system

Abstract

We provide the system. [Solution] Means of receiving company information, A means for automatically collecting information from publicly available data sources based on the aforementioned company information, A means of analyzing collected information to hypothesize the company's challenges, A means for generating the optimal commercial proposal for the aforementioned problem, A means of creating sales materials and anticipated questions and answers based on the generated proposals, A system that includes this.

Description

Technical Field

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

Background Art

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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern sales activities, a comprehensive understanding of target companies and quick and effective proposals based on such information are required. However, salespersons usually spend a lot of time and effort on information collection and analysis. As a result, the efficiency of the entire sales activity has decreased, and it has become difficult to concentrate on strategic activities. In addition, there are also significant additional burdens such as capturing real-time customer reactions during business negotiations and the speed of follow-up after business negotiations. An innovative system is needed to solve these problems.

Means for Solving the Problems

[0005] This invention provides an AI system that automatically collects information from publicly available data sources after receiving company information, and analyzes the company's challenges based on that information. Based on the analyzed information, it automatically generates optimal commercial proposals to address the challenges, significantly improving operational efficiency. Furthermore, it has a function to analyze the customer's real-time reactions during sales negotiations and propose necessary actions. It also automatically generates follow-up communications after sales negotiations, reducing the burden on sales representatives. With this system, the entire sales activity is automated, dramatically improving efficiency and effectiveness.

[0006] "Company information" refers to publicly available data related to a specific company, including investor relations information, financial results, stock price trends, press releases, official websites, and official social media information.

[0007] "Methods for automatically collecting information" refers to technologies that perform a process of mechanically acquiring relevant information from various data sources on the internet, based on specific company information.

[0008] "Means of information analysis" refers to technologies that process collected information using data analysis algorithms to derive the current state and potential challenges of a company.

[0009] A "commercial proposal" refers to a marketing plan or strategy that proposes a specific product or service to address a company's challenges.

[0010] "Sales materials" include presentation materials, reports, and other related documents used to effectively communicate the proposed content to the customer.

[0011] "Anticipated questions and answers" refers to a set of prepared answers to questions that customers are expected to ask.

[0012] "Customer response data" refers to various data that shows the customer's reaction during a business negotiation, and may include facial expressions, statements, and actions.

[0013] "Means of suggesting action" refers to technology that has the function of instructing or recommending appropriate responses to sales representatives based on customer responses analyzed in real time.

[0014] "Follow-up communication messages" refer to follow-up emails or text messages sent to customers after a business meeting, including summaries of the meeting and information about future contact.

[0015] "Sales representative's physical data" is a general term for data indicating the biological state of sales representatives, acquired using IoT devices, etc., and may include heart rate, stress indicators, body temperature, etc.

[0016] A "refreshment notification" is an alert that informs sales representatives of the need for breaks or refreshment based on their condition. [Brief explanation of the drawing]

[0017] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8]It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Embodiments for Carrying Out the Invention

[0018] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0019] First, the terms used in the following description will be explained.

[0020] In the following embodiments, the labeled processor (hereinafter simply referred to as "processor") may be one arithmetic unit or a combination of a plurality of arithmetic units. Also, the processor may be one type of arithmetic unit or a combination of a plurality of types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0021] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0022] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0023] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0024] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0025] [First Embodiment]

[0026] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0027] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0028] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0029] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0030] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0031] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0032] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0033] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0035] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0036] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0037] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0038] This invention is an AI-powered sales support system that efficiently supports sales activities by utilizing company information. When a user enters a company name via a terminal, the system automatically collects information related to that company from various publicly available data sources. This information includes investor relations (IR) information, financial results, stock price trends, press releases, and information from official websites and social media.

[0039] Next, the server analyzes the collected information and hypothesizes potential challenges for the company. Based on this analysis, the server refers to its own product database and generates appropriate commercial proposals. These proposals include materials for sales meetings, talk scripts, and anticipated questions and answers, all of which are automatically generated by the server.

[0040] Furthermore, the system supports the sales process by displaying materials provided from the server via the terminal when sales representatives conduct business negotiations. During negotiations, the server can acquire and analyze customer response data in real time. Based on this analysis, it also has a function to suggest appropriate actions to take during the negotiation. This enables immediate reactions and appropriate actions in the field of sales.

[0041] Furthermore, after the business meeting concludes, the server automatically generates a follow-up communication message. This message includes information about the next contact and a summary of the meeting's content, and is sent directly to the customer from the terminal.

[0042] This system also has a function to monitor the condition of sales representatives. The server periodically receives physical data from IoT devices and, depending on their stress levels and heart rate, can send notifications via the device prompting them to take a break if they need to refresh themselves.

[0043] As a concrete example, when a sales representative is conducting business negotiations with a new company, "XYZ Corporation," using this system allows for the rapid collection and analysis of a vast amount of information related to XYZ Corporation in advance, automatically generating an optimal proposal document based on the results. During the negotiation, the system guides the sales representative to respond appropriately while observing the customer's reactions, enabling them to work efficiently and effectively.

[0044] Thus, the present invention contributes to improving the efficiency of sales activities and reducing the burden on sales representatives.

[0045] The following describes the processing flow.

[0046] Step 1:

[0047] The user uses their device to enter the name of the target company. The device then sends the entered company name to the server.

[0048] Step 2:

[0049] Based on the company name it receives, the server collects relevant information from data sources on the internet. Specifically, it starts the process of acquiring data from IR information, financial statements, stock price trends, press releases, the company's official website, and social media.

[0050] Step 3:

[0051] The server analyzes the collected data. Using information analysis algorithms, it identifies the company's current situation and potential challenges. Based on the analysis results, it hypothesizes the challenges the target company may be facing.

[0052] Step 4:

[0053] The server references its product database to generate the optimal commercial proposal for the assumed problem. It then concretizes the proposal and automatically generates sales materials, talk scripts, and anticipated questions and answers based on it.

[0054] Step 5:

[0055] The terminal displays sales materials provided by the server. The user reviews the generated materials through the terminal and prepares for the sales negotiation. These materials are used to effectively advance the negotiation.

[0056] Step 6:

[0057] During a business negotiation, the server receives and analyzes user and customer interaction data in real time. Specifically, it analyzes the customer's facial expressions and reactions and provides a function to suggest the next action to take.

[0058] Step 7:

[0059] After the business meeting concludes, the server automatically generates a follow-up communication message and sends it to the user's device. The user reviews and edits the message before sending it to the customer.

