system

Abstract

We provide the system. [Solution] A means for inputting basic information of the target customer, Means for collecting and analyzing the latest industry and company data from external data sources, A method for identifying the challenges and needs of target companies from data collected using generative artificial intelligence, A means of automatically generating optimal proposals based on identified issues and needs, and formatting them as proposal documents, Means for users to review and edit proposals, A means of providing a proposal for use during business negotiations, A system that includes this.

Description

Technical Field

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

Background Art

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

Summary of the Invention

Problems to be Solved by the Invention

[0006] "Basic information about the target customer" refers to fundamental information about the target company or customer, such as their name, industry, and location.

[0007] "External data sources" refer to reliable information providers on the internet, APIs, databases, etc., and serve as sources of information for obtaining the latest data on industries and companies.

[0008] "Generative artificial intelligence" is a type of artificial intelligence that uses collected data, machine learning, and natural language generation technologies to identify the challenges and needs of target companies and automatically generate proposals based on them.

[0009] "Challenges and needs" refer to the problems the target company is facing, the areas that need improvement, or the business requirements and expectations.

[0010] A "proposal" refers to a formatted document that outlines solutions and strategies for a target company. This typically includes information such as statistics, trend data, and competitive analysis results.

[0011] A "template" provides a basic framework and format for generating proposals, serving as a guideline for maintaining a consistent format and design.

[0012] A "terminal" refers to a device used by a user to input information or to review and edit generated proposals, and includes computers, tablets, and smartphones.

[0013] A "user" refers to an individual or legal entity that operates this system and creates proposals for target customers. [Brief explanation of the drawing]

[0014] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11]It is a sequence diagram showing the processing flow of the data processing system in Embodiment 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined.

Mode for Carrying Out the Invention

[0015] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0016] First, the terms used in the following description will be explained.

[0017] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Also, the 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), an APU (Accelerated Processing Unit), and the like.

[0018] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0019] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0020] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0021] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0022] [First Embodiment]

[0023] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0024] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0025] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0026] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0027] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0028] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0029] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0030] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0032] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0033] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0034] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0035] This invention is a system for generating optimal proposals for target customers and using them to provide topics of discussion during business negotiations. The system's program processing will be explained below with specific examples.

[0036] 1. Inputting target customer information and collecting data

[0037] The user enters basic information about their target customer (e.g., company name, industry, location). Based on the entered information, the device sends a data collection request to the server. The server collects data about the relevant company and industry from external data sources. In this process, it uses reliable APIs and internet sources to obtain relevant news articles, financial data, and market trend data.

[0038] 2. Data analysis and identification of challenges and needs

[0039] The server uses generative artificial intelligence to analyze the collected data. The analysis extracts potential challenges and needs of the target company. For example, if a company's sales are declining, possible causes include increased competition and loss of market share. These challenges and needs are listed according to their importance and then prioritized.

[0040] 3. Automated generation and formatting of proposals

[0041] The server utilizes natural language generation (NLG) technology to generate optimal proposals based on identified challenges and needs. The generated proposals are formatted according to defined templates and include, for example, statistical information, trend data, and competitive analysis results. The resulting proposals become the final version ready for use during business negotiations.

[0042] 4. Review and edit the proposal.

[0043] The terminal displays a draft of the generated proposal to the user. The user can review the proposal and edit it as needed, adding data and graphs. Finally, the user can save the proposal as an electronic file, such as a PDF, and print it.

[0044] 5. Topics to discuss during business negotiations

[0045] Users conduct business negotiations based on the generated proposals, presenting industry trends and solutions to specific challenges faced by companies. During negotiations, they can cite collected data and analysis results from generative artificial intelligence to provide specific and reliable explanations. For example, users can gain customer trust by presenting specific challenges and solutions based on the company's current situation and trends.

[0046] The key feature of this system is that it handles all processes in-house, from external data collection and analysis using generative artificial intelligence to the automatic generation of optimal proposals. This significantly improves the efficiency of proposal creation and makes it possible to provide concrete and reliable materials that will lead to successful business negotiations.

[0047] The following describes the processing flow.

[0048] Program processing flow

[0049] Step 1: Enter target customer information

[0050] The user enters basic information about the target customer (company name, industry, location, etc.) into the terminal. They then register the necessary information using the input form.

[0051] Step 2: Submit data collection request

[0052] The device sends a data collection request to the server based on the information entered by the user. The request includes the type of data to be collected and the source of the data.

[0053] Step 3: External Data Collection

[0054] The server accesses external data sources (APIs, reliable internet sources, etc.) to collect up-to-date data about the target company and industry. Specifically, it retrieves news articles, financial data, market trend data, and so on.

[0055] Step 4: Data Analysis

[0056] The server analyzes the collected data. Using text analysis algorithms, it extracts key keywords and topics from news articles and reports, organizes financial and market trend data, and generates statistical information.

[0057] Step 5: Identifying Issues and Needs

[0058] The server uses generative artificial intelligence to analyze data and identify potential challenges and needs of target companies. For example, it analyzes the causes of declining sales and new market opportunities. In this process, the AI ​​model performs pattern recognition and trend analysis.

[0059] Step 6: List the issues and needs

[0060] The server lists identified issues and needs, prioritizes them, assigns scores based on impact and urgency, and sorts them by priority.

[0061] Step 7: Automatic generation of optimal proposals

[0062] The server automatically generates optimal proposals for target companies based on the analysis results. Using natural language generation (NLG) technology, it generates specific solutions and strategies in proposal format.

[0063] Step 8: Formatting the proposal

[0064] The server formats the generated proposals according to a defined template. The proposals include statistical information, trend data, competitive analysis results, and also incorporate company logos and design elements.

[0065] Step 9: View and edit the proposal

[0066] The device displays a draft of the generated proposal to the user. The user reviews the proposal and adds data or graphs, or revises the text as needed.

[0067] Step 10: Saving and distributing the proposal

[0068] Users can save their final proposal as an electronic file, such as a PDF, and print it. This allows them to use it during business negotiations with clients.

[0069] Step 11: Topics to discuss during business negotiations

[0070] The sales negotiation is based on the proposal generated by the user. While referring to the proposal, the discussion will present industry trends and solutions to the company's specific challenges, and provide topics based on concrete and reliable data.

[0071] These steps enable the efficient generation of optimal proposals for target customers through a consistent process, thereby increasing the success rate of business negotiations.

[0072] (Example 1)

[0073] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0074] Traditional proposal creation processes were inefficient and time-consuming because each step—from data collection and analysis to proposal generation and its use in business negotiations—was performed individually. Furthermore, the reliability of the collected data and the accuracy of the analysis were often insufficient, significantly impacting the success of business negotiations. Therefore, there is a need for a system that consistently collects and analyzes highly reliable data and efficiently creates and delivers optimal proposals.

[0075] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0076] In this invention, the server includes means for collecting basic information on target companies and obtaining the latest industry and company data from external data sources; means for analyzing the collected data using generative artificial intelligence to identify the challenges and needs of target companies; and means for automatically generating proposals based on the identified challenges and needs and formatting them as proposal documents. This makes it possible to efficiently collect and analyze highly reliable data and create and provide optimal proposal documents.

[0077] A "target company" is a company that is the focus of a proposal.

[0078] "Basic information" refers to initial data about the target company, including information such as company name, industry, and location.

[0079] "External data sources" refer to external sources of information used to collect information about target companies and industries, such as APIs and websites on the internet.

[0080] "Generative artificial intelligence" is an artificial intelligence technology that analyzes collected data and generates text using natural language generation techniques.

[0081] A "proposal" is a document that outlines the proposed content for a target company and may include statistical information, trend data, and competitive analysis results.

[0082] "Formatting" refers to organizing and arranging the content of a generated proposal according to a defined template.

[0083] An "electronic file" is a digital file that can be read by a computer, such as a PDF file.

[0084] "Reliability" refers to the degree to which the collected data and analysis results are accurate and the proposed content is justified.

[0085] This invention is a system for generating optimal proposals for target companies and using them to provide topics of discussion during business negotiations. The system is implemented as follows.

[0086] 1. Inputting target customer information and collecting data

[0087] The user enters basic information about the target company (company name, industry, location, etc.) into the terminal. Specifically, the user enters information such as "Company Name: ABC Manufacturing Inc.", "Industry: Manufacturing", and "Location: Tokyo" into the terminal's input form.

[0088] The terminal sends the entered information to the server. A secure communication protocol (e.g., HTTPS) is used for transmission.

[0089] Based on the information received, the server collects up-to-date industry and company data from reliable external data sources (e.g., Bloomberg API, Google® News API). This data includes news articles, financial data, and market trend data.

[0090] 2. Data analysis and identification of challenges and needs

[0091] The server analyzes the collected data using generative artificial intelligence (e.g., OpenAI's GPT-4). Natural language processing (NLP) techniques are used for the analysis.

[0092] The analysis identifies potential challenges and needs of the target company. For example, if a company's sales are declining, possible causes include increased competition or loss of market share. These challenges and needs are then listed according to their importance.

[0093] 3. Automated generation and formatting of proposals

[0094] Based on identified challenges and needs, the server utilizes natural language generation (NLG) technology to generate optimal suggestions. Specifically, these suggestions include statistical information, trend data, and competitive analysis results.

[0095] The generated proposals are formatted according to a defined template. Sections of the template include "Market Trend Analysis," "Competitor Comparison," and "Specific Solution Proposals."

[0096] 4. Review and edit the proposal.

[0097] The terminal displays a draft of the generated proposal to the user. The proposal is displayed in PDF preview format.

[0098] Users review the proposal and add data and graphs as needed. Editing options include adding text, modifying data, and inserting graphs.

[0099] Ultimately, users save the proposal in PDF or other electronic file formats and print it if necessary.

[0100] 5. Topics to discuss during business negotiations

[0101] Users conduct business negotiations based on the generated proposals. During negotiations, they quote the contents of the proposals and present specific challenges and solutions based on the company's current situation and trends. For example, they might present "decreased sales due to increased competition" and "strategies for expanding into new markets" to gain the customer's trust.

[0102] The following prompt statements are used as specific examples in this system:

[0103] Based on the target company's information (Company name: ABC Manufacturing Inc., Industry: Manufacturing, Location: Tokyo), analyze the problems causing the company's declining sales and generate an appropriate proposal.

[0104] The key feature of this system is its integrated approach, encompassing everything from external data collection and analysis using generative artificial intelligence to the automatic generation of optimal proposals. This significantly improves the efficiency of proposal creation and enables the provision of concrete and reliable materials for successful business negotiations.

[0105] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0106] Step 1:

[0107] The user enters basic information about the target customer.

[0108] Specifically, the user enters information such as "Company Name: ABC Manufacturing Inc.", "Industry: Manufacturing", and "Location: Tokyo" into the input form on the device.

[0109] Input: Company name, industry, location

[0110] Output: Basic information of the entered target customer

[0111] Step 2:

[0112] The terminal sends the input information to the server.

[0113] The terminal uses a secure communication protocol (e.g., HTTPS) to send the entered information to the server.

[0114] Input: Basic information of the target customer entered.

[0115] Output: Customer information request sent to the server

[0116] Step 3:

[0117] The server collects up-to-date industry and company data from external data sources.

[0118] The server collects necessary information, such as news articles, financial data, and market trend data, using APIs like the Bloomberg API and the Google News API.

[0119] Input: Submitted customer information request

[0120] Output: Collected industry and company data

[0121] Step 4:

[0122] The server analyzes the data it has collected.

[0123] The server uses generative artificial intelligence (e.g., OpenAI's GPT-4) to analyze the collected data and identify the challenges and needs of the target company.

[0124] Input: Collected industry and company data

[0125] Output: List of issues and needs based on analysis results

[0126] Step 5:

[0127] The server automatically generates proposals based on identified issues and needs, and formats them into a proposal document.

[0128] The server utilizes natural language generation (NLG) technology to format proposals according to a template. The generated proposals include statistical information, trend data, and competitive analysis results.

[0129] Input: List of issues and needs

[0130] Output: Formatted proposal

[0131] Step 6:

[0132] The terminal displays the draft of the generated proposal.

[0133] The terminal displays the proposal, generated in PDF preview format, to the user.

[0134] Input: Formatted proposal

[0135] Output: Draft proposal displayed to the user

[0136] Step 7:

[0137] The user reviews the proposal and edits it as needed.

[0138] Users review the proposal and make edits such as adding text, changing data, and inserting graphs.

[0139] Input: Draft proposal displayed to the user

[0140] Output: Revised proposal

[0141] Step 8:

[0142] The user saves the proposal as its final form and prints it as needed.

[0143] Users can save the completed proposal as an electronic file, such as a PDF, and print it as needed.

[0144] Input: Revised proposal

[0145] Output: Saved proposal in PDF format, and printed proposal.

[0146] Step 9:

[0147] The user conducts business negotiations based on the proposal and provides specific topics for discussion.

[0148] Users quote the content of the generated proposal and, during business negotiations, present specific challenges and solutions based on the company's current situation and trends.

[0149] Input: Saved proposal in PDF format

[0150] Output: Providing specific topics for business negotiations and their results.

[0151] (Application Example 1)

[0152] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0153] In traditional advertising proposal creation, data collection, analysis, and proposal writing for target customers are often done manually, resulting in significant time and effort. Furthermore, manual data analysis and proposal generation are prone to human error and bias, making it difficult to identify the optimal advertising strategy. This leads to a lower success rate in advertising negotiations. Therefore, there is a need for a system that efficiently generates optimal advertising proposals for target companies and effectively advances negotiations.

[0154] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0155] In this invention, the server includes means for inputting basic information of target customers, means for collecting and analyzing the latest industry and company data from external data sources, means for identifying the challenges and needs of target companies from the collected data using generative artificial intelligence, means for automatically generating optimal proposals based on the identified challenges and needs and formatting them as advertising proposals, means for users to review and edit advertising proposals, and means for providing advertising proposals for use during advertising negotiations. This makes it possible to efficiently and accurately perform a series of processes from data collection and analysis of target companies to the automatic generation of optimal advertising proposals. Furthermore, since the generated proposals are formatted based on templates that include statistical information, trend data, and competitive analysis results, they can be used effectively during negotiations.

[0156] "Basic information about the target customer" includes information such as the customer's company name, industry, location, and past advertising campaign information.

[0157] "External data sources" refer to reliable data sources that provide information such as corporate financial data, industry trend data, and relevant news articles.

[0158] "Generative artificial intelligence" is an artificial intelligence technology that analyzes collected data to identify the challenges and needs of target companies and automatically generates proposals.

[0159] An "advertising proposal" is a document used to propose an advertising strategy for a target company, and is formatted using a template that includes statistical information, trend data, and competitive analysis results.

[0160] "Means for users to review and edit advertising proposals" refers to an interface that allows users to review generated advertising proposals and edit their content as needed.

[0161] "A means of providing advertising proposals for use during advertising negotiations" refers to a system function that provides a final advertising proposal so that it can be used effectively during negotiations.

[0162] "Statistical information" refers to data that quantifies the effectiveness of advertising campaigns and market research data.

[0163] "Trend data" refers to data on the latest industry trends and consumer behavior.

[0164] "Competitive analysis results" refer to the results of an analysis of the advertising strategies and market share of competing companies.

[0165] This invention is a system for automatically generating optimal advertising proposals for target customers specifically for the advertising industry. The system provides a consistent service from inputting target customer information to data collection, analysis, and generation, review, and editing of advertising proposals, using the following means. Specific hardware and software components include a server, smartphone, generative artificial intelligence model, and data collection API.

[0166] Entering target customer information

[0167] Users use a smartphone application to input basic information about their target customers (such as company name, industry, location, and past advertising campaign information). This basic information forms the basis for the system to send requests to external data sources.

[0168] Data collection

[0169] The server collects relevant data from external data sources based on the target customer's basic information. Specific data sources include corporate financial data, industry trend data, and relevant news articles. Reliable APIs are used for data collection.

[0170] Data Analysis

[0171] The collected data is analyzed on a server. This analysis uses generative artificial intelligence to extract potential challenges and needs of the target company from the collected information. For example, if the cause of declining sales is increased competition or loss of market share, these challenges and needs will be listed.

[0172] Generating an advertising proposal

[0173] The server automatically generates optimal advertising proposals using natural language generation (NLG) technology based on the analysis results. These proposals include statistical information, trend data, and competitive analysis results. The generated proposals are formatted according to a template and await user review.

[0174] Review and editing of advertising proposals

[0175] Users review the generated advertising proposal draft using a smartphone application and edit the content as needed. The edited advertising proposal is saved in PDF format or other formats and is ultimately used during business negotiations.

[0176] Specific example

[0177] For example, if the target company for the cosmetics industry is "XYZ Corporation," the user inputs basic information about "XYZ Corporation" and past social media campaign information. Based on this, the server collects and analyzes the latest industry trend data, financial data, and competitive analysis results. Finally, a proposal for an influencer marketing campaign utilizing social media is generated.

[0178] Example of a prompt

[0179] Target company: XYZ Corporation

[0180] Industry: Cosmetics

[0181] Past Campaigns: Successful Social Media Campaigns

[0182] Collected data:

[0183] 1. Industry Trends

[0184] 2. News articles

[0185] 3. Financial Data

[0186] The generated proposal:

[0187] 1. Overview of Industry Trends

[0188] 2. Potential challenges

[0189] 3. Proposal for the optimal advertising campaign

[0190] In this way, the entire proposal creation process can be streamlined, increasing the success rate of business negotiations.

[0191] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0192] Step 1:

[0193] The user enters basic information about the target customer into a smartphone application. This information includes the customer's company name, industry, location, and past advertising campaign information. This generates basic information data for the target company.

[0194] Input: Customer's company name, industry, location, and past advertising campaign information.

[0195] Output: Basic information data of the target company

[0196] Step 2:

[0197] The device sends a data collection request to the server based on the target customer's basic information data. The server uses a reliable data collection API to collect industry trend data, corporate financial data, news articles, etc., from external data sources. This is how external data is collected.

[0198] Input: Basic information data of the target company

[0199] Output: Collected external data

[0200] Step 3:

[0201] The server analyzes the collected external data using generative artificial intelligence (generative AI model). The generative AI model extracts and lists the target company's potential challenges and needs from the collected data. This generates challenge and needs data.

[0202] Input: Collected external data

[0203] Output: Issue and needs data

[0204] Step 4:

[0205] The server automatically generates optimal advertising proposals using natural language generation (NLG) technology based on problem and needs data. The generated advertising proposals are formatted according to a template, including statistical information, trend data, and competitor analysis results. This process generates the advertising proposal data.

[0206] Input: Issue and needs data

[0207] Output: Advertising proposal data

[0208] Step 5:

[0209] The user reviews the draft advertising proposal generated on their device and edits it as needed. The edited advertising proposal is saved on the device in PDF format or another suitable format. This completes the final advertising proposal.

[0210] Input: Advertising proposal data

[0211] Output: Final advertising proposal

[0212] Step 6:

[0213] Users conduct business negotiations using the completed advertising proposal. During negotiations, they can increase their chances of success by citing the statistical information, trend data, and competitor analysis results included in the advertising proposal to make specific and reliable proposals.

[0214] Input: Final advertising proposal

[0215] Output: Improved success rate of business negotiations

[0216] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0217] This invention is a system for generating optimal proposals for target customers and using them to provide topics of conversation during business negotiations. Furthermore, it incorporates an emotion engine that recognizes the user's emotions and adjusts the content of the proposal. The system's program processing will be explained below with specific examples.

[0218] 1. Inputting target customer information and collecting data

[0219] The user enters basic information about their target customer (e.g., company name, industry, location). Based on the entered information, the terminal sends a data collection request to the server. The server collects the latest data on the relevant company and industry from external data sources. In this process, it uses reliable APIs and internet sources to obtain relevant news articles, financial data, and market trend data.

[0220] 2. Data analysis and identification of challenges and needs

[0221] The server uses generative artificial intelligence to analyze the collected data. The analysis extracts potential challenges and needs of the target company. For example, if a company's sales are declining, possible causes include increased competition and loss of market share. These challenges and needs are listed according to their importance and then prioritized.

[0222] 3. Automated generation and formatting of proposals

[0223] The server utilizes natural language generation (NLG) technology to generate optimal proposals based on identified challenges and needs. The generated proposals are formatted according to defined templates and include, for example, statistical information, trend data, and competitive analysis results. The resulting proposals become the final version ready for use during business negotiations.

[0224] 4. Emotion Recognition and Regulation

[0225] The server uses an emotion engine to analyze the user's emotions from their voice, facial expressions, and text input. For example, if the user is feeling nervous, the emotion engine can adjust the tone of the proposal to be more calming. This ensures that the best proposal is provided based on the user's emotional state.

