Collects and analyzes official documents, automatically generates and sends proposal materials.

[1422] Terminal: Receives, displays, and edits proposal materials.

[1423] Database: Stores collected official documents and generated proposal materials.

[1424] 2. Hardware and Software Used

[1425] Server hardware: A computer equipped with a high-performance processor and large memory capacity. For example, a server machine equipped with an NVIDIA GPU.

[1426] Software: Python programming language, requests library, natural language processing library spaCy, template engine Jinja2, library for sending emails smtplib.

[1427] 3. System Processing

[1428] The server collects official documents such as investor relations materials and financial reports from companies' official websites and public databases based on a pre-configured list, for example, by using the Python requests library to retrieve data from the Internet.

[1429] The collected official documents are stored in a database on the server, which then analyzes them using natural language processing technology, using the spaCy library to extract important keywords and phrases from the text and identify the company's business challenges.

[1430] Based on the analysis results, the server uses templates to automatically generate proposal materials. The template engine Jinja2 is used to create materials that include solutions to the company's challenges and the selling points of the service. The generated proposal materials are sent by email from the server to the user's device. The library used for sending emails is smtplib.

[1431] The terminal displays the received proposal documents and allows the user to review and edit them. For example, the user can open the received email, check the contents of the documents, and make supplementary input or corrections as necessary.

[1432] 4. Specific Examples

[1433] Company A's case

[1434] 1. The server collects the latest IR materials from the official website of Company A. The materials contain information about a "recent decline in revenue."

[1435] 2. The server's natural language processing algorithm identifies the keyword "revenue decline" and records it as a business issue.

[1436] 3. The server incorporates approaches to solving the declining revenue (e.g., new marketing strategies) into the proposal.

[1437] 4. The server sends the generated proposal materials to the terminal and displays the materials on the terminal.

[1438] Prompt Sentence Examples

[1439] "Collect the latest IR materials for Company A and generate a proposal to resolve the decline in revenue."

[1440] In this way, the server and terminals work together to automate the process of creating proposal materials, allowing sales representatives to focus on negotiating with customers.

[1441] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1442] System program processing flow

[1443] Step 1: Gather official documents

[1444] The server collects official documents such as investor relations materials and financial reports from companies' official websites and public databases based on a pre-configured list.

[1445] Input: Company website URL list

[1446] How it works: Uses Python's requests library to retrieve HTML data from each URL.

[1447] Output: A list of collected text data from official documents

[1448] Step 2: Save official documents

[1449] The server stores the collected official documents in a database.

[1450] Input: Text data from collected official documents

[1451] What it does: Query an SQL database for stored official documents.

[1452] Output: Official documents stored in a database

[1453] Step 3: Analyzing the official documentation

[1454] The server analyzes the collected official documents using natural language processing technology.

[1455] Input: Official documents stored in a database

[1456] What it does: It uses the spaCy library to parse text and extract keywords and phrases.

[1457] Output: A list of extracted keywords and phrases

[1458] Step 4: Identifying business issues

[1459] The server identifies business issues facing the company based on the analysis results.

[1460] Input: A list of extracted keywords or phrases

[1461] How it works: Uses a rules engine to match keyword lists to specific patterns and extract business issues.

[1462] Output: List of identified business issues

[1463] Step 5: Automatically generate proposal materials

[1464] The server automatically generates proposal materials using templates based on the identified management issues.

[1465] Input: List of identified business issues

[1466] How it works: Uses the Jinja2 template engine to generate proposals that detail solutions and services that address business challenges.

[1467] Output: Generated proposal document

[1468] Step 6: Submit your proposal

[1469] The server transmits the generated proposal material to the user's terminal.

[1470] Input: Generated proposal document

[1471] What it does: Uses the smtplib library to send the proposal to the user's email address.

[1472] Output: Proposal sent to the user's device

[1473] Step 7: View and edit your proposal

[1474] The terminal displays the received proposal material to the user, who can then edit it as necessary.

[1475] Input: Proposal sent to the user's device

[1476] How it works: The proposal document opens in the email client, and the user can edit and add to it.

[1477] Output: Proposal document reviewed and edited by the user

[1478] (Application example 1)

[1479] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1480] Ensuring efficient and safe operations is crucial in modern manufacturing. However, analyzing various official documents and maintenance reports requires a great deal of time and effort, making it difficult to quickly implement appropriate countermeasures. Furthermore, identifying manufacturing process and safety issues and generating specific proposals for improvement is also cumbersome, placing a significant burden on personnel. A solution to these issues and improving the efficiency and safety of manufacturing is needed.

[1481] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1482] In this invention, the server includes means for collecting official documents, means for analyzing the collected official documents using natural language processing technology and identifying issues, means for automatically generating proposal materials based on the analysis results, means for transmitting the generated proposal materials to a user terminal, means for collecting factory maintenance reports and official reports and identifying issues related to the manufacturing process, efficiency, and safety, means for incorporating specific solutions to the identified issues into the proposal materials, and means for displaying the proposal materials on a display installed in a factory robot. This enables personnel to efficiently identify issues and quickly implement solutions.