[0060] Step 8:

[0061] The server periodically checks data from IoT devices to monitor sales representatives' stress levels and heart rates. If certain thresholds are exceeded, it sends a notification to the sales representative via their device prompting them to take a break or refresh themselves.

[0062] (Example 1)

[0063] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0064] Traditional sales support systems faced challenges such as the enormous time and effort required for collecting and analyzing company information, as well as the difficulty for sales representatives to respond appropriately to customer reactions during negotiations. Furthermore, follow-up after negotiations and management of the sales representatives' own well-being were often insufficient. To address these issues, there was a need for a system that could support sales activities more efficiently and effectively.

[0065] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0066] In this invention, the server includes a device for receiving company names, a device for automatically collecting information from publicly available data sources based on the company names, a device for analyzing the collected information and hypothesizing organizational challenges, and a device for automatically generating commercial proposals and negotiation materials using a generative AI model. This enables the efficient execution of a series of sales support processes, from information collection and analysis to proposal material creation, responding to real-time reactions during business negotiations, and even follow-up and sales representative condition management, thereby maximizing the effectiveness of sales activities.

[0067] A "device for receiving company names" is an interface that acquires company name information entered by the user and incorporates it into the system.

[0068] A "device that automatically collects information from publicly available data sources" is part of a system that has the function of acquiring data from publicly available information sources on the internet based on predetermined search criteria.

[0069] A "device for analyzing information and hypothesizing organizational challenges" is a program component that analyzes collected data to estimate potential challenges an organization may face.

[0070] A "device for automatically generating commercial proposals and negotiation materials using a generative AI model" is a system component that utilizes AI technology to automatically create proposal content and materials necessary for business negotiations.

[0071] A "device for analyzing customer response data in real time" is part of a system that provides the functionality to immediately process and analyze data based on customer behavior and statements acquired during business negotiations.

[0072] A "device for automatically generating follow-up communication messages" is a system component that has the function of automatically creating and sending necessary follow-up emails and notifications after a business negotiation.

[0073] The "device that monitors physical data and sends notifications to encourage refreshment" is part of a system that monitors the biometric information of sales representatives and generates and delivers notifications to encourage rest as needed.

[0074] This invention is a system designed to support a company's sales activities, with a server, terminals, and users working together. The server first receives the company name entered by the user from the terminal. Based on this company name, the server automatically collects relevant company information using publicly available data sources on the internet, such as company information APIs and SNS APIs. The software used includes web scraping tools and API clients.

[0075] Next, the server uses generative AI models and natural language processing algorithms to analyze the collected data. This allows it to hypothesize potential challenges facing the company. Once the analysis is complete, the server refers to the company's product database and generates commercial proposals based on the analysis results. In the process of generating these proposal documents, the generative AI model is used to automatically create sales materials and talk scripts.

[0076] As a concrete example, when a user enters the name of a new client company, relevant information is quickly collected through this system. For instance, by entering a prompt message into the server such as, "Based on the latest market trends and competitive landscape related to this company, please create a proposal document for the next new product," the necessary proposal document is automatically generated.

[0077] As described above, this system supports the sales process and enables users to conduct efficient sales activities.

[0078] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0079] Step 1:

[0080] The user enters the company name into the input field on the terminal. The terminal sends this company name to the server. At this stage, the input is the company name, and the output is the company name information transferred to the server. In particular, the user is required to accurately enter the name of the new customer company.

[0081] Step 2:

[0082] Based on the received company name, the server uses corporate information APIs and web scraping tools to collect relevant information from publicly available data sources on the internet. Input includes company name and search criteria, while output includes corporate-related information such as IR information, financial statements, stock prices, and press releases. The server collects information accurately and quickly.

[0083] Step 3:

[0084] The server analyzes collected company-related information using natural language processing algorithms and generative AI models to hypothesize potential challenges facing the organization. It performs data analysis on the input company information and generates outputs that identify potential challenges the company may be facing and their hypotheses. This analysis makes it possible to understand the company's current situation and formulate appropriate strategies.

[0085] Step 4:

[0086] To address the analyzed issues, the server references the company's product database and utilizes a generation AI model to automatically generate commercial proposal materials and documents necessary for negotiations. Inputs include hypothetical issues and database information, while outputs include negotiation materials, talk scripts, and anticipated questions and answers. Specifically, the server aims to generate easily understandable documents.

[0087] Step 5:

[0088] During business negotiations, sales representatives use terminals to view negotiation materials provided by the server and proceed with the negotiation. The server acquires and analyzes customer response data through the terminals during the negotiation. Real-time response data is used as input, and instructions and suggestions based on customer responses are provided to the sales representatives as output. The terminals provide an intuitive interface to facilitate operation by the sales representatives.

[0089] Step 6:

[0090] After a sales meeting concludes, the server automatically generates a follow-up communication message and sends it to the customer via the terminal. Input includes the details and outcome of the meeting, and output generates a follow-up email or message. Furthermore, the server monitors the sales representative's condition via IoT devices and sends notifications for rest as needed. This ensures that customer support is maintained effectively even after sales activities have ended.

[0091] (Application Example 1)

[0092] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0093] In modern sales activities, there is a demand for quick and effective proposals that meet customer interests and needs. However, traditional methods make it difficult to respond in real time and adjust proposals based on customer feedback, which hinders maximizing sales efficiency. Furthermore, there is a need to increase sales while reducing the burden on sales representatives.

[0094] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0095] In this invention, the server includes means for receiving corporate information, means for automatically collecting information from publicly available data sources, and means for analyzing customer interests. This makes it possible to quickly and accurately grasp customer needs in sales activities and adjust proposals in real time accordingly.

[0096] "Means for receiving corporate information" refers to a function for electronically acquiring information about a company.

[0097] "Means for automatically collecting information" refers to a function that automatically collects necessary information from specified data sources.

[0098] "Means of analyzing information" refers to the function of analyzing a company's current situation and potential problems based on the collected information.

[0099] "Means for generating commercial proposals" refers to a function that automatically creates appropriate commercial strategies tailored to a company's challenges.

[0100] "Means for creating materials for business negotiations" refers to the function of preparing the necessary materials for business negotiations based on the generated commercial proposal.

[0101] "Means for analyzing customer interests" refers to a function that evaluates and analyzes customer interests and concerns based on customer response data.

[0102] "A means of adjusting proposals in real time" refers to a function that allows for the rapid adjustment and modification of commercial proposals in response to customer reactions during business negotiations.

