System

Abstract

A system is provided.SOLUTION: This system includes a means for automatically collecting customer information from an information source on the Internet, a means for analyzing the collected customer information and grasping the business environment and market trend of a customer company, a means for specifying the proposal need of the customer on the basis of an analysis result, a means for automatically generating a proposal material corresponding to the specified proposal need, and a means for transmitting the generated proposal material to a user terminal.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] The present invention solves the problem of the significant amount of time and effort required for preparing proposals and creating materials in corporate sales. In particular, the overlapping tasks of researching basic customer information, understanding the business environment, devising appropriate proposal content, and creating the actual materials makes it difficult for salespeople to devote sufficient time to proposal activities. Therefore, there is a need for a method to streamline proposal preparation, allowing salespeople to focus on high-value-added proposal activities. [Means for solving the problem]

[0005] The present invention solves the above problems by providing a system that includes: means for automatically collecting customer information from information sources on the Internet; means for analyzing the collected customer information to understand the business environment and market trends of the client company; means for identifying the customer's proposal needs based on the analysis results; means for automatically generating proposal materials according to the identified proposal needs; and means for transmitting the generated proposal materials to a user terminal. This allows salespeople to quickly and accurately prepare proposal materials even when a customer visit is suddenly decided upon, thereby improving the productivity of proposal activities.

[0006] "Customer information" refers to basic information and publicly available data related to the client company, including, among other things, the company's performance, major products and services, recent news, website information, and social media posts.

[0007] "Internet sources" is a general term for information providers accessible via the Internet, including official corporate websites, news sites, and social media platforms.

[0008] "Collection methods" refers to the technical methods and tools used to automatically or semi-automatically obtain customer information from sources on the Internet.

[0009] "Analytics" refers to the algorithms and software used to process collected customer information and extract meaningful data and insights.

[0010] "Business environment and market trends" refers to the business situation in which the client company finds itself and overall market trends, including industry sectors, economic trends, competitor strategies, and consumer trends.

[0011] "Means for identifying proposal needs" refers to methods and algorithms that, based on the analysis results, clarify the challenges and potential needs facing client companies.

[0012] A "proposal document" is a document that documents the content of a proposal to a client company, and refers to a presentation document or report that includes the proposal content, background information, specific solutions, expected results, etc.

[0013] "Automatic generation means" refers to algorithms or software that generate optimal proposal materials based on identified needs.

[0014] "User terminal" refers to a computer or mobile device used by a salesperson, and refers to a device to which the generated proposal materials are sent and displayed. [Brief explanation of the drawings]

[0015] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11]FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0016] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0017] First, the terms used in the following description will be explained.

[0018] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0019] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0020] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0021] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0022] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0023] [First embodiment]

[0024] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0025] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0026] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0027] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0028] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0029] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0030] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0031] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0033] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0034] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0035] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0036] The present invention relates to a system for reducing the time and man-hours required for salespeople to prepare proposals and documents. Specific embodiments for carrying out the present invention are described below. In the present invention, a server, a terminal, and a user work together.

[0037] 1. Information gathering

[0038] The user inputs the name and URL of the client company to which they are making a proposal to the server. The server then crawls the designated company's official website, news sites, social media, and other online sources to collect basic information and publicly available data related to the client company. The collected information includes the company's performance, major products and services, recent news, and social media posts.

[0039] Example: When a user enters the name of a company, such as "XYZ Corporation," into a server, the server analyzes the company profile page on the company's official website and collects related information from news sites and social media.

[0040] 2. Information analysis

[0041] The server analyzes the collected information using natural language processing (NLP) algorithms to understand the client company's business environment and market trends. The analysis results include information on industry sectors, economic trends, competitor strategies, consumer trends, etc.

[0042] Example: The server analyzes the latest news articles about XYZ Corporation, analyzing and organizing information about the company's new product launch and its market reaction.

[0043] 3. Identifying proposal needs

[0044] Based on the analysis results, the server identifies the specific issues and proposal needs of the client company, referring to past success stories and general business cases to identify appropriate proposal content.

[0045] Example: The server analyzes business environment data and market trends for XYZ Corporation to identify needs regarding promotion strategies for new products.

[0046] 4. Automatic generation of proposal materials

[0047] When a user inputs their proposal into the server, the server automatically generates the optimal document format for the identified proposal needs, embedding the collected data and analysis results in the appropriate format.

[0048] Example: A user inputs a "promotion support proposal for XYZ Corporation" into the server, and the server automatically generates presentation slides. The slides include an "outline of XYZ's new product," "current market trends," "proposed promotion strategy," and "past success stories."

[0049] 5. Submit your proposal

[0050] The server saves the generated proposal materials in PDF or PPT format and sends them to the user's device. The user can then review the received materials, make any necessary adjustments, and immediately begin their proposal activities.

[0051] Example: The server generates a "Promotion proposal document for XYZ Co., Ltd." and saves it in PDF format, then sends it to the user's device. The user receives the document, makes any necessary adjustments, and promptly starts the proposal process.

[0052] In this way, the present invention is a system that can significantly reduce the time it takes salespeople to prepare proposals and improve productivity by implementing a series of processes: collecting customer information, analyzing the information, identifying proposal needs, automatically generating proposal materials, and sending the proposal materials.

[0053] The processing flow will be explained below.

[0054] Step 1:

[0055] The user inputs the name and URL of the client company into the server, which identifies the company information to be collected.

[0056] Step 2:

[0057] The server crawls designated company official websites, news sites, social media sites, and other internet information sources by sending HTTP requests and retrieving the HTML content of each page.

[0058] Step 3:

[0059] The server analyzes the HTML content and extracts basic information about the company using techniques such as Document Object Model (DOM) analysis and regular expressions.

[0060] Step 4:

[0061] The server performs text analysis on the collected information using natural language processing (NLP) algorithms, which include text tokenization, part-of-speech tagging, and entity recognition.

[0062] Step 5:

[0063] The server evaluates the importance of the text and extracts key keywords and phrases related to the business environment and market trends, such as new products, marketing strategies, and market trends.

[0064] Step 6:

[0065] Based on the analysis results, the server identifies the customer's specific issues and proposal needs, referring to past success stories and a sales strategy database.

[0066] Step 7:

[0067] The user inputs the desired proposal details to the server, for example, specifying specific details such as "promotion support proposal for a new product."

[0068] Step 8:

[0069] The server selects the appropriate proposal format based on user input, including template selection.

[0070] Step 9:

[0071] The server embeds the collected data and analysis results into the selected template and automatically generates proposal materials in the form of presentation slides, reports, etc.

[0072] Step 10:

[0073] The server saves the generated proposal materials in PDF or PPT format and sends them to the user's terminal.

[0074] Step 11:

[0075] The user can review the proposal materials received on their device and make any necessary adjustments, after which they can immediately begin their proposal activities.

[0076] Through these steps, the system of the present invention efficiently collects and analyzes customer information, identifies proposal needs, automatically generates proposal materials, and finally sends the materials, significantly reducing the time salespeople spend preparing proposals and improving productivity.

[0077] Example 1

[0078] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0079] In modern sales activities, the time and effort required to prepare proposals and documents is a significant burden. Sales representatives must spend a great deal of time gathering and analyzing information about client companies and creating appropriate proposal materials. This process is often done manually and is inefficient, resulting in a decrease in the speed and productivity of sales activities.

[0080] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0081] In this invention, the server includes means for automatically collecting customer information from information sources on the Internet, means for analyzing the collected customer information using a natural language processing algorithm to grasp the business environment and market trends of the client company, means for identifying the proposal needs of the client based on the analysis results, means for automatically generating proposal materials according to the identified proposal needs, and means for generating the generated proposal materials in PDF or PPT format and transmitting them to a user terminal, thereby enabling a significant reduction in proposal preparation time and an improvement in the productivity of sales activities.

[0082] "Customer Information" refers to data including basic information, performance, products and services, and market trends regarding the target company.

[0083] "Internet sources" refers to various sources that provide public information accessible via the Internet, such as official websites, news sites, and social media.

[0084] A "natural language processing algorithm" is a collection of computer programs and methods for understanding and analyzing human language, and refers to technology for extracting keywords from text and analyzing relationships.

[0085] "Business environment" refers to the surrounding circumstances, including industry trends, economic conditions, and competitor activities, that affect the business operations of client companies.

[0086] "Market trends" refers to information that describes fluctuations and trends over time in demand for products or services, the competitive landscape, consumer preferences, and other factors in a particular market.

[0087] "Proposal needs" refers to the specific requests and requirements related to the challenges the client company faces and the solutions it seeks.

[0088] "Proposal materials" refer to documents such as presentation slides and reports that sales representatives use when making proposals to client companies.

[0089] "Automatic generation" refers to a process in which data collection, analysis, and documentation are performed programmatically without manual intervention.

[0090] "PDF and PPT formats" refer to standard file formats for electronic documents and presentations, such as the Portable Document Format (PDF) developed by Adobe Systems and the presentation format (PPT) used by Microsoft PowerPoint.

[0091] "User terminal" refers to devices such as computers, tablets, and smartphones operated by sales representatives and system users.

[0092] The present invention relates to a system for reducing the time and man-hours required for sales representatives to prepare proposals and documents. This system operates in cooperation with a server, terminals, and users, and uses the following hardware and software.

[0093] Hardware and software used

[0094] Server: Computing resources for data collection, analysis, and proposal generation, including web servers (e.g., Apache), crawling tools (e.g., Scrapy), natural language processing libraries (e.g., SpaCy, NLTK), and presentation generation tools (e.g., Python-PPTX).

[0095] Terminal: A PC or tablet is used as a device for users to operate the system. Terminals are primarily used for input and output, and have a web browser, PDF viewer, and presentation creation tool (e.g., Microsoft PowerPoint) installed.

[0096] Internet connection: A network connection is required to transmit data between the server and your device.

[0097] Program processing flow

[0098] Information gathering

[0099] The user accesses the server from their device and enters the name and URL of the client company to which they would like to make a proposal. As a specific example, let's say the user enters the name of "Company A." Based on this, the server uses a crawling tool (e.g., Scrapy) to collect information from the designated company's official website, news sites, and social media. The collected information is stored in a database on the server.

[0100] Information analysis

[0101] The server then analyzes the collected information using natural language processing (NLP) algorithms (e.g., SpaCy, NLTK). This allows the client company's business environment and market trends to be understood. Specifically, the analysis targets company performance data, new product information, recent news, social media posts, etc. For example, a report summarizing the market trends of "Company A" and customer reactions on social media is generated.

[0102] Identifying proposal needs

[0103] The server identifies the client company's specific issues and proposal needs based on the analysis results. In doing so, the server refers to past success stories and general business cases to identify appropriate proposal content. For example, if "Company A" launches a new product, the server identifies proposal needs regarding its promotion strategy.

[0104] Automatic generation of proposal materials

[0105] When a user inputs the details of their proposal into the server, the server automatically generates the optimal document format for the identified proposal needs. Using the Python-PPTX library, the collected data and analysis results are incorporated into presentation slides. For example, a "Proposal for Promotion Support for Company A" slide is generated that includes an overview of the company's new product, market trends, the proposed promotion strategy, and past success stories.

[0106] Submit a proposal

[0107] Finally, the server saves the generated proposal materials in PDF or PPT format and sends them to the user's device. The user can review the received materials and make any necessary adjustments, allowing them to quickly begin their proposal activities.

[0108] Prompt Sentence Examples

[0109] Here is an example prompt:

[0110] Company name: Company A

[0111] Proposal: Please create a proposal document for Company A's new product promotion strategy.

[0112] In this way, the present invention is a system that significantly reduces the burden on sales representatives and improves the efficiency of proposal activities by automating the entire process from data collection and analysis, identification of proposal needs, and automatic generation and transmission of materials.

[0113] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0114] Step 1:

[0115] The user accesses the server from their device and enters the name and URL of the client company to which they are making a proposal into a dedicated form. The input data is "Company Name: Company A" and "URL: www.example.com." Based on this input, the server launches a crawling tool (e.g., Scrapy) and collects information from the designated company's official website, news sites, and social media. Specifically, the server saves the collected data in a database and confirms that crawling is complete.

[0116] Step 2:

[0117] The server analyzes the information stored in the database using natural language processing algorithms (e.g., SpaCy, NLTK). The collected text data is used as input. Specific analysis operations include extracting keywords from the text, classifying documents, and analyzing sentiment, and outputting the results of the analysis: company performance, product overviews, market trends, and the behavior of competitors.

[0118] Step 3:

[0119] The server uses the analysis results to identify the specific issues and proposal needs of the client company. The input in this step is the analysis results obtained in the previous step. The server refers to past success stories and general business cases to identify the proposal content that best meets the client's needs. A specific example of how this works is to create a list of proposal needs regarding Company A's new product promotion strategy.

[0120] Step 4:

[0121] When a user inputs the proposal details into the server, the server automatically generates the proposal materials. In this case, the input data is "Proposal Details: Company A's New Product Promotion Strategy." The server uses the Python-PPTX library to create presentation slides based on the collected data and analysis results. Specifically, the slides include an overview of the company's new product, market trends, the proposed promotion strategy, and past success stories.

[0122] Step 5:

[0123] The server saves the generated proposal materials in PDF or PPT format and sends them to the user's terminal. In this step, the generated proposal materials are used as input. The server converts the materials into PDF format and sends them to the user's terminal. Specific operations include confirming the completion of transmission and recording a log. The user can then review the received materials and make any necessary adjustments to quickly begin proposal activities.

[0124] The above is the flow of processing of the program of this system.

[0125] (Application example 1)

[0126] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0127] In conventional sales support systems, the process from collecting customer information to creating proposal materials was done manually, requiring a great deal of time and effort. Furthermore, the collected information could not be analyzed quickly and the proposal needs based on that analysis could not be identified quickly, resulting in a decrease in the productivity of sales activities. Furthermore, there were few ways to utilize the collected information to provide appropriate materials, which was a burden for salespeople. A new approach was needed to solve these problems.

[0128] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0129] In this invention, the server includes means for automatically collecting customer information from information sources on the Internet, means for analyzing the collected customer information and grasping the business environment and market trends of the client organization, means for identifying the proposal needs of the client based on the analysis results, means for automatically generating proposal materials according to the identified proposal needs, means for transmitting the generated proposal materials to an end-user terminal, and means for inputting information and viewing materials using a smart device. This enables salespeople to quickly collect and analyze customer information and automatically generate and transmit appropriate proposal materials, significantly reducing the time and effort required for proposal preparation and improving productivity.

[0130] "Customer information" refers to data collected from online sources such as official websites published by companies and organizations, news sites, and social networking services (SNS).

[0131] "Client organization" refers to the company or organization to which the proposal is made, and its business environment and market trends are the subject of analysis.

[0132] "Proposal needs" refers to the content and strategies that should be proposed in sales activities, identified based on the client organization's business environment and market trends.

[0133] "Proposal materials" refer to presentation slides and documents that are automatically generated in response to identified proposal needs and are used in sales activities.

[0134] "End-user terminal" refers to the device used by salespeople who use the system, and includes PCs, smartphones, tablets, smart glasses, etc.

[0135] "Smart device" refers to devices such as smartphones, smart glasses, and tablets that are connected to the internet and offer additional functionality.

[0136] "Natural language processing algorithm" refers to the computational methods and models used to analyze collected customer information and understand and classify its content.

[0137] A "crawling engine" refers to a program that automatically searches for information on the Internet and collects the necessary data.

[0138] "Analysis results" refers to the analysis results of customer information extracted using natural language processing algorithms and data analysis tools.

[0139] "Sources" refer to reliable sources of data on the Internet, such as official websites, news sites, and social networking services (SNS).

[0140] To implement the present invention, a system is provided in which a server, a terminal, and a user work together, as will be described in detail below.

[0141] Overall structure

[0142] This system includes the following means for collecting customer information, analyzing the information, identifying proposal needs, automatically generating proposal materials, transmitting the generated materials, and inputting information and viewing materials using a smart device.

[0143] Collecting customer information

[0144] The user uses a smart device (e.g., smart glasses) to voice-input the name of the customer company. Based on this input, the server collects customer information from online sources (official websites, news sites, social media, etc.). This process uses a crawling engine (e.g., Beautiful Soup, Scrapy).

[0145] Information analysis

[0146] The collected information is sent to a server and analyzed using natural language processing algorithms (e.g., BERT, GPT). This allows us to understand the business environment and market trends of our client organizations. Data analysis tools such as Pandas and Scikit-learn are used for data analysis.

[0147] Identifying proposal needs

[0148] The server identifies the customer's proposal needs based on the analysis results, which is achieved by using a content recommendation system (e.g., Collaborative Filtering) and referencing past success stories and industry-standard data.

[0149] Automatic generation of proposal materials

[0150] The server automatically generates proposal materials based on the identified proposal needs, using document generation tools (e.g., LaTeX, MS Office API) to create presentation slides and PDF documents.

[0151] Sending generated materials

[0152] The generated proposal materials are sent to the end user's device in PDF or PPT format using an email sending API (e.g., SMTP) or cloud storage (e.g., Google Drive, AWS S3). These materials can be viewed in real time using a smart device.

[0153] Specific examples

[0154] For example, if a user uses smart glasses to voice-input the name of a client company, such as "ABC Co., Ltd.", the server will crawl online sources based on the company name and collect company information. The collected information is analyzed using a natural language processing algorithm to understand the company's business environment and market trends. Based on the analysis results, a proposal need, such as "promotion strategy for ABC Co., Ltd.'s new product," is then identified. The server then uses this information to automatically generate presentation slides and send them to the user's smart glasses. This allows the user to review the materials in real time while making proposals.

[0155] Prompt Sentence Examples

[0156] Examples of prompts from this generative AI model include:

[0157] "Please provide us with the latest information about ABC Corporation. Pay particular attention to new products, market trends, and competitor activity, and provide analysis that can be used in our advertising strategy."

[0158] In this way, the salesperson can make proposals to the customer quickly and efficiently.

[0159] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0160] Step 1:

[0161] The user uses a smart device (smart glasses) to input the name of the customer company by voice. This voice input is converted into text data by the device and sent to the server. This results in the input data of the customer company name.

[0162] Step 2:

[0163] The server uses the received text data to crawl the designated company's official website, news sites, social media, and other sources. Software such as Beautiful Soup and Scrapy are used. This process gathers relevant information from company profile pages on official websites, news articles, social media posts, and other sources to obtain the collected data.

[0164] Step 3:

[0165] The server analyzes the collected information using NLP (natural language processing) algorithms, such as the BERT or GPT model. This analysis extracts information about the client organization's business environment, market trends, and competitor trends, and produces analysis results.

[0166] Step 4:

[0167] The server uses the analysis results to refer to past success stories and general business cases, and utilizes a content recommendation system (Collaborative Filtering) to identify proposal needs. This clarifies the customer's specific issues and proposal needs, and provides data on identified proposal needs.

[0168] Step 5:

[0169] Based on the identified proposal needs, the server automatically generates proposal materials using document generation tools (LaTeX, MS Office API). The generated materials are in PDF or PPT format and include company information, market analysis, proposal details, etc. This process results in the automatically generated proposal materials.

[0170] Step 6:

[0171] The generated proposal materials are sent by the server to the end user's device using methods such as email sending API (SMTP) or cloud storage services (Google Drive, AWS S3). This sends the materials to the user's device (smart glasses, etc.), allowing the user to view the materials in real time.

[0172] Through the above processing steps, this system can efficiently carry out the process from collecting customer information to automatically generating and sending proposal materials.

[0173] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0174] The present invention relates to a system that reduces the time and man-hours required for salespeople to prepare proposals and create documents, and improves the quality of proposals by combining it with an emotion engine that recognizes user emotions. Specific embodiments for implementing the present invention are described below. In this invention, a server, terminals, and users work in cooperation with each other, and an emotion engine is also included.

[0175] 1. Information gathering

[0176] A user inputs the name or URL of a client company into the server. This identifies the company information to be collected. The server then crawls the designated company's official website, news sites, social media, and other online sources to collect basic information and publicly available data related to the client company. The collected information includes the company's performance, major products and services, recent news, and social media posts. For example, if a user inputs the company name "XYZ Corporation" into the server, the server will analyze the company profile page on the company's official website and collect related information from news sites and social media.