[0226] 5. Viewing and editing proposals

[0227] The terminal displays a draft of the generated proposal to the user. The user can review the proposal and edit it as needed, adding data and graphs as required. Finally, the user can save the proposal as an electronic file, such as a PDF, and print it.

[0228] 6. Topics to discuss during business negotiations

[0229] Users conduct business negotiations based on the generated proposals. While referring to the proposals, they are presented with industry trends and solutions to specific challenges faced by the company. Furthermore, based on the analysis results of the emotion engine, communication is tailored to the user's emotional state, and topics are provided based on reliable data.

[0230] As a concrete example, when a user is conducting a business negotiation with a target company, the proposal will include detailed information on the activities of competitors and market trends. Furthermore, if the user is feeling nervous, the emotional engine will adjust the tone of the proposal to soften it and change it to a format that is easier for the user to read. This will enable a more effective presentation during the business negotiation.

[0231] This system is characterized by its integrated approach, handling all processes from external data collection and analysis by generative artificial intelligence to the automatic generation of optimal proposals and the adjustment of proposal content using an emotion engine. This significantly improves the efficiency of proposal creation and enables the provision of concrete and reliable materials for successful business negotiations.

[0232] The following describes the processing flow.

[0233] Program processing flow

[0234] Step 1: Enter target customer information

[0235] The user enters basic information about the target customer (company name, industry, location, etc.) into the terminal. The user then registers the necessary information using a dedicated input form.

[0236] Step 2: Submit data collection request

[0237] The device sends a data collection request to the server based on the information entered by the user. The request includes the type of data to be collected and the source of the data.

[0238] Step 3: External Data Collection

[0239] The server accesses external data sources to collect the latest data on target companies and industries. These external data sources include news article APIs, financial data APIs, and market trend data APIs. Specifically, it retrieves relevant news articles, financial reports, market trend reports, and more.

[0240] Step 4: Data Analysis

[0241] The server analyzes the collected data. Using text analysis algorithms, it extracts key keywords and topics from news articles and reports, organizes financial and market trend data, and generates statistical information.

[0242] Step 5: Identifying Issues and Needs

[0243] The server uses generative artificial intelligence to analyze data and identify potential challenges and needs of target companies. For example, if a decline in sales is observed, possible causes include increased competition and a decrease in market share. The generative AI model performs pattern recognition and trend analysis to extract specific challenges and areas for improvement.

[0244] Step 6: List the issues and needs

[0245] The server lists identified issues and needs and prioritizes them. It assigns scores based on impact and urgency, and sorts them by priority. This ensures that solutions are considered starting with the most important issues.

[0246] Step 7: Automatic generation of optimal proposals

[0247] The server utilizes natural language generation (NLG) technology to generate optimal suggestions based on identified challenges and needs. These suggestions include solutions and recommendations, presenting specific strategies tailored to the company's specific situation.

[0248] Step 8: Formatting the proposal

[0249] The server formats the generated proposals according to a defined template. The proposals include statistical information, trend data, competitive analysis results, and also incorporate company logos and design elements.

[0250] Step 9: Emotion Recognition

[0251] The device collects emotions from the user's voice, facial expressions, and text input. This data is transmitted to the server in real time.

[0252] Step 10: Emotion Analysis

[0253] The server analyzes the collected emotional data through an emotion engine. For example, if the user is feeling nervous, the emotion engine recognizes this and adjusts the tone of the proposal accordingly.

[0254] Step 11: Adjusting the proposal based on emotions

[0255] The server adjusts the content and tone of the proposal based on the analysis results of the emotion engine. For example, if the user is relaxed, it uses a lighthearted tone, and if the user is stressed, it uses a calm tone.

[0256] Step 12: View and edit the proposal

[0257] The device displays a draft of the generated proposal to the user. The user can review the proposal and edit it as needed, adding data and graphs as required.

[0258] Step 13: Saving and distributing the proposal

[0259] Users can save their final proposal as an electronic file, such as a PDF, and print it. This allows them to use it during business negotiations with clients.

[0260] Step 14: Topics to discuss during business negotiations

[0261] The system conducts business negotiations based on proposals generated by the user. While referring to the proposals, it presents industry trends and solutions to the company's specific challenges. Furthermore, based on analysis results from the emotion engine, it communicates in a tone appropriate to the user's emotional state.

[0262] These steps enable the efficient generation of optimal proposals for target customers through a consistent process, allowing for communication that takes user emotions into consideration, and ultimately increasing the success rate of business negotiations.

[0263] (Example 2)

[0264] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0265] While conventional sales presentation systems could automatically generate proposals for target customers, their effectiveness was limited due to the uniformity of the proposal content and the lack of appropriate adjustments based on the user's emotional state. Furthermore, manual data collection and analysis were required, posing significant challenges in terms of time and effort. Additionally, improvements in the accuracy of data analysis and the reduction of the user's emotional burden during sales negotiations were insufficient.

[0266] The identification processing by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for inputting basic information of the target customer, means for collecting and analyzing the latest industry and company data from external data sources, means for identifying the challenges and needs of the target company from the collected data using generative artificial intelligence, means for automatically generating an optimal proposal based on the identified challenges and needs and formatting it as a proposal document, means for analyzing the user's emotions and adjusting the content and tone of the proposal document, means for the user to review and edit the proposal document, and means for providing the proposal document for use during business negotiations. This makes it possible to make optimal adjustments in response to the user's emotions during the automatic generation of the proposal document, maximizing the effectiveness of business negotiations while significantly reducing the time and effort required to create the proposal document.

[0267] "Means for inputting basic information of target customers" refers to the means by which users input basic information of their target customers, such as the company name, industry, and location, into a terminal.

[0268] "Means for collecting and analyzing up-to-date industry and company data from external data sources" refers to a method by which a server uses reliable APIs or internet information sources to obtain and analyze the latest data on the target industry and company.

[0269] "Methods for identifying the challenges and needs of target companies from data collected using generative artificial intelligence" refers to methods for analyzing collected data using generative artificial intelligence to extract the potential challenges and needs of target companies.

[0270] "A means of automatically generating optimal proposals based on identified issues and needs and formatting them as proposal documents" refers to a method of automatically generating optimal proposals using natural language generation technology based on analysis results and formatting them according to a predefined template.

[0271] "Means for analyzing user emotions and adjusting the content and tone of the proposal" refers to a method that uses an emotion engine to analyze the user's emotions from their voice, facial expressions, and text input, and then appropriately adjusts the tone and content of the proposal.

[0272] "Means for users to review and edit proposals" refers to the means by which the terminal displays the generated proposal to the user, allowing the user to review and edit its contents.

[0273] "Means of providing proposals for use during business negotiations" refers to methods of saving the final completed proposal in an electronic format such as PDF, and making it printable.

[0274] This invention is a system for generating optimal proposals for target customers and using them to provide topics of conversation during business negotiations. Furthermore, it incorporates an emotion engine that recognizes the user's emotions and adjusts the content of the proposal. The system's program processing will be explained below with specific examples.

[0275] 1. Inputting target customer information and collecting data

[0276] The user enters basic information about their target customer (e.g., company name, industry, location) into an input form on the device. After completing the input, the device sends a data collection request to the server based on this information. The server accesses reliable external data sources (e.g., Google News API, Yahoo Finance API) and collects the relevant data. The server stores the collected data in a database. This database may include, for example, the latest company news, financial data, and industry market trend information.

[0277] 2. Data analysis and identification of challenges and needs

[0278] The server retrieves data collected from the database and analyzes it using generative artificial intelligence (e.g., OpenAI GPT-4). The analysis extracts potential challenges and needs of the target company. For example, if a company's sales are declining, possible causes include increased competition and loss of market share. These challenges and needs are listed according to their importance and then prioritized.

[0279] 3. Automated generation and formatting of proposals

[0280] The server utilizes natural language generation (NLG) technology to generate optimal proposals based on identified challenges and needs. The generated proposals are formatted according to defined templates, including, for example, statistics, trend data, and competitive analysis results. The resulting proposals become the final version ready for use during business negotiations.

[0281] 4. Emotion Recognition and Regulation

[0282] The server uses an emotion engine to analyze emotions from the user's voice, expressions, and text inputs. The emotion engine uses, for example, the emotion analysis API of Microsoft (registered trademark) Azure (registered trademark) Cognitive Services. When the user is nervous, it adjusts the tone of the proposal in a soothing direction and provides an optimal proposal according to the user's emotional state.

[0283] 5. Display and Editing of the Proposal

[0284] The terminal displays the generated proposal draft to the user. The user checks the proposal and makes edits such as adding or setting data and graphs as needed. Finally, the user can save the proposal as an electronic file such as in PDF format and can also print it.

[0285] 6. Topic Provision during Negotiations

[0286] The user conducts negotiations based on the generated proposal. While referring to the proposal, industry trends and specific solutions to the company's problems are presented. Also, based on the analysis results of the emotion engine, communication is carried out in a tone according to the user's emotional state. As a specific example, when the user conducts negotiations with a certain target company, the proposal details the trends of other companies in the same industry and market trends. Furthermore, when the user is nervous, the emotion engine adjusts the tone of the proposal in a soothing direction and changes it to a format that is easy for the user to read. This enables a more effective presentation during the negotiation.

[0287] Prompt Sentences as Specific Examples <>

[0288] Use the following prompt sentences as input to the generation AI model:

[0289] Company Name: XYZ Corporation

[0290] Industry: Technology

[0291] Location: Tokyo

[0292] Description: XYZ Corporation is experiencing declining sales, while its competitors continue to grow.

[0293] Please generate the corresponding proposal.

[0294] Following this prompt, the generative AI model collects the necessary data and generates the optimal proposal. As described above, the system of the present invention can consistently perform all processes, from external data collection to analysis by generative artificial intelligence, automatic generation of the optimal proposal, and adjustment of the proposal content by an emotion engine. This significantly improves the efficiency of proposal creation and makes it possible to provide concrete and reliable materials for successful business negotiations.

[0295] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0296] Step 1:

[0297] The user enters basic information about the target customer (e.g., company name, industry, location, etc.) into the input form on the device.

[0298] Input: Basic information such as company name, industry, and location entered by the user.

[0299] Output: A data request in which basic information is sent from the terminal to the server.

[0300] Specific action: The user enters "ABC Corporation," "Technology," and "Tokyo" into the input form and presses the submit button.

[0301] Step 2:

[0302] The terminal sends a data collection request to the server based on the information entered.

[0303] Input: User input information.

[0304] Output: The data collection request sent to the server.

[0305] Specific operation: The terminal sends a request to the / collect-data endpoint, and the server accesses external data sources (e.g., Google News API, Yahoo Finance API).

[0306] Step 3:

[0307] The server collects relevant data from reliable external data sources.

[0308] Input: The data collection request from the terminal.

[0309] Output: News articles, financial data, market trend data, etc. obtained from external data sources.

[0310] Specific operation: The server calls the Google News API to obtain the latest news and uses the Yahoo Finance API to collect financial data. The server saves the collected data in the database.

[0311] Step 4:

[0312] The server retrieves the data collected from the database and analyzes the data using a generative artificial intelligence (e.g., OpenAI GPT-4).

[0313] Input: The collected data.

[0314] Output: A list of analyzed issues and needs.

[0315] Specific operation: The server sends a prompt text "Analyze the reasons for the decline in the sales of this company and list the main issues" to the GPT-4 model and saves the analysis results in the database.

[0316] Step 5:

[0317] Based on the identified challenges and needs, the server generates an optimal proposal and formats it using natural language generation (NLG) technology.

[0318] Input: A list of analyzed issues and needs.

[0319] Output: Formatted proposal draft.

[0320] Specific operation: The server uses NLG technology to generate proposals and incorporates statistical information and trend data into pre-prepared templates.

[0321] Step 6:

[0322] The server uses an emotion engine to analyze the user's emotions from their voice, facial expressions, and text input.

[0323] Input: User's voice, facial expressions, and text data.

[0324] Output: Analyzed sentiment data.

[0325] Specific operation: The server uses the Microsoft Azure Cognitive Services sentiment analysis API to analyze the user's emotional state in real time. It determines that the user is feeling stressed.

[0326] Step 7:

[0327] The server adjusts the tone and content of the proposal based on emotional data.

[0328] Input: Analyzed sentiment data and proposal draft.

[0329] Output: Revised proposal draft.

[0330] Specific operation: Based on the analyzed sentiment data, the server softens the tone of the proposal and changes it to a user-friendly format.

[0331] Step 8:

[0332] The terminal displays a draft of the generated proposal to the user.

[0333] Input: Revised proposal draft.

[0334] Output: Display of the proposal draft.

[0335] Specific operation: The device displays the proposal to the user and provides a UI for the user to review its contents.

[0336] Step 9:

[0337] The user reviews the proposal and adds data and graphs as needed.

[0338] Input: Draft proposal.

[0339] Output: Edited proposal.

[0340] Specific actions: The user clicks on a specific part of the proposal to perform edits such as inserting data or graphs.

[0341] Step 10:

[0342] Users can ultimately save the proposal as an electronic file, such as a PDF, and also print it.

[0343] Input: Edited proposal.

[0344] Output: Saved PDF file.

[0345] Specific action: The user presses the save button, exports the proposal in PDF format, and saves it to their device.

[0346] Step 11:

[0347] Users conduct business negotiations based on the generated proposals.

[0348] Input: Saved PDF proposal.

[0349] Output: Progress of the business negotiation.

[0350] Specific actions: The user opens a proposal during a business meeting and gives a presentation based on the proposal. Using the results of sentiment analysis, the system adjusts the tone of communication if the user appears nervous.

[0351] (Application Example 2)

[0352] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0353] In modern business negotiations and customer interactions, it is crucial to quickly generate optimal proposals based on detailed information about target customers, and further adjust the proposal content according to the user's emotional state. Traditional systems often require manual proposal generation and adjustment, which is time-consuming and inefficient. Furthermore, dynamically adjusting proposal content to reflect the user's emotions is difficult. As a result, providing the optimal proposal to maximize the effectiveness of business negotiations has been challenging.

[0354] In Application Example 2, the identification processing by the identification processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for inputting basic information of the target customer, means for collecting and analyzing the latest industry and company data from external data sources, means for identifying the challenges and needs of the target company from the collected data using generative artificial intelligence, means for automatically generating an optimal proposal based on the identified challenges and needs and formatting it as a proposal document, means for the user to review and edit the proposal document, means for providing the proposal document for use during business negotiations, means for adjusting the content of the proposal document according to the user's emotional state, and means for analyzing emotions from the user's voice, facial expressions, and text input. This makes it possible to quickly generate an optimal proposal document for the target customer and dynamically adjust the content of the proposal according to the user's emotional state.

[0355] A "target customer" is an individual or company from which the system generates proposals.

[0356] "Basic information" refers to fundamental data necessary for creating a proposal, such as the target customer's company name, industry, and location.

[0357] "External data sources" refer to reliable sources of information that can be obtained from external sources, such as APIs on the internet, news articles, and financial data.

[0358] "Latest industry and company data" refers to real-time data collected on specific industries and companies, including the latest market trends, competitor activities, and financial data.

[0359] "Generative artificial intelligence" refers to an artificial intelligence system that can analyze collected data and automatically generate proposals using natural language generation technology.

[0360] "Challenges and needs" refer to problems and requests faced by companies and customers, including declining sales and entry into new markets.

[0361] A "proposal" is a document that contains recommendations for solutions, products, or services to be offered to a target customer.

[0362] "Formatting" refers to organizing the content of a proposal according to a set template and making it easy to read.

[0363] A "user" is a person who uses this system to create proposals and use them in business negotiations.

[0364] "Means for reviewing and editing" refers to a system that provides an interface allowing users to review the content of the generated proposal and make changes or additions as needed.

[0365] "Means of provision" refers to methods that allow the generated proposal to be saved as an electronic file or printed.

[0366] "Emotional state" refers to the user's current feelings and psychological state, and is analyzed from factors such as voice tone and facial expressions.

[0367] The "emotion engine" is a system that analyzes a user's emotions from their voice, facial expressions, and text input.

[0368] "Voice, facial expressions, and text input" refers to the user's speaking voice, facial expressions, and entered text data, and analyzing these is a means of understanding the user's emotions.

[0369] This invention provides a system for in-store employees wearing smart glasses to make optimal product recommendations to target customers and to adjust the recommendations according to the user's emotional state. The system's program processing is described in detail below.

[0370] First, the user (store clerk) wears smart glasses and interacts with customers in a physical store. After the user inputs basic information about the target customer (e.g., past purchase history, preferences, allergy information, etc.) through the input interface of the smart glasses, the terminal (smart glasses) sends this information to a server.

[0371] The server collects up-to-date industry and company data from external data sources. It utilizes reliable APIs and internet information sources to obtain relevant news articles and market trend data. Next, the server analyzes the collected data using generative artificial intelligence to extract potential customer challenges and needs. Based on this analysis, the server generates optimal proposals and formats them into a proposal document.

[0372] This proposal is automatically formatted according to a template that includes statistical information, trend data, and competitive analysis results. Furthermore, the content of the proposal is adjusted according to the user's emotional state using an emotion engine. If the user is tense, the tone of the proposal can be softened, and if their facial expression is excited, it can be adjusted to an energetic tone. This emotional state analysis uses an emotion engine that analyzes emotions from the user's voice, facial expressions, and text input.

[0373] Ultimately, the proposal generated by the server is displayed on the terminal (smart glasses), and the user (salesperson) uses this to suggest products to target customers. For example, if a customer asks about a specific facial cleanser, recommended products and descriptions will be displayed on the smart glasses' screen. The user then explains the product to the customer according to this information. At this time, the system will instruct the user to explain in a gentle tone if the customer's expression looks anxious, and in an energetic tone if their expression looks excited.

[0374] As a concrete example, consider a scenario where a customer visits a store and consults with a salesperson wearing smart glasses about a product they are considering purchasing. If the customer is looking for a facial cleanser, the salesperson asks, "Hello. What are you looking for today?" and the customer replies, "I'm looking for a facial cleanser." At this moment, the system analyzes the customer's facial expression and voice and determines that they are nervous. As a result, recommended facial cleansers are displayed on the smart glasses' screen, and the system suggests an explanation in a calm tone.

[0375] An example of a prompt statement is as follows:

[0376] Please enter customer information and generate product recommendations.

[0377] customer_id = "12345"

[0378] face_image = capture_face_image()

[0379] voice_sample = capture_voice_sample()

[0380] get_customer_data(customer_id)

[0381] analyze_emotion(face_image, voice_sample)

[0382] recommendations = generate_recommendation(customer_id)

[0383] adjusted_explanation = adjust_tone(recommendations)

[0384] display_recommendation(adjusted_explanation)

[0385] This approach makes it possible to make more effective product proposals to target customers. Furthermore, by dynamically adjusting the proposal content according to the user's emotional state, the success rate of sales negotiations can be increased.

[0386] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0387] Step 1:

[0388] The user inputs basic information about their target customer through the smart glasses' input interface. For example, they might enter the customer's ID, past purchase history, preferences, and allergy information. This information is stored as input data on the device and sent to the next processing step.

[0389] Step 2:

[0390] The terminal sends the entered target customer's basic information to the server. The server receives this information, collects the latest industry and company data from external data sources, and begins analysis. It uses reliable APIs and internet sources to obtain relevant information and stores it in a database.

[0391] Step 3:

[0392] The server analyzes the collected data using generative artificial intelligence. Specifically, it analyzes market trends, competitor activities, financial data, etc., to extract potential challenges and needs of target customers. For example, it analyzes customer preferences from their purchase history and identifies key purchasing trends using sales and interest data. Based on this, the server generates a list of challenges and needs.

[0393] Step 4:

[0394] The server generates optimal solutions based on identified challenges and needs, and formats them as proposal documents. Using generative artificial intelligence, it performs natural language generation and automatically formats the proposal according to a template that includes statistical information, trend data, and competitive analysis results. This proposal is then saved on the server as an electronic file.

[0395] Step 5:

[0396] The server uses an emotion engine to analyze the user's emotional state. The emotion engine detects emotions based on the user's voice, facial expressions, and text input data transmitted from the smart glasses. For example, it identifies emotional states such as tension, excitement, and anxiety from the user's tone of voice and facial expressions. This prepares the server for making adjustments based on the emotional state.

[0397] Step 6:

[0398] The server adjusts the tone of the generated proposal based on the user's emotional state, which has been analyzed by the emotion engine. For example, if the user is nervous, the proposal's description will be changed to a calmer tone; if they are excited, it will be adjusted to an energetic tone. This adjusted proposal then becomes the draft for use in the final business negotiation.

[0399] Step 7:

[0400] Finally, the finalized proposal is sent to the device. The device (smart glasses) displays the adjusted proposal to the user. The user can then use this proposal to proceed with negotiations with target customers and review and edit the proposal content as needed. During negotiations, the system supports the user in responding with the appropriate tone based on their emotional state, while referring to the proposal.