[0103] This invention relates to an AI-powered sales support system that enables efficient and effective sales activities. At the heart of the system is a server that first receives company information and automatically collects detailed information about target companies from publicly available data sources. The collected information is analyzed using an AI model to hypothesize the company's challenges and potential needs.

[0104] Next, the server automatically generates a commercial proposal and, based on this proposal, prepares materials for the business negotiation and answers to anticipated customer questions. Furthermore, it acquires customer response data through the terminal during the negotiation and analyzes it in real time to evaluate and analyze customer interest. Based on this evaluation, the server can adjust the content of the negotiation in real time and provide the optimal proposal.

[0105] To perform this processing, the server utilizes AI technologies such as TENSORFLOW®, OpenCV, and Google® Cloud AI, and employs Google Speech-to-Text API as a speech recognition library, and Microsoft® Azure® Face API for facial recognition and sentiment analysis. For example, a robot could visit a home and propose smart home devices tailored to the lifestyle of the residents. This approach is expected to accurately meet customer needs and maximize the effectiveness of sales activities.

[0106] Examples of prompts for a generative AI model:

[0107] "Please generate a proposal document for smart home devices that the customer is likely to be interested in. His facial expression suggests he may be interested."

[0108] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0109] Step 1:

[0110] The server receives company information from the user. Based on the received company name, it automatically collects information from publicly available data sources. At this stage, the input is the company name, and the server uses Web APIs and scraping techniques to collect data such as IR information, financial statements, and stock price trends, and stores them in the database. The output is the collected company-related information.

[0111] Step 2:

[0112] The server uses an AI model to analyze the collected information. The input is company information collected in the previous step, and the AI ​​model (e.g., TensorFlow) is used to analyze data patterns and extract potential challenges and needs of the company. The output is a list of assumed company challenges and needs.

[0113] Step 3:

[0114] The server generates commercial proposals based on problems assumed by the AI ​​model. The input is a company's challenges and needs, which are then compared with the company's product database to create the most suitable proposal document. This proposal document includes information on specific products and services. The output is a commercial proposal document.

[0115] Step 4:

[0116] The user initiates a business negotiation with a customer and displays negotiation materials through their terminal. The server acquires the customer's voice and facial expression data during the negotiation and performs real-time analysis. The input is customer reaction data (voice and video), and based on this, the customer's emotional state is analyzed using an AI model (e.g., OpenCV, Microsoft Azure Face API). The output is the customer's interest and emotional state.

[0117] Step 5:

[0118] The server adjusts commercial proposals based on the customer's emotional state, which is acquired in real time. The input is the customer's emotional state, and a generative AI model (including the creation of prompts) is used to optimize the content and order of proposals and adjust the response during the sales negotiation. The output is the adjusted commercial proposal.

[0119] Step 6:

[0120] After a business meeting concludes, the server summarizes the meeting content and generates a follow-up message. The input is the meeting log data; natural language generation technology is used to create a summary and next contact information, preparing it for transmission. The output is the follow-up message.

[0121] Step 7:

[0122] The server periodically monitors the physical data of sales representatives to check their condition. Input is physical data obtained from IoT devices (e.g., heart rate, stress level). Based on the analysis results, it sends notifications to the terminal prompting refreshment as needed. Output is the refreshment notification.

[0123] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0124] This invention combines an AI-powered sales support system with an emotion engine, aiming to efficiently utilize company information and make sales activities more effective. In this system, the user inputs the company name via a terminal, and the server automatically collects relevant data from multiple sources on the internet based on that company information.

[0125] The collected data is analyzed on a server to hypothesize potential challenges for the company. The server then compares the analysis results with the company's product database to generate optimal commercial proposals. Based on these proposals, sales materials, talk scripts, and anticipated questions and answers are automatically created and delivered to the terminal.

[0126] During a business negotiation, the server uses an emotion engine to recognize the user's emotions in real time and optimize the commercial proposal. This engine analyzes the user's emotional state from voice and facial expression data and dynamically modifies the proposal according to the progress of the negotiation. This allows the user to flexibly adapt to the flow of the negotiation.

[0127] Furthermore, customer response data acquired during negotiations is analyzed in real time, and the system also has a function to suggest the next action based on the analysis results. This allows users to accurately grasp the customer's interests and reactions during negotiations and conduct effective negotiations.

[0128] After a sales meeting, the server automatically generates a follow-up communication message and sends it to the user's device. The user can then review and edit this message before sending it to the customer. This process allows for quick and efficient follow-up after a sales meeting has concluded.

[0129] Furthermore, the server monitors the sales representatives' physical data via IoT devices and sends notifications to their terminals prompting them to refresh themselves as needed. It also has a function that suggests taking breaks at appropriate times based on data such as stress levels and heart rate.

[0130] For example, when a user prepares for a business meeting with a new customer, "Ryuutsu Co., Ltd.," this system allows for the rapid collection of extensive information in advance, and the automatic generation of business meeting materials based on the analysis results. During the meeting, emotional data is acquired in real time, and commercial proposals are adjusted accordingly, enabling the user to respond flexibly to the customer's needs. In this way, the system of the present invention supports all stages of sales activities and improves operational efficiency.

[0131] The following describes the processing flow.

[0132] Step 1:

[0133] The user enters the name of the company they are negotiating with via their terminal. The terminal then sends the entered company name to the server.

[0134] Step 2:

[0135] Based on the received company name, the server automatically collects relevant information from various data sources on the internet (e.g., IR information, financial reports, stock price information, press releases, etc.). The server then stores the collected data in a database.

[0136] Step 3:

[0137] The server analyzes the accumulated data and performs an analysis to identify the company's challenges. Based on the results of this analysis, it hypothesizes the potential challenges the company faces.

[0138] Step 4:

[0139] The server, as a solution to the assumed problem, refers to the company's product database and generates the optimal commercial proposal. Based on this, it automatically generates sales materials, talk scripts, and anticipated questions and answers.

[0140] Step 5:

[0141] The terminal receives sales materials sent from the server and displays them to the user. The user reviews these materials and prepares for the sales meeting.

[0142] Step 6:

[0143] During business negotiations, the server uses an emotion engine to recognize the user's emotions in real time. It analyzes the user's voice and facial expression data and dynamically adjusts commercial proposals according to their emotional state.

[0144] Step 7:

[0145] The server analyzes customer response data acquired during the sales negotiation and, if necessary, suggests the next action to take for the user during the negotiation.