[1483] "Official documents" are documents such as reports, financial reports, and maintenance reports officially issued by companies or organizations.

[1484] "Collection methods" are techniques and methods for periodically obtaining official documents from multiple sources and storing them in a database.

[1485] "Natural language processing technology" is a technology that allows machines to understand and analyze text data, and can perform tasks such as extracting keywords and identifying relationships.

[1486] "Means of analysis" refers to techniques and methods for analyzing collected official documents using natural language processing technology and extracting specific issues or problems.

[1487] "Means for automatically generating proposal materials" refers to techniques or methods for automatically creating proposal materials in a standard format based on the analysis results.

[1488] A "user terminal" is a device such as a communication device or computer used by a user, and is a device that receives and displays proposal materials.

[1489] "Transmission means" refers to the technology or method for transferring data from the server to the user terminal.

[1490] A "maintenance report" is a document that records the results of regular maintenance inspections of factories and equipment.

[1491] "Manufacturing process" means the process and procedures for producing products in a factory.

[1492] "Efficiency" is a concept that refers to the optimal use of resources in the manufacturing process and the resulting high productivity.

[1493] "Safety" refers to the state of minimizing risks in the manufacturing environment and processes and preventing injuries and accidents.

[1494] A "problem" refers to a problem or challenge that requires a solution under specific circumstances or conditions.

[1495] A "solution" is a specific method or means for overcoming a particular problem.

[1496] A "factory robot" is an automated machine designed to perform work within a factory, and is a device equipped with a display for displaying proposal materials.

[1497] This invention is a system that aims to improve the efficiency and safety of manufacturing processes by having a server collect and analyze official documents and provide proposal materials to the user's factory robots. Specific embodiments for implementing this system are described below.

[1498] 1. System Overview

[1499] This system collects official documents, analyzes their contents to identify issues related to manufacturing processes, efficiency, and safety, and automatically generates proposal materials and sends them to the user's terminal.This system saves the user (factory personnel) the trouble of creating documents and enables them to quickly take efficient countermeasures to the identified issues.

[1500] 2. Hardware and Software

[1501] The system uses the following hardware and software:

[1502] Server: Collects data, analyzes it, and generates proposal materials.

[1503] Factory robot: Equipped with a display that displays the generated proposal materials.

[1504] Python: Used to implement the entire program.

[1505] Requests: Used to collect data on web pages.

[1506] BeautifulSoup: Used to extract text from HTML.

[1507] Transformers: Used to run natural language processing models (e.g., HuggingFace's Transformers library).

[1508] 3. Program Processing

[1509] 3.1 Collecting official documentation

[1510] The server automatically and periodically collects official documents (e.g., maintenance reports) from multiple official websites and public databases based on a pre-configured list and stores them in a database.

[1511] 3.2 Document Parsing

[1512] The server then analyzes the collected official documents using natural language processing techniques to extract key keywords and phrases, using HuggingFace's Transformers library, to identify issues related to manufacturing processes, efficiency, and safety.

[1513] 3.3 Automatic generation of proposal materials

[1514] Based on the analysis results, the server automatically generates proposal materials using templates. The proposal materials include solutions to the identified issues and their effects. For example, the proposals might include "application of a new process control system" or "regular maintenance and the introduction of high-performance sensors."

[1515] 3.4 Submitting and Displaying Proposals

[1516] The server sends the generated proposal materials to the user terminal (factory robot), which displays the received proposal materials on its display, allowing the user (factory staff) to check them and take appropriate measures as necessary.

[1517] 4. Specific Examples

[1518] For example, the server collects the latest maintenance reports from the factory's official website and uses analysis technology to identify mentions of "declining efficiency" or "safety issues." The server then generates a proposal document containing solutions such as "proposing a new process control system to address the efficiency decline" or "introducing high-performance sensors to address safety issues." The generated proposal document is then displayed on the factory robot's display for the user to review.

[1519] An example of a prompt sentence to be input into a generative AI model is, "Please extract issues and solutions from the factory report below and generate a proposal document."

[1520] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1521] Step 1:

[1522] Collecting official documentation

[1523] Input: A list of official website URLs pre-configured by the server.

[1524] How it works: The server periodically collects official documents (e.g., maintenance reports and financial reports) from multiple official websites based on a configured list of URLs. This process involves issuing HTTP requests using the Requests library to retrieve the contents of the web pages.

[1525] Data processing: The acquired HTML data is analyzed using the BeautifulSoup library and text data is extracted.

[1526] Output: A text data list of the official documentation.

[1527] Step 2:

[1528] Analysis of the official documentation

[1529] Input: A text data list of official documents collected in Step 1.