[0401] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0402] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0403] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0404] [Second Embodiment]

[0405] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0406] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0407] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0408] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0409] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0410] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0411] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0412] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0413] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0414] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0415] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0416] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0417] This invention is a system for generating optimal proposals for target customers and using them to provide topics of discussion during business negotiations. The system's program processing will be explained below with specific examples.

[0418] 1. Inputting target customer information and collecting data

[0419] The user enters basic information about their target customer (e.g., company name, industry, location). Based on the entered information, the device sends a data collection request to the server. The server collects data about the relevant company and industry from external data sources. In this process, it uses reliable APIs and internet sources to obtain relevant news articles, financial data, and market trend data.

[0420] 2. Data analysis and identification of challenges and needs

[0421] The server uses generative artificial intelligence to analyze the collected data. The analysis extracts potential challenges and needs of the target company. For example, if a company's sales are declining, possible causes include increased competition and loss of market share. These challenges and needs are listed according to their importance and then prioritized.

[0422] 3. Automated generation and formatting of proposals

[0423] The server utilizes natural language generation (NLG) technology to generate optimal proposals based on identified challenges and needs. The generated proposals are formatted according to defined templates and include, for example, statistical information, trend data, and competitive analysis results. The resulting proposals become the final version ready for use during business negotiations.

[0424] 4. Review and edit the proposal.

[0425] The terminal displays a draft of the generated proposal to the user. The user can review the proposal and edit it as needed, adding data and graphs. Finally, the user can save the proposal as an electronic file, such as a PDF, and print it.

[0426] 5. Topics to discuss during business negotiations

[0427] Users conduct business negotiations based on the generated proposals, presenting industry trends and solutions to specific challenges faced by companies. During negotiations, they can cite collected data and analysis results from generative artificial intelligence to provide specific and reliable explanations. For example, users can gain customer trust by presenting specific challenges and solutions based on the company's current situation and trends.

[0428] The key feature of this system is that it handles all processes in-house, from external data collection and analysis using generative artificial intelligence to the automatic generation of optimal proposals. This significantly improves the efficiency of proposal creation and makes it possible to provide concrete and reliable materials that will lead to successful business negotiations.

[0429] The following describes the processing flow.

[0430] Program processing flow

[0431] Step 1: Enter target customer information

[0432] The user enters basic information about the target customer (company name, industry, location, etc.) into the terminal. They then register the necessary information using the input form.

[0433] Step 2: Submit data collection request

[0434] The device sends a data collection request to the server based on the information entered by the user. The request includes the type of data to be collected and the source of the data.

[0435] Step 3: External Data Collection

[0436] The server accesses external data sources (APIs, reliable internet sources, etc.) to collect up-to-date data about the target company and industry. Specifically, it retrieves news articles, financial data, market trend data, and so on.

[0437] Step 4: Data Analysis

[0438] The server analyzes the collected data. Using text analysis algorithms, it extracts key keywords and topics from news articles and reports, organizes financial and market trend data, and generates statistical information.

[0439] Step 5: Identifying Issues and Needs

[0440] The server uses generative artificial intelligence to analyze data and identify potential challenges and needs of target companies. For example, it analyzes the causes of declining sales and new market opportunities. In this process, the AI ​​model performs pattern recognition and trend analysis.

[0441] Step 6: List the issues and needs

[0442] The server lists identified issues and needs, prioritizes them, assigns scores based on impact and urgency, and sorts them by priority.

[0443] Step 7: Automatic generation of optimal proposals

[0444] The server automatically generates optimal proposals for target companies based on the analysis results. Using natural language generation (NLG) technology, it generates specific solutions and strategies in proposal format.

[0445] Step 8: Formatting the proposal

[0446] The server formats the generated proposals according to a defined template. The proposals include statistical information, trend data, competitive analysis results, and also incorporate company logos and design elements.

[0447] Step 9: View and edit the proposal

[0448] The device displays a draft of the generated proposal to the user. The user reviews the proposal and adds data or graphs, or revises the text as needed.

[0449] Step 10: Saving and distributing the proposal

[0450] Users can save their final proposal as an electronic file, such as a PDF, and print it. This allows them to use it during business negotiations with clients.

[0451] Step 11: Topics to discuss during business negotiations

[0452] The sales negotiation is based on the proposal generated by the user. While referring to the proposal, the discussion will present industry trends and solutions to the company's specific challenges, and provide topics based on concrete and reliable data.

[0453] These steps enable the efficient generation of optimal proposals for target customers through a consistent process, thereby increasing the success rate of business negotiations.

[0454] (Example 1)

[0455] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0456] Traditional proposal creation processes were inefficient and time-consuming because each step—from data collection and analysis to proposal generation and its use in business negotiations—was performed individually. Furthermore, the reliability of the collected data and the accuracy of the analysis were often insufficient, significantly impacting the success of business negotiations. Therefore, there is a need for a system that consistently collects and analyzes highly reliable data and efficiently creates and delivers optimal proposals.

[0457] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0458] In this invention, the server includes means for collecting basic information on target companies and obtaining the latest industry and company data from external data sources; means for analyzing the collected data using generative artificial intelligence to identify the challenges and needs of target companies; and means for automatically generating proposals based on the identified challenges and needs and formatting them as proposal documents. This makes it possible to efficiently collect and analyze highly reliable data and create and provide optimal proposal documents.

[0459] A "target company" is a company that is the focus of a proposal.

[0460] "Basic information" refers to initial data about the target company, including information such as company name, industry, and location.

[0461] "External data sources" refer to external sources of information used to collect information about target companies and industries, such as APIs and websites on the internet.

[0462] "Generative artificial intelligence" is an artificial intelligence technology that analyzes collected data and generates text using natural language generation techniques.

[0463] A "proposal" is a document that outlines the proposed content for a target company and may include statistical information, trend data, and competitive analysis results.

[0464] "Formatting" refers to organizing and arranging the content of a generated proposal according to a defined template.

[0465] An "electronic file" is a digital file that can be read by a computer, such as a PDF file.

[0466] "Reliability" refers to the degree to which the collected data and analysis results are accurate and the proposed content is justified.

[0467] This invention is a system for generating optimal proposals for target companies and using them to provide topics of discussion during business negotiations. The system is implemented as follows.

[0468] 1. Inputting target customer information and collecting data

[0469] The user enters basic information about the target company (company name, industry, location, etc.) into the terminal. Specifically, the user enters information such as "Company Name: ABC Manufacturing Inc.", "Industry: Manufacturing", and "Location: Tokyo" into the terminal's input form.

[0470] The terminal sends the entered information to the server. A secure communication protocol (e.g., HTTPS) is used for transmission.

[0471] Based on the information it receives, the server collects up-to-date industry and company data from reliable external data sources (e.g., Bloomberg API, Google News API). This data includes news articles, financial data, and market trend data.

[0472] 2. Data analysis and identification of challenges and needs

[0473] The server analyzes the collected data using generative artificial intelligence (e.g., OpenAI's GPT-4). Natural language processing (NLP) techniques are used for the analysis.

[0474] The analysis identifies potential challenges and needs of the target company. For example, if a company's sales are declining, possible causes include increased competition or loss of market share. These challenges and needs are then listed according to their importance.

[0475] 3. Automated generation and formatting of proposals

[0476] Based on identified challenges and needs, the server utilizes natural language generation (NLG) technology to generate optimal suggestions. Specifically, these suggestions include statistical information, trend data, and competitive analysis results.

[0477] The generated proposals are formatted according to a defined template. Sections of the template include "Market Trend Analysis," "Competitor Comparison," and "Specific Solution Proposals."

[0478] 4. Review and edit the proposal.

[0479] The terminal displays a draft of the generated proposal to the user. The proposal is displayed in PDF preview format.

[0480] Users review the proposal and add data and graphs as needed. Editing options include adding text, modifying data, and inserting graphs.

[0481] Ultimately, users save the proposal in PDF or other electronic file formats and print it if necessary.

[0482] 5. Topics to discuss during business negotiations

[0483] Users conduct business negotiations based on the generated proposals. During negotiations, they quote the contents of the proposals and present specific challenges and solutions based on the company's current situation and trends. For example, they might present "decreased sales due to increased competition" and "strategies for expanding into new markets" to gain the customer's trust.

[0484] The following prompt statements are used as specific examples in this system:

[0485] Based on the target company's information (Company name: ABC Manufacturing Inc., Industry: Manufacturing, Location: Tokyo), analyze the problems causing the company's declining sales and generate an appropriate proposal.

[0486] The key feature of this system is its integrated approach, encompassing everything from external data collection and analysis using generative artificial intelligence to the automatic generation of optimal proposals. This significantly improves the efficiency of proposal creation and enables the provision of concrete and reliable materials for successful business negotiations.

[0487] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0488] Step 1:

[0489] The user enters basic information about the target customer.

[0490] Specifically, the user enters information such as "Company Name: ABC Manufacturing Inc.", "Industry: Manufacturing", and "Location: Tokyo" into the input form on the device.

[0491] Input: Company name, industry, location

[0492] Output: Basic information of the entered target customer

[0493] Step 2:

[0494] The terminal sends the input information to the server.

[0495] The terminal uses a secure communication protocol (e.g., HTTPS) to send the entered information to the server.

[0496] Input: Basic information of the target customer entered

[0497] Output: Customer information request sent to the server

[0498] Step 3:

[0499] The server collects up-to-date industry and company data from external data sources.

[0500] The server collects necessary information, such as news articles, financial data, and market trend data, using APIs like the Bloomberg API and the Google News API.

[0501] Input: Submitted customer information request

[0502] Output: Collected industry and company data

[0503] Step 4:

[0504] The server analyzes the data it has collected.

[0505] The server uses generative artificial intelligence (e.g., OpenAI's GPT-4) to analyze the collected data and identify the challenges and needs of the target company.

[0506] Input: Collected industry and company data

[0507] Output: List of issues and needs based on analysis results

[0508] Step 5:

[0509] The server automatically generates proposals based on identified issues and needs, and formats them into a proposal document.

[0510] The server utilizes natural language generation (NLG) technology to format proposals according to a template. The generated proposals include statistical information, trend data, and competitive analysis results.

[0511] Input: List of issues and needs

[0512] Output: Formatted proposal

[0513] Step 6:

[0514] The terminal displays the draft of the generated proposal.

[0515] The terminal displays the proposal, generated in PDF preview format, to the user.

[0516] Input: Formatted proposal

[0517] Output: Draft proposal displayed to the user

[0518] Step 7:

[0519] The user reviews the proposal and edits it as needed.

[0520] Users review the proposal and make edits such as adding text, changing data, and inserting graphs.

[0521] Input: Draft proposal displayed to the user

[0522] Output: Revised proposal

[0523] Step 8:

[0524] The user saves the proposal as its final form and prints it as needed.

[0525] Users can save the completed proposal as an electronic file, such as a PDF, and print it as needed.

[0526] Input: Revised proposal

[0527] Output: Saved proposal in PDF format, and printed proposal.

[0528] Step 9:

[0529] The user conducts business negotiations based on the proposal and provides specific topics for discussion.

[0530] Users quote the content of the generated proposal and, during business negotiations, present specific challenges and solutions based on the company's current situation and trends.

[0531] Input: Saved proposal in PDF format

[0532] Output: Providing specific topics for business negotiations and their results.

[0533] (Application Example 1)

[0534] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0535] In traditional advertising proposal creation, data collection, analysis, and proposal writing for target customers are often done manually, resulting in significant time and effort. Furthermore, manual data analysis and proposal generation are prone to human error and bias, making it difficult to identify the optimal advertising strategy. This leads to a lower success rate in advertising negotiations. Therefore, there is a need for a system that efficiently generates optimal advertising proposals for target companies and effectively advances negotiations.

[0536] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0537] In this invention, the server includes means for inputting basic information of target customers, means for collecting and analyzing the latest industry and company data from external data sources, means for identifying the challenges and needs of target companies from the collected data using generative artificial intelligence, means for automatically generating optimal proposals based on the identified challenges and needs and formatting them as advertising proposals, means for users to review and edit advertising proposals, and means for providing advertising proposals for use during advertising negotiations. This makes it possible to efficiently and accurately perform a series of processes from data collection and analysis of target companies to the automatic generation of optimal advertising proposals. Furthermore, since the generated proposals are formatted based on templates that include statistical information, trend data, and competitive analysis results, they can be used effectively during negotiations.

[0538] "Basic information about the target customer" includes information such as the customer's company name, industry, location, and past advertising campaign information.

[0539] "External data sources" refer to reliable data sources that provide information such as corporate financial data, industry trend data, and relevant news articles.

[0540] "Generative artificial intelligence" is an artificial intelligence technology that analyzes collected data to identify the challenges and needs of target companies and automatically generates proposals.

[0541] An "advertising proposal" is a document used to propose an advertising strategy for a target company, and is formatted using a template that includes statistical information, trend data, and competitive analysis results.

[0542] "Means for users to review and edit advertising proposals" refers to an interface that allows users to review generated advertising proposals and edit their content as needed.

[0543] "A means of providing advertising proposals for use during advertising negotiations" refers to a system function that provides a final advertising proposal so that it can be used effectively during negotiations.

[0544] "Statistical information" refers to data that quantifies the effectiveness of advertising campaigns and market research data.

[0545] "Trend data" refers to data on the latest industry trends and consumer behavior.

[0546] "Competitive analysis results" refer to the results of an analysis of the advertising strategies and market share of competing companies.

[0547] This invention is a system for automatically generating optimal advertising proposals for target customers specifically for the advertising industry. The system provides a consistent service from inputting target customer information to data collection, analysis, and generation, review, and editing of advertising proposals, using the following means. Specific hardware and software components include a server, smartphone, generative artificial intelligence model, and data collection API.

[0548] Entering target customer information

[0549] Users use a smartphone application to input basic information about their target customers (such as company name, industry, location, and past advertising campaign information). This basic information forms the basis for the system to send requests to external data sources.

[0550] Data collection

[0551] The server collects relevant data from external data sources based on the target customer's basic information. Specific data sources include corporate financial data, industry trend data, and relevant news articles. Reliable APIs are used for data collection.

[0552] Data Analysis

[0553] The collected data is analyzed on a server. This analysis uses generative artificial intelligence to extract potential challenges and needs of the target company from the collected information. For example, if the cause of declining sales is increased competition or loss of market share, these challenges and needs will be listed.

[0554] Generating an advertising proposal

[0555] The server automatically generates optimal advertising proposals using natural language generation (NLG) technology based on the analysis results. These proposals include statistical information, trend data, and competitive analysis results. The generated proposals are formatted according to a template and await user review.

[0556] Review and editing of advertising proposals

[0557] Users review the generated advertising proposal draft using a smartphone application and edit the content as needed. The edited advertising proposal is saved in PDF format or other formats and is ultimately used during business negotiations.

[0558] Specific example

[0559] For example, if the target company for the cosmetics industry is "XYZ Corporation," the user inputs basic information about "XYZ Corporation" and past social media campaign information. Based on this, the server collects and analyzes the latest industry trend data, financial data, and competitive analysis results. Finally, a proposal for an influencer marketing campaign utilizing social media is generated.

[0560] Example of a prompt

[0561] Target company: XYZ Corporation

[0562] Industry: Cosmetics

[0563] Past Campaigns: Successful Social Media Campaigns

[0564] Collected data:

[0565] 1. Industry Trends

[0566] 2. News articles

[0567] 3. Financial Data

[0568] The generated proposal:

[0569] 1. Overview of Industry Trends

[0570] 2. Potential challenges

[0571] 3. Proposal for the optimal advertising campaign

[0572] In this way, the entire proposal creation process can be streamlined, increasing the success rate of business negotiations.

[0573] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0574] Step 1:

[0575] The user enters basic information about the target customer into a smartphone application. This information includes the customer's company name, industry, location, and past advertising campaign information. This generates basic information data for the target company.

[0576] Input: Customer's company name, industry, location, and past advertising campaign information.

[0577] Output: Basic information data of the target company

[0578] Step 2:

[0579] The device sends a data collection request to the server based on the target customer's basic information data. The server uses a reliable data collection API to collect industry trend data, corporate financial data, news articles, etc., from external data sources. This is how external data is collected.

[0580] Input: Basic information data of the target company

[0581] Output: Collected external data

[0582] Step 3:

[0583] The server analyzes the collected external data using generative artificial intelligence (generative AI model). The generative AI model extracts and lists the target company's potential challenges and needs from the collected data. This generates challenge and needs data.

[0584] Input: Collected external data

[0585] Output: Issue and needs data

[0586] Step 4:

[0587] The server automatically generates optimal advertising proposals using natural language generation (NLG) technology based on problem and needs data. The generated advertising proposals are formatted according to a template, including statistical information, trend data, and competitor analysis results. This process generates the advertising proposal data.

[0588] Input: Issue and needs data

[0589] Output: Advertising proposal data

[0590] Step 5:

[0591] The user reviews the draft advertising proposal generated on their device and edits it as needed. The edited advertising proposal is saved on the device in PDF format or another suitable format. This completes the final advertising proposal.

[0592] Input: Advertising proposal data

[0593] Output: Final advertising proposal

[0594] Step 6:

[0595] Users conduct business negotiations using the completed advertising proposal. During negotiations, they can increase their chances of success by citing the statistical information, trend data, and competitor analysis results included in the advertising proposal to make specific and reliable proposals.

[0596] Input: Final advertising proposal

[0597] Output: Improved success rate of business negotiations

[0598] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0599] This invention is a system for generating optimal proposals for target customers and using them to provide topics of conversation during business negotiations. Furthermore, it incorporates an emotion engine that recognizes the user's emotions and adjusts the content of the proposal. The system's program processing will be explained below with specific examples.

[0600] 1. Inputting target customer information and collecting data

[0601] The user enters basic information about their target customer (e.g., company name, industry, location). Based on the entered information, the terminal sends a data collection request to the server. The server collects the latest data on the relevant company and industry from external data sources. In this process, it uses reliable APIs and internet sources to obtain relevant news articles, financial data, and market trend data.

[0602] 2. Data analysis and identification of challenges and needs

[0603] The server uses generative artificial intelligence to analyze the collected data. The analysis extracts potential challenges and needs of the target company. For example, if a company's sales are declining, possible causes include increased competition and loss of market share. These challenges and needs are listed according to their importance and then prioritized.

[0604] 3. Automated generation and formatting of proposals

[0605] The server utilizes natural language generation (NLG) technology to generate optimal proposals based on identified challenges and needs. The generated proposals are formatted according to defined templates and include, for example, statistical information, trend data, and competitive analysis results. The resulting proposals become the final version ready for use during business negotiations.

[0606] 4. Emotion Recognition and Regulation

[0607] The server uses an emotion engine to analyze the user's emotions from their voice, facial expressions, and text input. For example, if the user is feeling nervous, the emotion engine can adjust the tone of the proposal to be more calming. This ensures that the best proposal is provided based on the user's emotional state.

[0608] 5. Viewing and editing proposals

[0609] The terminal displays a draft of the generated proposal to the user. The user can review the proposal and edit it as needed, adding data and graphs as required. Finally, the user can save the proposal as an electronic file, such as a PDF, and print it.

[0610] 6. Topics to discuss during business negotiations

[0611] Users conduct business negotiations based on the generated proposals. While referring to the proposals, they are presented with industry trends and solutions to specific company challenges. Furthermore, based on the analysis results of the emotion engine, communication is tailored to the user's emotional state, and topics are provided based on reliable data.

[0612] As a concrete example, when a user is conducting a business negotiation with a target company, the proposal will include detailed information on the activities of competitors and market trends. Furthermore, if the user is feeling nervous, the emotional engine will adjust the tone of the proposal to soften it and change it to a format that is easier for the user to read. This will enable a more effective presentation during the business negotiation.

[0613] This system is characterized by its integrated approach, handling all processes from external data collection and analysis by generative artificial intelligence to the automatic generation of optimal proposals and the adjustment of proposal content using an emotion engine. This significantly improves the efficiency of proposal creation and enables the provision of concrete and reliable materials for successful business negotiations.

[0614] The following describes the processing flow.

[0615] Program processing flow

[0616] Step 1: Enter target customer information

[0617] The user enters basic information about the target customer (company name, industry, location, etc.) into the terminal. The user then registers the necessary information using a dedicated input form.

[0618] Step 2: Submit data collection request

[0619] The device sends a data collection request to the server based on the information entered by the user. The request includes the type of data to be collected and the source of the data.

[0620] Step 3: External Data Collection

[0621] The server accesses external data sources to collect the latest data on target companies and industries. These external data sources include news article APIs, financial data APIs, and market trend data APIs. Specifically, it retrieves relevant news articles, financial reports, market trend reports, and more.