[0146] Step 8:

[0147] After the business meeting concludes, the server automatically generates a follow-up message and sends it to the user's device. The user then reviews and edits this message, preparing to send it to the customer.

[0148] Step 9:

[0149] The server monitors the sales representative's physical data from IoT devices and sends notifications to the devices prompting them to refresh themselves, depending on their stress levels and other conditions. Users receive these notifications and take breaks at appropriate times.

[0150] (Example 2)

[0151] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0152] In today's sales environment, competition is fierce, and there is a demand for efficient and effective proposals that meet customer needs. However, traditional methods require a great deal of time and effort for information gathering and proposal preparation, and real-time responses during negotiations are difficult. Furthermore, managing the stress levels of sales representatives is also a crucial issue.

[0153] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0154] In this invention, the server includes a device for receiving information, a device for automatically collecting data from multiple publicly available information sources based on the information, and a device for analyzing the collected data to hypothesize potential challenges for the company. This enables sales representatives to quickly gain a deep understanding of customers, make appropriate proposals, respond flexibly to customer reactions during negotiations, improve work efficiency, and manage stress.

[0155] A "device for receiving information" is a device that receives data entered by a user and begins processing it.

[0156] "Publicly available information sources" refer to a collection of data that is made available to anyone on the internet or other accessible media.

[0157] A "device that automatically collects data" is a device that automatically retrieves relevant information from the internet or databases based on specified conditions.

[0158] A "device that analyzes collected data to hypothesize potential challenges for a company" is a device that uses acquired data to predict future problems and areas for improvement in a specific company.

[0159] A "business proposal generation device" is a device that automatically creates proposals as solutions to a company's challenges based on analysis results.

[0160] A "device for creating materials for business negotiations and anticipated questions and answers" is a device for preparing materials and question-and-answer sets that will be useful during business negotiations based on a proposal.

[0161] A "device that recognizes emotions in real time from voice and facial expression data and dynamically optimizes proposals" is a device that analyzes the user's voice and facial expressions to understand their emotions and adjusts the proposal for the business negotiation in real time based on the results.

[0162] A "device for analyzing customer reaction data in real time" is a device that instantly processes customer behavior and speech data acquired during business negotiations and obtains evaluation results.

[0163] A "device that automatically generates follow-up communication messages" is a device that automatically creates messages to build appropriate ongoing relationships after a business negotiation.

[0164] A "device that monitors biometric data and sends notifications to encourage refreshment based on stress indicators" is a device that constantly monitors the health status and stress levels of sales representatives and sends notifications to suggest rest as needed.

[0165] This invention is a sales support system that efficiently collects and analyzes corporate information from multiple sources and automatically generates commercial proposals based on that information. The system integrates functions for information reception, data collection, data analysis, commercial proposal generation, document creation, sentiment recognition, action suggestions, follow-up message generation, and biometric data monitoring.

[0166] The user first enters the name of a company they are interested in using a terminal. This terminal functions as an interface and sends data to the server as an information receiving device. The server is connected to the internet via a network interface and automatically collects relevant data from public sources such as news sites and industry databases using RESTful APIs and scraping techniques.

[0167] The server then analyzes the collected data using natural language processing (NLP) techniques. Open-source language analysis libraries (e.g., NLTK, spaCy) and sentiment analysis tools are used for this analysis. Based on the analysis results, the server hypothesizes hidden challenges within the company and generates commercial proposals using data mining algorithms (e.g., Scikit-learn).

[0168] Based on the generated proposal, the server automatically creates materials for the business negotiation, as well as anticipated questions and their answers, using a document generation tool (e.g., LaTeX, Docx), and sends them to the terminal. The user can then prepare for the negotiation based on these materials.

[0169] During business negotiations, the server utilizes an emotion engine to analyze the user's emotions from their voice and facial expression data. Automatic speech recognition (ASR) software (e.g., Google Speech-to-Text) is used for speech recognition, and facial recognition technology (e.g., OpenCV) is used for facial expression analysis. Based on the analysis results, proposals are optimized in real time, and actions are suggested to streamline the business negotiation process.

[0170] After a business meeting, the server generates follow-up communication messages and displays them on the end user's device in an editable format. Furthermore, the server works with wearable devices to collect biometric data, such as the sales representative's stress level and heart rate. Based on the collected data, it issues notifications encouraging refreshment and breaks, supporting improved work efficiency and health management.

[0171] As a concrete example, the system quickly and accurately generates proposals based on an input prompt such as, "Create a sales proposal for a new customer. Customize the proposal based on the latest company information and market trends," thereby supporting the user's sales activities.

[0172] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0173] Step 1:

[0174] The user enters the name of a company they are interested in via their device. The entered company name is sent to the server and functions as data that triggers information collection. This step prepares the necessary keywords to initiate information collection.

[0175] Step 2:

[0176] The server automatically collects relevant data from publicly available information sources on the internet using RESTful APIs and scraping techniques, based on the entered company name. Specifically, it retrieves the latest company trends, related news, and financial status from news sites and industry databases. The collected data is stored on the server in its raw state and becomes input data for the next analysis step.

[0177] Step 3:

[0178] The server analyzes the collected data using natural language processing techniques. The analysis utilizes open-source language analysis libraries to perform sentiment analysis, keyword extraction, and trend change detection on text data. This reveals market sentiment and potential challenges relevant to the company. The analyzed data is used as foundational information for generating commercial proposals.

[0179] Step 4:

[0180] The server automatically generates commercial proposals using an AI model based on the analysis results. The model proposes solutions to the assumed company's challenges and documents them. This process creates concrete proposals that serve as guidelines for sales negotiations provided by the user. The generated proposals are used as input data for creating sales negotiation materials.

[0181] Step 5:

[0182] The server automatically generates sales materials and anticipated questions and answers based on the generated commercial proposal using a document generation tool. These materials contain information that should be used during sales negotiations and serve as support materials to help users efficiently conduct negotiations. The generated materials are sent to the terminal and made available for the user to review.

[0183] Step 6:

[0184] During a business negotiation, the server uses speech recognition and facial expression analysis technologies to analyze the user's emotions in real time. The acquired emotional data is immediately reflected as a factor influencing the adjustment of the proposal and the flow of the negotiation. This enables dynamic feedback on the progress of the negotiation.

[0185] Step 7:

[0186] The server analyzes customer response data acquired during sales negotiations in real time and suggests appropriate actions to the user based on that analysis. This allows the user to take a flexible approach tailored to the progress of the negotiation. It serves as a basis for decision-making to maximize the outcome of the negotiation.