[0177] 2. Information analysis

[0178] The server analyzes the collected information using natural language processing (NLP) algorithms to understand the client company's business environment and market trends. The analysis results include information on the industry sector, economic trends, competitor strategies, consumer trends, etc. As a specific example, the server analyzes the latest news articles about XYZ Corporation, analyzing and organizing information about the company's new product launch and its market reaction.

[0179] 3. Identifying proposal needs

[0180] Based on the analysis results, the server identifies the client company's specific issues and proposal needs. In doing so, the server refers to past success stories and general business cases to identify appropriate proposal content. As a specific example, the server analyzes business environment data and market trends for XYZ Corporation to identify needs regarding a promotion strategy for a new product.

[0181] 4. Emotion Recognition by Emotion Engine

[0182] When a user inputs the content of a proposal they wish to make into the server, the emotion engine built into the server recognizes the emotion based on the user's input. For example, when a user inputs a "promotion support proposal for a new product," the emotion engine determines whether the emotional state is positive or negative. As a specific example, if a user inputs "I would like to propose a promotion strategy that will maximize the appeal of XYZ Corporation's new product," the emotion engine recognizes the user's positive emotion.

[0183] 5. Automatic generation and adjustment of proposal materials

[0184] The server adjusts the content of the proposal materials based on the user's emotional state recognized by the emotion engine. If positive emotions are recognized, the content of the materials is automatically generated with a more proactive tone, and if negative emotions are recognized, the content is adjusted to take a more cautious approach as necessary. For example, if the user's positive emotions are recognized, the proposal materials will include a proactive phrase such as, "We expect this promotional strategy to significantly increase product sales."

[0185] 6. Submitting proposal materials

[0186] The server saves the generated proposal materials in PDF or PPT format and sends them to the user's device. The user checks the proposal materials received on their device and makes any necessary adjustments. After this, the user can immediately begin their proposal activities. As a concrete example, the server saves the "Promotion proposal materials for XYZ Co., Ltd." generated in PDF format and sends it to the user's device. The user receives the materials, makes any necessary adjustments, and immediately begins their proposal activities.

[0187] This system efficiently implements a series of processes, from collecting and analyzing customer information, identifying proposal needs, recognizing emotions using an emotion engine, automatically generating proposal materials, and sending them. As a result, it is possible to significantly reduce the time salespeople spend preparing proposals and improve their productivity. Furthermore, by generating proposal materials that take the user's emotions into consideration, it is possible to improve the quality of proposals.

[0188] The processing flow will be explained below.

[0189] Step 1:

[0190] The user inputs the name and URL of the client company into the server, which identifies the company information to be collected.

[0191] Step 2:

[0192] The server crawls designated company official websites, news sites, social media sites, and other internet information sources. Specifically, it sends HTTP requests and retrieves the HTML content of each page.

[0193] Step 3:

[0194] The server analyzes the HTML content and extracts basic information about the company using techniques such as Document Object Model (DOM) analysis and regular expressions.

[0195] Step 4:

[0196] The server performs text analysis on the collected information using natural language processing (NLP) algorithms, which include text tokenization, part-of-speech tagging, and entity recognition.

[0197] Step 5:

[0198] The server evaluates the importance of the text and extracts key keywords and phrases related to the business environment and market trends, such as new products, marketing strategies, and market trends.

[0199] Step 6:

[0200] Based on the analysis results, the server identifies the customer's specific issues and proposal needs, referring to past success stories and a sales strategy database.

[0201] Step 7:

[0202] The user inputs the desired proposal details to the server, for example, specifying specific details such as "promotion support proposal for a new product."

[0203] Step 8:

[0204] The emotion engine built into the server recognizes emotions based on user input, analyzing the emotion from the text entered by the user and determining whether it is positive or negative.

[0205] Step 9:

[0206] The server adjusts the content of the proposal materials based on the user's emotional state as recognized by the emotion engine. If a positive emotion is recognized, the content of the materials is automatically generated with a more positive tone, and if a negative emotion is recognized, it is automatically generated with a more cautious approach.

[0207] Step 10:

[0208] The server selects a proposal format tailored to the user's sentiment, embeds the collected data and analysis results, and applies an algorithm to automatically generate presentation slides and reports.

[0209] Step 11:

[0210] The server saves the generated proposal materials in PDF or PPT format and sends them to the user's device. The user can then review the received materials and make any necessary adjustments.

[0211] Step 12:

[0212] The user can check the final materials on the device and immediately begin making proposals.

[0213] By following these steps, the system of the present invention efficiently realizes a series of processes, from collecting customer information to recognizing and analyzing the user's emotions using an emotion engine, identifying proposal needs, automatically generating proposal materials, and finally sending the materials. This not only significantly reduces the time salespeople spend preparing proposals and improves productivity, but also makes it possible to provide high-quality proposal materials that take emotions into consideration.

[0214] Example 2

[0215] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0216] In conventional sales activities, salespeople spend a lot of time and effort preparing proposals and creating materials, resulting in low efficiency. Furthermore, it is difficult to make proposals that take into account customer emotions, making it difficult to improve the quality of proposals. The present invention aims to provide a system that reduces the time and effort required for salespeople to prepare proposals and automatically generates high-quality proposal materials that take into account customer emotions.

[0217] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for automatically collecting customer information from information sources on the Internet, means for analyzing the collected customer information using a natural language processing algorithm to grasp the business environment and market trends of the client company, means for identifying the customer's proposal needs based on the analysis results, means for a user to input desired proposal content and for an emotion engine to recognize emotions based on the user's input, means for automatically generating proposal materials adjusted based on the recognized emotions, and means for saving the generated proposal materials in PDF or PPT format and sending them to the user terminal. This not only significantly reduces the time a salesperson needs to prepare a proposal and improves productivity, but also makes it possible to generate high-quality proposal materials that take customer emotions into consideration.

[0218] "Customer information" refers to data about the client company, such as its name, URL, performance, major products and services, recent news, and posts on social media.

[0219] "Internet sources" refers to any information that is publicly available on the Internet, such as official websites, news sites, and social media.

[0220] "Crawling" refers to the technology of automatically crawling through web pages on the Internet to collect information.

[0221] "Natural language processing algorithms" refer to the technology that enables computers to understand and analyze human language.

[0222] "Business environment" refers to the industry and economic conditions to which the client company belongs, as well as external factors such as competitors and market trends.

[0223] "Market trends" refers to market trends, consumer behavior patterns, changes in demand, etc.

[0224] "Proposal needs" refers to the solutions and services that should be proposed to address the specific problems and issues that client companies face.

[0225] "Emotion engine" refers to technology for recognizing and analyzing emotions from user input.

[0226] "Proposal materials" refers to documents that summarize the proposals to client companies.

[0227] "PDF" stands for Portable Document Format and refers to a file format for electronically storing and sharing documents.

[0228] "PPT format" refers to the file format used by Microsoft PowerPoint for saving and sharing presentations.

[0229] "User terminal" refers to electronic devices used by users, such as PCs, smartphones, and tablets.

[0230] This invention provides a system that automates a series of processes from collecting customer information to generating proposal materials. The system works in cooperation with the server, terminals, and users, and incorporates an emotion engine to improve the work efficiency of salespeople.

[0231] Hardware and software used

[0232] The server is a high-performance computer, and cloud servers (Amazon Web Services, Google Cloud Platform, Microsoft Azure, etc.) can generally be used. A relational database such as MySQL is used as the database. Crawling software (e.g., Scrapy) is used to collect information, natural language processing algorithms (e.g., SpaCy, NLTK) are used for information analysis, and Scikit-learn is used for machine learning. A template generation tool (e.g., LaTeX, ReportLab) is used to generate proposal materials. A sentiment analysis tool such as IBM Watson or Microsoft Azure Text Analytics is used as the emotion engine.

[0233] System Operation

[0234] The user accesses the server from their device and enters the name or URL of the client company. This identifies the company information to be collected. For example, if the user enters the company name "XYZ Corporation," the server will collect information about that company. Here, crawling tools such as Scrapy are used to collect information from the specified company's official website, news sites, social media, etc.

[0235] The collected information is stored in a database (e.g., MySQL) on the server. The server then analyzes the collected information using natural language processing algorithms (e.g., SpaCy, NLTK) to understand the company's business environment and market trends. For example, the latest news articles about XYZ Corporation can be analyzed to summarize the status of new product launches and their market reactions.

[0236] Based on the analysis results, the server identifies the client company's specific issues and proposal needs. It uses a machine learning algorithm (Scikit-learn) to reference past success stories and general business cases to generate appropriate proposals. For example, if XYZ Co., Ltd. launches a new product, it identifies the needs for its promotion strategy.

[0237] Next, when the user inputs the content of the proposal they want, the emotion engine built into the server recognizes emotions based on the user's input. For example, when a user inputs a "promotion support proposal for a new product," it determines whether the emotional state is positive or negative.

[0238] The server adjusts the content of the proposal materials based on the user's emotional state as recognized by the emotion engine. If a positive emotion is recognized, the content of the materials will be adjusted to a more proactive tone. If a negative emotion is recognized, the content will be adjusted to a more cautious approach as necessary. For example, a proactive phrase such as "We expect this promotional strategy to significantly increase product sales" will be inserted.

[0239] Finally, the server saves the generated proposal materials in PDF or PPT format and sends them to the user's device. The user can review the received materials on their device and fine-tune the content as needed. For example, the server generates a "Promotion Proposal Material for XYZ Co., Ltd." and sends it in PDF format, which the user can then receive and review.

[0240] An example of a prompt sentence that can be entered is, "Please create a promotion strategy proposal based on the latest news from XYZ Co., Ltd." This system efficiently collects and analyzes customer information, identifies proposal needs, recognizes emotions using an emotion engine, automatically generates proposal materials, and sends them.

[0241] This will significantly reduce the time it takes salespeople to prepare proposals, improving productivity and the quality of proposals.

[0242] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0243] Step 1:

[0244] The user enters the name and URL of the client company into the server. The entered name and URL are sent to the server, which identifies the company information to be collected.

[0245] Input: Name and URL of the customer company

[0246] Output: Specific information to be collected

[0247] Specific behavior:

[0248] The user enters a company name, such as "XYZ Co., Ltd.", into the server from the terminal and sends it.

[0249] The server generates a request to obtain information about the specified company.

[0250] Step 2:

[0251] The server uses crawling software (such as Scrapy) to crawl designated companies' official websites, news sites, social media, and other online sources to collect customer information.

[0252] Input: Specific information to be collected

[0253] Output: Collected customer information

[0254] Specific behavior:

[0255] Based on the URL request, the server crawls the company's official website for company overviews, news sites for the latest news, and social media posts.

[0256] The server stores the collected information in a database (such as MySQL).

[0257] Step 3:

[0258] The information collected by the server is analyzed using natural language processing (NLP) algorithms (e.g., SpaCy, NLTK) to understand the company's business environment and market trends.

[0259] Input: Collected customer information

[0260] Output: Parsed company information

[0261] Specific behavior:

[0262] The server reads the collected text data and performs topic modeling and sentiment analysis.

[0263] The server formats the analysis results and generates a report that helps client companies understand industry and economic trends.

[0264] The server stores the analysis results in a database.

[0265] Step 4:

[0266] Based on the analysis results, the server identifies the proposal needs of the client company by referring to past success stories and general business cases.

[0267] Input: Parsed company information

[0268] Output: Identified proposed needs

[0269] Specific behavior:

[0270] The server searches a database of past proposal examples and extracts similar cases.

[0271] The server uses machine learning algorithms (such as Scikit-learn) to compare the analysis results with past cases and identify the needs of the client company.

[0272] The server stores the identified proposed needs in a database.

[0273] Step 5:

[0274] When the user inputs the desired suggestion, an emotion engine built into the server recognizes emotions based on the user's input.

[0275] Input: The input text of the user's suggestion

[0276] Output: User's emotional state

[0277] Specific behavior:

[0278] The user inputs and submits a proposal such as "Proposal for new product promotion support."

[0279] The server uses sentiment analysis tools (e.g., IBM Watson, Microsoft Azure Text Analytics) to determine the emotional state of the user from the text they input.

[0280] The server stores the recognized emotional state of the user in a database.

[0281] Step 6:

[0282] The server automatically adjusts and generates the content of the proposal materials based on the user's emotional state recognized by the emotion engine.

[0283] Input: User's emotional state and suggestion needs

[0284] Output: Generated proposal

[0285] Specific behavior:

[0286] The server generates the proposal using a template generation tool (e.g., LaTeX, ReportLab).

[0287] The server adjusts the tone and content of the proposal based on the emotional state.

[0288] For positive emotions, include phrases and designs with a positive tone.

[0289] In the case of negative emotions, adopt a measured approach and a calm tone.

[0290] The server saves the generated proposal document in PDF format.

[0291] Step 7:

[0292] The server sends the generated proposal materials to the user's terminal, where the user can check the received proposal materials and make fine adjustments as necessary.

[0293] Input: Generated proposal document (PDF format)

[0294] Output: Received proposal materials

[0295] Specific behavior:

[0296] The server sends the generated PDF file to the user's device via email or a dedicated application.

[0297] The user checks the proposal materials through their inbox or application.

[0298] The user fine-tunes the necessary wording and design and finalizes the final proposal materials.

[0299] (Application example 2)

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

[0301] In traditional sales activities, salespeople spend a great deal of time and effort preparing proposals and creating materials for customers. Furthermore, the approach to customers is uniform, making it difficult to customize proposals that take into account the customer's emotional state. Furthermore, while effective customer service and proposals for best-selling products are required in brick-and-mortar stores, efficient information gathering and the creation of proposal materials are difficult. There is a need to solve these issues and improve the efficiency and quality of sales activities and store operations.

[0302] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0303] In this invention, the server includes: means for automatically collecting customer information from online information sources; means for analyzing the collected customer information to understand the business environment and market trends of the client company; means for identifying the customer's proposal needs based on the analysis results; means for automatically generating proposal materials according to the identified proposal needs; means for transmitting the generated proposal materials to a user terminal; and means for adjusting the content of the proposal materials based on the user's emotional state, including an emotion engine that recognizes the user's emotions when inputting the proposal content. This reduces the salesperson's proposal preparation time, improves productivity, and enables the automatic generation of high-quality proposal materials that take the user's emotions into consideration. Furthermore, effective proposals that meet customer needs can be made even in physical stores, which is expected to improve customer satisfaction.

[0304] "Customer information" is a general term for basic information and publicly available data about companies and individuals collected from sources on the Internet.

[0305] "Internet sources" refers to all media that provide information publicly available via the Internet, such as official websites, news sites, and social media.

[0306] "Means of collection" refers to the way the server automatically obtains the necessary information, such as by crawling the web or using an API.

[0307] "Means of analysis" refers to the use of natural language processing algorithms to analyze collected information and extract useful insights.

[0308] "The business environment of a client company" refers to the industry and market conditions to which a particular client company belongs, as well as the external environment that influences corporate activities.

[0309] "Market trends" refer to ongoing or predicted movements or trends in a particular industry or market.

[0310] "Proposal needs" refers to the specific solutions and approaches that should be proposed to address the challenges and business opportunities facing client companies.

[0311] "Means for automatic generation" refers to an algorithm that automatically assembles proposal materials based on collected and analyzed information.

[0312] "Transmission means" refers to a communication means or protocol for transmitting the generated proposal materials to a user terminal.

[0313] "Emotion engine" refers to a software component for recognizing and analyzing a user's emotional state from input data.

[0314] "Adjustment measures" refer to methods for appropriately changing the content and structure of proposal materials depending on the perceived emotional state.

[0315] The present invention provides a system for improving the efficiency and quality of customer service and sales strategy planning in a brick-and-mortar store. Specific embodiments for carrying out the present invention will be described below.

[0316] System Overview

[0317] The system consists of a server, tablets or smartphones used by store staff, and an application incorporating an emotion engine.

[0318] Hardware and software used

[0319] Hardware: Tablets, smartphones

[0320] Software: Python and Django are used on the server side, Flutter on the client side, scikit-learn and NLTK (Natural Language Toolkit) for data analysis, and Microsoft Azure Emotion API for emotion recognition.

[0321] Data processing and data calculation

[0322] The server automatically collects customer information from sources on the Internet, such as official websites, news sites, and social media, to obtain the latest information about customers and their companies.

[0323] The collected information is then analyzed using NLP (natural language processing) algorithms, which allows the company to understand its business environment and market trends. The analysis results include industry trends, economic conditions, competitor strategies, consumer trends, and more.

[0324] The server then identifies the customer's specific proposal needs based on the analysis results, referencing past success stories and general business cases to determine the appropriate proposal content.

[0325] Once the proposal content has been decided, the user (store staff) inputs the desired proposal content via a tablet or smartphone, and the emotion expressed at that time is recognized by an emotion engine built into the server. The emotion engine analyzes the input content and determines the user's emotional state.

[0326] The server adjusts the content of the proposal materials based on the user's emotional state, as recognized by the emotion engine. If a positive emotion is recognized, the content of the materials is automatically generated with a more positive tone. If a negative emotion is recognized, the content is adjusted to take a more cautious approach.

[0327] The proposal materials generated in this way are saved in PDF or PPT format and sent to the user's device, where the user can review the received proposal materials and make any necessary adjustments.

[0328] Specific examples

[0329] For example, suppose a store staff member operates a tablet and enters "Customer name: ABC Company, desired product category: home appliances." The server collects information related to ABC Company from its official website and social media, and uses an NLP algorithm to analyze the business environment and market trends. Next, based on the analysis results, the server identifies proposal needs. When the staff member enters "Proposal for new product promotion support," the emotion engine recognizes the staff member's positive emotions. The server takes these emotions into consideration and automatically generates proposal materials with a positive tone. The generated materials are then sent to the staff member's tablet.

[0330] Prompt Sentence Examples

[0331] Information gathering prompt:

[0332] Customer name: ABC Company

[0333] Desired product category: Home appliances

[0334] Information analysis prompt:

[0335] Information collected:

[0336] Company profile from the official website

[0337] Latest News Articles

[0338] SNS posts

[0339] Analysis details:

[0340] New product release information

[0341] market trends

[0342] Customer interests

[0343] Proposal needs specific prompt:

[0344] Needs based on analysis results:

[0345] New product promotion strategies

[0346] Differentiate yourself from your competitors

[0347] Emotion engine prompts for emotion recognition:

[0348] Input text: "I think ABC's new product is excellent, but I'm concerned about the promotion."

[0349] This makes it possible to efficiently create and provide high-quality proposal materials that meet customer needs, reducing the time store staff spend preparing proposals and enabling them to respond to customers quickly and effectively.

[0350] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0351] Step 1:

[0352] Collecting customer information

[0353] Input: The user enters the customer name and desired product category on a tablet or smartphone.

[0354] Specific operation: When the user enters "Customer name: ABC company, desired product category: home appliances", the information is sent to the server.

[0355] Data processing or data calculation: The server crawls and collects information related to the specified customer from online sources (official websites, news sites, social media, etc.).

[0356] Output: Collected customer information data is stored on the server.

[0357] Step 2:

[0358] Customer information analysis

[0359] Input: Customer information data collected by the server.

[0360] Specific operation: The server uses natural language processing (NLP) algorithms (using scikit-learn, NLTK, etc.) to understand the company's business environment and market trends.

[0361] Data processing or data calculation: NLP algorithms analyze collected text data to extract the latest developments of companies, industry trends, competitor strategies, etc.

[0362] Output: The analysis results in a useful set of data that can be used for proposals.

[0363] Step 3:

[0364] Identifying proposal needs

[0365] Input: Analysis result data.

[0366] Specific actions: The server identifies the customer's specific issues and needs based on the analysis results.

[0367] Data processing or data calculation: The server refers to past success stories and general business cases and determines appropriate proposals based on the extracted information.

[0368] Output: Data about customer proposal needs is identified.

[0369] Step 4:

[0370] Emotion recognition by emotion engine

[0371] Input: The user inputs the desired suggestions using a tablet or smartphone.

[0372] Specific operation: The user inputs "Proposal for new product promotion support." The emotion engine (Microsoft Azure Emotion API) built into the server analyzes the input in real time.

[0373] Data processing or data calculation: The emotion engine recognizes the user's emotional state (positive, negative, etc.) based on the text data.

[0374] Output: User emotional state data is generated.

[0375] Step 5:

[0376] Automatic generation and adjustment of proposal materials

[0377] Input: Customer suggestion needs data and user emotional state data.