[0622] Step 4: Data Analysis

[0623] The server analyzes the collected data. Using text analysis algorithms, it extracts key keywords and topics from news articles and reports, organizes financial and market trend data, and generates statistical information.

[0624] Step 5: Identifying Issues and Needs

[0625] The server uses generative artificial intelligence to analyze data and identify potential challenges and needs of target companies. For example, if a decline in sales is observed, possible causes include increased competition and a decrease in market share. The generative AI model performs pattern recognition and trend analysis to extract specific challenges and areas for improvement.

[0626] Step 6: List the issues and needs

[0627] The server lists identified issues and needs and prioritizes them. It assigns scores based on impact and urgency, and sorts them by priority. This ensures that solutions are considered starting with the most important issues.

[0628] Step 7: Automatic generation of optimal proposals

[0629] The server utilizes natural language generation (NLG) technology to generate optimal suggestions based on identified challenges and needs. These suggestions include solutions and recommendations, presenting specific strategies tailored to the company's specific situation.

[0630] Step 8: Formatting the proposal

[0631] The server formats the generated proposals according to a defined template. The proposals include statistical information, trend data, competitive analysis results, and also incorporate company logos and design elements.

[0632] Step 9: Emotion Recognition

[0633] The device collects emotions from the user's voice, facial expressions, and text input. This data is transmitted to the server in real time.

[0634] Step 10: Emotion Analysis

[0635] The server analyzes the collected emotional data through an emotion engine. For example, if the user is feeling nervous, the emotion engine recognizes this and adjusts the tone of the proposal accordingly.

[0636] Step 11: Adjusting the proposal based on emotions

[0637] The server adjusts the content and tone of the proposal based on the analysis results of the emotion engine. For example, if the user is relaxed, it uses a lighthearted tone, and if the user is stressed, it uses a calm tone.

[0638] Step 12: View and edit the proposal

[0639] The device displays a draft of the generated proposal to the user. The user can review the proposal and edit it as needed, adding data and graphs as required.

[0640] Step 13: Saving and distributing the proposal

[0641] Users can save their final proposal as an electronic file, such as a PDF, and print it. This allows them to use it during business negotiations with clients.

[0642] Step 14: Topics to discuss during business negotiations

[0643] The system conducts business negotiations based on proposals generated by the user. While referring to the proposals, it presents industry trends and solutions to the company's specific challenges. Furthermore, based on analysis results from the emotion engine, it communicates in a tone appropriate to the user's emotional state.

[0644] These steps enable the efficient generation of optimal proposals for target customers through a consistent process, allowing for communication that takes user emotions into consideration, and ultimately increasing the success rate of business negotiations.

[0645] (Example 2)

[0646] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0647] While conventional sales presentation systems could automatically generate proposals for target customers, their effectiveness was limited due to the uniformity of the proposal content and the lack of appropriate adjustments based on the user's emotional state. Furthermore, manual data collection and analysis were required, posing significant challenges in terms of time and effort. Additionally, improvements in the accuracy of data analysis and the reduction of the user's emotional burden during sales negotiations were insufficient.

[0648] The identification processing by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for inputting basic information of the target customer, means for collecting and analyzing the latest industry and company data from external data sources, means for identifying the challenges and needs of the target company from the collected data using generative artificial intelligence, means for automatically generating an optimal proposal based on the identified challenges and needs and formatting it as a proposal document, means for analyzing the user's emotions and adjusting the content and tone of the proposal document, means for the user to review and edit the proposal document, and means for providing the proposal document for use during business negotiations. This makes it possible to make optimal adjustments in response to the user's emotions during the automatic generation of the proposal document, maximizing the effectiveness of business negotiations while significantly reducing the time and effort required to create the proposal document.

[0649] "Means for inputting basic information of target customers" refers to the means by which users input basic information of their target customers, such as the company name, industry, and location, into a terminal.

[0650] "Means for collecting and analyzing up-to-date industry and company data from external data sources" refers to a method by which a server uses reliable APIs or internet information sources to obtain and analyze the latest data on the target industry and company.

[0651] "Methods for identifying the challenges and needs of target companies from data collected using generative artificial intelligence" refers to methods for analyzing collected data using generative artificial intelligence to extract the potential challenges and needs of target companies.

[0652] "A means of automatically generating optimal proposals based on identified issues and needs and formatting them as proposal documents" refers to a method of automatically generating optimal proposals using natural language generation technology based on analysis results and formatting them according to a predefined template.

[0653] "Means for analyzing user emotions and adjusting the content and tone of the proposal" refers to a method that uses an emotion engine to analyze the user's emotions from their voice, facial expressions, and text input, and then appropriately adjusts the tone and content of the proposal.

[0654] "Means for users to review and edit proposals" refers to the means by which the terminal displays the generated proposal to the user, allowing the user to review and edit its contents.

[0655] "Means of providing proposals for use during business negotiations" refers to methods of saving the final completed proposal in an electronic format such as PDF, and making it printable.

[0656] This invention is a system for generating optimal proposals for target customers and using them to provide topics of conversation during business negotiations. Furthermore, it incorporates an emotion engine that recognizes the user's emotions and adjusts the content of the proposal. The system's program processing will be explained below with specific examples.

[0657] 1. Inputting target customer information and collecting data

[0658] The user enters basic information about their target customer (e.g., company name, industry, location) into an input form on the device. After completing the input, the device sends a data collection request to the server based on this information. The server accesses reliable external data sources (e.g., Google News API, Yahoo Finance API) and collects the relevant data. The server stores the collected data in a database. This database may include, for example, the latest company news, financial data, and industry market trend information.

[0659] 2. Data analysis and identification of challenges and needs

[0660] The server retrieves data collected from the database and analyzes it using generative artificial intelligence (e.g., OpenAI GPT-4). The analysis extracts potential challenges and needs of the target company. For example, if a company's sales are declining, possible causes include increased competition and loss of market share. These challenges and needs are listed according to their importance and then prioritized.

[0661] 3. Automated generation and formatting of proposals

[0662] The server utilizes natural language generation (NLG) technology to generate optimal proposals based on identified challenges and needs. The generated proposals are formatted according to defined templates, including, for example, statistics, trend data, and competitive analysis results. The resulting proposals become the final version ready for use during business negotiations.

[0663] 4. Emotion Recognition and Regulation

[0664] The server uses an emotion engine to analyze the user's emotions from their voice, facial expressions, and text input. The emotion engine utilizes, for example, the emotion analysis API from Microsoft Azure Cognitive Services. If the user is feeling stressed, the server adjusts the tone of the proposal to soften it, providing the most appropriate proposal based on the user's emotional state.

[0665] 5. Viewing and editing proposals

[0666] The terminal displays a draft of the generated proposal to the user. The user reviews the proposal and edits it as needed, adding data and graphs. Finally, the user can save the proposal as an electronic file, such as a PDF, and print it.

[0667] 6. Topics to discuss during business negotiations

[0668] Users conduct business negotiations based on the generated proposals. They refer to the proposals while presenting industry trends and solutions to specific challenges faced by the company. Furthermore, based on the analysis results of the emotion engine, communication is tailored to the user's emotional state. For example, when a user is conducting a business negotiation with a target company, the proposal includes detailed information on the activities of competitors and market trends. Additionally, if the user is feeling nervous, the emotion engine adjusts the tone of the proposal to soften it and changes it to a more readable format. This enables more effective presentations during business negotiations.

[0669] Prompt statements as concrete examples

[0670] Use the following prompt text as input for the generating AI model:

[0671] Company name: XYZ Corporation

[0672] Industry: Technology

[0673] Location: Tokyo

[0674] Description: XYZ Corporation is experiencing declining sales, while its competitors continue to grow.

[0675] Please generate the corresponding proposal.

[0676] Following this prompt, the generative AI model collects the necessary data and generates the optimal proposal. As described above, the system of the present invention can consistently perform all processes, from external data collection to analysis by generative artificial intelligence, automatic generation of the optimal proposal, and adjustment of the proposal content by an emotion engine. This significantly improves the efficiency of proposal creation and makes it possible to provide concrete and reliable materials for successful business negotiations.

[0677] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0678] Step 1:

[0679] The user enters basic information about the target customer (e.g., company name, industry, location, etc.) into the input form on the device.

[0680] Input: Basic information such as company name, industry, and location entered by the user.

[0681] Output: A data request in which basic information is sent from the terminal to the server.

[0682] Specific action: The user enters "ABC Corporation," "Technology," and "Tokyo" into the input form and presses the submit button.

[0683] Step 2:

[0684] The terminal sends a data collection request to the server based on the information entered.

[0685] Input: User input information.

[0686] Output: Data collection request sent to the server.

[0687] Specific operation: The terminal sends a request to the / collect-data endpoint, and the server accesses an external data source (e.g., Google News API, Yahoo Finance API).

[0688] Step 3:

[0689] The server collects relevant data from reliable external data sources.

[0690] Input: Data collection request from the device.

[0691] Output: News articles, financial data, market trend data, etc., obtained from external data sources.

[0692] Specific operation: The server calls the Google News API to retrieve the latest news and uses the Yahoo Finance API to collect financial data. The server then stores the collected data in a database.

[0693] Step 4:

[0694] The server retrieves data collected from the database and analyzes it using generative artificial intelligence (e.g., OpenAI GPT-4).

[0695] Input: Collected data.

[0696] Output: A list of analyzed issues and needs.

[0697] Specific operation: The server sends a prompt message to the GPT-4 model saying, "Analyze the reasons for this company's decline in sales and list the main issues," and saves the analysis results to the database.

[0698] Step 5:

[0699] Based on the identified challenges and needs, the server generates an optimal proposal and formats it using natural language generation (NLG) technology.

[0700] Input: A list of analyzed issues and needs.

[0701] Output: Formatted proposal draft.

[0702] Specific operation: The server uses NLG technology to generate proposals and incorporates statistical information and trend data into pre-prepared templates.

[0703] Step 6:

[0704] The server uses an emotion engine to analyze the user's emotions from their voice, facial expressions, and text input.

[0705] Input: User's voice, facial expressions, and text data.

[0706] Output: Analyzed sentiment data.

[0707] Specific operation: The server uses the Microsoft Azure Cognitive Services sentiment analysis API to analyze the user's emotional state in real time. It determines that the user is feeling stressed.

[0708] Step 7:

[0709] The server adjusts the tone and content of the proposal based on emotional data.

[0710] Input: Analyzed sentiment data and proposal draft.

[0711] Output: Revised proposal draft.

[0712] Specific operation: Based on the analyzed sentiment data, the server softens the tone of the proposal and changes it to a user-friendly format.

[0713] Step 8:

[0714] The terminal displays a draft of the generated proposal to the user.

[0715] Input: Revised proposal draft.

[0716] Output: Display of the proposal draft.

[0717] Specific operation: The device displays the proposal to the user and provides a UI for the user to review its contents.

[0718] Step 9:

[0719] The user reviews the proposal and adds data and graphs as needed.

[0720] Input: Draft proposal.

[0721] Output: Edited proposal.

[0722] Specific actions: The user clicks on a specific part of the proposal to perform edits such as inserting data or graphs.

[0723] Step 10:

[0724] Users can ultimately save the proposal as an electronic file, such as a PDF, and also print it.

[0725] Input: Edited proposal.

[0726] Output: Saved PDF file.

[0727] Specific action: The user presses the save button, exports the proposal in PDF format, and saves it to their device.

[0728] Step 11:

[0729] Users conduct business negotiations based on the generated proposals.

[0730] Input: Saved PDF proposal.

[0731] Output: Progress of the business negotiation.

[0732] Specific actions: The user opens a proposal during a business meeting and gives a presentation based on the proposal. Using the results of sentiment analysis, the system adjusts the tone of communication if the user appears nervous.

[0733] (Application Example 2)

[0734] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0735] In modern business negotiations and customer interactions, it is crucial to quickly generate optimal proposals based on detailed information about target customers, and further adjust the proposal content according to the user's emotional state. Traditional systems often require manual proposal generation and adjustment, which is time-consuming and inefficient. Furthermore, dynamically adjusting proposal content to reflect the user's emotions is difficult. As a result, providing the optimal proposal to maximize the effectiveness of business negotiations has been challenging.

[0736] In Application Example 2, the identification processing by the identification processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for inputting basic information of the target customer, means for collecting and analyzing the latest industry and company data from external data sources, means for identifying the challenges and needs of the target company from the collected data using generative artificial intelligence, means for automatically generating an optimal proposal based on the identified challenges and needs and formatting it as a proposal document, means for the user to review and edit the proposal document, means for providing the proposal document for use during business negotiations, means for adjusting the content of the proposal document according to the user's emotional state, and means for analyzing emotions from the user's voice, facial expressions, and text input. This makes it possible to quickly generate an optimal proposal document for the target customer and dynamically adjust the content of the proposal according to the user's emotional state.

[0737] A "target customer" is an individual or company from which the system generates proposals.

[0738] "Basic information" refers to fundamental data necessary for creating a proposal, such as the target customer's company name, industry, and location.

[0739] "External data sources" refer to reliable sources of information that can be obtained from external sources, such as APIs on the internet, news articles, and financial data.

[0740] "Latest industry and company data" refers to real-time data collected on specific industries and companies, including the latest market trends, competitor activities, and financial data.

[0741] "Generative artificial intelligence" refers to an artificial intelligence system that can analyze collected data and automatically generate proposals using natural language generation technology.

[0742] "Challenges and needs" refer to problems and requests faced by companies and customers, including declining sales and entry into new markets.

[0743] A "proposal" is a document that contains recommendations for solutions, products, or services to be offered to a target customer.

[0744] "Formatting" refers to organizing the content of a proposal according to a set template and making it easy to read.

[0745] A "user" is a person who uses this system to create proposals and use them in business negotiations.

[0746] "Means for reviewing and editing" refers to a system that provides an interface allowing users to review the content of the generated proposal and make changes or additions as needed.

[0747] "Means of provision" refers to methods that allow the generated proposal to be saved as an electronic file or printed.

[0748] "Emotional state" refers to the user's current feelings and psychological state, and is analyzed from factors such as voice tone and facial expressions.

[0749] The "emotion engine" is a system that analyzes a user's emotions from their voice, facial expressions, and text input.

[0750] "Voice, facial expressions, and text input" refers to the user's speaking voice, facial expressions, and entered text data, and analyzing these is a means of understanding the user's emotions.

[0751] This invention provides a system for in-store employees wearing smart glasses to make optimal product recommendations to target customers and to adjust the recommendations according to the user's emotional state. The system's program processing is described in detail below.

[0752] First, the user (store clerk) wears smart glasses and interacts with customers in a physical store. After the user inputs basic information about the target customer (e.g., past purchase history, preferences, allergy information, etc.) through the input interface of the smart glasses, the terminal (smart glasses) sends this information to a server.

[0753] The server collects up-to-date industry and company data from external data sources. It utilizes reliable APIs and internet information sources to obtain relevant news articles and market trend data. Next, the server analyzes the collected data using generative artificial intelligence to extract potential customer challenges and needs. Based on this analysis, the server generates optimal proposals and formats them into a proposal document.

[0754] This proposal is automatically formatted according to a template that includes statistical information, trend data, and competitive analysis results. Furthermore, the content of the proposal is adjusted according to the user's emotional state using an emotion engine. If the user is tense, the tone of the proposal can be softened, and if their facial expression is excited, it can be adjusted to an energetic tone. This emotional state analysis uses an emotion engine that analyzes emotions from the user's voice, facial expressions, and text input.

[0755] Ultimately, the proposal generated by the server is displayed on the terminal (smart glasses), and the user (salesperson) uses this to suggest products to target customers. For example, if a customer asks about a specific facial cleanser, recommended products and descriptions will be displayed on the smart glasses' screen. The user then explains the product to the customer according to this information. At this time, the system will instruct the user to explain in a gentle tone if the customer's expression looks anxious, and in an energetic tone if their expression looks excited.

[0756] As a concrete example, consider a scenario where a customer visits a store and consults with a salesperson wearing smart glasses about a product they are considering purchasing. If the customer is looking for a facial cleanser, the salesperson asks, "Hello. What are you looking for today?" and the customer replies, "I'm looking for a facial cleanser." At this moment, the system analyzes the customer's facial expression and voice and determines that they are nervous. As a result, recommended facial cleansers are displayed on the smart glasses' screen, and the system suggests an explanation in a calm tone.

[0757] An example of a prompt statement is as follows:

[0758] Please enter customer information and generate product recommendations.

[0759] customer_id = "12345"

[0760] face_image = capture_face_image()

[0761] voice_sample = capture_voice_sample()

[0762] get_customer_data(customer_id)

[0763] analyze_emotion(face_image, voice_sample)

[0764] recommendations = generate_recommendation(customer_id)

[0765] adjusted_explanation = adjust_tone(recommendations)

[0766] display_recommendation(adjusted_explanation)

[0767] This approach makes it possible to make more effective product proposals to target customers. Furthermore, by dynamically adjusting the proposal content according to the user's emotional state, the success rate of sales negotiations can be increased.

[0768] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0769] Step 1:

[0770] The user inputs basic information about their target customer through the smart glasses' input interface. For example, they might enter the customer's ID, past purchase history, preferences, and allergy information. This information is stored as input data on the device and sent to the next processing step.

[0771] Step 2:

[0772] The terminal sends the entered target customer's basic information to the server. The server receives this information, collects the latest industry and company data from external data sources, and begins analysis. It uses reliable APIs and internet sources to obtain relevant information and stores it in a database.

[0773] Step 3:

[0774] The server analyzes the collected data using generative artificial intelligence. Specifically, it analyzes market trends, competitor activities, financial data, etc., to extract potential challenges and needs of target customers. For example, it analyzes customer preferences from their purchase history and identifies key purchasing trends using sales and interest data. Based on this, the server generates a list of challenges and needs.

[0775] Step 4:

[0776] The server generates optimal solutions based on identified challenges and needs, and formats them as proposal documents. Using generative artificial intelligence, it performs natural language generation and automatically formats the proposal according to a template that includes statistical information, trend data, and competitive analysis results. This proposal is then saved on the server as an electronic file.

[0777] Step 5:

[0778] The server uses an emotion engine to analyze the user's emotional state. The emotion engine detects emotions based on the user's voice, facial expressions, and text input data transmitted from the smart glasses. For example, it identifies emotional states such as tension, excitement, and anxiety from the user's tone of voice and facial expressions. This prepares the server for making adjustments based on the emotional state.

[0779] Step 6:

[0780] The server adjusts the tone of the generated proposal based on the user's emotional state, which has been analyzed by the emotion engine. For example, if the user is nervous, the proposal's description will be changed to a calmer tone; if they are excited, it will be adjusted to an energetic tone. This adjusted proposal then becomes the draft for use in the final business negotiation.

[0781] Step 7:

[0782] Finally, the finalized proposal is sent to the device. The device (smart glasses) displays the adjusted proposal to the user. The user can then use this proposal to proceed with negotiations with target customers and review and edit the proposal content as needed. During negotiations, the system supports the user in responding with the appropriate tone based on their emotional state, while referring to the proposal.

[0783] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0784] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0785] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0786] [Third Embodiment]

[0787] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0788] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0789] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0790] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0791] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0792] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0793] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0794] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0795] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0796] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0797] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0798] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0799] This invention is a system for generating optimal proposals for target customers and using them to provide topics of discussion during business negotiations. The system's program processing will be explained below with specific examples.

[0800] 1. Inputting target customer information and collecting data

[0801] The user enters basic information about their target customer (e.g., company name, industry, location). Based on the entered information, the device sends a data collection request to the server. The server collects data about the relevant company and industry from external data sources. In this process, it uses reliable APIs and internet sources to obtain relevant news articles, financial data, and market trend data.

[0802] 2. Data analysis and identification of challenges and needs

[0803] The server uses generative artificial intelligence to analyze the collected data. The analysis extracts potential challenges and needs of the target company. For example, if a company's sales are declining, possible causes include increased competition and loss of market share. These challenges and needs are listed according to their importance and then prioritized.

[0804] 3. Automated generation and formatting of proposals

[0805] The server utilizes natural language generation (NLG) technology to generate optimal proposals based on identified challenges and needs. The generated proposals are formatted according to defined templates and include, for example, statistical information, trend data, and competitive analysis results. The resulting proposals become the final version ready for use during business negotiations.

[0806] 4. Review and edit the proposal.

[0807] The terminal displays a draft of the generated proposal to the user. The user can review the proposal and edit it as needed, adding data and graphs. Finally, the user can save the proposal as an electronic file, such as a PDF, and print it.

[0808] 5. Topics to discuss during business negotiations

[0809] Users conduct business negotiations based on the generated proposals, presenting industry trends and solutions to specific challenges faced by companies. During negotiations, they can cite collected data and analysis results from generative artificial intelligence to provide specific and reliable explanations. For example, users can gain customer trust by presenting specific challenges and solutions based on the company's current situation and trends.