[1530] How it works: The server analyzes the collected official documents using natural language processing techniques, specifically using the Transformers library to extract keywords and important phrases from the text data.

[1531] Data Computing: Natural language processing models analyze text and identify issues (e.g., inefficiencies, safety issues, etc.) contained in each document.

[1532] Output: A list of identified issues.

[1533] Step 3:

[1534] Automatic generation of proposal materials

[1535] Input: The list of issues identified in step 2.

[1536] How it works: The server uses templates to automatically generate proposals containing solutions to identified issues, such as proposing a new process control system to address "decreased efficiency" or introducing high-performance sensors to address "safety issues."

[1537] Data processing: Combine the issue list and solution template to generate concrete proposal materials.

[1538] Output: The generated proposal (e.g. PDF, HTML format).

[1539] Step 4:

[1540] Submitting and viewing proposals

[1541] Input: The proposal generated in Step 3.

[1542] Operation: The server sends the generated proposal materials to the user terminal (factory robot). The factory robot displays the received proposal materials on its built-in display.

[1543] Data operations: file transfer and display.

[1544] Output: Proposal materials displayed on the user's terminal.

[1545] Step 5:

[1546] User verification and action taken

[1547] Input: Proposal materials displayed on the user's terminal.

[1548] Operation: The user (factory staff) checks the proposal documents displayed on the factory robot's display and takes appropriate measures as necessary. Based on the proposal documents, the user adjusts the manufacturing process and implements safety measures.

[1549] Output: Efficient manufacturing processes and improved safety.

[1550] The above are the specific processing steps of this system. By performing detailed operations for each step, it is possible to improve the efficiency and ensure safety of the manufacturing process.

[1551] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1552] This invention provides a system that automatically generates necessary proposal materials so that salespeople can concentrate on negotiating with customers, and furthermore, recognizes the user's emotions and adjusts the proposal content accordingly. Below, we will explain how this system is implemented by the server, terminal, and user.

[1553] 1. System Overview

[1554] This system collects official documents, analyzes their contents to identify management issues, and automatically generates proposal materials and sends them to the user's device. Furthermore, by incorporating an emotion engine that recognizes the user's (sales representative's) emotions, the system has the ability to adjust proposal materials according to the user's emotional state. This saves users the trouble of creating documents, allowing them to focus on negotiating with customers and closing deals.

[1555] 2. Program Processing

[1556] 2.1 Collecting official documentation

[1557] The server retrieves a pre-configured list of companies, which includes URLs of the target companies' official websites or public databases.

[1558] Based on the company list, the server automatically downloads the latest official documents (e.g., investor relations materials and financial reports) from each company's official website or public database.

[1559] The server stores the downloaded official documents in a database, along with metadata such as the company name, document name, and download date and time.

[1560] 2.2 Analysis of the official documentation

[1561] The server initializes natural language processing (NLP) algorithms, which include text analysis and keyword extraction.

[1562] The server retrieves official documents from the database and passes them to an NLP algorithm, which analyzes the documents and extracts key keywords and phrases, identifying business challenges for the company (e.g., "declining revenue" or "need to enter new markets").

[1563] 2.3 Automatic generation of proposal materials

[1564] Based on the analysis results, the server automatically generates proposal materials using templates. These proposal materials include solutions to the identified business issues and the selling points of the services and products to be offered. For example, they could be "Proposing a new marketing strategy to address declining revenues" or "Introducing services to support market entry."

[1565] 2.4 Submitting proposal materials and operation of the emotion engine

[1566] The server transmits the generated proposal materials to the user's terminal using a secure communication method.

[1567] The terminal displays the received proposal materials to the user and activates the emotion engine, which analyzes the user's facial expressions and tone of voice while reviewing the proposal materials to determine the user's emotional state.

[1568] 2.5 Adjustment of proposal materials

[1569] The device's emotion engine analyzes the user's emotions and automatically adjusts the content of the proposal based on the results. For example, if the user looks anxious, it will add additional reassuring information to the proposal.

[1570] 2.6 Preparation and implementation of business negotiations

[1571] The user can review the proposal materials adjusted by the emotion engine and make further edits or additions as necessary.

[1572] Users can prepare and efficiently conduct business negotiations with customers based on the proposal materials. At this point, the materials reflect the user's emotions and are optimized, improving the success rate of the negotiations.

[1573] 3. Specific Examples

[1574] 3.1 Analysis of Company A's IR materials and generation of proposal materials

[1575] The server collects the latest IR documents from the official website of Company A. The documents contain information about a "recent decline in revenue."

[1576] A natural language processing algorithm on the server identifies the keyword "revenue decline" and records it as a management issue.

[1577] The server automatically incorporates specific approaches to solving declining revenue (for example, cost reduction programs or methods for developing new markets) into the proposal materials.

[1578] 3.2 Data transmission and user emotion recognition

[1579] The server transmits the generated proposal materials to the user's terminal.