[0187] Step 8:

[0188] After a sales meeting, the server automatically generates a follow-up communication message and sends it to the terminal. The user can then review, edit, and quickly send this message to the customer. This process enables efficient post-sales follow-up.

[0189] Step 9:

[0190] The server monitors the sales representative's biometric data through a wearable device and sends notifications to the device prompting them to refresh themselves as needed, based on stress indicators. This supports the sales representative in continuing their work while taking their health into consideration.

[0191] (Application Example 2)

[0192] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0193] In today's business environment, security consultants and sales representatives are required to provide timely and accurate information during interactions with clients. However, collecting and analyzing useful data from vast amounts of information is time-consuming and laborious, and making appropriate proposals that take into account human emotions is a difficult challenge. Furthermore, efficient and effective processes are needed for follow-up after meetings and health management of the representatives.

[0194] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0195] In this invention, the server includes a device for receiving corporate information, a device for automatically collecting data from publicly available information sources based on the corporate information, and a device for analyzing the collected data and hypothesizing the company's challenges. This makes it possible to quickly grasp the company's security challenges and make appropriate proposals. Furthermore, by providing means for analyzing the user's emotional state during the interview and dynamically adjusting the content of the proposal, the quality of the response can be improved. In addition, by automatically generating follow-up communication messages after the interview and monitoring the user's biometric data, efficient follow-up and health management can be achieved.

[0196] "Corporate information" refers to data related to a specific corporation or organization, including its performance, social reputation, and the products and services it provides.

[0197] A "device" is a system that combines hardware and software for performing information processing.

[0198] "Publicly available information sources" refer to databases and media that are publicly accessible via the internet or other means.

[0199] "Data" refers to a unit of information that is collected and analyzed for a specific purpose.

[0200] "Challenges" refer to problems or challenges that a company or organization must overcome.

[0201] A "business proposal" refers to a recommended action plan or solution for improving specific business benefits.

[0202] "Dialogue" refers to the communication process that takes place between two or more parties.

[0203] "Materials" refers to documents or digital content provided for a specific purpose.

[0204] "Emotional state" refers to the state of emotions and moods experienced within a person's mind.

[0205] The system implementing this invention consists of multiple devices and software components. The server first receives company information entered by the user and automatically collects data from publicly available sources related to that company. This includes publicly available databases on the internet, news sites, etc. The server uses AWS® SageMaker to analyze the collected data and hypothesize the company's challenges.

[0206] Next, the system generates optimal business proposals for the given challenges. To generate these proposals, the server compares the analysis results with the company's database and optimizes the proposals using TensorFlow. Based on these proposals, interactive materials, anticipated problems, and example answers are created. An automated script written in Python is used to generate these materials.

[0207] During the interview, the system uses Azure Cognitive Services to analyze the user's voice and facial expressions in real time to determine their emotional state. Based on this analysis, the server dynamically adjusts the suggestions to improve the quality of the conversation. Optimized information is displayed on the user's device in real time.

[0208] After the interview, the server automatically generates a follow-up communication message and sends it to the user's device. This message is customizable by the user and ready to be sent. In addition, the system monitors the user's biometric data through IoT devices and suggests breaks based on stress levels and heart rate.

[0209] For example, when a user prepares for a meeting with a new financial institution client, the latest information on specific financial risks is automatically collected and analyzed. During the meeting, the proposal is dynamically adjusted in response to the client's reactions, resulting in more effective communication.

[0210] An example of an input prompt statement for a generative AI model is as follows:

[0211] "Gather up-to-date information on security risks for specific clients and begin data analysis to propose appropriate solutions."

[0212] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0213] Step 1:

[0214] The server receives company information from the user. Based on this entered company information, the server automatically collects relevant data from publicly available data sources on the internet (e.g., databases, news sites). The collected data is stored on the server as primary data for analysis.

[0215] Step 2:

[0216] The server uses AWS SageMaker to analyze the collected primary data. The analysis process applies machine learning algorithms to the data to extract potential challenges the company may face. Based on these analysis results, hypotheses about the challenges are formulated. The output of this process is a list of specific challenges.

[0217] Step 3:

[0218] The server uses TensorFlow to generate optimal business proposals by matching the analyzed list of issues with the company's internal database. Here, it executes a data matching algorithm to optimize the proposal content to suit the company's needs. The output of this step is an optimized proposal document and supporting materials.

[0219] Step 4:

[0220] During the interview, the user's voice and facial expression data are collected in real time via Azure Cognitive Services on the user's device. Based on this data, the server analyzes the user's emotional state and dynamically adjusts the proposed content based on the analysis results. The output is information about the adjusted proposal.

[0221] Step 5:

[0222] After the interview, the server automatically generates a follow-up communication message using the analyzed information. The user receives this message and can customize its content. The generated message is designed to facilitate the next action.

[0223] Step 6:

[0224] The user's biometric data is sent to a server via an IoT device. The server analyzes this data and monitors stress levels and heart rate. If necessary, it sends a notification to the user's device suggesting a break. The output is a refresh suggestion at the appropriate time.

[0225] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0226] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0227] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0228] [Second Embodiment]

[0229] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0230] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0231] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0232] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0233] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0234] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0235] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0236] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0237] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0238] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0239] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0240] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0241] This invention is an AI-powered sales support system that efficiently supports sales activities by utilizing company information. When a user enters a company name via a terminal, the system automatically collects information related to that company from various publicly available data sources. This information includes investor relations (IR) information, financial results, stock price trends, press releases, and information from official websites and social media.

[0242] Next, the server analyzes the collected information and hypothesizes potential challenges for the company. Based on this analysis, the server refers to its own product database and generates appropriate commercial proposals. These proposals include materials for sales meetings, talk scripts, and anticipated questions and answers, all of which are automatically generated by the server.

[0243] Furthermore, the system supports the sales process by displaying materials provided from the server via the terminal when sales representatives conduct business negotiations. During negotiations, the server can acquire and analyze customer response data in real time. Based on this analysis, it also has a function to suggest appropriate actions to take during the negotiation. This enables immediate reactions and appropriate actions in the field of sales.

[0244] Furthermore, after the business meeting concludes, the server automatically generates a follow-up communication message. This message includes information about the next contact and a summary of the meeting's content, and is sent directly to the customer from the terminal.

[0245] This system also has a function to monitor the condition of sales representatives. The server periodically receives physical data from IoT devices and, depending on their stress levels and heart rate, can send notifications via the device prompting them to take a break if they need to refresh themselves.