[0810] The key feature of this system is that it handles all processes in-house, from external data collection and analysis using generative artificial intelligence to the automatic generation of optimal proposals. This significantly improves the efficiency of proposal creation and makes it possible to provide concrete and reliable materials that will lead to successful business negotiations.

[0811] The following describes the processing flow.

[0812] Program processing flow

[0813] Step 1: Enter target customer information

[0814] The user enters basic information about the target customer (company name, industry, location, etc.) into the terminal. They then register the necessary information using the input form.

[0815] Step 2: Submit data collection request

[0816] The device sends a data collection request to the server based on the information entered by the user. The request includes the type of data to be collected and the source of the data.

[0817] Step 3: External Data Collection

[0818] The server accesses external data sources (APIs, reliable internet sources, etc.) to collect up-to-date data about the target company and industry. Specifically, it retrieves news articles, financial data, market trend data, and so on.

[0819] Step 4: Data Analysis

[0820] The server analyzes the collected data. Using text analysis algorithms, it extracts key keywords and topics from news articles and reports, organizes financial and market trend data, and generates statistical information.

[0821] Step 5: Identifying Issues and Needs

[0822] The server uses generative artificial intelligence to analyze data and identify potential challenges and needs of target companies. For example, it analyzes the causes of declining sales and new market opportunities. In this process, the AI ​​model performs pattern recognition and trend analysis.

[0823] Step 6: List the issues and needs

[0824] The server lists identified issues and needs, prioritizes them, assigns scores based on impact and urgency, and sorts them by priority.

[0825] Step 7: Automatic generation of optimal proposals

[0826] The server automatically generates optimal proposals for target companies based on the analysis results. Using natural language generation (NLG) technology, it generates specific solutions and strategies in proposal format.

[0827] Step 8: Formatting the proposal

[0828] The server formats the generated proposals according to a defined template. The proposals include statistical information, trend data, competitive analysis results, and also incorporate company logos and design elements.

[0829] Step 9: View and edit the proposal

[0830] The device displays a draft of the generated proposal to the user. The user reviews the proposal and adds data or graphs, or revises the text as needed.

[0831] Step 10: Saving and distributing the proposal

[0832] Users can save their final proposal as an electronic file, such as a PDF, and print it. This allows them to use it during business negotiations with clients.

[0833] Step 11: Topics to discuss during business negotiations

[0834] The sales negotiation is based on the proposal generated by the user. While referring to the proposal, the discussion will present industry trends and solutions to the company's specific challenges, and provide topics based on concrete and reliable data.

[0835] These steps enable the efficient generation of optimal proposals for target customers through a consistent process, thereby increasing the success rate of business negotiations.

[0836] (Example 1)

[0837] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0838] Traditional proposal creation processes were inefficient and time-consuming because each step—from data collection and analysis to proposal generation and its use in business negotiations—was performed individually. Furthermore, the reliability of the collected data and the accuracy of the analysis were often insufficient, significantly impacting the success of business negotiations. Therefore, there is a need for a system that consistently collects and analyzes highly reliable data and efficiently creates and delivers optimal proposals.

[0839] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0840] In this invention, the server includes means for collecting basic information on target companies and obtaining the latest industry and company data from external data sources; means for analyzing the collected data using generative artificial intelligence to identify the challenges and needs of target companies; and means for automatically generating proposals based on the identified challenges and needs and formatting them as proposal documents. This makes it possible to efficiently collect and analyze highly reliable data and create and provide optimal proposal documents.

[0841] A "target company" is a company that is the focus of a proposal.

[0842] "Basic information" refers to initial data about the target company, including information such as company name, industry, and location.

[0843] "External data sources" refer to external sources of information used to collect information about target companies and industries, such as APIs and websites on the internet.

[0844] "Generative artificial intelligence" is an artificial intelligence technology that analyzes collected data and generates text using natural language generation techniques.

[0845] A "proposal" is a document that outlines the proposed content for a target company and may include statistical information, trend data, and competitive analysis results.

[0846] "Formatting" refers to organizing and arranging the content of a generated proposal according to a defined template.

[0847] An "electronic file" is a digital file that can be read by a computer, such as a PDF file.

[0848] "Reliability" refers to the degree to which the collected data and analysis results are accurate and the proposed content is justified.

[0849] This invention is a system for generating optimal proposals for target companies and using them to provide topics of discussion during business negotiations. The system is implemented as follows.

[0850] 1. Inputting target customer information and collecting data

[0851] The user enters basic information about the target company (company name, industry, location, etc.) into the terminal. Specifically, the user enters information such as "Company Name: ABC Manufacturing Inc.", "Industry: Manufacturing", and "Location: Tokyo" into the terminal's input form.

[0852] The terminal sends the entered information to the server. A secure communication protocol (e.g., HTTPS) is used for transmission.

[0853] Based on the information it receives, the server collects up-to-date industry and company data from reliable external data sources (e.g., Bloomberg API, Google News API). This data includes news articles, financial data, and market trend data.

[0854] 2. Data analysis and identification of challenges and needs

[0855] The server analyzes the collected data using generative artificial intelligence (e.g., OpenAI's GPT-4). Natural language processing (NLP) techniques are used for the analysis.

[0856] The analysis identifies potential challenges and needs of the target company. For example, if a company's sales are declining, possible causes include increased competition or loss of market share. These challenges and needs are then listed according to their importance.

[0857] 3. Automated generation and formatting of proposals

[0858] Based on identified challenges and needs, the server utilizes natural language generation (NLG) technology to generate optimal suggestions. Specifically, these suggestions include statistical information, trend data, and competitive analysis results.

[0859] The generated proposals are formatted according to a defined template. Sections of the template include "Market Trend Analysis," "Competitor Comparison," and "Specific Solution Proposals."

[0860] 4. Review and edit the proposal.

[0861] The terminal displays a draft of the generated proposal to the user. The proposal is displayed in PDF preview format.

[0862] Users review the proposal and add data and graphs as needed. Editing options include adding text, modifying data, and inserting graphs.

[0863] Ultimately, users save the proposal in PDF or other electronic file formats and print it if necessary.

[0864] 5. Topics to discuss during business negotiations

[0865] Users conduct business negotiations based on the generated proposals. During negotiations, they quote the contents of the proposals and present specific challenges and solutions based on the company's current situation and trends. For example, they might present "decreased sales due to increased competition" and "strategies for expanding into new markets" to gain the customer's trust.

[0866] The following prompt statements are used as specific examples in this system:

[0867] Based on the target company's information (Company name: ABC Manufacturing Inc., Industry: Manufacturing, Location: Tokyo), analyze the problems causing the company's declining sales and generate an appropriate proposal.

[0868] The key feature of this system is its integrated approach, encompassing everything from external data collection and analysis using generative artificial intelligence to the automatic generation of optimal proposals. This significantly improves the efficiency of proposal creation and enables the provision of concrete and reliable materials for successful business negotiations.

[0869] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0870] Step 1:

[0871] The user enters basic information about the target customer.

[0872] Specifically, the user enters information such as "Company Name: ABC Manufacturing Inc.", "Industry: Manufacturing", and "Location: Tokyo" into the input form on the device.

[0873] Input: Company name, industry, location

[0874] Output: Basic information of the entered target customer

[0875] Step 2:

[0876] The terminal sends the input information to the server.

[0877] The terminal uses a secure communication protocol (e.g., HTTPS) to send the entered information to the server.

[0878] Input: Basic information of the target customer entered

[0879] Output: Customer information request sent to the server

[0880] Step 3:

[0881] The server collects up-to-date industry and company data from external data sources.

[0882] The server collects necessary information, such as news articles, financial data, and market trend data, using APIs like the Bloomberg API and the Google News API.

[0883] Input: Submitted customer information request

[0884] Output: Collected industry and company data

[0885] Step 4:

[0886] The server analyzes the data it has collected.

[0887] The server uses generative artificial intelligence (e.g., OpenAI's GPT-4) to analyze the collected data and identify the challenges and needs of the target company.

[0888] Input: Collected industry and company data

[0889] Output: List of issues and needs based on analysis results

[0890] Step 5:

[0891] The server automatically generates proposals based on identified issues and needs, and formats them into a proposal document.

[0892] The server utilizes natural language generation (NLG) technology to format proposals according to a template. The generated proposals include statistical information, trend data, and competitive analysis results.

[0893] Input: List of issues and needs

[0894] Output: Formatted proposal

[0895] Step 6:

[0896] The terminal displays the draft of the generated proposal.

[0897] The terminal displays the proposal, generated in PDF preview format, to the user.

[0898] Input: Formatted proposal

[0899] Output: Draft proposal displayed to the user

[0900] Step 7:

[0901] The user reviews the proposal and edits it as needed.

[0902] Users review the proposal and make edits such as adding text, changing data, and inserting graphs.

[0903] Input: Draft proposal displayed to the user

[0904] Output: Revised proposal

[0905] Step 8:

[0906] The user saves the proposal as its final form and prints it as needed.

[0907] Users can save the completed proposal as an electronic file, such as a PDF, and print it as needed.

[0908] Input: Revised proposal

[0909] Output: Saved proposal in PDF format, and printed proposal.

[0910] Step 9:

[0911] The user conducts business negotiations based on the proposal and provides specific topics for discussion.

[0912] Users quote the content of the generated proposal and, during business negotiations, present specific challenges and solutions based on the company's current situation and trends.

[0913] Input: Saved proposal in PDF format

[0914] Output: Providing specific topics for business negotiations and their results.

[0915] (Application Example 1)

[0916] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0917] In traditional advertising proposal creation, data collection, analysis, and proposal writing for target customers are often done manually, resulting in significant time and effort. Furthermore, manual data analysis and proposal generation are prone to human error and bias, making it difficult to identify the optimal advertising strategy. This leads to a lower success rate in advertising negotiations. Therefore, there is a need for a system that efficiently generates optimal advertising proposals for target companies and effectively advances negotiations.

[0918] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0919] In this invention, the server includes means for inputting basic information of target customers, means for collecting and analyzing the latest industry and company data from external data sources, means for identifying the challenges and needs of target companies from the collected data using generative artificial intelligence, means for automatically generating optimal proposals based on the identified challenges and needs and formatting them as advertising proposals, means for users to review and edit advertising proposals, and means for providing advertising proposals for use during advertising negotiations. This makes it possible to efficiently and accurately perform a series of processes from data collection and analysis of target companies to the automatic generation of optimal advertising proposals. Furthermore, since the generated proposals are formatted based on templates that include statistical information, trend data, and competitive analysis results, they can be used effectively during negotiations.

[0920] "Basic information about the target customer" includes information such as the customer's company name, industry, location, and past advertising campaign information.

[0921] "External data sources" refer to reliable data sources that provide information such as corporate financial data, industry trend data, and relevant news articles.

[0922] "Generative artificial intelligence" is an artificial intelligence technology that analyzes collected data to identify the challenges and needs of target companies and automatically generates proposals.

[0923] An "advertising proposal" is a document used to propose an advertising strategy for a target company, and is formatted using a template that includes statistical information, trend data, and competitive analysis results.

[0924] "Means for users to review and edit advertising proposals" refers to an interface that allows users to review generated advertising proposals and edit their content as needed.

[0925] "A means of providing advertising proposals for use during advertising negotiations" refers to a system function that provides a final advertising proposal so that it can be used effectively during negotiations.

[0926] "Statistical information" refers to data that quantifies the effectiveness of advertising campaigns and market research data.

[0927] "Trend data" refers to data on the latest industry trends and consumer behavior.

[0928] "Competitive analysis results" refer to the results of an analysis of the advertising strategies and market share of competing companies.

[0929] This invention is a system for automatically generating optimal advertising proposals for target customers specifically for the advertising industry. The system provides a consistent service from inputting target customer information to data collection, analysis, and generation, review, and editing of advertising proposals, using the following means. Specific hardware and software components include a server, smartphone, generative artificial intelligence model, and data collection API.

[0930] Entering target customer information

[0931] Users use a smartphone application to input basic information about their target customers (such as company name, industry, location, and past advertising campaign information). This basic information forms the basis for the system to send requests to external data sources.

[0932] Data collection

[0933] The server collects relevant data from external data sources based on the target customer's basic information. Specific data sources include corporate financial data, industry trend data, and relevant news articles. Reliable APIs are used for data collection.

[0934] Data Analysis

[0935] The collected data is analyzed on a server. This analysis uses generative artificial intelligence to extract potential challenges and needs of the target company from the collected information. For example, if the cause of declining sales is increased competition or loss of market share, these challenges and needs will be listed.

[0936] Generating an advertising proposal

[0937] The server automatically generates optimal advertising proposals using natural language generation (NLG) technology based on the analysis results. These proposals include statistical information, trend data, and competitive analysis results. The generated proposals are formatted according to a template and await user review.

[0938] Review and editing of advertising proposals

[0939] Users review the generated advertising proposal draft using a smartphone application and edit the content as needed. The edited advertising proposal is saved in PDF format or other formats and is ultimately used during business negotiations.

[0940] Specific example

[0941] For example, if the target company for the cosmetics industry is "XYZ Corporation," the user inputs basic information about "XYZ Corporation" and past social media campaign information. Based on this, the server collects and analyzes the latest industry trend data, financial data, and competitive analysis results. Finally, a proposal for an influencer marketing campaign utilizing social media is generated.

[0942] Example of a prompt

[0943] Target company: XYZ Corporation

[0944] Industry: Cosmetics

[0945] Past Campaigns: Successful Social Media Campaigns

[0946] Collected data:

[0947] 1. Industry Trends

[0948] 2. News articles

[0949] 3. Financial Data

[0950] The generated proposal:

[0951] 1. Overview of Industry Trends

[0952] 2. Potential challenges

[0953] 3. Proposal for the optimal advertising campaign

[0954] In this way, the entire proposal creation process can be streamlined, increasing the success rate of business negotiations.

[0955] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0956] Step 1:

[0957] The user enters basic information about the target customer into a smartphone application. This information includes the customer's company name, industry, location, and past advertising campaign information. This generates basic information data for the target company.

[0958] Input: Customer's company name, industry, location, and past advertising campaign information.

[0959] Output: Basic information data of the target company

[0960] Step 2:

[0961] The device sends a data collection request to the server based on the target customer's basic information data. The server uses a reliable data collection API to collect industry trend data, corporate financial data, news articles, etc., from external data sources. This is how external data is collected.

[0962] Input: Basic information data of the target company

[0963] Output: Collected external data

[0964] Step 3:

[0965] The server analyzes the collected external data using generative artificial intelligence (generative AI model). The generative AI model extracts and lists the target company's potential challenges and needs from the collected data. This generates challenge and needs data.

[0966] Input: Collected external data

[0967] Output: Issue and needs data

[0968] Step 4:

[0969] The server automatically generates optimal advertising proposals using natural language generation (NLG) technology based on problem and needs data. The generated advertising proposals are formatted according to a template, including statistical information, trend data, and competitor analysis results. This process generates the advertising proposal data.

[0970] Input: Issue and needs data

[0971] Output: Advertising proposal data

[0972] Step 5:

[0973] The user reviews the draft advertising proposal generated on their device and edits it as needed. The edited advertising proposal is saved on the device in PDF format or another suitable format. This completes the final advertising proposal.

[0974] Input: Advertising proposal data

[0975] Output: Final advertising proposal

[0976] Step 6:

[0977] Users conduct business negotiations using the completed advertising proposal. During negotiations, they can increase their chances of success by citing the statistical information, trend data, and competitor analysis results included in the advertising proposal to make specific and reliable proposals.

[0978] Input: Final advertising proposal

[0979] Output: Improved success rate of business negotiations

[0980] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0981] This invention is a system for generating optimal proposals for target customers and using them to provide topics of conversation during business negotiations. Furthermore, it incorporates an emotion engine that recognizes the user's emotions and adjusts the content of the proposal. The system's program processing will be explained below with specific examples.

[0982] 1. Inputting target customer information and collecting data

[0983] The user enters basic information about their target customer (e.g., company name, industry, location). Based on the entered information, the terminal sends a data collection request to the server. The server collects the latest data on the relevant company and industry from external data sources. In this process, it uses reliable APIs and internet sources to obtain relevant news articles, financial data, and market trend data.

[0984] 2. Data analysis and identification of challenges and needs

[0985] The server uses generative artificial intelligence to analyze the collected data. The analysis extracts potential challenges and needs of the target company. For example, if a company's sales are declining, possible causes include increased competition and loss of market share. These challenges and needs are listed according to their importance and then prioritized.

[0986] 3. Automated generation and formatting of proposals

[0987] The server utilizes natural language generation (NLG) technology to generate optimal proposals based on identified challenges and needs. The generated proposals are formatted according to defined templates and include, for example, statistical information, trend data, and competitive analysis results. The resulting proposals become the final version ready for use during business negotiations.

[0988] 4. Emotion Recognition and Regulation

[0989] The server uses an emotion engine to analyze the user's emotions from their voice, facial expressions, and text input. For example, if the user is feeling nervous, the emotion engine can adjust the tone of the proposal to be more calming. This ensures that the best proposal is provided based on the user's emotional state.

[0990] 5. Viewing and editing proposals

[0991] The terminal displays a draft of the generated proposal to the user. The user can review the proposal and edit it as needed, adding data and graphs as required. Finally, the user can save the proposal as an electronic file, such as a PDF, and print it.

[0992] 6. Topics to discuss during business negotiations

[0993] Users conduct business negotiations based on the generated proposals. While referring to the proposals, they are presented with industry trends and solutions to specific company challenges. Furthermore, based on the analysis results of the emotion engine, communication is tailored to the user's emotional state, and topics are provided based on reliable data.

[0994] As a concrete example, when a user is conducting a business negotiation with a target company, the proposal will include detailed information on the activities of competitors and market trends. Furthermore, if the user is feeling nervous, the emotional engine will adjust the tone of the proposal to soften it and change it to a format that is easier for the user to read. This will enable a more effective presentation during the business negotiation.

[0995] This system is characterized by its integrated approach, handling all processes from external data collection and analysis by generative artificial intelligence to the automatic generation of optimal proposals and the adjustment of proposal content using an emotion engine. This significantly improves the efficiency of proposal creation and enables the provision of concrete and reliable materials for successful business negotiations.

[0996] The following describes the processing flow.

[0997] Program processing flow

[0998] Step 1: Enter target customer information

[0999] The user enters basic information about the target customer (company name, industry, location, etc.) into the terminal. The user then registers the necessary information using a dedicated input form.

[1000] Step 2: Submit data collection request

[1001] The device sends a data collection request to the server based on the information entered by the user. The request includes the type of data to be collected and the source of the data.

[1002] Step 3: External Data Collection

[1003] The server accesses external data sources to collect the latest data on target companies and industries. These external data sources include news article APIs, financial data APIs, and market trend data APIs. Specifically, it retrieves relevant news articles, financial reports, market trend reports, and more.

[1004] Step 4: Data Analysis

[1005] The server analyzes the collected data. Using text analysis algorithms, it extracts key keywords and topics from news articles and reports, organizes financial and market trend data, and generates statistical information.

[1006] Step 5: Identifying Issues and Needs

[1007] The server uses generative artificial intelligence to analyze data and identify potential challenges and needs of target companies. For example, if a decline in sales is observed, possible causes include increased competition and a decrease in market share. The generative AI model performs pattern recognition and trend analysis to extract specific challenges and areas for improvement.

[1008] Step 6: List the issues and needs

[1009] The server lists identified issues and needs and prioritizes them. It assigns scores based on impact and urgency, and sorts them by priority. This ensures that solutions are considered starting with the most important issues.

[1010] Step 7: Automatic generation of optimal proposals

[1011] The server utilizes natural language generation (NLG) technology to generate optimal suggestions based on identified challenges and needs. These suggestions include solutions and recommendations, presenting specific strategies tailored to the company's specific situation.

[1012] Step 8: Formatting the proposal

[1013] The server formats the generated proposals according to a defined template. The proposals include statistical information, trend data, competitive analysis results, and also incorporate company logos and design elements.

[1014] Step 9: Emotion Recognition

[1015] The device collects emotions from the user's voice, facial expressions, and text input. This data is transmitted to the server in real time.

[1016] Step 10: Emotion Analysis

[1017] The server analyzes the collected emotional data through an emotion engine. For example, if the user is feeling nervous, the emotion engine recognizes this and adjusts the tone of the proposal accordingly.

[1018] Step 11: Adjusting the proposal based on emotions

[1019] The server adjusts the content and tone of the proposal based on the analysis results of the emotion engine. For example, if the user is relaxed, it uses a lighthearted tone, and if the user is stressed, it uses a calm tone.

[1020] Step 12: View and edit the proposal

[1021] The device displays a draft of the generated proposal to the user. The user can review the proposal and edit it as needed, adding data and graphs as required.