[0378] Specific operation: The server adjusts and automatically generates the content of proposal materials based on the recognized emotional state.

[0379] Data manipulation or data calculation: Generate materials with a positive tone when positive sentiment is perceived, and adjust to take a cautious approach when negative sentiment is perceived.

[0380] Output: A tailored proposal is generated in PDF and PPT formats.

[0381] Step 6:

[0382] Submit a proposal

[0383] Input: Proposal material automatically generated by the server.

[0384] Specific operation: The server sends the generated proposal materials to the user's terminal in PDF or PPT format.

[0385] Data processing or data calculation: Sending material data from the server to the terminal using a communication protocol.

[0386] Output: The proposal materials are sent to the user's device, allowing the user to receive and review the proposal materials.

[0387] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0388] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0389] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0390] [Second embodiment]

[0391] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0392] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0393] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0394] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0395] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0396] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0397] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0398] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0399] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0400] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0401] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0402] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0403] The present invention relates to a system for reducing the time and man-hours required for salespeople to prepare proposals and documents. Specific embodiments for carrying out the present invention are described below. In the present invention, a server, a terminal, and a user work together.

[0404] 1. Information gathering

[0405] The user inputs the name and URL of the client company to which they are making a proposal to the server. The server then crawls the designated company's official website, news sites, social media, and other online sources to collect basic information and publicly available data related to the client company. The collected information includes the company's performance, major products and services, recent news, and social media posts.

[0406] Example: When a user enters the name of a company, such as "XYZ Corporation," into a server, the server analyzes the company profile page on the company's official website and collects related information from news sites and social media.

[0407] 2. Information analysis

[0408] The server analyzes the collected information using natural language processing (NLP) algorithms to understand the client company's business environment and market trends. The analysis results include information on industry sectors, economic trends, competitor strategies, consumer trends, etc.

[0409] Example: The server analyzes the latest news articles about XYZ Corporation, analyzing and organizing information about the company's new product launch and its market reaction.

[0410] 3. Identifying proposal needs

[0411] Based on the analysis results, the server identifies the specific issues and proposal needs of the client company, referring to past success stories and general business cases to identify appropriate proposal content.

[0412] Example: The server analyzes business environment data and market trends for XYZ Corporation to identify needs regarding promotion strategies for new products.

[0413] 4. Automatic generation of proposal materials

[0414] When a user inputs their proposal into the server, the server automatically generates the optimal document format for the identified proposal needs, embedding the collected data and analysis results in the appropriate format.

[0415] Example: A user inputs a "promotion support proposal for XYZ Corporation" into the server, and the server automatically generates presentation slides. The slides include an "outline of XYZ's new product," "current market trends," "proposed promotion strategy," and "past success stories."

[0416] 5. Submit your proposal

[0417] The server saves the generated proposal materials in PDF or PPT format and sends them to the user's device. The user can then review the received materials, make any necessary adjustments, and immediately begin their proposal activities.

[0418] Example: The server generates a "Promotion proposal document for XYZ Co., Ltd." and saves it in PDF format, then sends it to the user's device. The user receives the document, makes any necessary adjustments, and promptly starts the proposal process.

[0419] In this way, the present invention is a system that can significantly reduce the time it takes salespeople to prepare proposals and improve productivity by implementing a series of processes: collecting customer information, analyzing the information, identifying proposal needs, automatically generating proposal materials, and sending the proposal materials.

[0420] The processing flow will be explained below.

[0421] Step 1:

[0422] The user inputs the name and URL of the client company into the server, which identifies the company information to be collected.

[0423] Step 2:

[0424] The server crawls designated company official websites, news sites, social media sites, and other internet information sources by sending HTTP requests and retrieving the HTML content of each page.

[0425] Step 3:

[0426] The server analyzes the HTML content and extracts basic information about the company using techniques such as Document Object Model (DOM) analysis and regular expressions.

[0427] Step 4:

[0428] The server performs text analysis on the collected information using natural language processing (NLP) algorithms, which include text tokenization, part-of-speech tagging, and entity recognition.

[0429] Step 5:

[0430] The server evaluates the importance of the text and extracts key keywords and phrases related to the business environment and market trends, such as new products, marketing strategies, and market trends.

[0431] Step 6:

[0432] Based on the analysis results, the server identifies the customer's specific issues and proposal needs, referring to past success stories and a sales strategy database.

[0433] Step 7:

[0434] The user inputs the desired proposal details to the server, for example, specifying specific details such as "promotion support proposal for a new product."

[0435] Step 8:

[0436] The server selects the appropriate proposal format based on user input, including template selection.

[0437] Step 9:

[0438] The server embeds the collected data and analysis results into the selected template and automatically generates proposal materials in the form of presentation slides, reports, etc.

[0439] Step 10:

[0440] The server saves the generated proposal materials in PDF or PPT format and sends them to the user's terminal.

[0441] Step 11:

[0442] The user can review the proposal materials received on their device and make any necessary adjustments, after which they can immediately begin their proposal activities.

[0443] Through these steps, the system of the present invention efficiently collects and analyzes customer information, identifies proposal needs, automatically generates proposal materials, and finally sends the materials, significantly reducing the time salespeople spend preparing proposals and improving productivity.

[0444] Example 1

[0445] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0446] In modern sales activities, the time and effort required to prepare proposals and documents is a significant burden. Sales representatives must spend a great deal of time gathering and analyzing information about client companies and creating appropriate proposal materials. This process is often done manually and is inefficient, resulting in a decrease in the speed and productivity of sales activities.

[0447] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0448] In this invention, the server includes means for automatically collecting customer information from information sources on the Internet, means for analyzing the collected customer information using a natural language processing algorithm to grasp the business environment and market trends of the client company, means for identifying the proposal needs of the client based on the analysis results, means for automatically generating proposal materials according to the identified proposal needs, and means for generating the generated proposal materials in PDF or PPT format and transmitting them to a user terminal, thereby enabling a significant reduction in proposal preparation time and an improvement in the productivity of sales activities.

[0449] "Customer Information" refers to data including basic information, performance, products and services, and market trends regarding the target company.

[0450] "Internet sources" refers to various sources that provide public information accessible via the Internet, such as official websites, news sites, and social media.

[0451] A "natural language processing algorithm" is a collection of computer programs and methods for understanding and analyzing human language, and refers to technology for extracting keywords from text and analyzing relationships.

[0452] "Business environment" refers to the surrounding circumstances, including industry trends, economic conditions, and competitor activities, that affect the business operations of client companies.

[0453] "Market trends" refers to information that describes fluctuations and trends over time in demand for products or services, the competitive landscape, consumer preferences, and other factors in a particular market.

[0454] "Proposal needs" refers to the specific requests and requirements related to the challenges the client company faces and the solutions it seeks.

[0455] "Proposal materials" refer to documents such as presentation slides and reports that sales representatives use when making proposals to client companies.

[0456] "Automatic generation" refers to a process in which data collection, analysis, and documentation are performed programmatically without manual intervention.

[0457] "PDF and PPT formats" refer to standard file formats for electronic documents and presentations, such as the Portable Document Format (PDF) developed by Adobe Systems and the presentation format (PPT) used by Microsoft PowerPoint.

[0458] "User terminal" refers to devices such as computers, tablets, and smartphones operated by sales representatives and system users.

[0459] The present invention relates to a system for reducing the time and man-hours required for sales representatives to prepare proposals and documents. This system operates in cooperation with a server, terminals, and users, and uses the following hardware and software.

[0460] Hardware and software used

[0461] Server: Computing resources for data collection, analysis, and proposal generation, including web servers (e.g., Apache), crawling tools (e.g., Scrapy), natural language processing libraries (e.g., SpaCy, NLTK), and presentation generation tools (e.g., Python-PPTX).

[0462] Terminal: A PC or tablet is used as a device for users to operate the system. Terminals are primarily used for input and output, and have a web browser, PDF viewer, and presentation creation tool (e.g., Microsoft PowerPoint) installed.

[0463] Internet connection: A network connection is required to transmit data between the server and your device.

[0464] Program processing flow

[0465] Information gathering

[0466] The user accesses the server from their device and enters the name and URL of the client company to which they would like to make a proposal. As a specific example, let's say the user enters the name of "Company A." Based on this, the server uses a crawling tool (e.g., Scrapy) to collect information from the designated company's official website, news sites, and social media. The collected information is stored in a database on the server.

[0467] Information analysis

[0468] The server then analyzes the collected information using natural language processing (NLP) algorithms (e.g., SpaCy, NLTK). This allows the client company's business environment and market trends to be understood. Specifically, the analysis targets company performance data, new product information, recent news, social media posts, etc. For example, a report summarizing the market trends of "Company A" and customer reactions on social media is generated.

[0469] Identifying proposal needs

[0470] The server identifies the client company's specific issues and proposal needs based on the analysis results. In doing so, the server refers to past success stories and general business cases to identify appropriate proposal content. For example, if "Company A" launches a new product, the server identifies proposal needs regarding its promotion strategy.

[0471] Automatic generation of proposal materials

[0472] When a user inputs the details of their proposal into the server, the server automatically generates the optimal document format for the identified proposal needs. Using the Python-PPTX library, the collected data and analysis results are incorporated into presentation slides. For example, a "Proposal for Promotion Support for Company A" slide is generated that includes an overview of the company's new product, market trends, the proposed promotion strategy, and past success stories.

[0473] Submit a proposal

[0474] Finally, the server saves the generated proposal materials in PDF or PPT format and sends them to the user's device. The user can review the received materials and make any necessary adjustments, allowing them to quickly begin their proposal activities.

[0475] Prompt Sentence Examples

[0476] Here is an example prompt:

[0477] Company name: Company A

[0478] Proposal: Please create a proposal document for Company A's new product promotion strategy.

[0479] In this way, the present invention is a system that significantly reduces the burden on sales representatives and improves the efficiency of proposal activities by automating the entire process from data collection and analysis, identification of proposal needs, and automatic generation and transmission of materials.

[0480] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0481] Step 1:

[0482] The user accesses the server from their device and enters the name and URL of the client company to which they are making a proposal into a dedicated form. The input data is "Company Name: Company A" and "URL: www.example.com." Based on this input, the server launches a crawling tool (e.g., Scrapy) and collects information from the designated company's official website, news sites, and social media. Specifically, the server saves the collected data in a database and confirms that crawling is complete.

[0483] Step 2:

[0484] The server analyzes the information stored in the database using natural language processing algorithms (e.g., SpaCy, NLTK). The collected text data is used as input. Specific analysis operations include extracting keywords from the text, classifying documents, and analyzing sentiment, and outputting the results of the analysis: company performance, product overviews, market trends, and the behavior of competitors.

[0485] Step 3:

[0486] The server uses the analysis results to identify the specific issues and proposal needs of the client company. The input in this step is the analysis results obtained in the previous step. The server refers to past success stories and general business cases to identify the proposal content that best meets the client's needs. A specific example of how this works is to create a list of proposal needs regarding Company A's new product promotion strategy.

[0487] Step 4:

[0488] When a user inputs the proposal details into the server, the server automatically generates the proposal materials. In this case, the input data is "Proposal Details: Company A's New Product Promotion Strategy." The server uses the Python-PPTX library to create presentation slides based on the collected data and analysis results. Specifically, the slides include an overview of the company's new product, market trends, the proposed promotion strategy, and past success stories.

[0489] Step 5:

[0490] The server saves the generated proposal materials in PDF or PPT format and sends them to the user's terminal. In this step, the generated proposal materials are used as input. The server converts the materials into PDF format and sends them to the user's terminal. Specific operations include confirming the completion of transmission and recording a log. The user can then review the received materials and make any necessary adjustments to quickly begin proposal activities.

[0491] The above is the flow of processing of the program of this system.

[0492] (Application example 1)

[0493] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0494] In conventional sales support systems, the process from collecting customer information to creating proposal materials was done manually, requiring a great deal of time and effort. Furthermore, the collected information could not be analyzed quickly and the proposal needs based on that analysis could not be identified quickly, resulting in a decrease in the productivity of sales activities. Furthermore, there were few ways to utilize the collected information to provide appropriate materials, which was a burden for salespeople. A new approach was needed to solve these problems.

[0495] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0496] In this invention, the server includes means for automatically collecting customer information from information sources on the Internet, means for analyzing the collected customer information and grasping the business environment and market trends of the client organization, means for identifying the proposal needs of the client based on the analysis results, means for automatically generating proposal materials according to the identified proposal needs, means for transmitting the generated proposal materials to an end-user terminal, and means for inputting information and viewing materials using a smart device. This enables salespeople to quickly collect and analyze customer information and automatically generate and transmit appropriate proposal materials, significantly reducing the time and effort required for proposal preparation and improving productivity.

[0497] "Customer information" refers to data collected from online sources such as official websites published by companies and organizations, news sites, and social networking services (SNS).

[0498] "Client organization" refers to the company or organization to which the proposal is made, and its business environment and market trends are the subject of analysis.

[0499] "Proposal needs" refers to the content and strategies that should be proposed in sales activities, identified based on the client organization's business environment and market trends.

[0500] "Proposal materials" refer to presentation slides and documents that are automatically generated in response to identified proposal needs and are used in sales activities.

[0501] "End-user terminal" refers to the device used by salespeople who use the system, and includes PCs, smartphones, tablets, smart glasses, etc.

[0502] "Smart device" refers to devices such as smartphones, smart glasses, and tablets that are connected to the internet and offer additional functionality.

[0503] "Natural language processing algorithm" refers to the computational methods and models used to analyze collected customer information and understand and classify its content.

[0504] A "crawling engine" refers to a program that automatically searches for information on the Internet and collects the necessary data.

[0505] "Analysis results" refers to the analysis results of customer information extracted using natural language processing algorithms and data analysis tools.

[0506] "Sources" refer to reliable sources of data on the Internet, such as official websites, news sites, and social networking services (SNS).

[0507] To implement the present invention, a system is provided in which a server, a terminal, and a user work together, as will be described in detail below.

[0508] Overall structure

[0509] This system includes the following means for collecting customer information, analyzing the information, identifying proposal needs, automatically generating proposal materials, transmitting the generated materials, and inputting information and viewing materials using a smart device.

[0510] Collecting customer information

[0511] The user uses a smart device (e.g., smart glasses) to voice-input the name of the customer company. Based on this input, the server collects customer information from online sources (official websites, news sites, social media, etc.). This process uses a crawling engine (e.g., Beautiful Soup, Scrapy).

[0512] Information analysis

[0513] The collected information is sent to a server and analyzed using natural language processing algorithms (e.g., BERT, GPT). This allows us to understand the business environment and market trends of our client organizations. Data analysis tools such as Pandas and Scikit-learn are used for data analysis.

[0514] Identifying proposal needs

[0515] The server identifies the customer's proposal needs based on the analysis results, which is achieved by using a content recommendation system (e.g., Collaborative Filtering) and referencing past success stories and industry-standard data.

[0516] Automatic generation of proposal materials

[0517] The server automatically generates proposal materials based on the identified proposal needs, using document generation tools (e.g., LaTeX, MS Office API) to create presentation slides and PDF documents.

[0518] Sending generated materials

[0519] The generated proposal materials are sent to the end user's device in PDF or PPT format using an email sending API (e.g., SMTP) or cloud storage (e.g., Google Drive, AWS S3). These materials can be viewed in real time using a smart device.

[0520] Specific examples

[0521] For example, if a user uses smart glasses to voice-input the name of a client company, such as "ABC Co., Ltd.", the server will crawl online sources based on the company name and collect company information. The collected information is analyzed using a natural language processing algorithm to understand the company's business environment and market trends. Based on the analysis results, a proposal need, such as "promotion strategy for ABC Co., Ltd.'s new product," is then identified. The server then uses this information to automatically generate presentation slides and send them to the user's smart glasses. This allows the user to review the materials in real time while making proposals.

[0522] Prompt Sentence Examples

[0523] Examples of prompts from this generative AI model include:

[0524] "Please provide us with the latest information about ABC Corporation. Pay particular attention to new products, market trends, and competitor activity, and provide analysis that can be used in our advertising strategy."

[0525] In this way, the salesperson can make proposals to the customer quickly and efficiently.

[0526] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0527] Step 1:

[0528] The user uses a smart device (smart glasses) to input the name of the customer company by voice. This voice input is converted into text data by the device and sent to the server. This results in the input data of the customer company name.

[0529] Step 2:

[0530] The server uses the received text data to crawl the designated company's official website, news sites, social media, and other sources. Software such as Beautiful Soup and Scrapy are used. This process gathers relevant information from company profile pages on official websites, news articles, social media posts, and other sources to obtain the collected data.

[0531] Step 3:

[0532] The server analyzes the collected information using NLP (natural language processing) algorithms, such as the BERT or GPT model. This analysis extracts information about the client organization's business environment, market trends, and competitor trends, and produces analysis results.

[0533] Step 4:

[0534] The server uses the analysis results to refer to past success stories and general business cases, and utilizes a content recommendation system (Collaborative Filtering) to identify proposal needs. This clarifies the customer's specific issues and proposal needs, and provides data on identified proposal needs.

[0535] Step 5:

[0536] Based on the identified proposal needs, the server automatically generates proposal materials using document generation tools (LaTeX, MS Office API). The generated materials are in PDF or PPT format and include company information, market analysis, proposal details, etc. This process results in the automatically generated proposal materials.

[0537] Step 6:

[0538] The generated proposal materials are sent by the server to the end user's device using methods such as email sending API (SMTP) or cloud storage services (Google Drive, AWS S3). This sends the materials to the user's device (smart glasses, etc.), allowing the user to view the materials in real time.

[0539] Through the above processing steps, this system can efficiently carry out the process from collecting customer information to automatically generating and sending proposal materials.

[0540] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0541] The present invention relates to a system that reduces the time and man-hours required for salespeople to prepare proposals and create documents, and improves the quality of proposals by combining it with an emotion engine that recognizes user emotions. Specific embodiments for implementing the present invention are described below. In this invention, a server, terminals, and users work in cooperation with each other, and an emotion engine is also included.

[0542] 1. Information gathering

[0543] A user inputs the name or URL of a client company into the server. This identifies the company information to be collected. The server then crawls the designated company's official website, news sites, social media, and other online sources to collect basic information and publicly available data related to the client company. The collected information includes the company's performance, major products and services, recent news, and social media posts. For example, if a user inputs the company name "XYZ Corporation" into the server, the server will analyze the company profile page on the company's official website and collect related information from news sites and social media.

[0544] 2. Information analysis

[0545] The server analyzes the collected information using natural language processing (NLP) algorithms to understand the client company's business environment and market trends. The analysis results include information on the industry sector, economic trends, competitor strategies, consumer trends, etc. As a specific example, the server analyzes the latest news articles about XYZ Corporation, analyzing and organizing information about the company's new product launch and its market reaction.

[0546] 3. Identifying proposal needs

[0547] Based on the analysis results, the server identifies the client company's specific issues and proposal needs. In doing so, the server refers to past success stories and general business cases to identify appropriate proposal content. As a specific example, the server analyzes business environment data and market trends for XYZ Corporation to identify needs regarding a promotion strategy for a new product.

[0548] 4. Emotion Recognition by Emotion Engine

[0549] When a user inputs the content of a proposal they wish to make into the server, the emotion engine built into the server recognizes the emotion based on the user's input. For example, when a user inputs a "promotion support proposal for a new product," the emotion engine determines whether the emotional state is positive or negative. As a specific example, if a user inputs "I would like to propose a promotion strategy that will maximize the appeal of XYZ Corporation's new product," the emotion engine recognizes the user's positive emotion.

[0550] 5. Automatic generation and adjustment of proposal materials

[0551] The server adjusts the content of the proposal materials based on the user's emotional state recognized by the emotion engine. If positive emotions are recognized, the content of the materials is automatically generated with a more proactive tone, and if negative emotions are recognized, the content is adjusted to take a more cautious approach as necessary. For example, if the user's positive emotions are recognized, the proposal materials will include a proactive phrase such as, "We expect this promotional strategy to significantly increase product sales."

[0552] 6. Submitting proposal materials

[0553] The server saves the generated proposal materials in PDF or PPT format and sends them to the user's device. The user checks the proposal materials received on their device and makes any necessary adjustments. After this, the user can immediately begin their proposal activities. As a concrete example, the server saves the "Promotion proposal materials for XYZ Co., Ltd." generated in PDF format and sends it to the user's device. The user receives the materials, makes any necessary adjustments, and immediately begins their proposal activities.