[0246] As a concrete example, when a sales representative is conducting business negotiations with a new company, "XYZ Corporation," using this system allows for the rapid collection and analysis of a vast amount of information related to XYZ Corporation in advance, automatically generating an optimal proposal document based on the results. During the negotiation, the system guides the sales representative to respond appropriately while observing the customer's reactions, enabling them to work efficiently and effectively.

[0247] Thus, the present invention contributes to improving the efficiency of sales activities and reducing the burden on sales representatives.

[0248] The following describes the processing flow.

[0249] Step 1:

[0250] The user uses their device to enter the name of the target company. The device then sends the entered company name to the server.

[0251] Step 2:

[0252] Based on the company name it receives, the server collects relevant information from data sources on the internet. Specifically, it starts the process of acquiring data from IR information, financial statements, stock price trends, press releases, the company's official website, and social media.

[0253] Step 3:

[0254] The server analyzes the collected data. Using information analysis algorithms, it identifies the company's current situation and potential challenges. Based on the analysis results, it hypothesizes the challenges the target company may be facing.

[0255] Step 4:

[0256] The server references its product database to generate the optimal commercial proposal for the assumed problem. It then concretizes the proposal and automatically generates sales materials, talk scripts, and anticipated questions and answers based on it.

[0257] Step 5:

[0258] The terminal displays sales materials provided by the server. The user reviews the generated materials through the terminal and prepares for the sales negotiation. These materials are used to effectively advance the negotiation.

[0259] Step 6:

[0260] During a business negotiation, the server receives and analyzes user and customer interaction data in real time. Specifically, it analyzes the customer's facial expressions and reactions and provides a function to suggest the next action to take.

[0261] Step 7:

[0262] After the business meeting concludes, the server automatically generates a follow-up communication message and sends it to the user's device. The user reviews and edits the message before sending it to the customer.

[0263] Step 8:

[0264] The server periodically checks data from IoT devices to monitor sales representatives' stress levels and heart rates. If certain thresholds are exceeded, it sends a notification to the sales representative via their device prompting them to take a break or refresh themselves.

[0265] (Example 1)

[0266] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0267] Traditional sales support systems faced challenges such as the enormous time and effort required for collecting and analyzing company information, as well as the difficulty for sales representatives to respond appropriately to customer reactions during negotiations. Furthermore, follow-up after negotiations and management of the sales representatives' own well-being were often insufficient. To address these issues, there was a need for a system that could support sales activities more efficiently and effectively.

[0268] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0269] In this invention, the server includes a device for receiving company names, a device for automatically collecting information from publicly available data sources based on the company names, a device for analyzing the collected information and hypothesizing organizational challenges, and a device for automatically generating commercial proposals and negotiation materials using a generative AI model. This enables the efficient execution of a series of sales support processes, from information collection and analysis to proposal material creation, responding to real-time reactions during business negotiations, and even follow-up and sales representative condition management, thereby maximizing the effectiveness of sales activities.

[0270] A "device for receiving company names" is an interface that acquires company name information entered by the user and incorporates it into the system.

[0271] A "device that automatically collects information from publicly available data sources" is part of a system that has the function of acquiring data from publicly available information sources on the internet based on predetermined search criteria.

[0272] A "device for analyzing information and hypothesizing organizational challenges" is a program component that analyzes collected data to estimate potential challenges an organization may face.

[0273] A "device for automatically generating commercial proposals and negotiation materials using a generative AI model" is a system component that utilizes AI technology to automatically create proposal content and materials necessary for business negotiations.

[0274] A "device for analyzing customer response data in real time" is part of a system that provides the functionality to immediately process and analyze data based on customer behavior and statements acquired during business negotiations.

[0275] A "device for automatically generating follow-up communication messages" is a system component that has the function of automatically creating and sending necessary follow-up emails and notifications after a business negotiation.

[0276] The "device that monitors physical data and sends notifications to encourage refreshment" is part of a system that monitors the biometric information of sales representatives and generates and delivers notifications to encourage rest as needed.

[0277] This invention is a system designed to support a company's sales activities, with a server, terminals, and users working together. The server first receives the company name entered by the user from the terminal. Based on this company name, the server automatically collects relevant company information using publicly available data sources on the internet, such as company information APIs and SNS APIs. The software used includes web scraping tools and API clients.

[0278] Next, the server uses generative AI models and natural language processing algorithms to analyze the collected data. This allows it to hypothesize potential challenges facing the company. Once the analysis is complete, the server refers to the company's product database and generates commercial proposals based on the analysis results. In the process of generating these proposal documents, the generative AI model is used to automatically create sales materials and talk scripts.

[0279] As a concrete example, when a user enters the name of a new client company, relevant information is quickly collected through this system. For instance, by entering a prompt message into the server such as, "Based on the latest market trends and competitive landscape related to this company, please create a proposal document for the next new product," the necessary proposal document is automatically generated.

[0280] As described above, this system supports the sales process and enables users to conduct efficient sales activities.

[0281] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0282] Step 1:

[0283] The user enters the company name in the input field of the terminal. The terminal sends this company name to the server. At this stage, the input is the company name, and the output is the company name information transferred to the server. In particular, the user is required to accurately enter the name of the new customer company.

[0284] Step 2:

[0285] Based on the received company name, the server uses corporate information APIs and web scraping tools to collect relevant information from public data sources on the Internet. The inputs are the company name and search conditions, and the outputs are corporate-related information such as IR information, financial statements, stock price information, and press releases. The server conducts accurate and rapid information collection.

[0286] Step 3:

[0287] Based on the collected corporate-related information, the server analyzes the data by leveraging natural language processing algorithms and generative AI models to hypothesize potential issues of the organization. Data analysis is performed on the input corporate information, and the outputs are the potential issues the company may face and their hypotheses. Through this analysis, it becomes possible to formulate appropriate strategies after understanding the current situation of the company.

[0288] Step 4:

[0289] To address the analyzed issues, the server refers to its own product database and utilizes a generative AI model to automatically generate business proposal materials and documents necessary for negotiations. The inputs are the hypotheses of the issues and database information, and the outputs include negotiation materials, talk scripts, and assumed questions and answers. As a specific operation, the server aims to generate materials that are easy to understand.

[0290] Step 5:

[0291] During business negotiations, sales representatives use terminals to view negotiation materials provided by the server and proceed with the negotiation. The server acquires and analyzes customer response data through the terminals during the negotiation. Real-time response data is used as input, and instructions and suggestions based on customer responses are provided to the sales representatives as output. The terminals provide an intuitive interface to facilitate operation by the sales representatives.