[1022] Step 13: Saving and distributing the proposal

[1023] Users can save their final proposal as an electronic file, such as a PDF, and print it. This allows them to use it during business negotiations with clients.

[1024] Step 14: Topics to discuss during business negotiations

[1025] The system conducts business negotiations based on proposals generated by the user. While referring to the proposals, it presents industry trends and solutions to the company's specific challenges. Furthermore, based on analysis results from the emotion engine, it communicates in a tone appropriate to the user's emotional state.

[1026] These steps enable the efficient generation of optimal proposals for target customers through a consistent process, allowing for communication that takes user emotions into consideration, and ultimately increasing the success rate of business negotiations.

[1027] (Example 2)

[1028] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1029] While conventional sales presentation systems could automatically generate proposals for target customers, their effectiveness was limited due to the uniformity of the proposal content and the lack of appropriate adjustments based on the user's emotional state. Furthermore, manual data collection and analysis were required, posing significant challenges in terms of time and effort. Additionally, improvements in the accuracy of data analysis and the reduction of the user's emotional burden during sales negotiations were insufficient.

[1030] The identification processing by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for inputting basic information of the target customer, means for collecting and analyzing the latest industry and company data from external data sources, means for identifying the challenges and needs of the target company from the collected data using generative artificial intelligence, means for automatically generating an optimal proposal based on the identified challenges and needs and formatting it as a proposal document, means for analyzing the user's emotions and adjusting the content and tone of the proposal document, means for the user to review and edit the proposal document, and means for providing the proposal document for use during business negotiations. This makes it possible to make optimal adjustments in response to the user's emotions during the automatic generation of the proposal document, maximizing the effectiveness of business negotiations while significantly reducing the time and effort required to create the proposal document.

[1031] "Means for inputting basic information of target customers" refers to the means by which users input basic information of their target customers, such as the company name, industry, and location, into a terminal.

[1032] "Means for collecting and analyzing up-to-date industry and company data from external data sources" refers to a method by which a server uses reliable APIs or internet information sources to obtain and analyze the latest data on the target industry and company.

[1033] "Methods for identifying the challenges and needs of target companies from data collected using generative artificial intelligence" refers to methods for analyzing collected data using generative artificial intelligence to extract the potential challenges and needs of target companies.

[1034] "A means of automatically generating optimal proposals based on identified issues and needs and formatting them as proposal documents" refers to a method of automatically generating optimal proposals using natural language generation technology based on analysis results and formatting them according to a predefined template.

[1035] "Means for analyzing user emotions and adjusting the content and tone of the proposal" refers to a method that uses an emotion engine to analyze the user's emotions from their voice, facial expressions, and text input, and then appropriately adjusts the tone and content of the proposal.

[1036] "Means for users to review and edit proposals" refers to the means by which the terminal displays the generated proposal to the user, allowing the user to review and edit its contents.

[1037] "Means of providing proposals for use during business negotiations" refers to methods of saving the final completed proposal in an electronic format such as PDF, and making it printable.

[1038] This invention is a system for generating optimal proposals for target customers and using them to provide topics of conversation during business negotiations. Furthermore, it incorporates an emotion engine that recognizes the user's emotions and adjusts the content of the proposal. The system's program processing will be explained below with specific examples.

[1039] 1. Inputting target customer information and collecting data

[1040] The user enters basic information about their target customer (e.g., company name, industry, location) into an input form on the device. After completing the input, the device sends a data collection request to the server based on this information. The server accesses reliable external data sources (e.g., Google News API, Yahoo Finance API) and collects the relevant data. The server stores the collected data in a database. This database may include, for example, the latest company news, financial data, and industry market trend information.

[1041] 2. Data analysis and identification of challenges and needs

[1042] The server retrieves data collected from the database and analyzes it using generative artificial intelligence (e.g., OpenAI GPT-4). The analysis extracts potential challenges and needs of the target company. For example, if a company's sales are declining, possible causes include increased competition and loss of market share. These challenges and needs are listed according to their importance and then prioritized.

[1043] 3. Automated generation and formatting of proposals

[1044] The server utilizes natural language generation (NLG) technology to generate optimal proposals based on identified challenges and needs. The generated proposals are formatted according to defined templates, including, for example, statistics, trend data, and competitive analysis results. The resulting proposals become the final version ready for use during business negotiations.

[1045] 4. Emotion Recognition and Regulation

[1046] The server uses an emotion engine to analyze the user's emotions from their voice, facial expressions, and text input. The emotion engine utilizes, for example, the emotion analysis API from Microsoft Azure Cognitive Services. If the user is feeling stressed, the server adjusts the tone of the proposal to soften it, providing the most appropriate proposal based on the user's emotional state.

[1047] 5. Viewing and editing proposals

[1048] The terminal displays a draft of the generated proposal to the user. The user reviews the proposal and edits it as needed, adding data and graphs. Finally, the user can save the proposal as an electronic file, such as a PDF, and print it.

[1049] 6. Topics to discuss during business negotiations

[1050] Users conduct business negotiations based on the generated proposals. They refer to the proposals while presenting industry trends and solutions to specific challenges faced by the company. Furthermore, based on the analysis results of the emotion engine, communication is tailored to the user's emotional state. For example, when a user is conducting a business negotiation with a target company, the proposal includes detailed information on the activities of competitors and market trends. Additionally, if the user is feeling nervous, the emotion engine adjusts the tone of the proposal to soften it and changes it to a more readable format. This enables more effective presentations during business negotiations.

[1051] Prompt statements as concrete examples

[1052] Use the following prompt text as input for the generating AI model:

[1053] Company name: XYZ Corporation

[1054] Industry: Technology

[1055] Location: Tokyo

[1056] Description: XYZ Corporation is experiencing declining sales, while its competitors continue to grow.

[1057] Please generate the corresponding proposal.

[1058] Following this prompt, the generative AI model collects the necessary data and generates the optimal proposal. As described above, the system of the present invention can consistently perform all processes, from external data collection to analysis by generative artificial intelligence, automatic generation of the optimal proposal, and adjustment of the proposal content by an emotion engine. This significantly improves the efficiency of proposal creation and makes it possible to provide concrete and reliable materials for successful business negotiations.

[1059] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1060] Step 1:

[1061] The user enters basic information about the target customer (e.g., company name, industry, location, etc.) into the input form on the device.

[1062] Input: Basic information such as company name, industry, and location entered by the user.

[1063] Output: A data request in which basic information is sent from the terminal to the server.

[1064] Specific action: The user enters "ABC Corporation," "Technology," and "Tokyo" into the input form and presses the submit button.

[1065] Step 2:

[1066] The terminal sends a data collection request to the server based on the information entered.

[1067] Input: User input information.

[1068] Output: Data collection request sent to the server.

[1069] Specific operation: The terminal sends a request to the / collect-data endpoint, and the server accesses an external data source (e.g., Google News API, Yahoo Finance API).

[1070] Step 3:

[1071] The server collects relevant data from reliable external data sources.

[1072] Input: Data collection request from the device.

[1073] Output: News articles, financial data, market trend data, etc., obtained from external data sources.

[1074] Specific operation: The server calls the Google News API to retrieve the latest news and uses the Yahoo Finance API to collect financial data. The server then stores the collected data in a database.

[1075] Step 4:

[1076] The server retrieves data collected from the database and analyzes it using generative artificial intelligence (e.g., OpenAI GPT-4).

[1077] Input: Collected data.

[1078] Output: A list of analyzed issues and needs.

[1079] Specific operation: The server sends a prompt message to the GPT-4 model saying, "Analyze the reasons for this company's decline in sales and list the main issues," and saves the analysis results to the database.

[1080] Step 5:

[1081] Based on the identified challenges and needs, the server generates an optimal proposal and formats it using natural language generation (NLG) technology.

[1082] Input: A list of analyzed issues and needs.

[1083] Output: Formatted proposal draft.

[1084] Specific operation: The server uses NLG technology to generate proposals and incorporates statistical information and trend data into pre-prepared templates.

[1085] Step 6:

[1086] The server uses an emotion engine to analyze the user's emotions from their voice, facial expressions, and text input.

[1087] Input: User's voice, facial expressions, and text data.

[1088] Output: Analyzed sentiment data.

[1089] Specific operation: The server uses the Microsoft Azure Cognitive Services sentiment analysis API to analyze the user's emotional state in real time. It determines that the user is feeling stressed.

[1090] Step 7:

[1091] The server adjusts the tone and content of the proposal based on emotional data.

[1092] Input: Analyzed sentiment data and proposal draft.

[1093] Output: Revised proposal draft.

[1094] Specific operation: Based on the analyzed sentiment data, the server softens the tone of the proposal and changes it to a user-friendly format.

[1095] Step 8:

[1096] The terminal displays a draft of the generated proposal to the user.

[1097] Input: Revised proposal draft.

[1098] Output: Display of the proposal draft.

[1099] Specific operation: The device displays the proposal to the user and provides a UI for the user to review its contents.

[1100] Step 9:

[1101] The user reviews the proposal and adds data and graphs as needed.

[1102] Input: Draft proposal.

[1103] Output: Edited proposal.

[1104] Specific actions: The user clicks on a specific part of the proposal to perform edits such as inserting data or graphs.

[1105] Step 10:

[1106] Users can ultimately save the proposal as an electronic file, such as a PDF, and also print it.

[1107] Input: Edited proposal.

[1108] Output: Saved PDF file.

[1109] Specific action: The user presses the save button, exports the proposal in PDF format, and saves it to their device.

[1110] Step 11:

[1111] Users conduct business negotiations based on the generated proposals.

[1112] Input: Saved PDF proposal.

[1113] Output: Progress of the business negotiation.

[1114] Specific actions: The user opens a proposal during a business meeting and gives a presentation based on the proposal. Using the results of sentiment analysis, the system adjusts the tone of communication if the user appears nervous.

[1115] (Application Example 2)

[1116] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1117] In modern business negotiations and customer interactions, it is crucial to quickly generate optimal proposals based on detailed information about target customers, and further adjust the proposal content according to the user's emotional state. Traditional systems often require manual proposal generation and adjustment, which is time-consuming and inefficient. Furthermore, dynamically adjusting proposal content to reflect the user's emotions is difficult. As a result, providing the optimal proposal to maximize the effectiveness of business negotiations has been challenging.

[1118] In Application Example 2, the identification processing by the identification processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for inputting basic information of the target customer, means for collecting and analyzing the latest industry and company data from external data sources, means for identifying the challenges and needs of the target company from the collected data using generative artificial intelligence, means for automatically generating an optimal proposal based on the identified challenges and needs and formatting it as a proposal document, means for the user to review and edit the proposal document, means for providing the proposal document for use during business negotiations, means for adjusting the content of the proposal document according to the user's emotional state, and means for analyzing emotions from the user's voice, facial expressions, and text input. This makes it possible to quickly generate an optimal proposal document for the target customer and dynamically adjust the content of the proposal according to the user's emotional state.

[1119] A "target customer" is an individual or company from which the system generates proposals.

[1120] "Basic information" refers to fundamental data necessary for creating a proposal, such as the target customer's company name, industry, and location.

[1121] "External data sources" refer to reliable sources of information that can be obtained from external sources, such as APIs on the internet, news articles, and financial data.

[1122] "Latest industry and company data" refers to real-time data collected on specific industries and companies, including the latest market trends, competitor activities, and financial data.

[1123] "Generative artificial intelligence" refers to an artificial intelligence system that can analyze collected data and automatically generate proposals using natural language generation technology.

[1124] "Challenges and needs" refer to problems and requests faced by companies and customers, including declining sales and entry into new markets.

[1125] A "proposal" is a document that contains recommendations for solutions, products, or services to be offered to a target customer.

[1126] "Formatting" refers to organizing the content of a proposal according to a set template and making it easy to read.

[1127] A "user" is a person who uses this system to create proposals and use them in business negotiations.

[1128] "Means for reviewing and editing" refers to a system that provides an interface allowing users to review the content of the generated proposal and make changes or additions as needed.

[1129] "Means of provision" refers to methods that allow the generated proposal to be saved as an electronic file or printed.

[1130] "Emotional state" refers to the user's current feelings and psychological state, and is analyzed from factors such as voice tone and facial expressions.

[1131] The "emotion engine" is a system that analyzes a user's emotions from their voice, facial expressions, and text input.

[1132] "Voice, facial expressions, and text input" refers to the user's speaking voice, facial expressions, and entered text data, and analyzing these is a means of understanding the user's emotions.

[1133] This invention provides a system for in-store employees wearing smart glasses to make optimal product recommendations to target customers and to adjust the recommendations according to the user's emotional state. The system's program processing is described in detail below.

[1134] First, the user (store clerk) wears smart glasses and interacts with customers in a physical store. After the user inputs basic information about the target customer (e.g., past purchase history, preferences, allergy information, etc.) through the input interface of the smart glasses, the terminal (smart glasses) sends this information to a server.

[1135] The server collects up-to-date industry and company data from external data sources. It utilizes reliable APIs and internet information sources to obtain relevant news articles and market trend data. Next, the server analyzes the collected data using generative artificial intelligence to extract potential customer challenges and needs. Based on this analysis, the server generates optimal proposals and formats them into a proposal document.

[1136] This proposal is automatically formatted according to a template that includes statistical information, trend data, and competitive analysis results. Furthermore, the content of the proposal is adjusted according to the user's emotional state using an emotion engine. If the user is tense, the tone of the proposal can be softened, and if their facial expression is excited, it can be adjusted to an energetic tone. This emotional state analysis uses an emotion engine that analyzes emotions from the user's voice, facial expressions, and text input.

[1137] Ultimately, the proposal generated by the server is displayed on the terminal (smart glasses), and the user (salesperson) uses this to suggest products to target customers. For example, if a customer asks about a specific facial cleanser, recommended products and descriptions will be displayed on the smart glasses' screen. The user then explains the product to the customer according to this information. At this time, the system will instruct the user to explain in a gentle tone if the customer's expression looks anxious, and in an energetic tone if their expression looks excited.

[1138] As a concrete example, consider a scenario where a customer visits a store and consults with a salesperson wearing smart glasses about a product they are considering purchasing. If the customer is looking for a facial cleanser, the salesperson asks, "Hello. What are you looking for today?" and the customer replies, "I'm looking for a facial cleanser." At this moment, the system analyzes the customer's facial expression and voice and determines that they are nervous. As a result, recommended facial cleansers are displayed on the smart glasses' screen, and the system suggests an explanation in a calm tone.

[1139] An example of a prompt statement is as follows:

[1140] Please enter customer information and generate product recommendations.

[1141] customer_id = "12345"

[1142] face_image = capture_face_image()

[1143] voice_sample = capture_voice_sample()

[1144] get_customer_data(customer_id)

[1145] analyze_emotion(face_image, voice_sample)

[1146] recommendations = generate_recommendation(customer_id)

[1147] adjusted_explanation = adjust_tone(recommendations)

[1148] display_recommendation(adjusted_explanation)

[1149] This approach makes it possible to make more effective product proposals to target customers. Furthermore, by dynamically adjusting the proposal content according to the user's emotional state, the success rate of sales negotiations can be increased.

[1150] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1151] Step 1:

[1152] The user inputs basic information about their target customer through the smart glasses' input interface. For example, they might enter the customer's ID, past purchase history, preferences, and allergy information. This information is stored as input data on the device and sent to the next processing step.

[1153] Step 2:

[1154] The terminal sends the entered target customer's basic information to the server. The server receives this information, collects the latest industry and company data from external data sources, and begins analysis. It uses reliable APIs and internet sources to obtain relevant information and stores it in a database.

[1155] Step 3:

[1156] The server analyzes the collected data using generative artificial intelligence. Specifically, it analyzes market trends, competitor activities, financial data, etc., to extract potential challenges and needs of target customers. For example, it analyzes customer preferences from their purchase history and identifies key purchasing trends using sales and interest data. Based on this, the server generates a list of challenges and needs.

[1157] Step 4:

[1158] The server generates optimal solutions based on identified challenges and needs, and formats them as proposal documents. Using generative artificial intelligence, it performs natural language generation and automatically formats the proposal according to a template that includes statistical information, trend data, and competitive analysis results. This proposal is then saved on the server as an electronic file.

[1159] Step 5:

[1160] The server uses an emotion engine to analyze the user's emotional state. The emotion engine detects emotions based on the user's voice, facial expressions, and text input data transmitted from the smart glasses. For example, it identifies emotional states such as tension, excitement, and anxiety from the user's tone of voice and facial expressions. This prepares the server for making adjustments based on the emotional state.

[1161] Step 6:

[1162] The server adjusts the tone of the generated proposal based on the user's emotional state, which has been analyzed by the emotion engine. For example, if the user is nervous, the proposal's description will be changed to a calmer tone; if they are excited, it will be adjusted to an energetic tone. This adjusted proposal then becomes the draft for use in the final business negotiation.

[1163] Step 7:

[1164] Finally, the finalized proposal is sent to the device. The device (smart glasses) displays the adjusted proposal to the user. The user can then use this proposal to proceed with negotiations with target customers and review and edit the proposal content as needed. During negotiations, the system supports the user in responding with the appropriate tone based on their emotional state, while referring to the proposal.

[1165] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1166] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1167] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[1168] [Fourth Embodiment]

[1169] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[1170] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1171] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1172] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[1173] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[1174] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[1175] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[1176] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[1177] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[1178] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1179] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1180] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[1181] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1182] This invention is a system for generating optimal proposals for target customers and using them to provide topics of discussion during business negotiations. The system's program processing will be explained below with specific examples.

[1183] 1. Inputting target customer information and collecting data

[1184] The user enters basic information about their target customer (e.g., company name, industry, location). Based on the entered information, the device sends a data collection request to the server. The server collects data about the relevant company and industry from external data sources. In this process, it uses reliable APIs and internet sources to obtain relevant news articles, financial data, and market trend data.

[1185] 2. Data analysis and identification of challenges and needs

[1186] The server uses generative artificial intelligence to analyze the collected data. The analysis extracts potential challenges and needs of the target company. For example, if a company's sales are declining, possible causes include increased competition and loss of market share. These challenges and needs are listed according to their importance and then prioritized.

[1187] 3. Automated generation and formatting of proposals

[1188] The server utilizes natural language generation (NLG) technology to generate optimal proposals based on identified challenges and needs. The generated proposals are formatted according to defined templates and include, for example, statistical information, trend data, and competitive analysis results. The resulting proposals become the final version ready for use during business negotiations.

[1189] 4. Review and edit the proposal.

[1190] The terminal displays a draft of the generated proposal to the user. The user can review the proposal and edit it as needed, adding data and graphs. Finally, the user can save the proposal as an electronic file, such as a PDF, and print it.

[1191] 5. Topics to discuss during business negotiations

[1192] Users conduct business negotiations based on the generated proposals, presenting industry trends and solutions to specific challenges faced by companies. During negotiations, they can cite collected data and analysis results from generative artificial intelligence to provide specific and reliable explanations. For example, users can gain customer trust by presenting specific challenges and solutions based on the company's current situation and trends.

[1193] The key feature of this system is that it handles all processes in-house, from external data collection and analysis using generative artificial intelligence to the automatic generation of optimal proposals. This significantly improves the efficiency of proposal creation and makes it possible to provide concrete and reliable materials that will lead to successful business negotiations.

[1194] The following describes the processing flow.

[1195] Program processing flow

[1196] Step 1: Enter target customer information

[1197] The user enters basic information about the target customer (company name, industry, location, etc.) into the terminal. They then register the necessary information using the input form.

[1198] Step 2: Submit data collection request

[1199] The device sends a data collection request to the server based on the information entered by the user. The request includes the type of data to be collected and the source of the data.

[1200] Step 3: External Data Collection

[1201] The server accesses external data sources (APIs, reliable internet sources, etc.) to collect up-to-date data about the target company and industry. Specifically, it retrieves news articles, financial data, market trend data, and so on.

[1202] Step 4: Data Analysis

[1203] The server analyzes the collected data. Using text analysis algorithms, it extracts key keywords and topics from news articles and reports, organizes financial and market trend data, and generates statistical information.

[1204] Step 5: Identifying Issues and Needs

[1205] The server uses generative artificial intelligence to analyze data and identify potential challenges and needs of target companies. For example, it analyzes the causes of declining sales and new market opportunities. In this process, the AI ​​model performs pattern recognition and trend analysis.

[1206] Step 6: List the issues and needs

[1207] The server lists identified issues and needs, prioritizes them, assigns scores based on impact and urgency, and sorts them by priority.

[1208] Step 7: Automatic generation of optimal proposals

[1209] The server automatically generates optimal proposals for target companies based on the analysis results. Using natural language generation (NLG) technology, it generates specific solutions and strategies in proposal format.

[1210] Step 8: Formatting the proposal

[1211] The server formats the generated proposals according to a defined template. The proposals include statistical information, trend data, competitive analysis results, and also incorporate company logos and design elements.