[0554] This system efficiently implements a series of processes, from collecting and analyzing customer information, identifying proposal needs, recognizing emotions using an emotion engine, automatically generating proposal materials, and sending them. As a result, it is possible to significantly reduce the time salespeople spend preparing proposals and improve their productivity. Furthermore, by generating proposal materials that take the user's emotions into consideration, it is possible to improve the quality of proposals.

[0555] The processing flow will be explained below.

[0556] Step 1:

[0557] The user inputs the name and URL of the client company into the server, which identifies the company information to be collected.

[0558] Step 2:

[0559] The server crawls designated company official websites, news sites, social media sites, and other internet information sources. Specifically, it sends HTTP requests and retrieves the HTML content of each page.

[0560] Step 3:

[0561] The server analyzes the HTML content and extracts basic information about the company using techniques such as Document Object Model (DOM) analysis and regular expressions.

[0562] Step 4:

[0563] The server performs text analysis on the collected information using natural language processing (NLP) algorithms, which include text tokenization, part-of-speech tagging, and entity recognition.

[0564] Step 5:

[0565] The server evaluates the importance of the text and extracts key keywords and phrases related to the business environment and market trends, such as new products, marketing strategies, and market trends.

[0566] Step 6:

[0567] Based on the analysis results, the server identifies the customer's specific issues and proposal needs, referring to past success stories and a sales strategy database.

[0568] Step 7:

[0569] The user inputs the desired proposal details to the server, for example, specifying specific details such as "promotion support proposal for a new product."

[0570] Step 8:

[0571] The emotion engine built into the server recognizes emotions based on user input, analyzing the emotion from the text entered by the user and determining whether it is positive or negative.

[0572] Step 9:

[0573] The server adjusts the content of the proposal materials based on the user's emotional state as recognized by the emotion engine. If a positive emotion is recognized, the content of the materials is automatically generated with a more positive tone, and if a negative emotion is recognized, it is automatically generated with a more cautious approach.

[0574] Step 10:

[0575] The server selects a proposal format tailored to the user's sentiment, embeds the collected data and analysis results, and applies an algorithm to automatically generate presentation slides and reports.

[0576] Step 11:

[0577] The server saves the generated proposal materials in PDF or PPT format and sends them to the user's device. The user can then review the received materials and make any necessary adjustments.

[0578] Step 12:

[0579] The user can check the final materials on the device and immediately begin making proposals.

[0580] By following these steps, the system of the present invention efficiently realizes a series of processes, from collecting customer information to recognizing and analyzing the user's emotions using an emotion engine, identifying proposal needs, automatically generating proposal materials, and finally sending the materials. This not only significantly reduces the time salespeople spend preparing proposals and improves productivity, but also makes it possible to provide high-quality proposal materials that take emotions into consideration.

[0581] Example 2

[0582] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0583] In conventional sales activities, salespeople spend a lot of time and effort preparing proposals and creating materials, resulting in low efficiency. Furthermore, it is difficult to make proposals that take into account customer emotions, making it difficult to improve the quality of proposals. The present invention aims to provide a system that reduces the time and effort required for salespeople to prepare proposals and automatically generates high-quality proposal materials that take into account customer emotions.

[0584] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for automatically collecting customer information from information sources on the Internet, means for analyzing the collected customer information using a natural language processing algorithm to grasp the business environment and market trends of the client company, means for identifying the customer's proposal needs based on the analysis results, means for a user to input desired proposal content and for an emotion engine to recognize emotions based on the user's input, means for automatically generating proposal materials adjusted based on the recognized emotions, and means for saving the generated proposal materials in PDF or PPT format and sending them to the user terminal. This not only significantly reduces the time a salesperson needs to prepare a proposal and improves productivity, but also makes it possible to generate high-quality proposal materials that take customer emotions into consideration.

[0585] "Customer information" refers to data about the client company, such as its name, URL, performance, major products and services, recent news, and posts on social media.

[0586] "Internet sources" refers to any information that is publicly available on the Internet, such as official websites, news sites, and social media.

[0587] "Crawling" refers to the technology of automatically crawling through web pages on the Internet to collect information.

[0588] "Natural language processing algorithms" refer to the technology that enables computers to understand and analyze human language.

[0589] "Business environment" refers to the industry and economic conditions to which the client company belongs, as well as external factors such as competitors and market trends.

[0590] "Market trends" refers to market trends, consumer behavior patterns, changes in demand, etc.

[0591] "Proposal needs" refers to the solutions and services that should be proposed to address the specific problems and issues that client companies face.

[0592] "Emotion engine" refers to technology for recognizing and analyzing emotions from user input.

[0593] "Proposal materials" refers to documents that summarize the proposals to client companies.

[0594] "PDF" stands for Portable Document Format and refers to a file format for electronically storing and sharing documents.

[0595] "PPT format" refers to the file format used by Microsoft PowerPoint for saving and sharing presentations.

[0596] "User terminal" refers to electronic devices used by users, such as PCs, smartphones, and tablets.

[0597] This invention provides a system that automates a series of processes from collecting customer information to generating proposal materials. The system works in cooperation with the server, terminals, and users, and incorporates an emotion engine to improve the work efficiency of salespeople.

[0598] Hardware and software used

[0599] The server is a high-performance computer, and cloud servers (Amazon Web Services, Google Cloud Platform, Microsoft Azure, etc.) can generally be used. A relational database such as MySQL is used as the database. Crawling software (e.g., Scrapy) is used to collect information, natural language processing algorithms (e.g., SpaCy, NLTK) are used for information analysis, and Scikit-learn is used for machine learning. A template generation tool (e.g., LaTeX, ReportLab) is used to generate proposal materials. A sentiment analysis tool such as IBM Watson or Microsoft Azure Text Analytics is used as the emotion engine.

[0600] System Operation

[0601] The user accesses the server from their device and enters the name or URL of the client company. This identifies the company information to be collected. For example, if the user enters the company name "XYZ Corporation," the server will collect information about that company. Here, crawling tools such as Scrapy are used to collect information from the specified company's official website, news sites, social media, etc.

[0602] The collected information is stored in a database (e.g., MySQL) on the server. The server then analyzes the collected information using natural language processing algorithms (e.g., SpaCy, NLTK) to understand the company's business environment and market trends. For example, the latest news articles about XYZ Corporation can be analyzed to summarize the status of new product launches and their market reactions.

[0603] Based on the analysis results, the server identifies the client company's specific issues and proposal needs. It uses a machine learning algorithm (Scikit-learn) to reference past success stories and general business cases to generate appropriate proposals. For example, if XYZ Co., Ltd. launches a new product, it identifies the needs for its promotion strategy.

[0604] Next, when the user inputs the content of the proposal they want, the emotion engine built into the server recognizes emotions based on the user's input. For example, when a user inputs a "promotion support proposal for a new product," it determines whether the emotional state is positive or negative.

[0605] The server adjusts the content of the proposal materials based on the user's emotional state as recognized by the emotion engine. If a positive emotion is recognized, the content of the materials will be adjusted to a more proactive tone. If a negative emotion is recognized, the content will be adjusted to a more cautious approach as necessary. For example, a proactive phrase such as "We expect this promotional strategy to significantly increase product sales" will be inserted.

[0606] Finally, the server saves the generated proposal materials in PDF or PPT format and sends them to the user's device. The user can review the received materials on their device and fine-tune the content as needed. For example, the server generates a "Promotion Proposal Material for XYZ Co., Ltd." and sends it in PDF format, which the user can then receive and review.

[0607] An example of a prompt sentence that can be entered is, "Please create a promotion strategy proposal based on the latest news from XYZ Co., Ltd." This system efficiently collects and analyzes customer information, identifies proposal needs, recognizes emotions using an emotion engine, automatically generates proposal materials, and sends them.

[0608] This will significantly reduce the time it takes salespeople to prepare proposals, improving productivity and the quality of proposals.

[0609] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0610] Step 1:

[0611] The user enters the name and URL of the client company into the server. The entered name and URL are sent to the server, which identifies the company information to be collected.

[0612] Input: Name and URL of the customer company

[0613] Output: Specific information to be collected

[0614] Specific behavior:

[0615] The user enters a company name, such as "XYZ Co., Ltd.", into the server from the terminal and sends it.

[0616] The server generates a request to obtain information about the specified company.

[0617] Step 2:

[0618] The server uses crawling software (such as Scrapy) to crawl designated companies' official websites, news sites, social media, and other online sources to collect customer information.

[0619] Input: Specific information to be collected

[0620] Output: Collected customer information

[0621] Specific behavior:

[0622] Based on the URL request, the server crawls the company's official website for company overviews, news sites for the latest news, and social media posts.

[0623] The server stores the collected information in a database (such as MySQL).

[0624] Step 3:

[0625] The information collected by the server is analyzed using natural language processing (NLP) algorithms (e.g., SpaCy, NLTK) to understand the company's business environment and market trends.

[0626] Input: Collected customer information

[0627] Output: Parsed company information

[0628] Specific behavior:

[0629] The server reads the collected text data and performs topic modeling and sentiment analysis.

[0630] The server formats the analysis results and generates a report that helps client companies understand industry and economic trends.

[0631] The server stores the analysis results in a database.

[0632] Step 4:

[0633] Based on the analysis results, the server identifies the proposal needs of the client company by referring to past success stories and general business cases.

[0634] Input: Parsed company information

[0635] Output: Identified proposed needs

[0636] Specific behavior:

[0637] The server searches a database of past proposal examples and extracts similar cases.

[0638] The server uses machine learning algorithms (such as Scikit-learn) to compare the analysis results with past cases and identify the needs of the client company.

[0639] The server stores the identified proposed needs in a database.

[0640] Step 5:

[0641] When the user inputs the desired suggestion, an emotion engine built into the server recognizes emotions based on the user's input.

[0642] Input: The input text of the user's suggestion

[0643] Output: User's emotional state

[0644] Specific behavior:

[0645] The user inputs and submits a proposal such as "Proposal for new product promotion support."

[0646] The server uses sentiment analysis tools (e.g., IBM Watson, Microsoft Azure Text Analytics) to determine the emotional state of the user from the text they input.

[0647] The server stores the recognized emotional state of the user in a database.

[0648] Step 6:

[0649] The server automatically adjusts and generates the content of the proposal materials based on the user's emotional state recognized by the emotion engine.

[0650] Input: User's emotional state and suggestion needs

[0651] Output: Generated proposal

[0652] Specific behavior:

[0653] The server generates the proposal using a template generation tool (e.g., LaTeX, ReportLab).

[0654] The server adjusts the tone and content of the proposal based on the emotional state.

[0655] For positive emotions, include phrases and designs with a positive tone.

[0656] In the case of negative emotions, adopt a measured approach and a calm tone.

[0657] The server saves the generated proposal document in PDF format.

[0658] Step 7:

[0659] The server sends the generated proposal materials to the user's terminal, where the user can check the received proposal materials and make fine adjustments as necessary.

[0660] Input: Generated proposal document (PDF format)

[0661] Output: Received proposal materials

[0662] Specific behavior:

[0663] The server sends the generated PDF file to the user's device via email or a dedicated application.

[0664] The user checks the proposal materials through their inbox or application.

[0665] The user fine-tunes the necessary wording and design and finalizes the final proposal materials.

[0666] (Application example 2)

[0667] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0668] In traditional sales activities, salespeople spend a great deal of time and effort preparing proposals and creating materials for customers. Furthermore, the approach to customers is uniform, making it difficult to customize proposals that take into account the customer's emotional state. Furthermore, while effective customer service and proposals for best-selling products are required in brick-and-mortar stores, efficient information gathering and the creation of proposal materials are difficult. There is a need to solve these issues and improve the efficiency and quality of sales activities and store operations.

[0669] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0670] In this invention, the server includes: means for automatically collecting customer information from online information sources; means for analyzing the collected customer information to understand the business environment and market trends of the client company; means for identifying the customer's proposal needs based on the analysis results; means for automatically generating proposal materials according to the identified proposal needs; means for transmitting the generated proposal materials to a user terminal; and means for adjusting the content of the proposal materials based on the user's emotional state, including an emotion engine that recognizes the user's emotions when inputting the proposal content. This reduces the salesperson's proposal preparation time, improves productivity, and enables the automatic generation of high-quality proposal materials that take the user's emotions into consideration. Furthermore, effective proposals that meet customer needs can be made even in physical stores, which is expected to improve customer satisfaction.

[0671] "Customer information" is a general term for basic information and publicly available data about companies and individuals collected from sources on the Internet.

[0672] "Internet sources" refers to all media that provide information publicly available via the Internet, such as official websites, news sites, and social media.

[0673] "Means of collection" refers to the way the server automatically obtains the necessary information, such as by crawling the web or using an API.

[0674] "Means of analysis" refers to the use of natural language processing algorithms to analyze collected information and extract useful insights.

[0675] "The business environment of a client company" refers to the industry and market conditions to which a particular client company belongs, as well as the external environment that influences corporate activities.

[0676] "Market trends" refer to ongoing or predicted movements or trends in a particular industry or market.

[0677] "Proposal needs" refers to the specific solutions and approaches that should be proposed to address the challenges and business opportunities facing client companies.

[0678] "Means for automatic generation" refers to an algorithm that automatically assembles proposal materials based on collected and analyzed information.

[0679] "Transmission means" refers to a communication means or protocol for transmitting the generated proposal materials to a user terminal.

[0680] "Emotion engine" refers to a software component for recognizing and analyzing a user's emotional state from input data.

[0681] "Adjustment measures" refer to methods for appropriately changing the content and structure of proposal materials depending on the perceived emotional state.

[0682] The present invention provides a system for improving the efficiency and quality of customer service and sales strategy planning in a brick-and-mortar store. Specific embodiments for carrying out the present invention will be described below.

[0683] System Overview

[0684] The system consists of a server, tablets or smartphones used by store staff, and an application incorporating an emotion engine.

[0685] Hardware and software used

[0686] Hardware: Tablets, smartphones

[0687] Software: Python and Django are used on the server side, Flutter on the client side, scikit-learn and NLTK (Natural Language Toolkit) for data analysis, and Microsoft Azure Emotion API for emotion recognition.

[0688] Data processing and data calculation

[0689] The server automatically collects customer information from sources on the Internet, such as official websites, news sites, and social media, to obtain the latest information about customers and their companies.

[0690] The collected information is then analyzed using NLP (natural language processing) algorithms, which allows the company to understand its business environment and market trends. The analysis results include industry trends, economic conditions, competitor strategies, consumer trends, and more.

[0691] The server then identifies the customer's specific proposal needs based on the analysis results, referencing past success stories and general business cases to determine the appropriate proposal content.

[0692] Once the proposal content has been decided, the user (store staff) inputs the desired proposal content via a tablet or smartphone, and the emotion expressed at that time is recognized by an emotion engine built into the server. The emotion engine analyzes the input content and determines the user's emotional state.

[0693] The server adjusts the content of the proposal materials based on the user's emotional state, as recognized by the emotion engine. If a positive emotion is recognized, the content of the materials is automatically generated with a more positive tone. If a negative emotion is recognized, the content is adjusted to take a more cautious approach.

[0694] The proposal materials generated in this way are saved in PDF or PPT format and sent to the user's device, where the user can review the received proposal materials and make any necessary adjustments.

[0695] Specific examples

[0696] For example, suppose a store staff member operates a tablet and enters "Customer name: ABC Company, desired product category: home appliances." The server collects information related to ABC Company from its official website and social media, and uses an NLP algorithm to analyze the business environment and market trends. Next, based on the analysis results, the server identifies proposal needs. When the staff member enters "Proposal for new product promotion support," the emotion engine recognizes the staff member's positive emotions. The server takes these emotions into consideration and automatically generates proposal materials with a positive tone. The generated materials are then sent to the staff member's tablet.

[0697] Prompt Sentence Examples

[0698] Information gathering prompt:

[0699] Customer name: ABC Company

[0700] Desired product category: Home appliances

[0701] Information analysis prompt:

[0702] Information collected:

[0703] Company profile from the official website

[0704] Latest News Articles

[0705] SNS posts

[0706] Analysis details:

[0707] New product release information

[0708] market trends

[0709] Customer interests

[0710] Proposal needs specific prompt:

[0711] Needs based on analysis results:

[0712] New product promotion strategies

[0713] Differentiate yourself from your competitors

[0714] Emotion engine prompts for emotion recognition:

[0715] Input text: "I think ABC's new product is excellent, but I'm concerned about the promotion."

[0716] This makes it possible to efficiently create and provide high-quality proposal materials that meet customer needs, reducing the time store staff spend preparing proposals and enabling them to respond to customers quickly and effectively.

[0717] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0718] Step 1:

[0719] Collecting customer information

[0720] Input: The user enters the customer name and desired product category on a tablet or smartphone.

[0721] Specific operation: When the user enters "Customer name: ABC company, desired product category: home appliances", the information is sent to the server.

[0722] Data processing or data calculation: The server crawls and collects information related to the specified customer from online sources (official websites, news sites, social media, etc.).

[0723] Output: Collected customer information data is stored on the server.

[0724] Step 2:

[0725] Customer information analysis

[0726] Input: Customer information data collected by the server.

[0727] Specific operation: The server uses natural language processing (NLP) algorithms (using scikit-learn, NLTK, etc.) to understand the company's business environment and market trends.

[0728] Data processing or data calculation: NLP algorithms analyze collected text data to extract the latest developments of companies, industry trends, competitor strategies, etc.

[0729] Output: The analysis results in a useful set of data that can be used for proposals.

[0730] Step 3:

[0731] Identifying proposal needs

[0732] Input: Analysis result data.

[0733] Specific actions: The server identifies the customer's specific issues and needs based on the analysis results.

[0734] Data processing or data calculation: The server refers to past success stories and general business cases and determines appropriate proposals based on the extracted information.

[0735] Output: Data about customer proposal needs is identified.

[0736] Step 4:

[0737] Emotion recognition by emotion engine

[0738] Input: The user inputs the desired suggestions using a tablet or smartphone.

[0739] Specific operation: The user inputs "Proposal for new product promotion support." The emotion engine (Microsoft Azure Emotion API) built into the server analyzes the input in real time.

[0740] Data processing or data calculation: The emotion engine recognizes the user's emotional state (positive, negative, etc.) based on the text data.

[0741] Output: User emotional state data is generated.

[0742] Step 5:

[0743] Automatic generation and adjustment of proposal materials

[0744] Input: Customer suggestion needs data and user emotional state data.

[0745] Specific operation: The server adjusts and automatically generates the content of proposal materials based on the recognized emotional state.

[0746] Data manipulation or data calculation: Generate materials with a positive tone when positive sentiment is perceived, and adjust to take a cautious approach when negative sentiment is perceived.

[0747] Output: A tailored proposal is generated in PDF and PPT formats.

[0748] Step 6:

[0749] Submit a proposal

[0750] Input: Proposal material automatically generated by the server.

[0751] Specific operation: The server sends the generated proposal materials to the user's terminal in PDF or PPT format.

[0752] Data processing or data calculation: Sending material data from the server to the terminal using a communication protocol.

[0753] Output: The proposal materials are sent to the user's device, allowing the user to receive and review the proposal materials.

[0754] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0755] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0756] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0757] [Third embodiment]

[0758] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0759] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0760] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0761] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0762] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0763] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0764] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0765] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0766] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0767] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0768] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0769] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0770] The present invention relates to a system for reducing the time and man-hours required for salespeople to prepare proposals and documents. Specific embodiments for carrying out the present invention are described below. In the present invention, a server, a terminal, and a user work together.

[0771] 1. Information gathering

[0772] The user inputs the name and URL of the client company to which they are making a proposal to the server. The server then crawls the designated company's official website, news sites, social media, and other online sources to collect basic information and publicly available data related to the client company. The collected information includes the company's performance, major products and services, recent news, and social media posts.

[0773] Example: When a user enters the name of a company, such as "XYZ Corporation," into a server, the server analyzes the company profile page on the company's official website and collects related information from news sites and social media.

[0774] 2. Information analysis

[0775] The server analyzes the collected information using natural language processing (NLP) algorithms to understand the client company's business environment and market trends. The analysis results include information on industry sectors, economic trends, competitor strategies, consumer trends, etc.