[1580] The terminal displays the document to the user, and the emotion engine recognizes the user's emotions. The emotion engine may detect that the user has an anxious expression while checking the document.

[1581] 3.3 Adjusting materials and preparing for business meetings

[1582] The device's emotion engine detects the user's anxiety and automatically inserts additional supporting information into the proposal materials to increase the user's sense of security.

[1583] The user prepares and conducts business negotiations with the client (Company A) based on the adjusted proposal materials. By using materials optimized by the emotion engine, the negotiations proceed more effectively and the probability of successfully closing the contract increases.

[1584] In this way, the system automates a series of processes, from collecting official documents to analyzing them, generating proposal materials, and adjusting them using an emotion engine, allowing sales representatives to devote more time to customer service and increasing the success rate of sales negotiations.

[1585] The processing flow will be explained below.

[1586] Step 1:

[1587] The server retrieves a pre-configured list of companies, which includes URLs of the target companies' official websites or public databases.

[1588] Step 2:

[1589] Based on the company list, the server automatically downloads the latest official documents (e.g., investor relations materials and financial reports) from each company's official website or public database.

[1590] Step 3:

[1591] The server stores the downloaded official documents in a database, along with metadata such as the company name, document name, and download date and time.

[1592] Step 4:

[1593] The server initializes natural language processing (NLP) algorithms, which include text analysis and keyword extraction.

[1594] Step 5:

[1595] The server retrieves official documents from the database and passes them to an NLP algorithm, which analyzes the documents and extracts key keywords and phrases, identifying business challenges for the company (e.g., "declining revenue" or "need to enter new markets").

[1596] Step 6:

[1597] The server identifies business challenges based on keywords extracted by the NLP algorithm, such as specific problems like "declining revenue" or "barriers to market entry."

[1598] Step 7:

[1599] The server selects a proposal template corresponding to the identified business problem, the template containing a proposal for solving the particular problem.

[1600] Step 8:

[1601] The server then embeds specific proposal content based on the analysis results into the proposal template and automatically generates proposal materials, which include solutions and selling points for the company's services.

[1602] Step 9:

[1603] The server transmits the generated proposal materials to the user's terminal using a secure communication method.

[1604] Step 10:

[1605] The device displays the received proposal materials to the user and activates the emotion engine, which analyzes the user's facial expressions and tone of voice while reviewing the proposal materials to determine the user's emotional state.

[1606] Step 11:

[1607] The device's emotion engine analyzes the user's emotions and automatically adjusts the content of the proposal based on the results. For example, if the user looks anxious, it will add additional reassuring information to the proposal.

[1608] Step 12:

[1609] The user can review the proposal materials adjusted by the emotion engine and make further edits or additions as necessary.

[1610] Step 13:

[1611] Users can use the proposal documents to prepare and efficiently conduct business negotiations with customers. At this point, the documents reflect the user's emotions and are optimized, improving the success rate of the negotiations.

[1612] Step 14:

[1613] The user starts negotiations with the customer and proposes specific solutions to the problem using proposal materials. After the negotiations, the user focuses on closing the deal and finalizing the contract.

[1614] Example 2

[1615] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1616] In order to allow sales representatives to concentrate on business negotiations with customers, it is necessary to improve the efficiency of the creation of proposal materials and the accuracy of proposals. Another challenge is to improve the success rate of sales negotiations by enabling the adjustment of proposal materials according to the user's emotional state.

[1617] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1618] In this invention, the server includes means for collecting company information, means for analyzing the collected company information using natural language processing technology and identifying management issues, means for automatically generating proposal materials based on the analysis results, means for transmitting the generated proposal materials to a user terminal, and means for recognizing the user's emotions and adjusting the proposal materials. This automates the creation of proposal materials and optimizes the materials according to the user's emotions, thereby reducing the burden on the user during business negotiations and improving the success rate.

[1619] "Corporate information" refers to official documents such as financial reports and investor relations materials collected from a company's official website or public databases.

[1620] "Natural language processing technology" refers to technology that enables computers to understand and analyze human language, and in particular technology that extracts keywords and analyzes text.

[1621] "Business challenges" are operational problems or obstacles facing a company, such as declining revenue or the need to enter new markets.

[1622] A "proposal document" is a document generated based on the analysis results, which describes solutions to identified business issues and the selling points of the services and products to be offered.

[1623] A "user terminal" is a device that receives proposal materials sent from the server and allows the user to check and operate them, and includes a PC, tablet, etc.

[1624] "Means for recognizing emotions" refers to technology that determines a user's emotional state by analyzing their facial expressions, tone of voice, etc., and includes, for example, facial recognition technology and voice analysis technology.

[1625] "Data storage" refers to a storage device or database for storing collected company information and generated proposal materials.

[1626] This invention provides a system that automatically generates necessary proposal materials so that salespeople can concentrate on negotiating with customers, and furthermore, recognizes the user's emotions and adjusts the proposal content accordingly. Below, we will explain how this system is implemented by the server, terminal, and user.