[0292] Step 6:

[0293] After a sales meeting concludes, the server automatically generates a follow-up communication message and sends it to the customer via the terminal. Input includes the details and outcome of the meeting, and output generates a follow-up email or message. Furthermore, the server monitors the sales representative's condition via IoT devices and sends notifications for rest as needed. This ensures that customer support is maintained effectively even after sales activities have ended.

[0294] (Application Example 1)

[0295] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0296] In modern sales activities, there is a demand for quick and effective proposals that meet customer interests and needs. However, traditional methods make it difficult to respond in real time and adjust proposals based on customer feedback, which hinders maximizing sales efficiency. Furthermore, there is a need to increase sales while reducing the burden on sales representatives.

[0297] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0298] In this invention, the server includes means for receiving corporate information, means for automatically collecting information from publicly available data sources, and means for analyzing customer interests. This makes it possible to quickly and accurately grasp customer needs in sales activities and adjust proposals in real time accordingly.

[0299] "Means for receiving corporate information" refers to a function for electronically acquiring information about a company.

[0300] "Means for automatically collecting information" refers to a function that automatically collects necessary information from specified data sources.

[0301] "Means of analyzing information" refers to the function of analyzing a company's current situation and potential problems based on the collected information.

[0302] "Means for generating commercial proposals" refers to a function that automatically creates appropriate commercial strategies tailored to a company's challenges.

[0303] "Means for creating materials for business negotiations" refers to the function of preparing the necessary materials for business negotiations based on the generated commercial proposal.

[0304] "Means for analyzing customer interests" refers to a function that evaluates and analyzes customer interests and concerns based on customer response data.

[0305] "A means of adjusting proposals in real time" refers to a function that allows for the rapid adjustment and modification of commercial proposals in response to customer reactions during business negotiations.

[0306] This invention relates to an AI-powered sales support system that enables efficient and effective sales activities. At the heart of the system is a server that first receives company information and automatically collects detailed information about target companies from publicly available data sources. The collected information is analyzed using an AI model to hypothesize the company's challenges and potential needs.

[0307] Next, the server automatically generates a commercial proposal and prepares negotiation materials and answers to assumed questions from customers based on this proposal. Also, during the negotiation through the terminal, the server acquires the customer's reaction data and evaluates and analyzes the customer's interests by analyzing this data in real time. Based on this evaluation, the server can adjust the content of the negotiation in real time and provide an optimal proposal.

[0308] To implement such processing, the server utilizes AI technologies such as TensorFlow, OpenCV, and Google Cloud AI, and uses Google Speech-to-Text API as a speech recognition library, Microsoft Azure Face API for face recognition and sentiment analysis, etc. As a specific example, it is possible for a robot to visit a home and propose smart home devices suitable for the lifestyle of the people living there. With this approach, it is expected that the customer's needs can be accurately met and the effectiveness of sales activities can be maximized.

[0309] Examples of prompt sentences for the generative AI model:

[0310] "Please generate proposal materials for smart home devices that may indicate the customer's interest. It is judged that he may show interest from his expression."

[0311] The flow of specific processing in Application Example 1 will be described using FIG. 12.

[0312] Step 1:

[0313] The server receives company information from the user. Based on the received company name, it automatically collects information from publicly available data sources. The input at this stage is the company name, and the server uses Web API and scraping technologies to collect data such as IR information, financial statement information, and stock price trends, and stores it in the database. The output is the collected company-related information.

[0314] Step 2:

[0315] The server uses an AI model to analyze the collected information. The input is company information collected in the previous step, and the AI ​​model (e.g., TensorFlow) is used to analyze data patterns and extract potential challenges and needs of the company. The output is a list of assumed company challenges and needs.

[0316] Step 3:

[0317] The server generates commercial proposals based on problems assumed by the AI ​​model. The input is a company's challenges and needs, which are then compared with the company's product database to create the most suitable proposal document. This proposal document includes information on specific products and services. The output is a commercial proposal document.

[0318] Step 4:

[0319] The user initiates a business negotiation with a customer and displays negotiation materials through their terminal. The server acquires the customer's voice and facial expression data during the negotiation and performs real-time analysis. The input is customer reaction data (voice and video), and based on this, the customer's emotional state is analyzed using an AI model (e.g., OpenCV, Microsoft Azure Face API). The output is the customer's interest and emotional state.

[0320] Step 5:

[0321] The server adjusts commercial proposals based on the customer's emotional state, which is acquired in real time. The input is the customer's emotional state, and a generative AI model (including the creation of prompts) is used to optimize the content and order of proposals and adjust the response during the sales negotiation. The output is the adjusted commercial proposal.

[0322] Step 6:

[0323] After a business meeting concludes, the server summarizes the meeting content and generates a follow-up message. The input is the meeting log data; natural language generation technology is used to create a summary and next contact information, preparing it for transmission. The output is the follow-up message.

[0324] Step 7:

[0325] The server periodically monitors the physical data of sales representatives to check their condition. Input is physical data obtained from IoT devices (e.g., heart rate, stress level). Based on the analysis results, it sends notifications to the terminal prompting refreshment as needed. Output is the refreshment notification.

[0326] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0327] This invention combines an AI-powered sales support system with an emotion engine, aiming to efficiently utilize company information and make sales activities more effective. In this system, the user inputs the company name via a terminal, and the server automatically collects relevant data from multiple sources on the internet based on that company information.

[0328] The collected data is analyzed on a server to hypothesize potential challenges for the company. The server then compares the analysis results with the company's product database to generate optimal commercial proposals. Based on these proposals, sales materials, talk scripts, and anticipated questions and answers are automatically created and delivered to the terminal.

[0329] During a business negotiation, the server uses an emotion engine to recognize the user's emotions in real time and optimize the commercial proposal. This engine analyzes the user's emotional state from voice and facial expression data and dynamically modifies the proposal according to the progress of the negotiation. This allows the user to flexibly adapt to the flow of the negotiation.

[0330] Furthermore, customer response data acquired during negotiations is analyzed in real time, and the system also has a function to suggest the next action based on the analysis results. This allows users to accurately grasp the customer's interests and reactions during negotiations and conduct effective negotiations.

[0331] After a sales meeting, the server automatically generates a follow-up communication message and sends it to the user's device. The user can then review and edit this message before sending it to the customer. This process allows for quick and efficient follow-up after a sales meeting has concluded.