[0776] Example: The server analyzes the latest news articles about XYZ Corporation, analyzing and organizing information about the company's new product launch and its market reaction.

[0777] 3. Identifying proposal needs

[0778] Based on the analysis results, the server identifies the specific issues and proposal needs of the client company, referring to past success stories and general business cases to identify appropriate proposal content.

[0779] Example: The server analyzes business environment data and market trends for XYZ Corporation to identify needs regarding promotion strategies for new products.

[0780] 4. Automatic generation of proposal materials

[0781] When a user inputs their proposal into the server, the server automatically generates the optimal document format for the identified proposal needs, embedding the collected data and analysis results in the appropriate format.

[0782] Example: A user inputs a "promotion support proposal for XYZ Corporation" into the server, and the server automatically generates presentation slides. The slides include an "outline of XYZ's new product," "current market trends," "proposed promotion strategy," and "past success stories."

[0783] 5. Submit your proposal

[0784] The server saves the generated proposal materials in PDF or PPT format and sends them to the user's device. The user can then review the received materials, make any necessary adjustments, and immediately begin their proposal activities.

[0785] Example: The server generates a "Promotion proposal document for XYZ Co., Ltd." and saves it in PDF format, then sends it to the user's device. The user receives the document, makes any necessary adjustments, and promptly starts the proposal process.

[0786] In this way, the present invention is a system that can significantly reduce the time it takes salespeople to prepare proposals and improve productivity by implementing a series of processes: collecting customer information, analyzing the information, identifying proposal needs, automatically generating proposal materials, and sending the proposal materials.

[0787] The processing flow will be explained below.

[0788] Step 1:

[0789] The user inputs the name and URL of the client company into the server, which identifies the company information to be collected.

[0790] Step 2:

[0791] The server crawls designated company official websites, news sites, social media sites, and other internet information sources by sending HTTP requests and retrieving the HTML content of each page.

[0792] Step 3:

[0793] The server analyzes the HTML content and extracts basic information about the company using techniques such as Document Object Model (DOM) analysis and regular expressions.

[0794] Step 4:

[0795] The server performs text analysis on the collected information using natural language processing (NLP) algorithms, which include text tokenization, part-of-speech tagging, and entity recognition.

[0796] Step 5:

[0797] The server evaluates the importance of the text and extracts key keywords and phrases related to the business environment and market trends, such as new products, marketing strategies, and market trends.

[0798] Step 6:

[0799] Based on the analysis results, the server identifies the customer's specific issues and proposal needs, referring to past success stories and a sales strategy database.

[0800] Step 7:

[0801] The user inputs the desired proposal details to the server, for example, specifying specific details such as "promotion support proposal for a new product."

[0802] Step 8:

[0803] The server selects the appropriate proposal format based on user input, including template selection.

[0804] Step 9:

[0805] The server embeds the collected data and analysis results into the selected template and automatically generates proposal materials in the form of presentation slides, reports, etc.

[0806] Step 10:

[0807] The server saves the generated proposal materials in PDF or PPT format and sends them to the user's terminal.

[0808] Step 11:

[0809] The user can review the proposal materials received on their device and make any necessary adjustments, after which they can immediately begin their proposal activities.

[0810] Through these steps, the system of the present invention efficiently collects and analyzes customer information, identifies proposal needs, automatically generates proposal materials, and finally sends the materials, significantly reducing the time salespeople spend preparing proposals and improving productivity.

[0811] Example 1

[0812] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0813] In modern sales activities, the time and effort required to prepare proposals and documents is a significant burden. Sales representatives must spend a great deal of time gathering and analyzing information about client companies and creating appropriate proposal materials. This process is often done manually and is inefficient, resulting in a decrease in the speed and productivity of sales activities.

[0814] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0815] In this invention, the server includes means for automatically collecting customer information from information sources on the Internet, means for analyzing the collected customer information using a natural language processing algorithm to grasp the business environment and market trends of the client company, means for identifying the proposal needs of the client based on the analysis results, means for automatically generating proposal materials according to the identified proposal needs, and means for generating the generated proposal materials in PDF or PPT format and transmitting them to a user terminal, thereby enabling a significant reduction in proposal preparation time and an improvement in the productivity of sales activities.

[0816] "Customer Information" refers to data including basic information, performance, products and services, and market trends regarding the target company.

[0817] "Internet sources" refers to various sources that provide public information accessible via the Internet, such as official websites, news sites, and social media.

[0818] A "natural language processing algorithm" is a collection of computer programs and methods for understanding and analyzing human language, and refers to technology for extracting keywords from text and analyzing relationships.

[0819] "Business environment" refers to the surrounding circumstances, including industry trends, economic conditions, and competitor activities, that affect the business operations of client companies.

[0820] "Market trends" refers to information that describes fluctuations and trends over time in demand for products or services, the competitive landscape, consumer preferences, and other factors in a particular market.

[0821] "Proposal needs" refers to the specific requests and requirements related to the challenges the client company faces and the solutions it seeks.

[0822] "Proposal materials" refer to documents such as presentation slides and reports that sales representatives use when making proposals to client companies.

[0823] "Automatic generation" refers to a process in which data collection, analysis, and documentation are performed programmatically without manual intervention.

[0824] "PDF and PPT formats" refer to standard file formats for electronic documents and presentations, such as the Portable Document Format (PDF) developed by Adobe Systems and the presentation format (PPT) used by Microsoft PowerPoint.

[0825] "User terminal" refers to devices such as computers, tablets, and smartphones operated by sales representatives and system users.

[0826] The present invention relates to a system for reducing the time and man-hours required for sales representatives to prepare proposals and documents. This system operates in cooperation with a server, terminals, and users, and uses the following hardware and software.

[0827] Hardware and software used

[0828] Server: Computing resources for data collection, analysis, and proposal generation, including web servers (e.g., Apache), crawling tools (e.g., Scrapy), natural language processing libraries (e.g., SpaCy, NLTK), and presentation generation tools (e.g., Python-PPTX).

[0829] Terminal: A PC or tablet is used as a device for users to operate the system. Terminals are primarily used for input and output, and have a web browser, PDF viewer, and presentation creation tool (e.g., Microsoft PowerPoint) installed.

[0830] Internet connection: A network connection is required to transmit data between the server and your device.

[0831] Program processing flow

[0832] Information gathering

[0833] The user accesses the server from their device and enters the name and URL of the client company to which they would like to make a proposal. As a specific example, let's say the user enters the name of "Company A." Based on this, the server uses a crawling tool (e.g., Scrapy) to collect information from the designated company's official website, news sites, and social media. The collected information is stored in a database on the server.

[0834] Information analysis

[0835] The server then analyzes the collected information using natural language processing (NLP) algorithms (e.g., SpaCy, NLTK). This allows the client company's business environment and market trends to be understood. Specifically, the analysis targets company performance data, new product information, recent news, social media posts, etc. For example, a report summarizing the market trends of "Company A" and customer reactions on social media is generated.

[0836] Identifying proposal needs

[0837] The server identifies the client company's specific issues and proposal needs based on the analysis results. In doing so, the server refers to past success stories and general business cases to identify appropriate proposal content. For example, if "Company A" launches a new product, the server identifies proposal needs regarding its promotion strategy.

[0838] Automatic generation of proposal materials

[0839] When a user inputs the details of their proposal into the server, the server automatically generates the optimal document format for the identified proposal needs. Using the Python-PPTX library, the collected data and analysis results are incorporated into presentation slides. For example, a "Proposal for Promotion Support for Company A" slide is generated that includes an overview of the company's new product, market trends, the proposed promotion strategy, and past success stories.

[0840] Submit a proposal

[0841] Finally, the server saves the generated proposal materials in PDF or PPT format and sends them to the user's device. The user can review the received materials and make any necessary adjustments, allowing them to quickly begin their proposal activities.

[0842] Prompt Sentence Examples

[0843] Here is an example prompt:

[0844] Company name: Company A

[0845] Proposal: Please create a proposal document for Company A's new product promotion strategy.

[0846] In this way, the present invention is a system that significantly reduces the burden on sales representatives and improves the efficiency of proposal activities by automating the entire process from data collection and analysis, identification of proposal needs, and automatic generation and transmission of materials.

[0847] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0848] Step 1:

[0849] The user accesses the server from their device and enters the name and URL of the client company to which they are making a proposal into a dedicated form. The input data is "Company Name: Company A" and "URL: www.example.com." Based on this input, the server launches a crawling tool (e.g., Scrapy) and collects information from the designated company's official website, news sites, and social media. Specifically, the server saves the collected data in a database and confirms that crawling is complete.

[0850] Step 2:

[0851] The server analyzes the information stored in the database using natural language processing algorithms (e.g., SpaCy, NLTK). The collected text data is used as input. Specific analysis operations include extracting keywords from the text, classifying documents, and analyzing sentiment, and outputting the results of the analysis: company performance, product overviews, market trends, and the behavior of competitors.

[0852] Step 3:

[0853] The server uses the analysis results to identify the specific issues and proposal needs of the client company. The input in this step is the analysis results obtained in the previous step. The server refers to past success stories and general business cases to identify the proposal content that best meets the client's needs. A specific example of how this works is to create a list of proposal needs regarding Company A's new product promotion strategy.

[0854] Step 4:

[0855] When a user inputs the proposal details into the server, the server automatically generates the proposal materials. In this case, the input data is "Proposal Details: Company A's New Product Promotion Strategy." The server uses the Python-PPTX library to create presentation slides based on the collected data and analysis results. Specifically, the slides include an overview of the company's new product, market trends, the proposed promotion strategy, and past success stories.

[0856] Step 5:

[0857] The server saves the generated proposal materials in PDF or PPT format and sends them to the user's terminal. In this step, the generated proposal materials are used as input. The server converts the materials into PDF format and sends them to the user's terminal. Specific operations include confirming the completion of transmission and recording a log. The user can then review the received materials and make any necessary adjustments to quickly begin proposal activities.

[0858] The above is the flow of processing of the program of this system.

[0859] (Application example 1)

[0860] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0861] In conventional sales support systems, the process from collecting customer information to creating proposal materials was done manually, requiring a great deal of time and effort. Furthermore, the collected information could not be analyzed quickly and the proposal needs based on that analysis could not be identified quickly, resulting in a decrease in the productivity of sales activities. Furthermore, there were few ways to utilize the collected information to provide appropriate materials, which was a burden for salespeople. A new approach was needed to solve these problems.

[0862] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0863] In this invention, the server includes means for automatically collecting customer information from information sources on the Internet, means for analyzing the collected customer information and grasping the business environment and market trends of the client organization, means for identifying the proposal needs of the client based on the analysis results, means for automatically generating proposal materials according to the identified proposal needs, means for transmitting the generated proposal materials to an end-user terminal, and means for inputting information and viewing materials using a smart device. This enables salespeople to quickly collect and analyze customer information and automatically generate and transmit appropriate proposal materials, significantly reducing the time and effort required for proposal preparation and improving productivity.

[0864] "Customer information" refers to data collected from online sources such as official websites published by companies and organizations, news sites, and social networking services (SNS).

[0865] "Client organization" refers to the company or organization to which the proposal is made, and its business environment and market trends are the subject of analysis.

[0866] "Proposal needs" refers to the content and strategies that should be proposed in sales activities, identified based on the client organization's business environment and market trends.

[0867] "Proposal materials" refer to presentation slides and documents that are automatically generated in response to identified proposal needs and are used in sales activities.

[0868] "End-user terminal" refers to the device used by salespeople who use the system, and includes PCs, smartphones, tablets, smart glasses, etc.

[0869] "Smart device" refers to devices such as smartphones, smart glasses, and tablets that are connected to the internet and offer additional functionality.

[0870] "Natural language processing algorithm" refers to the computational methods and models used to analyze collected customer information and understand and classify its content.

[0871] A "crawling engine" refers to a program that automatically searches for information on the Internet and collects the necessary data.

[0872] "Analysis results" refers to the analysis results of customer information extracted using natural language processing algorithms and data analysis tools.

[0873] "Sources" refer to reliable sources of data on the Internet, such as official websites, news sites, and social networking services (SNS).

[0874] To implement the present invention, a system is provided in which a server, a terminal, and a user work together, as will be described in detail below.

[0875] Overall structure

[0876] This system includes the following means for collecting customer information, analyzing the information, identifying proposal needs, automatically generating proposal materials, transmitting the generated materials, and inputting information and viewing materials using a smart device.

[0877] Collecting customer information

[0878] The user uses a smart device (e.g., smart glasses) to voice-input the name of the customer company. Based on this input, the server collects customer information from online sources (official websites, news sites, social media, etc.). This process uses a crawling engine (e.g., Beautiful Soup, Scrapy).

[0879] Information analysis

[0880] The collected information is sent to a server and analyzed using natural language processing algorithms (e.g., BERT, GPT). This allows us to understand the business environment and market trends of our client organizations. Data analysis tools such as Pandas and Scikit-learn are used for data analysis.

[0881] Identifying proposal needs

[0882] The server identifies the customer's proposal needs based on the analysis results, which is achieved by using a content recommendation system (e.g., Collaborative Filtering) and referencing past success stories and industry-standard data.

[0883] Automatic generation of proposal materials

[0884] The server automatically generates proposal materials based on the identified proposal needs, using document generation tools (e.g., LaTeX, MS Office API) to create presentation slides and PDF documents.

[0885] Sending generated materials

[0886] The generated proposal materials are sent to the end user's device in PDF or PPT format using an email sending API (e.g., SMTP) or cloud storage (e.g., Google Drive, AWS S3). These materials can be viewed in real time using a smart device.

[0887] Specific examples

[0888] For example, if a user uses smart glasses to voice-input the name of a client company, such as "ABC Co., Ltd.", the server will crawl online sources based on the company name and collect company information. The collected information is analyzed using a natural language processing algorithm to understand the company's business environment and market trends. Based on the analysis results, a proposal need, such as "promotion strategy for ABC Co., Ltd.'s new product," is then identified. The server then uses this information to automatically generate presentation slides and send them to the user's smart glasses. This allows the user to review the materials in real time while making proposals.

[0889] Prompt Sentence Examples

[0890] Examples of prompts from this generative AI model include:

[0891] "Please provide us with the latest information about ABC Corporation. Pay particular attention to new products, market trends, and competitor activity, and provide analysis that can be used in our advertising strategy."

[0892] In this way, the salesperson can make proposals to the customer quickly and efficiently.

[0893] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0894] Step 1:

[0895] The user uses a smart device (smart glasses) to input the name of the customer company by voice. This voice input is converted into text data by the device and sent to the server. This results in the input data of the customer company name.

[0896] Step 2:

[0897] The server uses the received text data to crawl the designated company's official website, news sites, social media, and other sources. Software such as Beautiful Soup and Scrapy are used. This process gathers relevant information from company profile pages on official websites, news articles, social media posts, and other sources to obtain the collected data.

[0898] Step 3:

[0899] The server analyzes the collected information using NLP (natural language processing) algorithms, such as the BERT or GPT model. This analysis extracts information about the client organization's business environment, market trends, and competitor trends, and produces analysis results.

[0900] Step 4:

[0901] The server uses the analysis results to refer to past success stories and general business cases, and utilizes a content recommendation system (Collaborative Filtering) to identify proposal needs. This clarifies the customer's specific issues and proposal needs, and provides data on identified proposal needs.

[0902] Step 5:

[0903] Based on the identified proposal needs, the server automatically generates proposal materials using document generation tools (LaTeX, MS Office API). The generated materials are in PDF or PPT format and include company information, market analysis, proposal details, etc. This process results in the automatically generated proposal materials.

[0904] Step 6:

[0905] The generated proposal materials are sent by the server to the end user's device using methods such as email sending API (SMTP) or cloud storage services (Google Drive, AWS S3). This sends the materials to the user's device (smart glasses, etc.), allowing the user to view the materials in real time.

[0906] Through the above processing steps, this system can efficiently carry out the process from collecting customer information to automatically generating and sending proposal materials.

[0907] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0908] The present invention relates to a system that reduces the time and man-hours required for salespeople to prepare proposals and create documents, and improves the quality of proposals by combining it with an emotion engine that recognizes user emotions. Specific embodiments for implementing the present invention are described below. In this invention, a server, terminals, and users work in cooperation with each other, and an emotion engine is also included.

[0909] 1. Information gathering

[0910] A user inputs the name or URL of a client company into the server. This identifies the company information to be collected. The server then crawls the designated company's official website, news sites, social media, and other online sources to collect basic information and publicly available data related to the client company. The collected information includes the company's performance, major products and services, recent news, and social media posts. For example, if a user inputs the company name "XYZ Corporation" into the server, the server will analyze the company profile page on the company's official website and collect related information from news sites and social media.

[0911] 2. Information analysis

[0912] The server analyzes the collected information using natural language processing (NLP) algorithms to understand the client company's business environment and market trends. The analysis results include information on the industry sector, economic trends, competitor strategies, consumer trends, etc. As a specific example, the server analyzes the latest news articles about XYZ Corporation, analyzing and organizing information about the company's new product launch and its market reaction.

[0913] 3. Identifying proposal needs

[0914] Based on the analysis results, the server identifies the client company's specific issues and proposal needs. In doing so, the server refers to past success stories and general business cases to identify appropriate proposal content. As a specific example, the server analyzes business environment data and market trends for XYZ Corporation to identify needs regarding a promotion strategy for a new product.

[0915] 4. Emotion Recognition by Emotion Engine

[0916] When a user inputs the content of a proposal they wish to make into the server, the emotion engine built into the server recognizes the emotion based on the user's input. For example, when a user inputs a "promotion support proposal for a new product," the emotion engine determines whether the emotional state is positive or negative. As a specific example, if a user inputs "I would like to propose a promotion strategy that will maximize the appeal of XYZ Corporation's new product," the emotion engine recognizes the user's positive emotion.

[0917] 5. Automatic generation and adjustment of proposal materials

[0918] The server adjusts the content of the proposal materials based on the user's emotional state recognized by the emotion engine. If positive emotions are recognized, the content of the materials is automatically generated with a more proactive tone, and if negative emotions are recognized, the content is adjusted to take a more cautious approach as necessary. For example, if the user's positive emotions are recognized, the proposal materials will include a proactive phrase such as, "We expect this promotional strategy to significantly increase product sales."

[0919] 6. Submitting proposal materials

[0920] The server saves the generated proposal materials in PDF or PPT format and sends them to the user's device. The user checks the proposal materials received on their device and makes any necessary adjustments. After this, the user can immediately begin their proposal activities. As a concrete example, the server saves the "Promotion proposal materials for XYZ Co., Ltd." generated in PDF format and sends it to the user's device. The user receives the materials, makes any necessary adjustments, and immediately begins their proposal activities.

[0921] This system efficiently implements a series of processes, from collecting and analyzing customer information, identifying proposal needs, recognizing emotions using an emotion engine, automatically generating proposal materials, and sending them. As a result, it is possible to significantly reduce the time salespeople spend preparing proposals and improve their productivity. Furthermore, by generating proposal materials that take the user's emotions into consideration, it is possible to improve the quality of proposals.

[0922] The processing flow will be explained below.

[0923] Step 1:

[0924] The user inputs the name and URL of the client company into the server, which identifies the company information to be collected.

[0925] Step 2:

[0926] The server crawls designated company official websites, news sites, social media sites, and other internet information sources. Specifically, it sends HTTP requests and retrieves the HTML content of each page.

[0927] Step 3:

[0928] The server analyzes the HTML content and extracts basic information about the company using techniques such as Document Object Model (DOM) analysis and regular expressions.

[0929] Step 4:

[0930] The server performs text analysis on the collected information using natural language processing (NLP) algorithms, which include text tokenization, part-of-speech tagging, and entity recognition.

[0931] Step 5:

[0932] The server evaluates the importance of the text and extracts key keywords and phrases related to the business environment and market trends, such as new products, marketing strategies, and market trends.

[0933] Step 6:

[0934] Based on the analysis results, the server identifies the customer's specific issues and proposal needs, referring to past success stories and a sales strategy database.

[0935] Step 7:

[0936] The user inputs the desired proposal details to the server, for example, specifying specific details such as "promotion support proposal for a new product."

[0937] Step 8:

[0938] The emotion engine built into the server recognizes emotions based on user input, analyzing the emotion from the text entered by the user and determining whether it is positive or negative.