[1212] Step 9: View and edit the proposal

[1213] The device displays a draft of the generated proposal to the user. The user reviews the proposal and adds data or graphs, or revises the text as needed.

[1214] Step 10: Saving and distributing the proposal

[1215] Users can save their final proposal as an electronic file, such as a PDF, and print it. This allows them to use it during business negotiations with clients.

[1216] Step 11: Topics to discuss during business negotiations

[1217] The sales negotiation is based on the proposal generated by the user. While referring to the proposal, the discussion will present industry trends and solutions to the company's specific challenges, and provide topics based on concrete and reliable data.

[1218] These steps enable the efficient generation of optimal proposals for target customers through a consistent process, thereby increasing the success rate of business negotiations.

[1219] (Example 1)

[1220] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1221] Traditional proposal creation processes were inefficient and time-consuming because each step—from data collection and analysis to proposal generation and its use in business negotiations—was performed individually. Furthermore, the reliability of the collected data and the accuracy of the analysis were often insufficient, significantly impacting the success of business negotiations. Therefore, there is a need for a system that consistently collects and analyzes highly reliable data and efficiently creates and delivers optimal proposals.

[1222] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[1223] In this invention, the server includes means for collecting basic information on target companies and obtaining the latest industry and company data from external data sources; means for analyzing the collected data using generative artificial intelligence to identify the challenges and needs of target companies; and means for automatically generating proposals based on the identified challenges and needs and formatting them as proposal documents. This makes it possible to efficiently collect and analyze highly reliable data and create and provide optimal proposal documents.

[1224] A "target company" is a company that is the focus of a proposal.

[1225] "Basic information" refers to initial data about the target company, including information such as company name, industry, and location.

[1226] "External data sources" refer to external sources of information used to collect information about target companies and industries, such as APIs and websites on the internet.

[1227] "Generative artificial intelligence" is an artificial intelligence technology that analyzes collected data and generates text using natural language generation techniques.

[1228] A "proposal" is a document that outlines the proposed content for a target company and may include statistical information, trend data, and competitive analysis results.

[1229] "Formatting" refers to organizing and arranging the content of a generated proposal according to a defined template.

[1230] An "electronic file" is a digital file that can be read by a computer, such as a PDF file.

[1231] "Reliability" refers to the degree to which the collected data and analysis results are accurate and the proposed content is justified.

[1232] This invention is a system for generating optimal proposals for target companies and using them to provide topics of discussion during business negotiations. The system is implemented as follows.

[1233] 1. Inputting target customer information and collecting data

[1234] The user enters basic information about the target company (company name, industry, location, etc.) into the terminal. Specifically, the user enters information such as "Company Name: ABC Manufacturing Inc.", "Industry: Manufacturing", and "Location: Tokyo" into the terminal's input form.

[1235] The terminal sends the entered information to the server. A secure communication protocol (e.g., HTTPS) is used for transmission.

[1236] Based on the information it receives, the server collects up-to-date industry and company data from reliable external data sources (e.g., Bloomberg API, Google News API). This data includes news articles, financial data, and market trend data.

[1237] 2. Data analysis and identification of challenges and needs

[1238] The server analyzes the collected data using generative artificial intelligence (e.g., OpenAI's GPT-4). Natural language processing (NLP) techniques are used for the analysis.

[1239] The analysis identifies potential challenges and needs of the target company. For example, if a company's sales are declining, possible causes include increased competition or loss of market share. These challenges and needs are then listed according to their importance.

[1240] 3. Automated generation and formatting of proposals

[1241] Based on identified challenges and needs, the server utilizes natural language generation (NLG) technology to generate optimal suggestions. Specifically, these suggestions include statistical information, trend data, and competitive analysis results.

[1242] The generated proposals are formatted according to a defined template. Sections of the template include "Market Trend Analysis," "Competitor Comparison," and "Specific Solution Proposals."

[1243] 4. Review and edit the proposal.

[1244] The terminal displays a draft of the generated proposal to the user. The proposal is displayed in PDF preview format.

[1245] Users review the proposal and add data and graphs as needed. Editing options include adding text, modifying data, and inserting graphs.

[1246] Ultimately, users save the proposal in PDF or other electronic file formats and print it if necessary.

[1247] 5. Topics to discuss during business negotiations

[1248] Users conduct business negotiations based on the generated proposals. During negotiations, they quote the contents of the proposals and present specific challenges and solutions based on the company's current situation and trends. For example, they might present "decreased sales due to increased competition" and "strategies for expanding into new markets" to gain the customer's trust.

[1249] The following prompt statements are used as specific examples in this system:

[1250] Based on the target company's information (Company name: ABC Manufacturing Inc., Industry: Manufacturing, Location: Tokyo), analyze the problems causing the company's declining sales and generate an appropriate proposal.

[1251] The key feature of this system is its integrated approach, encompassing everything from external data collection and analysis using generative artificial intelligence to the automatic generation of optimal proposals. This significantly improves the efficiency of proposal creation and enables the provision of concrete and reliable materials for successful business negotiations.

[1252] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1253] Step 1:

[1254] The user enters basic information about the target customer.

[1255] Specifically, the user enters information such as "Company Name: ABC Manufacturing Inc.", "Industry: Manufacturing", and "Location: Tokyo" into the input form on the device.

[1256] Input: Company name, industry, location

[1257] Output: Basic information of the entered target customer

[1258] Step 2:

[1259] The terminal sends the input information to the server.

[1260] The terminal uses a secure communication protocol (e.g., HTTPS) to send the entered information to the server.

[1261] Input: Basic information of the target customer entered

[1262] Output: Customer information request sent to the server

[1263] Step 3:

[1264] The server collects up-to-date industry and company data from external data sources.

[1265] The server collects necessary information, such as news articles, financial data, and market trend data, using APIs like the Bloomberg API and the Google News API.

[1266] Input: Submitted customer information request

[1267] Output: Collected industry and company data

[1268] Step 4:

[1269] The server analyzes the data it has collected.

[1270] The server uses generative artificial intelligence (e.g., OpenAI's GPT-4) to analyze the collected data and identify the challenges and needs of the target company.

[1271] Input: Collected industry and company data

[1272] Output: List of issues and needs based on analysis results

[1273] Step 5:

[1274] The server automatically generates proposals based on identified issues and needs, and formats them into a proposal document.

[1275] The server utilizes natural language generation (NLG) technology to format proposals according to a template. The generated proposals include statistical information, trend data, and competitive analysis results.

[1276] Input: List of issues and needs

[1277] Output: Formatted proposal

[1278] Step 6:

[1279] The terminal displays the draft of the generated proposal.

[1280] The terminal displays the proposal, generated in PDF preview format, to the user.

[1281] Input: Formatted proposal

[1282] Output: Draft proposal displayed to the user

[1283] Step 7:

[1284] The user reviews the proposal and edits it as needed.

[1285] Users review the proposal and make edits such as adding text, changing data, and inserting graphs.

[1286] Input: Draft proposal displayed to the user

[1287] Output: Revised proposal

[1288] Step 8:

[1289] The user saves the proposal as its final form and prints it as needed.

[1290] Users can save the completed proposal as an electronic file, such as a PDF, and print it as needed.

[1291] Input: Revised proposal

[1292] Output: Saved proposal in PDF format, and printed proposal.

[1293] Step 9:

[1294] The user conducts business negotiations based on the proposal and provides specific topics for discussion.

[1295] Users quote the content of the generated proposal and, during business negotiations, present specific challenges and solutions based on the company's current situation and trends.

[1296] Input: Saved proposal in PDF format

[1297] Output: Providing specific topics for business negotiations and their results.

[1298] (Application Example 1)

[1299] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1300] In traditional advertising proposal creation, data collection, analysis, and proposal writing for target customers are often done manually, resulting in significant time and effort. Furthermore, manual data analysis and proposal generation are prone to human error and bias, making it difficult to identify the optimal advertising strategy. This leads to a lower success rate in advertising negotiations. Therefore, there is a need for a system that efficiently generates optimal advertising proposals for target companies and effectively advances negotiations.

[1301] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[1302] In this invention, the server includes means for inputting basic information of target customers, means for collecting and analyzing the latest industry and company data from external data sources, means for identifying the challenges and needs of target companies from the collected data using generative artificial intelligence, means for automatically generating optimal proposals based on the identified challenges and needs and formatting them as advertising proposals, means for users to review and edit advertising proposals, and means for providing advertising proposals for use during advertising negotiations. This makes it possible to efficiently and accurately perform a series of processes from data collection and analysis of target companies to the automatic generation of optimal advertising proposals. Furthermore, since the generated proposals are formatted based on templates that include statistical information, trend data, and competitive analysis results, they can be used effectively during negotiations.

[1303] "Basic information about the target customer" includes information such as the customer's company name, industry, location, and past advertising campaign information.

[1304] "External data sources" refer to reliable data sources that provide information such as corporate financial data, industry trend data, and relevant news articles.

[1305] "Generative artificial intelligence" is an artificial intelligence technology that analyzes collected data to identify the challenges and needs of target companies and automatically generates proposals.

[1306] An "advertising proposal" is a document used to propose an advertising strategy for a target company, and is formatted using a template that includes statistical information, trend data, and competitive analysis results.

[1307] "Means for users to review and edit advertising proposals" refers to an interface that allows users to review generated advertising proposals and edit their content as needed.

[1308] "A means of providing advertising proposals for use during advertising negotiations" refers to a system function that provides a final advertising proposal so that it can be used effectively during negotiations.

[1309] "Statistical information" refers to data that quantifies the effectiveness of advertising campaigns and market research data.

[1310] "Trend data" refers to data on the latest industry trends and consumer behavior.

[1311] "Competitive analysis results" refer to the results of an analysis of the advertising strategies and market share of competing companies.

[1312] This invention is a system for automatically generating optimal advertising proposals for target customers specifically for the advertising industry. The system provides a consistent service from inputting target customer information to data collection, analysis, and generation, review, and editing of advertising proposals, using the following means. Specific hardware and software components include a server, smartphone, generative artificial intelligence model, and data collection API.

[1313] Entering target customer information

[1314] Users use a smartphone application to input basic information about their target customers (such as company name, industry, location, and past advertising campaign information). This basic information forms the basis for the system to send requests to external data sources.

[1315] Data collection

[1316] The server collects relevant data from external data sources based on the target customer's basic information. Specific data sources include corporate financial data, industry trend data, and relevant news articles. Reliable APIs are used for data collection.

[1317] Data Analysis

[1318] The collected data is analyzed on a server. This analysis uses generative artificial intelligence to extract potential challenges and needs of the target company from the collected information. For example, if the cause of declining sales is increased competition or loss of market share, these challenges and needs will be listed.

[1319] Generating an advertising proposal

[1320] The server automatically generates optimal advertising proposals using natural language generation (NLG) technology based on the analysis results. These proposals include statistical information, trend data, and competitive analysis results. The generated proposals are formatted according to a template and await user review.

[1321] Review and editing of advertising proposals

[1322] Users review the generated advertising proposal draft using a smartphone application and edit the content as needed. The edited advertising proposal is saved in PDF format or other formats and is ultimately used during business negotiations.

[1323] Specific example

[1324] For example, if the target company for the cosmetics industry is "XYZ Corporation," the user inputs basic information about "XYZ Corporation" and past social media campaign information. Based on this, the server collects and analyzes the latest industry trend data, financial data, and competitive analysis results. Finally, a proposal for an influencer marketing campaign utilizing social media is generated.

[1325] Example of a prompt

[1326] Target company: XYZ Corporation

[1327] Industry: Cosmetics

[1328] Past Campaigns: Successful Social Media Campaigns

[1329] Collected data:

[1330] 1. Industry Trends

[1331] 2. News articles

[1332] 3. Financial Data

[1333] The generated proposal:

[1334] 1. Overview of Industry Trends

[1335] 2. Potential challenges

[1336] 3. Proposal for the optimal advertising campaign

[1337] In this way, the entire proposal creation process can be streamlined, increasing the success rate of business negotiations.

[1338] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1339] Step 1:

[1340] The user enters basic information about the target customer into a smartphone application. This information includes the customer's company name, industry, location, and past advertising campaign information. This generates basic information data for the target company.

[1341] Input: Customer's company name, industry, location, and past advertising campaign information.

[1342] Output: Basic information data of the target company

[1343] Step 2:

[1344] The device sends a data collection request to the server based on the target customer's basic information data. The server uses a reliable data collection API to collect industry trend data, corporate financial data, news articles, etc., from external data sources. This is how external data is collected.

[1345] Input: Basic information data of the target company

[1346] Output: Collected external data

[1347] Step 3:

[1348] The server analyzes the collected external data using generative artificial intelligence (generative AI model). The generative AI model extracts and lists the target company's potential challenges and needs from the collected data. This generates challenge and needs data.

[1349] Input: Collected external data

[1350] Output: Issue and needs data

[1351] Step 4:

[1352] The server automatically generates optimal advertising proposals using natural language generation (NLG) technology based on problem and needs data. The generated advertising proposals are formatted according to a template, including statistical information, trend data, and competitor analysis results. This process generates the advertising proposal data.

[1353] Input: Issue and needs data

[1354] Output: Advertising proposal data

[1355] Step 5:

[1356] The user reviews the draft advertising proposal generated on their device and edits it as needed. The edited advertising proposal is saved on the device in PDF format or another suitable format. This completes the final advertising proposal.

[1357] Input: Advertising proposal data

[1358] Output: Final advertising proposal

[1359] Step 6:

[1360] Users conduct business negotiations using the completed advertising proposal. During negotiations, they can increase their chances of success by citing the statistical information, trend data, and competitor analysis results included in the advertising proposal to make specific and reliable proposals.

[1361] Input: Final advertising proposal

[1362] Output: Improved success rate of business negotiations

[1363] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1364] This invention is a system for generating optimal proposals for target customers and using them to provide topics of conversation during business negotiations. Furthermore, it incorporates an emotion engine that recognizes the user's emotions and adjusts the content of the proposal. The system's program processing will be explained below with specific examples.

[1365] 1. Inputting target customer information and collecting data

[1366] The user enters basic information about their target customer (e.g., company name, industry, location). Based on the entered information, the terminal sends a data collection request to the server. The server collects the latest data on the relevant company and industry from external data sources. In this process, it uses reliable APIs and internet sources to obtain relevant news articles, financial data, and market trend data.

[1367] 2. Data analysis and identification of challenges and needs

[1368] The server uses generative artificial intelligence to analyze the collected data. The analysis extracts potential challenges and needs of the target company. For example, if a company's sales are declining, possible causes include increased competition and loss of market share. These challenges and needs are listed according to their importance and then prioritized.

[1369] 3. Automated generation and formatting of proposals

[1370] The server utilizes natural language generation (NLG) technology to generate optimal proposals based on identified challenges and needs. The generated proposals are formatted according to defined templates and include, for example, statistical information, trend data, and competitive analysis results. The resulting proposals become the final version ready for use during business negotiations.

[1371] 4. Emotion Recognition and Regulation

[1372] The server uses an emotion engine to analyze the user's emotions from their voice, facial expressions, and text input. For example, if the user is feeling nervous, the emotion engine can adjust the tone of the proposal to be more calming. This ensures that the best proposal is provided based on the user's emotional state.

[1373] 5. Viewing and editing proposals

[1374] The terminal displays a draft of the generated proposal to the user. The user can review the proposal and edit it as needed, adding data and graphs as required. Finally, the user can save the proposal as an electronic file, such as a PDF, and print it.

[1375] 6. Topics to discuss during business negotiations

[1376] Users conduct business negotiations based on the generated proposals. While referring to the proposals, they are presented with industry trends and solutions to specific company challenges. Furthermore, based on the analysis results of the emotion engine, communication is tailored to the user's emotional state, and topics are provided based on reliable data.

[1377] As a concrete example, when a user is conducting a business negotiation with a target company, the proposal will include detailed information on the activities of competitors and market trends. Furthermore, if the user is feeling nervous, the emotional engine will adjust the tone of the proposal to soften it and change it to a format that is easier for the user to read. This will enable a more effective presentation during the business negotiation.

[1378] This system is characterized by its integrated approach, handling all processes from external data collection and analysis by generative artificial intelligence to the automatic generation of optimal proposals and the adjustment of proposal content using an emotion engine. This significantly improves the efficiency of proposal creation and enables the provision of concrete and reliable materials for successful business negotiations.

[1379] The following describes the processing flow.

[1380] Program processing flow

[1381] Step 1: Enter target customer information

[1382] The user enters basic information about the target customer (company name, industry, location, etc.) into the terminal. The user then registers the necessary information using a dedicated input form.

[1383] Step 2: Submit data collection request

[1384] The device sends a data collection request to the server based on the information entered by the user. The request includes the type of data to be collected and the source of the data.

[1385] Step 3: External Data Collection

[1386] The server accesses external data sources to collect the latest data on target companies and industries. These external data sources include news article APIs, financial data APIs, and market trend data APIs. Specifically, it retrieves relevant news articles, financial reports, market trend reports, and more.

[1387] Step 4: Data Analysis

[1388] The server analyzes the collected data. Using text analysis algorithms, it extracts key keywords and topics from news articles and reports, organizes financial and market trend data, and generates statistical information.

[1389] Step 5: Identifying Issues and Needs

[1390] The server uses generative artificial intelligence to analyze data and identify potential challenges and needs of target companies. For example, if a decline in sales is observed, possible causes include increased competition and a decrease in market share. The generative AI model performs pattern recognition and trend analysis to extract specific challenges and areas for improvement.

[1391] Step 6: List the issues and needs

[1392] The server lists identified issues and needs and prioritizes them. It assigns scores based on impact and urgency, and sorts them by priority. This ensures that solutions are considered starting with the most important issues.

[1393] Step 7: Automatic generation of optimal proposals

[1394] The server utilizes natural language generation (NLG) technology to generate optimal suggestions based on identified challenges and needs. These suggestions include solutions and recommendations, presenting specific strategies tailored to the company's specific situation.

[1395] Step 8: Formatting the proposal

[1396] The server formats the generated proposals according to a defined template. The proposals include statistical information, trend data, competitive analysis results, and also incorporate company logos and design elements.

[1397] Step 9: Emotion Recognition

[1398] The device collects emotions from the user's voice, facial expressions, and text input. This data is transmitted to the server in real time.

[1399] Step 10: Emotion Analysis

[1400] The server analyzes the collected emotional data through an emotion engine. For example, if the user is feeling nervous, the emotion engine recognizes this and adjusts the tone of the proposal accordingly.

[1401] Step 11: Adjusting the proposal based on emotions

[1402] The server adjusts the content and tone of the proposal based on the analysis results of the emotion engine. For example, if the user is relaxed, it uses a lighthearted tone, and if the user is stressed, it uses a calm tone.

[1403] Step 12: View and edit the proposal

[1404] The device displays a draft of the generated proposal to the user. The user can review the proposal and edit it as needed, adding data and graphs as required.

[1405] Step 13: Saving and distributing the proposal

[1406] Users can save their final proposal as an electronic file, such as a PDF, and print it. This allows them to use it during business negotiations with clients.

[1407] Step 14: Topics to discuss during business negotiations

[1408] The system conducts business negotiations based on proposals generated by the user. While referring to the proposals, it presents industry trends and solutions to the company's specific challenges. Furthermore, based on analysis results from the emotion engine, it communicates in a tone appropriate to the user's emotional state.

[1409] These steps enable the efficient generation of optimal proposals for target customers through a consistent process, allowing for communication that takes user emotions into consideration, and ultimately increasing the success rate of business negotiations.

[1410] (Example 2)

[1411] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1412] While conventional sales presentation systems could automatically generate proposals for target customers, their effectiveness was limited due to the uniformity of the proposal content and the lack of appropriate adjustments based on the user's emotional state. Furthermore, manual data collection and analysis were required, posing significant challenges in terms of time and effort. Additionally, improvements in the accuracy of data analysis and the reduction of the user's emotional burden during sales negotiations were insufficient.

[1413] The identification processing by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for inputting basic information of the target customer, means for collecting and analyzing the latest industry and company data from external data sources, means for identifying the challenges and needs of the target company from the collected data using generative artificial intelligence, means for automatically generating an optimal proposal based on the identified challenges and needs and formatting it as a proposal document, means for analyzing the user's emotions and adjusting the content and tone of the proposal document, means for the user to review and edit the proposal document, and means for providing the proposal document for use during business negotiations. This makes it possible to make optimal adjustments in response to the user's emotions during the automatic generation of the proposal document, maximizing the effectiveness of business negotiations while significantly reducing the time and effort required to create the proposal document.

[1414] "Means for inputting basic information of target customers" refers to the means by which users input basic information of their target customers, such as the company name, industry, and location, into a terminal.