[1627] System Overview

[1628] This system collects corporate information, analyzes the content to identify management issues, and automatically generates proposal materials and sends them to the user's device. Furthermore, by incorporating an emotion engine that recognizes the user's (salesperson's) emotions, the system has the ability to adjust proposal materials according to the user's emotional state. This saves users the trouble of creating materials, allowing them to focus on negotiating and closing deals with customers.

[1629] Hardware and software used

[1630] This system uses the following hardware and software:

[1631] Server: Collects and analyzes data, generates and sends proposal materials.

[1632] Terminal: Displays proposal materials and performs emotion recognition using an emotion engine.

[1633] Natural language processing algorithm: Uses Google TensorFlow.

[1634] Proposal generation tool: Uses the Microsoft Word API.

[1635] Sentiment Engine: Uses Microsoft Azure Cognitive Services.

[1636] Specific processing

[1637] 1. Collection of company information

[1638] The server retrieves a pre-defined list of companies, which includes the URLs of the target companies' official websites and public databases. Based on the list, the server automatically downloads the latest official documents from each company's official website or public database. For example, it retrieves PDF files from websites using HTTP requests. The downloaded official documents are stored in data storage, along with metadata such as the company name, document name, and download date and time.

[1639] Specific working example:

[1640] The server collects the latest IR documents from the official website of Company A. The documents contain information about a "recent decline in revenue."

[1641] 2. Analysis of company information

[1642] The server initializes TensorFlow, retrieves the official document from data storage, and passes it to the NLP algorithm, which analyzes the document and extracts important keywords and phrases. For example, the keyword "revenue decline" is extracted.

[1643] Specific working example:

[1644] TensorFlow identifies the keyword "revenue decline" from Company A's IR materials.

[1645] 3. Automatic generation of proposal materials

[1646] Based on the analysis results, the server uses Microsoft Word API to automatically generate a proposal document using a template, which includes solutions to the identified business issues and the selling points of the services and products to be offered.

[1647] Specific working example:

[1648] The server automatically incorporates specific approaches (for example, cost reduction programs and methods for developing new markets) for solving Company A's declining profits into the proposal materials.

[1649] Example prompt sentence:

[1650] "Generate proposal materials to address revenue declines based on Company A's IR materials"

[1651] 4. Sending proposal materials and running the emotion engine

[1652] The server sends the generated proposal materials to the user's device. SSL / TLS is used as a secure communication method for transmission. The device displays the received proposal materials to the user and uses Azure Cognitive Services to start the emotion engine. The emotion engine analyzes facial expressions and tone of voice while the user is reviewing the proposal materials to determine the user's emotional state.

[1653] Specific working example:

[1654] The server sends the generated proposal materials to the user's device, where Azure Cognitive Services, the device's emotion engine, recognizes the user's emotions.

[1655] 5. Adjusting the proposal materials

[1656] The device's emotion engine analyzes the user's emotions and automatically adjusts the content of the proposal based on the results. For example, if the user looks anxious, it will add additional information to the proposal to make them feel more reassured.

[1657] Specific working example:

[1658] The device's emotion engine detects the user's anxiety and automatically inserts supplementary information into the proposal materials to provide reassurance.

[1659] 6. Preparation and implementation of business negotiations

[1660] The user can review the proposal materials adjusted by the emotion engine and make further edits or additions as necessary. Based on the proposal materials, the user can prepare and efficiently proceed with business negotiations with customers. Because the materials are optimized to reflect the user's emotions, the success rate of business negotiations increases.

[1661] Specific working example:

[1662] Based on the adjusted proposal materials, the user prepares and conducts a business negotiation with Company A. By using materials optimized by the emotion engine, the business negotiation can be conducted effectively, increasing the probability of successfully closing the contract.

[1663] In this way, by automating a series of processes, from collecting corporate information to analyzing it, generating proposal materials, and adjusting them using an emotion engine, the system allows sales representatives to devote more time to customer service and increases the success rate of sales negotiations.

[1664] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1665] Step 1: Gather company information

[1666] The server takes as input a list of companies, which includes URLs of their official websites and public databases.

[1667] Input: Company list (including URL)

[1668] The server automatically downloads the latest official documents from each company's official website or public database based on the company list using HTTP requests.

[1669] Specific operation: Download IR materials (example_IR.pdf) from Company A's official website (https: / / example.com)

[1670] Output: Downloaded official documentation

[1671] Step 2: Save your company information

[1672] The server saves the downloaded official documents in data storage, including metadata such as the company name, document name, and download date and time.

[1673] Input: Downloaded official documentation

[1674] Data processing: Save the document in the data storage along with the company name, document name, and download date and time.

[1675] Specific operation: Save a document with metadata such as Company A, example_IR.pdf, 2023-10-01

[1676] Output: Official documents stored in data storage

[1677] Step 3: Analyze company information

[1678] The server retrieves official documents from data storage and passes them to a natural language processing (NLP) algorithm, which uses TensorFlow for analysis.