[0939] Step 9:

[0940] The server adjusts the content of the proposal materials based on the user's emotional state as recognized by the emotion engine. If a positive emotion is recognized, the content of the materials is automatically generated with a more positive tone, and if a negative emotion is recognized, it is automatically generated with a more cautious approach.

[0941] Step 10:

[0942] The server selects a proposal format tailored to the user's sentiment, embeds the collected data and analysis results, and applies an algorithm to automatically generate presentation slides and reports.

[0943] Step 11:

[0944] The server saves the generated proposal materials in PDF or PPT format and sends them to the user's device. The user can then review the received materials and make any necessary adjustments.

[0945] Step 12:

[0946] The user can check the final materials on the device and immediately begin making proposals.

[0947] By following these steps, the system of the present invention efficiently realizes a series of processes, from collecting customer information to recognizing and analyzing the user's emotions using an emotion engine, identifying proposal needs, automatically generating proposal materials, and finally sending the materials. This not only significantly reduces the time salespeople spend preparing proposals and improves productivity, but also makes it possible to provide high-quality proposal materials that take emotions into consideration.

[0948] Example 2

[0949] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0950] In conventional sales activities, salespeople spend a lot of time and effort preparing proposals and creating materials, resulting in low efficiency. Furthermore, it is difficult to make proposals that take into account customer emotions, making it difficult to improve the quality of proposals. The present invention aims to provide a system that reduces the time and effort required for salespeople to prepare proposals and automatically generates high-quality proposal materials that take into account customer emotions.

[0951] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for automatically collecting customer information from information sources on the Internet, means for analyzing the collected customer information using a natural language processing algorithm to grasp the business environment and market trends of the client company, means for identifying the customer's proposal needs based on the analysis results, means for a user to input desired proposal content and for an emotion engine to recognize emotions based on the user's input, means for automatically generating proposal materials adjusted based on the recognized emotions, and means for saving the generated proposal materials in PDF or PPT format and sending them to the user terminal. This not only significantly reduces the time a salesperson needs to prepare a proposal and improves productivity, but also makes it possible to generate high-quality proposal materials that take customer emotions into consideration.

[0952] "Customer information" refers to data about the client company, such as its name, URL, performance, major products and services, recent news, and posts on social media.

[0953] "Internet sources" refers to any information that is publicly available on the Internet, such as official websites, news sites, and social media.

[0954] "Crawling" refers to the technology of automatically crawling through web pages on the Internet to collect information.

[0955] "Natural language processing algorithms" refer to the technology that enables computers to understand and analyze human language.

[0956] "Business environment" refers to the industry and economic conditions to which the client company belongs, as well as external factors such as competitors and market trends.

[0957] "Market trends" refers to market trends, consumer behavior patterns, changes in demand, etc.

[0958] "Proposal needs" refers to the solutions and services that should be proposed to address the specific problems and issues that client companies face.

[0959] "Emotion engine" refers to technology for recognizing and analyzing emotions from user input.

[0960] "Proposal materials" refers to documents that summarize the proposals to client companies.

[0961] "PDF" stands for Portable Document Format and refers to a file format for electronically storing and sharing documents.

[0962] "PPT format" refers to the file format used by Microsoft PowerPoint for saving and sharing presentations.

[0963] "User terminal" refers to electronic devices used by users, such as PCs, smartphones, and tablets.

[0964] This invention provides a system that automates a series of processes from collecting customer information to generating proposal materials. The system works in cooperation with the server, terminals, and users, and incorporates an emotion engine to improve the work efficiency of salespeople.

[0965] Hardware and software used

[0966] The server is a high-performance computer, and cloud servers (Amazon Web Services, Google Cloud Platform, Microsoft Azure, etc.) can generally be used. A relational database such as MySQL is used as the database. Crawling software (e.g., Scrapy) is used to collect information, natural language processing algorithms (e.g., SpaCy, NLTK) are used for information analysis, and Scikit-learn is used for machine learning. A template generation tool (e.g., LaTeX, ReportLab) is used to generate proposal materials. A sentiment analysis tool such as IBM Watson or Microsoft Azure Text Analytics is used as the emotion engine.

[0967] System Operation

[0968] The user accesses the server from their device and enters the name or URL of the client company. This identifies the company information to be collected. For example, if the user enters the company name "XYZ Corporation," the server will collect information about that company. Here, crawling tools such as Scrapy are used to collect information from the specified company's official website, news sites, social media, etc.

[0969] The collected information is stored in a database (e.g., MySQL) on the server. The server then analyzes the collected information using natural language processing algorithms (e.g., SpaCy, NLTK) to understand the company's business environment and market trends. For example, the latest news articles about XYZ Corporation can be analyzed to summarize the status of new product launches and their market reactions.

[0970] Based on the analysis results, the server identifies the client company's specific issues and proposal needs. It uses a machine learning algorithm (Scikit-learn) to reference past success stories and general business cases to generate appropriate proposals. For example, if XYZ Co., Ltd. launches a new product, it identifies the needs for its promotion strategy.

[0971] Next, when the user inputs the content of the proposal they want, the emotion engine built into the server recognizes emotions based on the user's input. For example, when a user inputs a "promotion support proposal for a new product," it determines whether the emotional state is positive or negative.

[0972] The server adjusts the content of the proposal materials based on the user's emotional state as recognized by the emotion engine. If a positive emotion is recognized, the content of the materials will be adjusted to a more proactive tone. If a negative emotion is recognized, the content will be adjusted to a more cautious approach as necessary. For example, a proactive phrase such as "We expect this promotional strategy to significantly increase product sales" will be inserted.

[0973] Finally, the server saves the generated proposal materials in PDF or PPT format and sends them to the user's device. The user can review the received materials on their device and fine-tune the content as needed. For example, the server generates a "Promotion Proposal Material for XYZ Co., Ltd." and sends it in PDF format, which the user can then receive and review.

[0974] An example of a prompt sentence that can be entered is, "Please create a promotion strategy proposal based on the latest news from XYZ Co., Ltd." This system efficiently collects and analyzes customer information, identifies proposal needs, recognizes emotions using an emotion engine, automatically generates proposal materials, and sends them.

[0975] This will significantly reduce the time it takes salespeople to prepare proposals, improving productivity and the quality of proposals.

[0976] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0977] Step 1:

[0978] The user enters the name and URL of the client company into the server. The entered name and URL are sent to the server, which identifies the company information to be collected.

[0979] Input: Name and URL of the customer company

[0980] Output: Specific information to be collected

[0981] Specific behavior:

[0982] The user enters a company name, such as "XYZ Co., Ltd.", into the server from the terminal and sends it.

[0983] The server generates a request to obtain information about the specified company.

[0984] Step 2:

[0985] The server uses crawling software (such as Scrapy) to crawl designated companies' official websites, news sites, social media, and other online sources to collect customer information.

[0986] Input: Specific information to be collected

[0987] Output: Collected customer information

[0988] Specific behavior:

[0989] Based on the URL request, the server crawls the company's official website for company overviews, news sites for the latest news, and social media posts.

[0990] The server stores the collected information in a database (such as MySQL).

[0991] Step 3:

[0992] The information collected by the server is analyzed using natural language processing (NLP) algorithms (e.g., SpaCy, NLTK) to understand the company's business environment and market trends.

[0993] Input: Collected customer information

[0994] Output: Parsed company information

[0995] Specific behavior:

[0996] The server reads the collected text data and performs topic modeling and sentiment analysis.

[0997] The server formats the analysis results and generates a report that helps client companies understand industry and economic trends.

[0998] The server stores the analysis results in a database.

[0999] Step 4:

[1000] Based on the analysis results, the server identifies the proposal needs of the client company by referring to past success stories and general business cases.

[1001] Input: Parsed company information

[1002] Output: Identified proposed needs

[1003] Specific behavior:

[1004] The server searches a database of past proposal examples and extracts similar cases.

[1005] The server uses machine learning algorithms (such as Scikit-learn) to compare the analysis results with past cases and identify the needs of the client company.

[1006] The server stores the identified proposed needs in a database.

[1007] Step 5:

[1008] When the user inputs the desired suggestion, an emotion engine built into the server recognizes emotions based on the user's input.

[1009] Input: The input text of the user's suggestion

[1010] Output: User's emotional state

[1011] Specific behavior:

[1012] The user inputs and submits a proposal such as "Proposal for new product promotion support."

[1013] The server uses sentiment analysis tools (e.g., IBM Watson, Microsoft Azure Text Analytics) to determine the emotional state of the user from the text they input.

[1014] The server stores the recognized emotional state of the user in a database.

[1015] Step 6:

[1016] The server automatically adjusts and generates the content of the proposal materials based on the user's emotional state recognized by the emotion engine.

[1017] Input: User's emotional state and suggestion needs

[1018] Output: Generated proposal

[1019] Specific behavior:

[1020] The server generates the proposal using a template generation tool (e.g., LaTeX, ReportLab).

[1021] The server adjusts the tone and content of the proposal based on the emotional state.

[1022] For positive emotions, include phrases and designs with a positive tone.

[1023] In the case of negative emotions, adopt a measured approach and a calm tone.

[1024] The server saves the generated proposal document in PDF format.

[1025] Step 7:

[1026] The server sends the generated proposal materials to the user's terminal, where the user can check the received proposal materials and make fine adjustments as necessary.

[1027] Input: Generated proposal document (PDF format)

[1028] Output: Received proposal materials

[1029] Specific behavior:

[1030] The server sends the generated PDF file to the user's device via email or a dedicated application.

[1031] The user checks the proposal materials through their inbox or application.

[1032] The user fine-tunes the necessary wording and design and finalizes the final proposal materials.

[1033] (Application example 2)

[1034] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1035] In traditional sales activities, salespeople spend a great deal of time and effort preparing proposals and creating materials for customers. Furthermore, the approach to customers is uniform, making it difficult to customize proposals that take into account the customer's emotional state. Furthermore, while effective customer service and proposals for best-selling products are required in brick-and-mortar stores, efficient information gathering and the creation of proposal materials are difficult. There is a need to solve these issues and improve the efficiency and quality of sales activities and store operations.

[1036] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1037] In this invention, the server includes: means for automatically collecting customer information from online information sources; means for analyzing the collected customer information to understand the business environment and market trends of the client company; means for identifying the customer's proposal needs based on the analysis results; means for automatically generating proposal materials according to the identified proposal needs; means for transmitting the generated proposal materials to a user terminal; and means for adjusting the content of the proposal materials based on the user's emotional state, including an emotion engine that recognizes the user's emotions when inputting the proposal content. This reduces the salesperson's proposal preparation time, improves productivity, and enables the automatic generation of high-quality proposal materials that take the user's emotions into consideration. Furthermore, effective proposals that meet customer needs can be made even in physical stores, which is expected to improve customer satisfaction.

[1038] "Customer information" is a general term for basic information and publicly available data about companies and individuals collected from sources on the Internet.

[1039] "Internet sources" refers to all media that provide information publicly available via the Internet, such as official websites, news sites, and social media.

[1040] "Means of collection" refers to the way the server automatically obtains the necessary information, such as by crawling the web or using an API.

[1041] "Means of analysis" refers to the use of natural language processing algorithms to analyze collected information and extract useful insights.

[1042] "The business environment of a client company" refers to the industry and market conditions to which a particular client company belongs, as well as the external environment that influences corporate activities.

[1043] "Market trends" refer to ongoing or predicted movements or trends in a particular industry or market.

[1044] "Proposal needs" refers to the specific solutions and approaches that should be proposed to address the challenges and business opportunities facing client companies.

[1045] "Means for automatic generation" refers to an algorithm that automatically assembles proposal materials based on collected and analyzed information.

[1046] "Transmission means" refers to a communication means or protocol for transmitting the generated proposal materials to a user terminal.

[1047] "Emotion engine" refers to a software component for recognizing and analyzing a user's emotional state from input data.

[1048] "Adjustment measures" refer to methods for appropriately changing the content and structure of proposal materials depending on the perceived emotional state.

[1049] The present invention provides a system for improving the efficiency and quality of customer service and sales strategy planning in a brick-and-mortar store. Specific embodiments for carrying out the present invention will be described below.

[1050] System Overview

[1051] The system consists of a server, tablets or smartphones used by store staff, and an application incorporating an emotion engine.

[1052] Hardware and software used

[1053] Hardware: Tablets, smartphones

[1054] Software: Python and Django are used on the server side, Flutter on the client side, scikit-learn and NLTK (Natural Language Toolkit) for data analysis, and Microsoft Azure Emotion API for emotion recognition.

[1055] Data processing and data calculation

[1056] The server automatically collects customer information from sources on the Internet, such as official websites, news sites, and social media, to obtain the latest information about customers and their companies.

[1057] The collected information is then analyzed using NLP (natural language processing) algorithms, which allows the company to understand its business environment and market trends. The analysis results include industry trends, economic conditions, competitor strategies, consumer trends, and more.

[1058] The server then identifies the customer's specific proposal needs based on the analysis results, referencing past success stories and general business cases to determine the appropriate proposal content.

[1059] Once the proposal content has been decided, the user (store staff) inputs the desired proposal content via a tablet or smartphone, and the emotion expressed at that time is recognized by an emotion engine built into the server. The emotion engine analyzes the input content and determines the user's emotional state.

[1060] The server adjusts the content of the proposal materials based on the user's emotional state, as recognized by the emotion engine. If a positive emotion is recognized, the content of the materials is automatically generated with a more positive tone. If a negative emotion is recognized, the content is adjusted to take a more cautious approach.

[1061] The proposal materials generated in this way are saved in PDF or PPT format and sent to the user's device, where the user can review the received proposal materials and make any necessary adjustments.

[1062] Specific examples

[1063] For example, suppose a store staff member operates a tablet and enters "Customer name: ABC Company, desired product category: home appliances." The server collects information related to ABC Company from its official website and social media, and uses an NLP algorithm to analyze the business environment and market trends. Next, based on the analysis results, the server identifies proposal needs. When the staff member enters "Proposal for new product promotion support," the emotion engine recognizes the staff member's positive emotions. The server takes these emotions into consideration and automatically generates proposal materials with a positive tone. The generated materials are then sent to the staff member's tablet.

[1064] Prompt Sentence Examples

[1065] Information gathering prompt:

[1066] Customer name: ABC Company

[1067] Desired product category: Home appliances

[1068] Information analysis prompt:

[1069] Information collected:

[1070] Company profile from the official website

[1071] Latest News Articles

[1072] SNS posts

[1073] Analysis details:

[1074] New product release information

[1075] market trends

[1076] Customer interests

[1077] Proposal needs specific prompt:

[1078] Needs based on analysis results:

[1079] New product promotion strategies

[1080] Differentiate yourself from your competitors

[1081] Emotion engine prompts for emotion recognition:

[1082] Input text: "I think ABC's new product is excellent, but I'm concerned about the promotion."

[1083] This makes it possible to efficiently create and provide high-quality proposal materials that meet customer needs, reducing the time store staff spend preparing proposals and enabling them to respond to customers quickly and effectively.

[1084] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1085] Step 1:

[1086] Collecting customer information

[1087] Input: The user enters the customer name and desired product category on a tablet or smartphone.

[1088] Specific operation: When the user enters "Customer name: ABC company, desired product category: home appliances", the information is sent to the server.

[1089] Data processing or data calculation: The server crawls and collects information related to the specified customer from online sources (official websites, news sites, social media, etc.).

[1090] Output: Collected customer information data is stored on the server.

[1091] Step 2:

[1092] Customer information analysis

[1093] Input: Customer information data collected by the server.

[1094] Specific operation: The server uses natural language processing (NLP) algorithms (using scikit-learn, NLTK, etc.) to understand the company's business environment and market trends.

[1095] Data processing or data calculation: NLP algorithms analyze collected text data to extract the latest developments of companies, industry trends, competitor strategies, etc.

[1096] Output: The analysis results in a useful set of data that can be used for proposals.

[1097] Step 3:

[1098] Identifying proposal needs

[1099] Input: Analysis result data.

[1100] Specific actions: The server identifies the customer's specific issues and needs based on the analysis results.

[1101] Data processing or data calculation: The server refers to past success stories and general business cases and determines appropriate proposals based on the extracted information.

[1102] Output: Data about customer proposal needs is identified.

[1103] Step 4:

[1104] Emotion recognition by emotion engine

[1105] Input: The user inputs the desired suggestions using a tablet or smartphone.

[1106] Specific operation: The user inputs "Proposal for new product promotion support." The emotion engine (Microsoft Azure Emotion API) built into the server analyzes the input in real time.

[1107] Data processing or data calculation: The emotion engine recognizes the user's emotional state (positive, negative, etc.) based on the text data.

[1108] Output: User emotional state data is generated.

[1109] Step 5:

[1110] Automatic generation and adjustment of proposal materials

[1111] Input: Customer suggestion needs data and user emotional state data.

[1112] Specific operation: The server adjusts and automatically generates the content of proposal materials based on the recognized emotional state.

[1113] Data manipulation or data calculation: Generate materials with a positive tone when positive sentiment is perceived, and adjust to take a cautious approach when negative sentiment is perceived.

[1114] Output: A tailored proposal is generated in PDF and PPT formats.

[1115] Step 6:

[1116] Submit a proposal

[1117] Input: Proposal material automatically generated by the server.

[1118] Specific operation: The server sends the generated proposal materials to the user's terminal in PDF or PPT format.

[1119] Data processing or data calculation: Sending material data from the server to the terminal using a communication protocol.

[1120] Output: The proposal materials are sent to the user's device, allowing the user to receive and review the proposal materials.

[1121] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1122] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1123] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1124] [Fourth embodiment]

[1125] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1126] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1127] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1128] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1129] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1130] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1131] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1132] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1133] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1134] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1135] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1136] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1137] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1138] The present invention relates to a system for reducing the time and man-hours required for salespeople to prepare proposals and documents. Specific embodiments for carrying out the present invention are described below. In the present invention, a server, a terminal, and a user work together.

[1139] 1. Information gathering

[1140] The user inputs the name and URL of the client company to which they are making a proposal to the server. The server then crawls the designated company's official website, news sites, social media, and other online sources to collect basic information and publicly available data related to the client company. The collected information includes the company's performance, major products and services, recent news, and social media posts.

[1141] Example: When a user enters the name of a company, such as "XYZ Corporation," into a server, the server analyzes the company profile page on the company's official website and collects related information from news sites and social media.

[1142] 2. Information analysis

[1143] The server analyzes the collected information using natural language processing (NLP) algorithms to understand the client company's business environment and market trends. The analysis results include information on industry sectors, economic trends, competitor strategies, consumer trends, etc.

[1144] Example: The server analyzes the latest news articles about XYZ Corporation, analyzing and organizing information about the company's new product launch and its market reaction.

[1145] 3. Identifying proposal needs

[1146] Based on the analysis results, the server identifies the specific issues and proposal needs of the client company, referring to past success stories and general business cases to identify appropriate proposal content.

[1147] Example: The server analyzes business environment data and market trends for XYZ Corporation to identify needs regarding promotion strategies for new products.

[1148] 4. Automatic generation of proposal materials

[1149] When a user inputs their proposal into the server, the server automatically generates the optimal document format for the identified proposal needs, embedding the collected data and analysis results in the appropriate format.

[1150] Example: A user inputs a "promotion support proposal for XYZ Corporation" into the server, and the server automatically generates presentation slides. The slides include an "outline of XYZ's new product," "current market trends," "proposed promotion strategy," and "past success stories."

[1151] 5. Submit your proposal

[1152] The server saves the generated proposal materials in PDF or PPT format and sends them to the user's device. The user can then review the received materials, make any necessary adjustments, and immediately begin their proposal activities.

[1153] Example: The server generates a "Promotion proposal document for XYZ Co., Ltd." and saves it in PDF format, then sends it to the user's device. The user receives the document, makes any necessary adjustments, and promptly starts the proposal process.

[1154] In this way, the present invention is a system that can significantly reduce the time it takes salespeople to prepare proposals and improve productivity by implementing a series of processes: collecting customer information, analyzing the information, identifying proposal needs, automatically generating proposal materials, and sending the proposal materials.

[1155] The processing flow will be explained below.

[1156] Step 1:

[1157] The user inputs the name and URL of the client company into the server, which identifies the company information to be collected.

[1158] Step 2:

[1159] The server crawls designated company official websites, news sites, social media sites, and other internet information sources by sending HTTP requests and retrieving the HTML content of each page.