[1415] "Means for collecting and analyzing up-to-date industry and company data from external data sources" refers to a method by which a server uses reliable APIs or internet information sources to obtain and analyze the latest data on the target industry and company.

[1416] "Methods for identifying the challenges and needs of target companies from data collected using generative artificial intelligence" refers to methods for analyzing collected data using generative artificial intelligence to extract the potential challenges and needs of target companies.

[1417] "A means of automatically generating optimal proposals based on identified issues and needs and formatting them as proposal documents" refers to a method of automatically generating optimal proposals using natural language generation technology based on analysis results and formatting them according to a predefined template.

[1418] "Means for analyzing user emotions and adjusting the content and tone of the proposal" refers to a method that uses an emotion engine to analyze the user's emotions from their voice, facial expressions, and text input, and then appropriately adjusts the tone and content of the proposal.

[1419] "Means for users to review and edit proposals" refers to the means by which the terminal displays the generated proposal to the user, allowing the user to review and edit its contents.

[1420] "Means of providing proposals for use during business negotiations" refers to methods of saving the final completed proposal in an electronic format such as PDF, and making it printable.

[1421] This invention is a system for generating optimal proposals for target customers and using them to provide topics of conversation during business negotiations. Furthermore, it incorporates an emotion engine that recognizes the user's emotions and adjusts the content of the proposal. The system's program processing will be explained below with specific examples.

[1422] 1. Inputting target customer information and collecting data

[1423] The user enters basic information about their target customer (e.g., company name, industry, location) into an input form on the device. After completing the input, the device sends a data collection request to the server based on this information. The server accesses reliable external data sources (e.g., Google News API, Yahoo Finance API) and collects the relevant data. The server stores the collected data in a database. This database may include, for example, the latest company news, financial data, and industry market trend information.

[1424] 2. Data analysis and identification of challenges and needs

[1425] The server retrieves data collected from the database and analyzes it using generative artificial intelligence (e.g., OpenAI GPT-4). The analysis extracts potential challenges and needs of the target company. For example, if a company's sales are declining, possible causes include increased competition and loss of market share. These challenges and needs are listed according to their importance and then prioritized.

[1426] 3. Automated generation and formatting of proposals

[1427] The server utilizes natural language generation (NLG) technology to generate optimal proposals based on identified challenges and needs. The generated proposals are formatted according to defined templates, including, for example, statistics, trend data, and competitive analysis results. The resulting proposals become the final version ready for use during business negotiations.

[1428] 4. Emotion Recognition and Regulation

[1429] The server uses an emotion engine to analyze the user's emotions from their voice, facial expressions, and text input. The emotion engine utilizes, for example, the emotion analysis API from Microsoft Azure Cognitive Services. If the user is feeling stressed, the server adjusts the tone of the proposal to soften it, providing the most appropriate proposal based on the user's emotional state.

[1430] 5. Viewing and editing proposals

[1431] The terminal displays a draft of the generated proposal to the user. The user reviews the proposal and edits it as needed, adding data and graphs. Finally, the user can save the proposal as an electronic file, such as a PDF, and print it.

[1432] 6. Topics to discuss during business negotiations

[1433] Users conduct business negotiations based on the generated proposals. They refer to the proposals while presenting industry trends and solutions to specific challenges faced by the company. Furthermore, based on the analysis results of the emotion engine, communication is tailored to the user's emotional state. For example, when a user is conducting a business negotiation with a target company, the proposal includes detailed information on the activities of competitors and market trends. Additionally, if the user is feeling nervous, the emotion engine adjusts the tone of the proposal to soften it and changes it to a more readable format. This enables more effective presentations during business negotiations.

[1434] Prompt statements as concrete examples

[1435] Use the following prompt text as input for the generating AI model:

[1436] Company name: XYZ Corporation

[1437] Industry: Technology

[1438] Location: Tokyo

[1439] Description: XYZ Corporation is experiencing declining sales, while its competitors continue to grow.

[1440] Please generate the corresponding proposal.

[1441] Following this prompt, the generative AI model collects the necessary data and generates the optimal proposal. As described above, the system of the present invention can consistently perform all processes, from external data collection to analysis by generative artificial intelligence, automatic generation of the optimal proposal, and adjustment of the proposal content by an emotion engine. This significantly improves the efficiency of proposal creation and makes it possible to provide concrete and reliable materials for successful business negotiations.

[1442] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1443] Step 1:

[1444] The user enters basic information about the target customer (e.g., company name, industry, location, etc.) into the input form on the device.

[1445] Input: Basic information such as company name, industry, and location entered by the user.

[1446] Output: A data request in which basic information is sent from the terminal to the server.

[1447] Specific action: The user enters "ABC Corporation," "Technology," and "Tokyo" into the input form and presses the submit button.

[1448] Step 2:

[1449] The terminal sends a data collection request to the server based on the information entered.

[1450] Input: User input information.

[1451] Output: Data collection request sent to the server.

[1452] Specific operation: The terminal sends a request to the / collect-data endpoint, and the server accesses an external data source (e.g., Google News API, Yahoo Finance API).

[1453] Step 3:

[1454] The server collects relevant data from reliable external data sources.

[1455] Input: Data collection request from the device.

[1456] Output: News articles, financial data, market trend data, etc., obtained from external data sources.

[1457] Specific operation: The server calls the Google News API to retrieve the latest news and uses the Yahoo Finance API to collect financial data. The server then stores the collected data in a database.

[1458] Step 4:

[1459] The server retrieves data collected from the database and analyzes it using generative artificial intelligence (e.g., OpenAI GPT-4).

[1460] Input: Collected data.

[1461] Output: A list of analyzed issues and needs.

[1462] Specific operation: The server sends a prompt message to the GPT-4 model saying, "Analyze the reasons for this company's decline in sales and list the main issues," and saves the analysis results to the database.

[1463] Step 5:

[1464] Based on the identified challenges and needs, the server generates an optimal proposal and formats it using natural language generation (NLG) technology.

[1465] Input: A list of analyzed issues and needs.

[1466] Output: Formatted proposal draft.

[1467] Specific operation: The server uses NLG technology to generate proposals and incorporates statistical information and trend data into pre-prepared templates.

[1468] Step 6:

[1469] The server uses an emotion engine to analyze the user's emotions from their voice, facial expressions, and text input.

[1470] Input: User's voice, facial expressions, and text data.

[1471] Output: Analyzed sentiment data.

[1472] Specific operation: The server uses the Microsoft Azure Cognitive Services sentiment analysis API to analyze the user's emotional state in real time. It determines that the user is feeling stressed.

[1473] Step 7:

[1474] The server adjusts the tone and content of the proposal based on emotional data.

[1475] Input: Analyzed sentiment data and proposal draft.

[1476] Output: Revised proposal draft.

[1477] Specific operation: Based on the analyzed sentiment data, the server softens the tone of the proposal and changes it to a user-friendly format.

[1478] Step 8:

[1479] The terminal displays a draft of the generated proposal to the user.

[1480] Input: Revised proposal draft.

[1481] Output: Display of the proposal draft.

[1482] Specific operation: The device displays the proposal to the user and provides a UI for the user to review its contents.

[1483] Step 9:

[1484] The user reviews the proposal and adds data and graphs as needed.

[1485] Input: Draft proposal.

[1486] Output: Edited proposal.

[1487] Specific actions: The user clicks on a specific part of the proposal to perform edits such as inserting data or graphs.

[1488] Step 10:

[1489] Users can ultimately save the proposal as an electronic file, such as a PDF, and also print it.

[1490] Input: Edited proposal.

[1491] Output: Saved PDF file.

[1492] Specific action: The user presses the save button, exports the proposal in PDF format, and saves it to their device.

[1493] Step 11:

[1494] Users conduct business negotiations based on the generated proposals.

[1495] Input: Saved PDF proposal.

[1496] Output: Progress of the business negotiation.

[1497] Specific actions: The user opens a proposal during a business meeting and gives a presentation based on the proposal. Using the results of sentiment analysis, the system adjusts the tone of communication if the user appears nervous.

[1498] (Application Example 2)

[1499] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1500] In modern business negotiations and customer interactions, it is crucial to quickly generate optimal proposals based on detailed information about target customers, and further adjust the proposal content according to the user's emotional state. Traditional systems often require manual proposal generation and adjustment, which is time-consuming and inefficient. Furthermore, dynamically adjusting proposal content to reflect the user's emotions is difficult. As a result, providing the optimal proposal to maximize the effectiveness of business negotiations has been challenging.

[1501] In Application Example 2, the identification processing by the identification processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for inputting basic information of the target customer, means for collecting and analyzing the latest industry and company data from external data sources, means for identifying the challenges and needs of the target company from the collected data using generative artificial intelligence, means for automatically generating an optimal proposal based on the identified challenges and needs and formatting it as a proposal document, means for the user to review and edit the proposal document, means for providing the proposal document for use during business negotiations, means for adjusting the content of the proposal document according to the user's emotional state, and means for analyzing emotions from the user's voice, facial expressions, and text input. This makes it possible to quickly generate an optimal proposal document for the target customer and dynamically adjust the content of the proposal according to the user's emotional state.

[1502] A "target customer" is an individual or company from which the system generates proposals.

[1503] "Basic information" refers to fundamental data necessary for creating a proposal, such as the target customer's company name, industry, and location.

[1504] "External data sources" refer to reliable sources of information that can be obtained from external sources, such as APIs on the internet, news articles, and financial data.

[1505] "Latest industry and company data" refers to real-time data collected on specific industries and companies, including the latest market trends, competitor activities, and financial data.

[1506] "Generative artificial intelligence" refers to an artificial intelligence system that can analyze collected data and automatically generate proposals using natural language generation technology.

[1507] "Challenges and needs" refer to problems and requests faced by companies and customers, including declining sales and entry into new markets.

[1508] A "proposal" is a document that contains recommendations for solutions, products, or services to be offered to a target customer.

[1509] "Formatting" refers to organizing the content of a proposal according to a set template and making it easy to read.

[1510] A "user" is a person who uses this system to create proposals and use them in business negotiations.

[1511] "Means for reviewing and editing" refers to a system that provides an interface allowing users to review the content of the generated proposal and make changes or additions as needed.

[1512] "Means of provision" refers to methods that allow the generated proposal to be saved as an electronic file or printed.

[1513] "Emotional state" refers to the user's current feelings and psychological state, and is analyzed from factors such as voice tone and facial expressions.

[1514] The "emotion engine" is a system that analyzes a user's emotions from their voice, facial expressions, and text input.

[1515] "Voice, facial expressions, and text input" refers to the user's speaking voice, facial expressions, and entered text data, and analyzing these is a means of understanding the user's emotions.

[1516] This invention provides a system for in-store employees wearing smart glasses to make optimal product recommendations to target customers and to adjust the recommendations according to the user's emotional state. The system's program processing is described in detail below.

[1517] First, the user (store clerk) wears smart glasses and interacts with customers in a physical store. After the user inputs basic information about the target customer (e.g., past purchase history, preferences, allergy information, etc.) through the input interface of the smart glasses, the terminal (smart glasses) sends this information to a server.

[1518] The server collects up-to-date industry and company data from external data sources. It utilizes reliable APIs and internet information sources to obtain relevant news articles and market trend data. Next, the server analyzes the collected data using generative artificial intelligence to extract potential customer challenges and needs. Based on this analysis, the server generates optimal proposals and formats them into a proposal document.

[1519] This proposal is automatically formatted according to a template that includes statistical information, trend data, and competitive analysis results. Furthermore, the content of the proposal is adjusted according to the user's emotional state using an emotion engine. If the user is tense, the tone of the proposal can be softened, and if their facial expression is excited, it can be adjusted to an energetic tone. This emotional state analysis uses an emotion engine that analyzes emotions from the user's voice, facial expressions, and text input.

[1520] Ultimately, the proposal generated by the server is displayed on the terminal (smart glasses), and the user (salesperson) uses this to suggest products to target customers. For example, if a customer asks about a specific facial cleanser, recommended products and descriptions will be displayed on the smart glasses' screen. The user then explains the product to the customer according to this information. At this time, the system will instruct the user to explain in a gentle tone if the customer's expression looks anxious, and in an energetic tone if their expression looks excited.

[1521] As a concrete example, consider a scenario where a customer visits a store and consults with a salesperson wearing smart glasses about a product they are considering purchasing. If the customer is looking for a facial cleanser, the salesperson asks, "Hello. What are you looking for today?" and the customer replies, "I'm looking for a facial cleanser." At this moment, the system analyzes the customer's facial expression and voice and determines that they are nervous. As a result, recommended facial cleansers are displayed on the smart glasses' screen, and the system suggests an explanation in a calm tone.

[1522] An example of a prompt statement is as follows:

[1523] Please enter customer information and generate product recommendations.

[1524] customer_id = "12345"

[1525] face_image = capture_face_image()

[1526] voice_sample = capture_voice_sample()

[1527] get_customer_data(customer_id)

[1528] analyze_emotion(face_image, voice_sample)

[1529] recommendations = generate_recommendation(customer_id)

[1530] adjusted_explanation = adjust_tone(recommendations)

[1531] display_recommendation(adjusted_explanation)

[1532] This approach makes it possible to make more effective product proposals to target customers. Furthermore, by dynamically adjusting the proposal content according to the user's emotional state, the success rate of sales negotiations can be increased.

[1533] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1534] Step 1:

[1535] The user inputs basic information about their target customer through the smart glasses' input interface. For example, they might enter the customer's ID, past purchase history, preferences, and allergy information. This information is stored as input data on the device and sent to the next processing step.

[1536] Step 2:

[1537] The terminal sends the entered target customer's basic information to the server. The server receives this information, collects the latest industry and company data from external data sources, and begins analysis. It uses reliable APIs and internet sources to obtain relevant information and stores it in a database.

[1538] Step 3:

[1539] The server analyzes the collected data using generative artificial intelligence. Specifically, it analyzes market trends, competitor activities, financial data, etc., to extract potential challenges and needs of target customers. For example, it analyzes customer preferences from their purchase history and identifies key purchasing trends using sales and interest data. Based on this, the server generates a list of challenges and needs.

[1540] Step 4:

[1541] The server generates optimal solutions based on identified challenges and needs, and formats them as proposal documents. Using generative artificial intelligence, it performs natural language generation and automatically formats the proposal according to a template that includes statistical information, trend data, and competitive analysis results. This proposal is then saved on the server as an electronic file.

[1542] Step 5:

[1543] The server uses an emotion engine to analyze the user's emotional state. The emotion engine detects emotions based on the user's voice, facial expressions, and text input data transmitted from the smart glasses. For example, it identifies emotional states such as tension, excitement, and anxiety from the user's tone of voice and facial expressions. This prepares the server for making adjustments based on the emotional state.

[1544] Step 6:

[1545] The server adjusts the tone of the generated proposal based on the user's emotional state, which has been analyzed by the emotion engine. For example, if the user is nervous, the proposal's description will be changed to a calmer tone; if they are excited, it will be adjusted to an energetic tone. This adjusted proposal then becomes the draft for use in the final business negotiation.

[1546] Step 7:

[1547] Finally, the finalized proposal is sent to the device. The device (smart glasses) displays the adjusted proposal to the user. The user can then use this proposal to proceed with negotiations with target customers and review and edit the proposal content as needed. During negotiations, the system supports the user in responding with the appropriate tone based on their emotional state, while referring to the proposal.

[1548] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1549] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1550] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[1551] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1552] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[1553] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[1554] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[1555] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[1556] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[1557] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[1558] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[1559] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[1560] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[1561] 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.

[1562] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[1563] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[1564] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[1565] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[1566] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[1567] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[1568] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[1569] The following is further disclosed regarding the embodiments described above.

[1570] (Claim 1)

[1571] A means of inputting basic information of the target customer,

[1572] Means for collecting and analyzing the latest industry and company data from external data sources,

[1573] A method for identifying the challenges and needs of target companies from data collected using generative artificial intelligence,

[1574] A means of automatically generating optimal proposals based on identified issues and needs, and formatting them as proposal documents,

[1575] Means for users to review and edit proposals,

[1576] A means of providing a proposal for use during business negotiations,

[1577] A system that includes this.

[1578] (Claim 2)

[1579] The system according to claim 1, characterized in that a generative artificial intelligence analyzes the collected data and lists the challenges and needs of the target company in order of priority.

[1580] (Claim 3)

[1581] The system according to claim 1, characterized in that the proposal is formatted into a template that includes statistical information, trend data, and competitive analysis results.

[1582] "Example 1"

[1583] (Claim 1)

[1584] A means of inputting basic information of the target customer,

[1585] Means for collecting and analyzing the latest industry and company data from external data sources,

[1586] A method for identifying the challenges and needs of target companies from data collected using generative artificial intelligence,

[1587] A means of automatically generating optimal proposals based on identified issues and needs, and formatting them as proposal documents,

[1588] A means for users to review and edit proposals, ultimately save them as electronic files, and also enable printing.

[1589] A means of providing a proposal for use during business negotiations,

[1590] A means of providing specific topics of discussion during business negotiations based on the content of the generated proposal,

[1591] A system that includes this.

[1592] (Claim 2)

[1593] The system according to claim 1, characterized in that a generative artificial intelligence analyzes the collected data and lists the challenges and needs of the target company in order of priority.

[1594] (Claim 3)

[1595] The system according to claim 1, characterized in that the proposal is formatted into a template that includes statistical information, trend data, and competitive analysis results.

[1596] "Application Example 1"

[1597] (Claim 1)

[1598] A means of inputting basic information of the target customer,

[1599] Means for collecting and analyzing the latest industry and company data from external data sources,

[1600] A method for identifying the challenges and needs of target companies from data collected using generative artificial intelligence,

[1601] A method for automatically generating optimal proposals based on identified issues and needs, and formatting them as advertising proposals,

[1602] A means for users to review and edit advertising proposals,

[1603] A means of providing advertising proposals for use during advertising negotiations,

[1604] A system that includes this.

[1605] (Claim 2)

[1606] The system according to claim 1, characterized in that a generative artificial intelligence analyzes the collected data and lists the challenges and needs of the target company in order of priority.

[1607] (Claim 3)

[1608] The system according to claim 1, characterized in that advertising proposals are formatted into templates that include statistical information, trend data, and competitive analysis results.

[1609] "Example 2 of combining an emotion engine"

[1610] (Claim 1)

[1611] A means of inputting basic information of the target customer,

[1612] Means for collecting and analyzing the latest industry and company data from external data sources,

[1613] A method for identifying the challenges and needs of target companies from data collected using generative artificial intelligence,

[1614] A means of automatically generating optimal proposals based on identified issues and needs, and formatting them as proposal documents,

[1615] A means of analyzing user emotions and adjusting the content and tone of the proposal,

[1616] Means for users to review and edit proposals,

[1617] A means of providing a proposal for use during business negotiations,

[1618] A system that includes this.

[1619] (Claim 2)

[1620] The system according to claim 1, characterized in that a generative artificial intelligence analyzes the collected data and lists the challenges and needs of the target company in order of priority.

[1621] (Claim 3)

[1622] The system according to claim 1, characterized in that the proposal is formatted into a template that includes statistical information, trend data, and competitive analysis results.

[1623] "Application example 2 when combining with an emotional engine"

[1624] (Claim 1)

[1625] A means of inputting basic information of the target customer,

[1626] Means for collecting and analyzing the latest industry and company data from external data sources,

[1627] A method for identifying the challenges and needs of target companies from data collected using generative artificial intelligence,

[1628] A means of automatically generating optimal proposals based on identified issues and needs, and formatting them as proposal documents,

[1629] Means for users to review and edit proposals,

[1630] A means of providing a proposal for use during business negotiations,

[1631] A means of adjusting the content of the proposal according to the user's emotional state,

[1632] A means of analyzing emotions from the user's voice, facial expressions, and text input,

[1633] A system that includes this.

[1634] (Claim 2)

[1635] The system according to claim 1, characterized in that a generative artificial intelligence analyzes the collected data and lists the challenges and needs of the target company in order of priority.

[1636] (Claim 3)

[1637] The system according to claim 1, characterized in that the proposal is formatted into a template that includes statistical information, trend data, and competitive analysis results, and the tone of the proposal is adjusted by an emotion engine. [Explanation of symbols]

[1638] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of inputting basic information of the target customer, Means for collecting and analyzing the latest industry and company data from external data sources, A method for identifying the challenges and needs of target companies from data collected using generative artificial intelligence, A means of automatically generating optimal proposals based on identified issues and needs, and formatting them as proposal documents, Means for users to review and edit proposals, A means of providing a proposal for use during business negotiations, A system that includes this.

2. The system according to claim 1, characterized in that a generative artificial intelligence analyzes the collected data and lists the challenges and needs of the target company in order of priority.

3. The system according to claim 1, characterized in that the proposal is formatted into a template that includes statistical information, trend data, and competitive analysis results.

Images