[1679] Input: Official documents stored in data storage

[1680] The server uses NLP algorithms to analyze the document and extract important keywords and phrases.

[1681] Specific behavior: TensorFlow extracts "Diminishing Revenue" from example_IR.pdf

[1682] Output: Extracted keywords and phrases

[1683] Step 4: Automatically generate proposal materials

[1684] Based on the analysis results, the server automatically generates proposal materials using templates using the Microsoft Word API.

[1685] Input: Extracted keywords or phrases (e.g., "revenue decline")

[1686] Data processing: Describe solutions to the management issues identified in the proposal materials and the selling points of the services and products to be offered.

[1687] Specific action: Include a marketing strategy for "revenue decline" in the proposal materials

[1688] Output: Auto-generated proposal

[1689] Step 5: Submit your proposal

[1690] The server sends the generated proposal materials to the user's device, securely using SSL / TLS.

[1691] Input: Auto-generated proposal

[1692] Data calculation: Proposal materials are sent to the user terminal.

[1693] Specific operation: Proposal materials are sent to the user's device using SSL / TLS

[1694] Output: Proposal materials sent to the user's device

[1695] Step 6: Activating the Emotion Engine

[1696] The device displays the received proposal to the user and activates the emotion engine, analyzing the user's facial expressions and tone of voice using Azure Cognitive Services.

[1697] Input: Proposal document being reviewed by the user

[1698] Data calculation: The emotion engine analyzes the user's facial expressions and tone of voice to determine their emotional state.

[1699] Specific operation: Launch Azure Cognitive Services to read the user's facial expression

[1700] Output: Parsed user's emotional state

[1701] Step 7: Adjust your proposal

[1702] The device's emotion engine analyzes the user's emotions and automatically adjusts the content of the proposal materials based on the results.

[1703] Input: Parsed emotional state of the user

[1704] Data processing: If the user looks anxious, add supplementary information to the proposal materials to make them feel more reassured.

[1705] Specific behavior: Automatically insert supplemental information into proposal materials

[1706] Output: Tailored proposal

[1707] Step 8: Prepare and conduct the deal

[1708] The user can then review the proposal documents adjusted by the emotion engine, and make further edits or additions as necessary. Based on the proposal documents, the user can prepare and efficiently conduct business negotiations with customers.

[1709] Input: Adjusted proposal

[1710] Data Computing: Prepare for customer meetings with tailored proposals.

[1711] Specific action: Add or correct the proposal document

[1712] Output: Deal preparation and execution completed

[1713] Through these specific processing steps, the system enables salespeople to efficiently create proposal materials and increase the success rate of business negotiations with customers.

[1714] (Application example 2)

[1715] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1716] In today's virtual stores, customers often lack the proper information to select products, which can lead to a loss of motivation to purchase. Furthermore, it is difficult to understand customer emotions and provide optimal product recommendations in real time, ultimately hindering the improvement of the customer experience. There is a need for a method to solve these problems and provide customers with a more personalized shopping experience.

[1717] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1718] In this invention, the server includes means for collecting official data, means for analyzing the collected official data using natural language processing technology to identify management issues, and means for automatically generating proposal materials based on the analysis results, thereby enabling means for transmitting the generated proposal materials to an end-user terminal, activating an emotion engine for analyzing the end-user's emotional state, and means for adjusting the content of the proposal materials in real time based on the analysis results of the emotion engine.

[1719] "Official data" refers to written information such as reports, financial documents, and news releases that are officially made public by companies and organizations.

[1720] "Natural language processing technology" is a technology that allows computers to understand, analyze, and generate human language, and is used to analyze text data and extract keywords.

[1721] "Management issues" refer to problems and challenges that companies and organizations face that need to be solved, such as declining performance, entering new markets, and cost reduction.

[1722] A "proposal document" is a document automatically generated based on collected and analyzed data, which includes solutions and selling points.

[1723] "End-user terminal" refers to the device ultimately used by the user, including smartphones, tablets, and PCs.

[1724] An "emotion engine" refers to software or functionality that analyzes a user's facial expressions and tone of voice to determine their emotional state.

[1725] "Real-time adjustment" refers to the process of instantly analyzing the user's emotional state and instantly changing or modifying the content of the proposal materials based on the results.

[1726] "Means of collection" refers to methods or devices for automatically obtaining and storing the official data in question.

[1727] "Analysis means" refers to a set of processing techniques and algorithms used to process the data obtained and identify important information and issues.

[1728] This invention provides a system that automatically generates necessary proposal materials so that sales representatives can concentrate on negotiating with customers, and furthermore, adjusts the proposal content by recognizing the user's emotions. This system collects official data, analyzes its contents to identify management issues, automatically generates proposal materials, and sends them to the end user's terminal. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it has the function of adjusting the proposal materials according to the user's emotional state. Below, we will explain how this system is implemented by the server, terminal, and user.