[1160] Step 3:

[1161] The server analyzes the HTML content and extracts basic information about the company using techniques such as Document Object Model (DOM) analysis and regular expressions.

[1162] Step 4:

[1163] The server performs text analysis on the collected information using natural language processing (NLP) algorithms, which include text tokenization, part-of-speech tagging, and entity recognition.

[1164] Step 5:

[1165] The server evaluates the importance of the text and extracts key keywords and phrases related to the business environment and market trends, such as new products, marketing strategies, and market trends.

[1166] Step 6:

[1167] Based on the analysis results, the server identifies the customer's specific issues and proposal needs, referring to past success stories and a sales strategy database.

[1168] Step 7:

[1169] The user inputs the desired proposal details to the server, for example, specifying specific details such as "promotion support proposal for a new product."

[1170] Step 8:

[1171] The server selects the appropriate proposal format based on user input, including template selection.

[1172] Step 9:

[1173] The server embeds the collected data and analysis results into the selected template and automatically generates proposal materials in the form of presentation slides, reports, etc.

[1174] Step 10:

[1175] The server saves the generated proposal materials in PDF or PPT format and sends them to the user's terminal.

[1176] Step 11:

[1177] The user can review the proposal materials received on their device and make any necessary adjustments, after which they can immediately begin their proposal activities.

[1178] Through these steps, the system of the present invention efficiently collects and analyzes customer information, identifies proposal needs, automatically generates proposal materials, and finally sends the materials, significantly reducing the time salespeople spend preparing proposals and improving productivity.

[1179] Example 1

[1180] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1181] In modern sales activities, the time and effort required to prepare proposals and documents is a significant burden. Sales representatives must spend a great deal of time gathering and analyzing information about client companies and creating appropriate proposal materials. This process is often done manually and is inefficient, resulting in a decrease in the speed and productivity of sales activities.

[1182] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1183] In this invention, the server includes means for automatically collecting customer information from information sources on the Internet, means for analyzing the collected customer information using a natural language processing algorithm to grasp the business environment and market trends of the client company, means for identifying the proposal needs of the client based on the analysis results, means for automatically generating proposal materials according to the identified proposal needs, and means for generating the generated proposal materials in PDF or PPT format and transmitting them to a user terminal, thereby enabling a significant reduction in proposal preparation time and an improvement in the productivity of sales activities.

[1184] "Customer Information" refers to data including basic information, performance, products and services, and market trends regarding the target company.

[1185] "Internet sources" refers to various sources that provide public information accessible via the Internet, such as official websites, news sites, and social media.

[1186] A "natural language processing algorithm" is a collection of computer programs and methods for understanding and analyzing human language, and refers to technology for extracting keywords from text and analyzing relationships.

[1187] "Business environment" refers to the surrounding circumstances, including industry trends, economic conditions, and competitor activities, that affect the business operations of client companies.

[1188] "Market trends" refers to information that describes fluctuations and trends over time in demand for products or services, the competitive landscape, consumer preferences, and other factors in a particular market.

[1189] "Proposal needs" refers to the specific requests and requirements related to the challenges the client company faces and the solutions it seeks.

[1190] "Proposal materials" refer to documents such as presentation slides and reports that sales representatives use when making proposals to client companies.

[1191] "Automatic generation" refers to a process in which data collection, analysis, and documentation are performed programmatically without manual intervention.

[1192] "PDF and PPT formats" refer to standard file formats for electronic documents and presentations, such as the Portable Document Format (PDF) developed by Adobe Systems and the presentation format (PPT) used by Microsoft PowerPoint.

[1193] "User terminal" refers to devices such as computers, tablets, and smartphones operated by sales representatives and system users.

[1194] The present invention relates to a system for reducing the time and man-hours required for sales representatives to prepare proposals and documents. This system operates in cooperation with a server, terminals, and users, and uses the following hardware and software.

[1195] Hardware and software used

[1196] Server: Computing resources for data collection, analysis, and proposal generation, including web servers (e.g., Apache), crawling tools (e.g., Scrapy), natural language processing libraries (e.g., SpaCy, NLTK), and presentation generation tools (e.g., Python-PPTX).

[1197] Terminal: A PC or tablet is used as a device for users to operate the system. Terminals are primarily used for input and output, and have a web browser, PDF viewer, and presentation creation tool (e.g., Microsoft PowerPoint) installed.

[1198] Internet connection: A network connection is required to transmit data between the server and your device.

[1199] Program processing flow

[1200] Information gathering

[1201] The user accesses the server from their device and enters the name and URL of the client company to which they would like to make a proposal. As a specific example, let's say the user enters the name of "Company A." Based on this, the server uses a crawling tool (e.g., Scrapy) to collect information from the designated company's official website, news sites, and social media. The collected information is stored in a database on the server.

[1202] Information analysis

[1203] The server then analyzes the collected information using natural language processing (NLP) algorithms (e.g., SpaCy, NLTK). This allows the client company's business environment and market trends to be understood. Specifically, the analysis targets company performance data, new product information, recent news, social media posts, etc. For example, a report summarizing the market trends of "Company A" and customer reactions on social media is generated.

[1204] Identifying proposal needs

[1205] The server identifies the client company's specific issues and proposal needs based on the analysis results. In doing so, the server refers to past success stories and general business cases to identify appropriate proposal content. For example, if "Company A" launches a new product, the server identifies proposal needs regarding its promotion strategy.

[1206] Automatic generation of proposal materials

[1207] When a user inputs the details of their proposal into the server, the server automatically generates the optimal document format for the identified proposal needs. Using the Python-PPTX library, the collected data and analysis results are incorporated into presentation slides. For example, a "Proposal for Promotion Support for Company A" slide is generated that includes an overview of the company's new product, market trends, the proposed promotion strategy, and past success stories.

[1208] Submit a proposal

[1209] Finally, the server saves the generated proposal materials in PDF or PPT format and sends them to the user's device. The user can review the received materials and make any necessary adjustments, allowing them to quickly begin their proposal activities.

[1210] Prompt Sentence Examples

[1211] Here is an example prompt:

[1212] Company name: Company A

[1213] Proposal: Please create a proposal document for Company A's new product promotion strategy.

[1214] In this way, the present invention is a system that significantly reduces the burden on sales representatives and improves the efficiency of proposal activities by automating the entire process from data collection and analysis, identification of proposal needs, and automatic generation and transmission of materials.

[1215] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1216] Step 1:

[1217] The user accesses the server from their device and enters the name and URL of the client company to which they are making a proposal into a dedicated form. The input data is "Company Name: Company A" and "URL: www.example.com." Based on this input, the server launches a crawling tool (e.g., Scrapy) and collects information from the designated company's official website, news sites, and social media. Specifically, the server saves the collected data in a database and confirms that crawling is complete.

[1218] Step 2:

[1219] The server analyzes the information stored in the database using natural language processing algorithms (e.g., SpaCy, NLTK). The collected text data is used as input. Specific analysis operations include extracting keywords from the text, classifying documents, and analyzing sentiment, and outputting the results of the analysis: company performance, product overviews, market trends, and the behavior of competitors.

[1220] Step 3:

[1221] The server uses the analysis results to identify the specific issues and proposal needs of the client company. The input in this step is the analysis results obtained in the previous step. The server refers to past success stories and general business cases to identify the proposal content that best meets the client's needs. A specific example of how this works is to create a list of proposal needs regarding Company A's new product promotion strategy.

[1222] Step 4:

[1223] When a user inputs the proposal details into the server, the server automatically generates the proposal materials. In this case, the input data is "Proposal Details: Company A's New Product Promotion Strategy." The server uses the Python-PPTX library to create presentation slides based on the collected data and analysis results. Specifically, the slides include an overview of the company's new product, market trends, the proposed promotion strategy, and past success stories.

[1224] Step 5:

[1225] The server saves the generated proposal materials in PDF or PPT format and sends them to the user's terminal. In this step, the generated proposal materials are used as input. The server converts the materials into PDF format and sends them to the user's terminal. Specific operations include confirming the completion of transmission and recording a log. The user can then review the received materials and make any necessary adjustments to quickly begin proposal activities.

[1226] The above is the flow of processing of the program of this system.

[1227] (Application example 1)

[1228] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1229] In conventional sales support systems, the process from collecting customer information to creating proposal materials was done manually, requiring a great deal of time and effort. Furthermore, the collected information could not be analyzed quickly and the proposal needs based on that analysis could not be identified quickly, resulting in a decrease in the productivity of sales activities. Furthermore, there were few ways to utilize the collected information to provide appropriate materials, which was a burden for salespeople. A new approach was needed to solve these problems.

[1230] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1231] In this invention, the server includes means for automatically collecting customer information from information sources on the Internet, means for analyzing the collected customer information and grasping the business environment and market trends of the client organization, means for identifying the proposal needs of the client based on the analysis results, means for automatically generating proposal materials according to the identified proposal needs, means for transmitting the generated proposal materials to an end-user terminal, and means for inputting information and viewing materials using a smart device. This enables salespeople to quickly collect and analyze customer information and automatically generate and transmit appropriate proposal materials, significantly reducing the time and effort required for proposal preparation and improving productivity.

[1232] "Customer information" refers to data collected from online sources such as official websites published by companies and organizations, news sites, and social networking services (SNS).

[1233] "Client organization" refers to the company or organization to which the proposal is made, and its business environment and market trends are the subject of analysis.

[1234] "Proposal needs" refers to the content and strategies that should be proposed in sales activities, identified based on the client organization's business environment and market trends.

[1235] "Proposal materials" refer to presentation slides and documents that are automatically generated in response to identified proposal needs and are used in sales activities.

[1236] "End-user terminal" refers to the device used by salespeople who use the system, and includes PCs, smartphones, tablets, smart glasses, etc.

[1237] "Smart device" refers to devices such as smartphones, smart glasses, and tablets that are connected to the internet and offer additional functionality.

[1238] "Natural language processing algorithm" refers to the computational methods and models used to analyze collected customer information and understand and classify its content.

[1239] A "crawling engine" refers to a program that automatically searches for information on the Internet and collects the necessary data.

[1240] "Analysis results" refers to the analysis results of customer information extracted using natural language processing algorithms and data analysis tools.

[1241] "Sources" refer to reliable sources of data on the Internet, such as official websites, news sites, and social networking services (SNS).

[1242] To implement the present invention, a system is provided in which a server, a terminal, and a user work together, as will be described in detail below.

[1243] Overall structure

[1244] This system includes the following means for collecting customer information, analyzing the information, identifying proposal needs, automatically generating proposal materials, transmitting the generated materials, and inputting information and viewing materials using a smart device.

[1245] Collecting customer information

[1246] The user uses a smart device (e.g., smart glasses) to voice-input the name of the customer company. Based on this input, the server collects customer information from online sources (official websites, news sites, social media, etc.). This process uses a crawling engine (e.g., Beautiful Soup, Scrapy).

[1247] Information analysis

[1248] The collected information is sent to a server and analyzed using natural language processing algorithms (e.g., BERT, GPT). This allows us to understand the business environment and market trends of our client organizations. Data analysis tools such as Pandas and Scikit-learn are used for data analysis.

[1249] Identifying proposal needs

[1250] The server identifies the customer's proposal needs based on the analysis results, which is achieved by using a content recommendation system (e.g., Collaborative Filtering) and referencing past success stories and industry-standard data.

[1251] Automatic generation of proposal materials

[1252] The server automatically generates proposal materials based on the identified proposal needs, using document generation tools (e.g., LaTeX, MS Office API) to create presentation slides and PDF documents.

[1253] Sending generated materials

[1254] The generated proposal materials are sent to the end user's device in PDF or PPT format using an email sending API (e.g., SMTP) or cloud storage (e.g., Google Drive, AWS S3). These materials can be viewed in real time using a smart device.

[1255] Specific examples

[1256] For example, if a user uses smart glasses to voice-input the name of a client company, such as "ABC Co., Ltd.", the server will crawl online sources based on the company name and collect company information. The collected information is analyzed using a natural language processing algorithm to understand the company's business environment and market trends. Based on the analysis results, a proposal need, such as "promotion strategy for ABC Co., Ltd.'s new product," is then identified. The server then uses this information to automatically generate presentation slides and send them to the user's smart glasses. This allows the user to review the materials in real time while making proposals.

[1257] Prompt Sentence Examples

[1258] Examples of prompts from this generative AI model include:

[1259] "Please provide us with the latest information about ABC Corporation. Pay particular attention to new products, market trends, and competitor activity, and provide analysis that can be used in our advertising strategy."

[1260] In this way, the salesperson can make proposals to the customer quickly and efficiently.

[1261] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1262] Step 1:

[1263] The user uses a smart device (smart glasses) to input the name of the customer company by voice. This voice input is converted into text data by the device and sent to the server. This results in the input data of the customer company name.

[1264] Step 2:

[1265] The server uses the received text data to crawl the designated company's official website, news sites, social media, and other sources. Software such as Beautiful Soup and Scrapy are used. This process gathers relevant information from company profile pages on official websites, news articles, social media posts, and other sources to obtain the collected data.

[1266] Step 3:

[1267] The server analyzes the collected information using NLP (natural language processing) algorithms, such as the BERT or GPT model. This analysis extracts information about the client organization's business environment, market trends, and competitor trends, and produces analysis results.

[1268] Step 4:

[1269] The server uses the analysis results to refer to past success stories and general business cases, and utilizes a content recommendation system (Collaborative Filtering) to identify proposal needs. This clarifies the customer's specific issues and proposal needs, and provides data on identified proposal needs.

[1270] Step 5:

[1271] Based on the identified proposal needs, the server automatically generates proposal materials using document generation tools (LaTeX, MS Office API). The generated materials are in PDF or PPT format and include company information, market analysis, proposal details, etc. This process results in the automatically generated proposal materials.

[1272] Step 6:

[1273] The generated proposal materials are sent by the server to the end user's device using methods such as email sending API (SMTP) or cloud storage services (Google Drive, AWS S3). This sends the materials to the user's device (smart glasses, etc.), allowing the user to view the materials in real time.

[1274] Through the above processing steps, this system can efficiently carry out the process from collecting customer information to automatically generating and sending proposal materials.

[1275] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1276] The present invention relates to a system that reduces the time and man-hours required for salespeople to prepare proposals and create documents, and improves the quality of proposals by combining it with an emotion engine that recognizes user emotions. Specific embodiments for implementing the present invention are described below. In this invention, a server, terminals, and users work in cooperation with each other, and an emotion engine is also included.

[1277] 1. Information gathering

[1278] A user inputs the name or URL of a client company into the server. This identifies the company information to be collected. The server then crawls the designated company's official website, news sites, social media, and other online sources to collect basic information and publicly available data related to the client company. The collected information includes the company's performance, major products and services, recent news, and social media posts. For example, if a user inputs the company name "XYZ Corporation" into the server, the server will analyze the company profile page on the company's official website and collect related information from news sites and social media.

[1279] 2. Information analysis

[1280] The server analyzes the collected information using natural language processing (NLP) algorithms to understand the client company's business environment and market trends. The analysis results include information on the industry sector, economic trends, competitor strategies, consumer trends, etc. As a specific example, the server analyzes the latest news articles about XYZ Corporation, analyzing and organizing information about the company's new product launch and its market reaction.

[1281] 3. Identifying proposal needs

[1282] Based on the analysis results, the server identifies the client company's specific issues and proposal needs. In doing so, the server refers to past success stories and general business cases to identify appropriate proposal content. As a specific example, the server analyzes business environment data and market trends for XYZ Corporation to identify needs regarding a promotion strategy for a new product.

[1283] 4. Emotion Recognition by Emotion Engine

[1284] When a user inputs the content of a proposal they wish to make into the server, the emotion engine built into the server recognizes the emotion based on the user's input. For example, when a user inputs a "promotion support proposal for a new product," the emotion engine determines whether the emotional state is positive or negative. As a specific example, if a user inputs "I would like to propose a promotion strategy that will maximize the appeal of XYZ Corporation's new product," the emotion engine recognizes the user's positive emotion.

[1285] 5. Automatic generation and adjustment of proposal materials

[1286] The server adjusts the content of the proposal materials based on the user's emotional state recognized by the emotion engine. If positive emotions are recognized, the content of the materials is automatically generated with a more proactive tone, and if negative emotions are recognized, the content is adjusted to take a more cautious approach as necessary. For example, if the user's positive emotions are recognized, the proposal materials will include a proactive phrase such as, "We expect this promotional strategy to significantly increase product sales."

[1287] 6. Submitting proposal materials

[1288] The server saves the generated proposal materials in PDF or PPT format and sends them to the user's device. The user checks the proposal materials received on their device and makes any necessary adjustments. After this, the user can immediately begin their proposal activities. As a concrete example, the server saves the "Promotion proposal materials for XYZ Co., Ltd." generated in PDF format and sends it to the user's device. The user receives the materials, makes any necessary adjustments, and immediately begins their proposal activities.

[1289] This system efficiently implements a series of processes, from collecting and analyzing customer information, identifying proposal needs, recognizing emotions using an emotion engine, automatically generating proposal materials, and sending them. As a result, it is possible to significantly reduce the time salespeople spend preparing proposals and improve their productivity. Furthermore, by generating proposal materials that take the user's emotions into consideration, it is possible to improve the quality of proposals.

[1290] The processing flow will be explained below.

[1291] Step 1:

[1292] The user inputs the name and URL of the client company into the server, which identifies the company information to be collected.

[1293] Step 2:

[1294] The server crawls designated company official websites, news sites, social media sites, and other internet information sources. Specifically, it sends HTTP requests and retrieves the HTML content of each page.

[1295] Step 3:

[1296] The server analyzes the HTML content and extracts basic information about the company using techniques such as Document Object Model (DOM) analysis and regular expressions.

[1297] Step 4:

[1298] The server performs text analysis on the collected information using natural language processing (NLP) algorithms, which include text tokenization, part-of-speech tagging, and entity recognition.

[1299] Step 5:

[1300] The server evaluates the importance of the text and extracts key keywords and phrases related to the business environment and market trends, such as new products, marketing strategies, and market trends.

[1301] Step 6:

[1302] Based on the analysis results, the server identifies the customer's specific issues and proposal needs, referring to past success stories and a sales strategy database.

[1303] Step 7:

[1304] The user inputs the desired proposal details to the server, for example, specifying specific details such as "promotion support proposal for a new product."

[1305] Step 8:

[1306] The emotion engine built into the server recognizes emotions based on user input, analyzing the emotion from the text entered by the user and determining whether it is positive or negative.

[1307] Step 9:

[1308] The server adjusts the content of the proposal materials based on the user's emotional state as recognized by the emotion engine. If a positive emotion is recognized, the content of the materials is automatically generated with a more positive tone, and if a negative emotion is recognized, it is automatically generated with a more cautious approach.

[1309] Step 10:

[1310] The server selects a proposal format tailored to the user's sentiment, embeds the collected data and analysis results, and applies an algorithm to automatically generate presentation slides and reports.

[1311] Step 11:

[1312] The server saves the generated proposal materials in PDF or PPT format and sends them to the user's device. The user can then review the received materials and make any necessary adjustments.

[1313] Step 12:

[1314] The user can check the final materials on the device and immediately begin making proposals.

[1315] By following these steps, the system of the present invention efficiently realizes a series of processes, from collecting customer information to recognizing and analyzing the user's emotions using an emotion engine, identifying proposal needs, automatically generating proposal materials, and finally sending the materials. This not only significantly reduces the time salespeople spend preparing proposals and improves productivity, but also makes it possible to provide high-quality proposal materials that take emotions into consideration.

[1316] Example 2

[1317] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1318] In conventional sales activities, salespeople spend a lot of time and effort preparing proposals and creating materials, resulting in low efficiency. Furthermore, it is difficult to make proposals that take into account customer emotions, making it difficult to improve the quality of proposals. The present invention aims to provide a system that reduces the time and effort required for salespeople to prepare proposals and automatically generates high-quality proposal materials that take into account customer emotions.

[1319] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for automatically collecting customer information from information sources on the Internet, means for analyzing the collected customer information using a natural language processing algorithm to grasp the business environment and market trends of the client company, means for identifying the customer's proposal needs based on the analysis results, means for a user to input desired proposal content and for an emotion engine to recognize emotions based on the user's input, means for automatically generating proposal materials adjusted based on the recognized emotions, and means for saving the generated proposal materials in PDF or PPT format and sending them to the user terminal. This not only significantly reduces the time a salesperson needs to prepare a proposal and improves productivity, but also makes it possible to generate high-quality proposal materials that take customer emotions into consideration.