[1729] 1. System Overview

[1730] This system collects "official data" such as reports and financial documents officially released by companies and organizations, and analyzes them using "natural language processing technology." It identifies "management issues" from the analysis results and automatically generates "proposal materials" based on those. The proposal materials are sent to the "end-user device," which then activates an emotion engine to analyze the user's emotions and adjusts the content of the proposal materials in real time based on the results.

[1731] 2. Program Processing

[1732] 1. Collection Method:

[1733] The server retrieves the URLs of official data from a pre-defined list of companies, then automatically downloads the latest official data from official websites and public databases, and stores the collected data in a database along with metadata such as company name, document name, and download date and time.

[1734] 2. Analysis method:

[1735] The server initializes a natural language processing engine (e.g., Google Cloud Natural Language API). By retrieving official data from the database and passing it to the analysis engine, important keywords and phrases are extracted and the company's management issues are identified.

[1736] 3. Automatic generation of proposal materials:

[1737] Based on the analysis results, a proposal document is automatically generated using a template, which includes solutions to the identified business issues and the selling points of the services and products to be offered.

[1738] 4. Submit your proposal:

[1739] The generated proposal material is transmitted to the end user terminal via a secure communication means.

[1740] 5. Emotion Engine in Action:

[1741] The end user's device displays the proposal documents and activates an emotion engine (e.g., Microsoft Azure Emotion API). The emotion engine analyzes the user's facial expressions and tone of voice while reviewing the documents to determine the user's emotional state.

[1742] 6. Adjustment of proposal materials:

[1743] The emotion engine on the end-user device analyzes the user's emotions and automatically adjusts the content of the proposal in real time based on the results. For example, if the user looks anxious, it will add supplemental information to the proposal to make them feel reassured.

[1744] 3. Specific Examples

[1745] Product "Smartwatch" proposal

[1746] 1. Collection of official data:

[1747] When a customer views a "smartwatch," product information is collected and recorded in a database.

[1748] 2. Automatic generation of proposal materials:

[1749] The server automatically generates a proposal document based on the collected information, including information such as tracking function, battery life, and waterproof performance.

[1750] 3. Emotion recognition:

[1751] The end-user device activates an emotion engine that analyzes the customer's facial expressions and voice as they review the materials. If the customer looks surprised, the system emphasizes "special discounts" and "limited-time offers." Conversely, if the customer looks anxious, the system adds more detailed information about the return policy and product warranty.

[1752] Prompt Sentence Examples

[1753] "Generate a proposal for a smartwatch."

[1754] Features: Tracking, battery life, waterproof

[1755] Additional Information: User Reviews, Special Offers, Return Policy

[1756] Customer facial expression and voice data: Surprise, anxiety

[1757] Proposal adjustments: surprise (emphasis on special discounts), anxiety (details of return policy)

[1758] In this way, the system automates a series of processes, from collecting official data to analyzing it, generating proposal materials, and adjusting them using an emotion engine, allowing end users to enjoy a personalized purchasing experience.

[1759] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1760] Step 1:

[1761] The server retrieves official data URLs from a pre-defined list of companies. This list includes URLs of the target companies' official websites and public databases. The input is the company list, and the output is the retrieved URLs. Based on this, the server automatically downloads official data (reports, financial documents, etc.) from the official websites and databases and stores them in a database.

[1762] Step 2:

[1763] The server retrieves the stored official data and initializes a natural language processing engine (e.g., Google Cloud Natural Language API). The official data is input, and analysis results (important keywords and phrases) are obtained as output. The server uses this analysis engine to extract important keywords and phrases from the official data and identify the company's management issues.

[1764] Step 3:

[1765] The server uses templates based on the analysis results of natural language processing to automatically generate proposal materials. The analysis results are input, and proposal materials are obtained as output. These proposal materials include solutions to identified business issues and the selling points of the products and services offered. For example, they can be embodied as "proposing a new marketing strategy" in response to "declining revenue."

[1766] Step 4:

[1767] The server transmits the generated proposal materials to the end user terminal via a secure communication means. The proposal materials are input, and the proposal materials transmitted to the end user terminal are obtained as output.

[1768] Step 5:

[1769] The device displays the received proposal documents and launches an emotion engine (e.g., Microsoft Azure Emotion API). The proposal documents are input, and the end user's emotional data is obtained as output. The emotion engine analyzes the end user's facial expressions and tone of voice when reviewing the proposal documents to identify their emotional state.

[1770] Step 6:

[1771] The device adjusts the content of the proposal materials in real time based on the results of analysis by the emotion engine. The input is emotion data and the proposal materials, and the output is the adjusted proposal materials. For example, if the user shows an anxious expression, additional information that gives a sense of security is inserted into the proposal materials.