[1320] "Customer information" refers to data about the client company, such as its name, URL, performance, major products and services, recent news, and posts on social media.

[1321] "Internet sources" refers to any information that is publicly available on the Internet, such as official websites, news sites, and social media.

[1322] "Crawling" refers to the technology of automatically crawling through web pages on the Internet to collect information.

[1323] "Natural language processing algorithms" refer to the technology that enables computers to understand and analyze human language.

[1324] "Business environment" refers to the industry and economic conditions to which the client company belongs, as well as external factors such as competitors and market trends.

[1325] "Market trends" refers to market trends, consumer behavior patterns, changes in demand, etc.

[1326] "Proposal needs" refers to the solutions and services that should be proposed to address the specific problems and issues that client companies face.

[1327] "Emotion engine" refers to technology for recognizing and analyzing emotions from user input.

[1328] "Proposal materials" refers to documents that summarize the proposals to client companies.

[1329] "PDF" stands for Portable Document Format and refers to a file format for electronically storing and sharing documents.

[1330] "PPT format" refers to the file format used by Microsoft PowerPoint for saving and sharing presentations.

[1331] "User terminal" refers to electronic devices used by users, such as PCs, smartphones, and tablets.

[1332] This invention provides a system that automates a series of processes from collecting customer information to generating proposal materials. The system works in cooperation with the server, terminals, and users, and incorporates an emotion engine to improve the work efficiency of salespeople.

[1333] Hardware and software used

[1334] The server is a high-performance computer, and cloud servers (Amazon Web Services, Google Cloud Platform, Microsoft Azure, etc.) can generally be used. A relational database such as MySQL is used as the database. Crawling software (e.g., Scrapy) is used to collect information, natural language processing algorithms (e.g., SpaCy, NLTK) are used for information analysis, and Scikit-learn is used for machine learning. A template generation tool (e.g., LaTeX, ReportLab) is used to generate proposal materials. A sentiment analysis tool such as IBM Watson or Microsoft Azure Text Analytics is used as the emotion engine.

[1335] System Operation

[1336] The user accesses the server from their device and enters the name or URL of the client company. This identifies the company information to be collected. For example, if the user enters the company name "XYZ Corporation," the server will collect information about that company. Here, crawling tools such as Scrapy are used to collect information from the specified company's official website, news sites, social media, etc.

[1337] The collected information is stored in a database (e.g., MySQL) on the server. The server then analyzes the collected information using natural language processing algorithms (e.g., SpaCy, NLTK) to understand the company's business environment and market trends. For example, the latest news articles about XYZ Corporation can be analyzed to summarize the status of new product launches and their market reactions.

[1338] Based on the analysis results, the server identifies the client company's specific issues and proposal needs. It uses a machine learning algorithm (Scikit-learn) to reference past success stories and general business cases to generate appropriate proposals. For example, if XYZ Co., Ltd. launches a new product, it identifies the needs for its promotion strategy.

[1339] Next, when the user inputs the content of the proposal they want, the emotion engine built into the server recognizes emotions based on the user's input. For example, when a user inputs a "promotion support proposal for a new product," it determines whether the emotional state is positive or negative.

[1340] The server adjusts the content of the proposal materials based on the user's emotional state as recognized by the emotion engine. If a positive emotion is recognized, the content of the materials will be adjusted to a more proactive tone. If a negative emotion is recognized, the content will be adjusted to a more cautious approach as necessary. For example, a proactive phrase such as "We expect this promotional strategy to significantly increase product sales" will be inserted.

[1341] Finally, the server saves the generated proposal materials in PDF or PPT format and sends them to the user's device. The user can review the received materials on their device and fine-tune the content as needed. For example, the server generates a "Promotion Proposal Material for XYZ Co., Ltd." and sends it in PDF format, which the user can then receive and review.

[1342] An example of a prompt sentence that can be entered is, "Please create a promotion strategy proposal based on the latest news from XYZ Co., Ltd." This system efficiently collects and analyzes customer information, identifies proposal needs, recognizes emotions using an emotion engine, automatically generates proposal materials, and sends them.

[1343] This will significantly reduce the time it takes salespeople to prepare proposals, improving productivity and the quality of proposals.

[1344] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1345] Step 1:

[1346] The user enters the name and URL of the client company into the server. The entered name and URL are sent to the server, which identifies the company information to be collected.

[1347] Input: Name and URL of the customer company

[1348] Output: Specific information to be collected

[1349] Specific behavior:

[1350] The user enters a company name, such as "XYZ Co., Ltd.", into the server from the terminal and sends it.

[1351] The server generates a request to obtain information about the specified company.

[1352] Step 2:

[1353] The server uses crawling software (such as Scrapy) to crawl designated companies' official websites, news sites, social media, and other online sources to collect customer information.

[1354] Input: Specific information to be collected

[1355] Output: Collected customer information

[1356] Specific behavior:

[1357] Based on the URL request, the server crawls the company's official website for company overviews, news sites for the latest news, and social media posts.

[1358] The server stores the collected information in a database (such as MySQL).

[1359] Step 3:

[1360] The information collected by the server is analyzed using natural language processing (NLP) algorithms (e.g., SpaCy, NLTK) to understand the company's business environment and market trends.

[1361] Input: Collected customer information

[1362] Output: Parsed company information

[1363] Specific behavior:

[1364] The server reads the collected text data and performs topic modeling and sentiment analysis.

[1365] The server formats the analysis results and generates a report that helps client companies understand industry and economic trends.

[1366] The server stores the analysis results in a database.

[1367] Step 4:

[1368] Based on the analysis results, the server identifies the proposal needs of the client company by referring to past success stories and general business cases.

[1369] Input: Parsed company information

[1370] Output: Identified proposed needs

[1371] Specific behavior:

[1372] The server searches a database of past proposal examples and extracts similar cases.

[1373] The server uses machine learning algorithms (such as Scikit-learn) to compare the analysis results with past cases and identify the needs of the client company.

[1374] The server stores the identified proposed needs in a database.

[1375] Step 5:

[1376] When the user inputs the desired suggestion, an emotion engine built into the server recognizes emotions based on the user's input.

[1377] Input: The input text of the user's suggestion

[1378] Output: User's emotional state

[1379] Specific behavior:

[1380] The user inputs and submits a proposal such as "Proposal for new product promotion support."

[1381] The server uses sentiment analysis tools (e.g., IBM Watson, Microsoft Azure Text Analytics) to determine the emotional state of the user from the text they input.

[1382] The server stores the recognized emotional state of the user in a database.

[1383] Step 6:

[1384] The server automatically adjusts and generates the content of the proposal materials based on the user's emotional state recognized by the emotion engine.

[1385] Input: User's emotional state and suggestion needs

[1386] Output: Generated proposal

[1387] Specific behavior:

[1388] The server generates the proposal using a template generation tool (e.g., LaTeX, ReportLab).

[1389] The server adjusts the tone and content of the proposal based on the emotional state.

[1390] For positive emotions, include phrases and designs with a positive tone.

[1391] In the case of negative emotions, adopt a measured approach and a calm tone.

[1392] The server saves the generated proposal document in PDF format.

[1393] Step 7:

[1394] The server sends the generated proposal materials to the user's terminal, where the user can check the received proposal materials and make fine adjustments as necessary.

[1395] Input: Generated proposal document (PDF format)

[1396] Output: Received proposal materials

[1397] Specific behavior:

[1398] The server sends the generated PDF file to the user's device via email or a dedicated application.

[1399] The user checks the proposal materials through their inbox or application.

[1400] The user fine-tunes the necessary wording and design and finalizes the final proposal materials.

[1401] (Application example 2)

[1402] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1403] In traditional sales activities, salespeople spend a great deal of time and effort preparing proposals and creating materials for customers. Furthermore, the approach to customers is uniform, making it difficult to customize proposals that take into account the customer's emotional state. Furthermore, while effective customer service and proposals for best-selling products are required in brick-and-mortar stores, efficient information gathering and the creation of proposal materials are difficult. There is a need to solve these issues and improve the efficiency and quality of sales activities and store operations.

[1404] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1405] In this invention, the server includes: means for automatically collecting customer information from online information sources; means for analyzing the collected customer information to understand the business environment and market trends of the client company; means for identifying the customer's proposal needs based on the analysis results; means for automatically generating proposal materials according to the identified proposal needs; means for transmitting the generated proposal materials to a user terminal; and means for adjusting the content of the proposal materials based on the user's emotional state, including an emotion engine that recognizes the user's emotions when inputting the proposal content. This reduces the salesperson's proposal preparation time, improves productivity, and enables the automatic generation of high-quality proposal materials that take the user's emotions into consideration. Furthermore, effective proposals that meet customer needs can be made even in physical stores, which is expected to improve customer satisfaction.

[1406] "Customer information" is a general term for basic information and publicly available data about companies and individuals collected from sources on the Internet.

[1407] "Internet sources" refers to all media that provide information publicly available via the Internet, such as official websites, news sites, and social media.

[1408] "Means of collection" refers to the way the server automatically obtains the necessary information, such as by crawling the web or using an API.

[1409] "Means of analysis" refers to the use of natural language processing algorithms to analyze collected information and extract useful insights.

[1410] "The business environment of a client company" refers to the industry and market conditions to which a particular client company belongs, as well as the external environment that influences corporate activities.

[1411] "Market trends" refer to ongoing or predicted movements or trends in a particular industry or market.

[1412] "Proposal needs" refers to the specific solutions and approaches that should be proposed to address the challenges and business opportunities facing client companies.

[1413] "Means for automatic generation" refers to an algorithm that automatically assembles proposal materials based on collected and analyzed information.

[1414] "Transmission means" refers to a communication means or protocol for transmitting the generated proposal materials to a user terminal.

[1415] "Emotion engine" refers to a software component for recognizing and analyzing a user's emotional state from input data.

[1416] "Adjustment measures" refer to methods for appropriately changing the content and structure of proposal materials depending on the perceived emotional state.

[1417] The present invention provides a system for improving the efficiency and quality of customer service and sales strategy planning in a brick-and-mortar store. Specific embodiments for carrying out the present invention will be described below.

[1418] System Overview

[1419] The system consists of a server, tablets or smartphones used by store staff, and an application incorporating an emotion engine.

[1420] Hardware and software used

[1421] Hardware: Tablets, smartphones

[1422] Software: Python and Django are used on the server side, Flutter on the client side, scikit-learn and NLTK (Natural Language Toolkit) for data analysis, and Microsoft Azure Emotion API for emotion recognition.

[1423] Data processing and data calculation

[1424] The server automatically collects customer information from sources on the Internet, such as official websites, news sites, and social media, to obtain the latest information about customers and their companies.

[1425] The collected information is then analyzed using NLP (natural language processing) algorithms, which allows the company to understand its business environment and market trends. The analysis results include industry trends, economic conditions, competitor strategies, consumer trends, and more.

[1426] The server then identifies the customer's specific proposal needs based on the analysis results, referencing past success stories and general business cases to determine the appropriate proposal content.

[1427] Once the proposal content has been decided, the user (store staff) inputs the desired proposal content via a tablet or smartphone, and the emotion expressed at that time is recognized by an emotion engine built into the server. The emotion engine analyzes the input content and determines the user's emotional state.

[1428] The server adjusts the content of the proposal materials based on the user's emotional state, as recognized by the emotion engine. If a positive emotion is recognized, the content of the materials is automatically generated with a more positive tone. If a negative emotion is recognized, the content is adjusted to take a more cautious approach.

[1429] The proposal materials generated in this way are saved in PDF or PPT format and sent to the user's device, where the user can review the received proposal materials and make any necessary adjustments.

[1430] Specific examples

[1431] For example, suppose a store staff member operates a tablet and enters "Customer name: ABC Company, desired product category: home appliances." The server collects information related to ABC Company from its official website and social media, and uses an NLP algorithm to analyze the business environment and market trends. Next, based on the analysis results, the server identifies proposal needs. When the staff member enters "Proposal for new product promotion support," the emotion engine recognizes the staff member's positive emotions. The server takes these emotions into consideration and automatically generates proposal materials with a positive tone. The generated materials are then sent to the staff member's tablet.

[1432] Prompt Sentence Examples

[1433] Information gathering prompt:

[1434] Customer name: ABC Company

[1435] Desired product category: Home appliances

[1436] Information analysis prompt:

[1437] Information collected:

[1438] Company profile from the official website

[1439] Latest News Articles

[1440] SNS posts

[1441] Analysis details:

[1442] New product release information

[1443] market trends

[1444] Customer interests

[1445] Proposal needs specific prompt:

[1446] Needs based on analysis results:

[1447] New product promotion strategies

[1448] Differentiate yourself from your competitors

[1449] Emotion engine prompts for emotion recognition:

[1450] Input text: "I think ABC's new product is excellent, but I'm concerned about the promotion."

[1451] This makes it possible to efficiently create and provide high-quality proposal materials that meet customer needs, reducing the time store staff spend preparing proposals and enabling them to respond to customers quickly and effectively.

[1452] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1453] Step 1:

[1454] Collecting customer information

[1455] Input: The user enters the customer name and desired product category on a tablet or smartphone.

[1456] Specific operation: When the user enters "Customer name: ABC company, desired product category: home appliances", the information is sent to the server.

[1457] Data processing or data calculation: The server crawls and collects information related to the specified customer from online sources (official websites, news sites, social media, etc.).

[1458] Output: Collected customer information data is stored on the server.

[1459] Step 2:

[1460] Customer information analysis

[1461] Input: Customer information data collected by the server.

[1462] Specific operation: The server uses natural language processing (NLP) algorithms (using scikit-learn, NLTK, etc.) to understand the company's business environment and market trends.

[1463] Data processing or data calculation: NLP algorithms analyze collected text data to extract the latest developments of companies, industry trends, competitor strategies, etc.

[1464] Output: The analysis results in a useful set of data that can be used for proposals.

[1465] Step 3:

[1466] Identifying proposal needs

[1467] Input: Analysis result data.

[1468] Specific actions: The server identifies the customer's specific issues and needs based on the analysis results.

[1469] Data processing or data calculation: The server refers to past success stories and general business cases and determines appropriate proposals based on the extracted information.

[1470] Output: Data about customer proposal needs is identified.

[1471] Step 4:

[1472] Emotion recognition by emotion engine

[1473] Input: The user inputs the desired suggestions using a tablet or smartphone.

[1474] Specific operation: The user inputs "Proposal for new product promotion support." The emotion engine (Microsoft Azure Emotion API) built into the server analyzes the input in real time.

[1475] Data processing or data calculation: The emotion engine recognizes the user's emotional state (positive, negative, etc.) based on the text data.

[1476] Output: User emotional state data is generated.

[1477] Step 5:

[1478] Automatic generation and adjustment of proposal materials

[1479] Input: Customer suggestion needs data and user emotional state data.

[1480] Specific operation: The server adjusts and automatically generates the content of proposal materials based on the recognized emotional state.

[1481] Data manipulation or data calculation: Generate materials with a positive tone when positive sentiment is perceived, and adjust to take a cautious approach when negative sentiment is perceived.

[1482] Output: A tailored proposal is generated in PDF and PPT formats.

[1483] Step 6:

[1484] Submit a proposal

[1485] Input: Proposal material automatically generated by the server.

[1486] Specific operation: The server sends the generated proposal materials to the user's terminal in PDF or PPT format.

[1487] Data processing or data calculation: Sending material data from the server to the terminal using a communication protocol.

[1488] Output: The proposal materials are sent to the user's device, allowing the user to receive and review the proposal materials.

[1489] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1490] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1491] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1492] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1493] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1494] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1495] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1496] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1497] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1498] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1499] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1500] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1501] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1502] 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.

[1503] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1504] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1505] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1506] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1507] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1508] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1509] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1510] The following is further disclosed regarding the above embodiment.

[1511] (Claim 1)

[1512] means for automatically collecting customer information from internet sources;

[1513] A means of analyzing collected customer information to understand the business environment and market trends of client companies,

[1514] A means for identifying customer proposal needs based on the analysis results;

[1515] A means for automatically generating proposal materials according to the identified proposal needs;

[1516] means for transmitting the generated proposal material to a user terminal;

[1517] A system including:

[1518] (Claim 2)

[1519] 2. The system of claim 1, wherein the collected customer information is analyzed using a natural language processing algorithm.

[1520] (Claim 3)

[1521] The system according to claim 1, wherein the proposal materials are generated and transmitted in PDF or PPT format.

[1522] "Example 1"

[1523] Claims

[1524] (Claim 1)

[1525] means for automatically collecting customer information from internet sources;

[1526] The collected customer information is analyzed using natural language processing algorithms to understand the business environment and market trends of the client company.

[1527] A means for identifying customer proposal needs based on the analysis results;

[1528] A means for automatically generating proposal materials according to the identified proposal needs;

[1529] A means to generate the generated proposal materials in PDF or PPT format and send them to the user's terminal;

[1530] A system including:

[1531] (Claim 2)

[1532] The system according to claim 1, wherein the user inputs the name of the company to which the proposal is made and the URL of its website into the interface, and collection and analysis are performed based on the input.

[1533] (Claim 3)

[1534] 2. The system according to claim 1, wherein the automatically generated proposal material embeds the collected data and analysis results according to a specific format and is immediately sent to the user's terminal.

[1535] "Application Example 1"

[1536] (Claim 1)

[1537] means for automatically collecting customer information from internet sources;

[1538] A means of analyzing collected customer information to understand the business environment and market trends of the client organization,

[1539] A means for identifying customer proposal needs based on the analysis results;

[1540] A means for automatically generating proposal materials according to the identified proposal needs;

[1541] means for transmitting the generated proposal material to an end user terminal;

[1542] A means of inputting information and viewing documents using a smart device,

[1543] A system including:

[1544] (Claim 2)

[1545] 2. The system of claim 1, wherein the collected customer information is analyzed using a natural language processing algorithm.

[1546] (Claim 3)

[1547] The system according to claim 1, wherein the proposal materials are generated and transmitted in PDF or PPT format.

[1548] "Example 2: Combining Emotion Engines"

[1549] (Claim 1)

[1550] means for automatically collecting customer information from internet sources;

[1551] The collected customer information is analyzed using natural language processing algorithms to understand the business environment and market trends of the client company.

[1552] A means for identifying customer proposal needs based on the analysis results;

[1553] A means for a user to input desired proposal content and for an emotion engine to recognize emotions based on the user's input;

[1554] a means for automatically generating a proposal document tailored based on the recognized emotion;

[1555] A means to save the generated proposal materials in PDF or PPT format and send them to the user's terminal,

[1556] A system including:

[1557] (Claim 2)

[1558] 2. The system of claim 1, wherein the emotion engine recognizes an emotional state input by a user and automatically generates proposal materials based on the emotional state.

[1559] (Claim 3)

[1560] 2. The system according to claim 1, wherein the user can check the proposal materials received on the terminal and make fine adjustments as necessary.

[1561] "Application example 2 when combining emotion engines"

[1562] (Claim 1)

[1563] means for automatically collecting customer information from internet sources;

[1564] A means of analyzing collected customer information to understand the business environment and market trends of client companies,

[1565] A means for identifying customer proposal needs based on the analysis results;

[1566] A means for automatically generating proposal materials according to the identified proposal needs;

[1567] means for transmitting the generated proposal material to a user terminal;

[1568] an emotion engine for recognizing the emotion of a user when inputting the proposal content, and means for adjusting the content of the proposal material based on the emotional state of the user;

[1569] A system including:

[1570] (Claim 2)

[1571] 2. The system of claim 1, wherein the collected customer information is analyzed using a natural language processing algorithm.

[1572] (Claim 3)

[1573] The system according to claim 1, wherein the proposal materials are generated and transmitted in PDF or PPT format. [Explanation of symbols]

[1574] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for automatically collecting customer information from internet sources; A means of analyzing collected customer information to understand the business environment and market trends of client companies, A means for identifying customer proposal needs based on the analysis results; A means for automatically generating proposal materials according to the identified proposal needs; means for transmitting the generated proposal material to a user terminal; A system including:

2. 10. The system of claim 1, wherein the collected customer information is analyzed using a natural language processing algorithm.

3. The system according to claim 1, wherein the proposal materials are generated and transmitted in PDF or PPT format.

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