[1772] Step 7:

[1773] The user checks the adjusted proposal document and makes further edits or additions as necessary. The adjusted proposal document is the input, and the final proposal document is the output. The user uses this as a basis to prepare and proceed with business negotiations with the customer.

[1774] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1775] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1776] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1777] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1778] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1779] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1780] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1781] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1782] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1783] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1784] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1785] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1786] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1787] 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.

[1788] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1789] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1790] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1791] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1792] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1793] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1794] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1795] The following is further disclosed regarding the above embodiment.

[1796] (Claim 1)

[1797] A means of collecting official documentation;

[1798] A method for analyzing collected official documents using natural language processing technology and identifying management issues;

[1799] A means for automatically generating proposal materials based on the analysis results;

[1800] means for transmitting the generated proposal material to a user terminal;

[1801] A system including:

[1802] (Claim 2)

[1803] a database means for storing official documents;

[1804] A means for retrieving official documents from the database and providing them to the analysis means;

[1805] The system of claim 1 further comprising:

[1806] (Claim 3)

[1807] 10. The system of claim 1, further comprising means for enabling the user terminal to display and edit the proposal material.

[1808] "Example 1"

[1809] (Claim 1)

[1810] means of collecting official documents;

[1811] A method for analyzing collected official documents using natural language processing technology and identifying business issues;

[1812] A means for automatically generating proposal materials based on the analysis results;

[1813] means for transmitting the generated proposal material to a terminal;

[1814] a database means for storing the collected official documents;

[1815] A system including:

[1816] (Claim 2)

[1817] A method for analyzing collected official documents using natural language processing technology and extracting keywords and phrases;

[1818] 2. The system according to claim 1, further comprising means for automatically generating a solution corresponding to the business problem identified by said means based on a template.

[1819] (Claim 3)

[1820] 2. The system of claim 1, wherein the terminal includes means for displaying and enabling editing of the proposal material.

[1821] "Application Example 1"

[1822] (Claim 1)

[1823] A means of collecting official documentation;

[1824] A method for analyzing collected official documents using natural language processing technology and identifying issues;

[1825] A means for automatically generating proposal materials based on the analysis results;

[1826] means for transmitting the generated proposal material to a user terminal;

[1827] A means to collect factory maintenance and official reports and identify issues with manufacturing processes, efficiency, and safety;

[1828] A means of incorporating specific solutions to the identified issues into the proposal materials;

[1829] A means for displaying the proposal materials on a display installed in the factory robot;

[1830] A system including:

[1831] (Claim 2)

[1832] a database means for storing official documents;

[1833] A means for retrieving official documents from the database and providing them to the analysis means;

[1834] The system of claim 1 further comprising:

[1835] (Claim 3)

[1836] 10. The system of claim 1, further comprising means for enabling the user terminal to display and edit the proposal material.

[1837] "Example 2: Combining Emotion Engines"

[1838] (Claim 1)

[1839] A means of collecting company information;

[1840] A means of analyzing collected corporate information using natural language processing technology and identifying management issues;

[1841] A means for automatically generating proposal materials based on the analysis results;

[1842] means for transmitting the generated proposal material to a user terminal;

[1843] A means for recognizing user emotions and adjusting proposal materials;

[1844] A system including:

[1845] (Claim 2)

[1846] a data storage means for storing business information;

[1847] A means for retrieving company information from the data storage and providing it to an analytical means;

[1848] The system of claim 1 further comprising:

[1849] (Claim 3)

[1850] 10. The system of claim 1, further comprising means for enabling the user terminal to display and edit the proposal material.

[1851] "Application example 2 when combining emotion engines"

[1852] (Claim 1)

[1853] means of collecting official data;

[1854] A means of analyzing collected official data using natural language processing technology and identifying management issues;

[1855] A means for automatically generating proposal materials based on the analysis results;

[1856] means for transmitting the generated proposal material to an end user terminal;

[1857] means for invoking an emotion engine for analyzing an end-user's emotional state;

[1858] A means of adjusting the content of proposal materials in real time based on the analysis results of the emotion engine

[1859] A system including:

[1860] (Claim 2)

[1861] database means for storing official data;

[1862] a means for retrieving official data from the database and providing it to the analytical means;

[1863] 10. The system of claim 1.

[1864] (Claim 3)

[1865] a means for the end user terminal to recognize the end user's emotions in real time using an emotion engine and adjust the proposal materials;

[1866] The end user terminal further includes means for displaying and editing the proposal material.

[1867] 10. The system of claim 1. [Explanation of symbols]

[1868] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of collecting official documentation; A method for analyzing collected official documents using natural language processing technology and identifying management issues; A means for automatically generating proposal materials based on the analysis results; means for transmitting the generated proposal material to a user terminal; A system including:

2. a database means for storing official documents; A means for retrieving official documents from the database and providing them to the analysis means; The system of claim 1 further comprising:

3. 2. The system of claim 1, further comprising means for enabling the user terminal to display and edit the proposal material.

Images