[0332] Furthermore, the server monitors the sales representatives' physical data via IoT devices and sends notifications to their terminals prompting them to refresh themselves as needed. It also has a function that suggests taking breaks at appropriate times based on data such as stress levels and heart rate.

[0333] For example, when a user prepares for a business meeting with a new customer, "Ryuutsu Co., Ltd.," this system allows for the rapid collection of extensive information in advance, and the automatic generation of business meeting materials based on the analysis results. During the meeting, emotional data is acquired in real time, and commercial proposals are adjusted accordingly, enabling the user to respond flexibly to the customer's needs. In this way, the system of the present invention supports all stages of sales activities and improves operational efficiency.

[0334] The following describes the processing flow.

[0335] Step 1:

[0336] The user enters the name of the company they are negotiating with via their terminal. The terminal then sends the entered company name to the server.

[0337] Step 2:

[0338] Based on the received company name, the server automatically collects relevant information from various data sources on the internet (e.g., IR information, financial reports, stock price information, press releases, etc.). The server then stores the collected data in a database.

[0339] Step 3:

[0340] The server analyzes the accumulated data and performs an analysis to identify the company's challenges. Based on the results of this analysis, it hypothesizes the potential challenges the company faces.

[0341] Step 4:

[0342] The server, as a solution to the assumed problem, refers to the company's product database and generates the optimal commercial proposal. Based on this, it automatically generates sales materials, talk scripts, and anticipated questions and answers.

[0343] Step 5:

[0344] The terminal receives sales materials sent from the server and displays them to the user. The user reviews these materials and prepares for the sales meeting.

[0345] Step 6:

[0346] During business negotiations, the server uses an emotion engine to recognize the user's emotions in real time. It analyzes the user's voice and facial expression data and dynamically adjusts commercial proposals according to their emotional state.

[0347] Step 7:

[0348] The server analyzes customer response data acquired during the sales negotiation and, if necessary, suggests the next action to take for the user during the negotiation.

[0349] Step 8:

[0350] After the business meeting concludes, the server automatically generates a follow-up message and sends it to the user's device. The user then reviews and edits this message, preparing to send it to the customer.

[0351] Step 9:

[0352] The server monitors the sales representative's physical data from IoT devices and sends notifications to the devices prompting them to refresh themselves, depending on their stress levels and other conditions. Users receive these notifications and take breaks at appropriate times.

[0353] (Example 2)

[0354] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0355] In today's sales environment, competition is fierce, and there is a demand for efficient and effective proposals that meet customer needs. However, traditional methods require a great deal of time and effort for information gathering and proposal preparation, and real-time responses during negotiations are difficult. Furthermore, managing the stress levels of sales representatives is also a crucial issue.

[0356] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0357] In this invention, the server includes a device for receiving information, a device for automatically collecting data from multiple publicly available information sources based on the information, and a device for analyzing the collected data to hypothesize potential challenges for the company. This enables sales representatives to quickly gain a deep understanding of customers, make appropriate proposals, respond flexibly to customer reactions during negotiations, improve work efficiency, and manage stress.

[0358] A "device for receiving information" is a device that receives data entered by a user and begins processing it.

[0359] "Publicly available information sources" refer to a collection of data that is made available to anyone on the internet or other accessible media.

[0360] A "device that automatically collects data" is a device that automatically retrieves relevant information from the internet or databases based on specified conditions.

[0361] A "device that analyzes collected data to hypothesize potential challenges for a company" is a device that uses acquired data to predict future problems and areas for improvement in a specific company.

[0362] A "business proposal generation device" is a device that automatically creates proposals as solutions to a company's challenges based on analysis results.

[0363] A "device for creating materials for business negotiations and anticipated questions and answers" is a device for preparing materials and question-and-answer sets that will be useful during business negotiations based on a proposal.

[0364] A "device that recognizes emotions in real time from voice and facial expression data and dynamically optimizes proposals" is a device that analyzes the user's voice and facial expressions to understand their emotions and adjusts the proposal for the business negotiation in real time based on the results.

[0365] A "device for analyzing customer reaction data in real time" is a device that instantly processes customer behavior and speech data acquired during business negotiations and obtains evaluation results.

[0366] A "device that automatically generates follow-up communication messages" is a device that automatically creates messages to build appropriate ongoing relationships after a business negotiation.

[0367] A "device that monitors biometric data and sends notifications to encourage refreshment based on stress indicators" is a device that constantly monitors the health status and stress levels of sales representatives and sends notifications to suggest rest as needed.

[0368] This invention is a sales support system that efficiently collects and analyzes corporate information from multiple sources and automatically generates commercial proposals based on that information. The system integrates functions for information reception, data collection, data analysis, commercial proposal generation, document creation, sentiment recognition, action suggestions, follow-up message generation, and biometric data monitoring.

[0369] The user first enters the name of a company they are interested in using a terminal. This terminal functions as an interface and sends data to the server as an information receiving device. The server is connected to the internet via a network interface and automatically collects relevant data from public sources such as news sites and industry databases using RESTful APIs and scraping techniques.

[0370] The server then analyzes the collected data using natural language processing (NLP) techniques. Open-source language analysis libraries (e.g., NLTK, spaCy) and sentiment analysis tools are used for this analysis. Based on the analysis results, the server hypothesizes hidden challenges within the company and generates commercial proposals using data mining algorithms (e.g., Scikit-learn).

[0371] Based on the generated proposal, the server automatically creates materials for the business negotiation, as well as anticipated questions and their answers, using a document generation tool (e.g., LaTeX, Docx), and sends them to the terminal. The user can then prepare for the negotiation based on these materials.

[0372] During business negotiations, the server utilizes an emotion engine to analyze the user's emotions from their voice and facial expression data. Automatic speech recognition (ASR) software (e.g., Google Speech-to-Text) is used for speech recognition, and facial recognition technology (e.g., OpenCV) is used for facial expression analysis. Based on the analysis results, proposals are optimized in real time, and actions are suggested to streamline the business negotiation process.

[0373] After a business meeting, the server generates follow-up communication messages and displays them on the end user's device in an editable format. Furthermore, the server works with wearable devices to collect biometric data, such as the sales representative's stress level and heart rate. Based on the collected data, it issues notifications encouraging refreshment and breaks, supporting improved work efficiency and health management.

[0374] As a concrete example, the system quickly and accurately generates proposals based on an input prompt such as, "Create a sales proposal for a new customer. Customize the proposal based on the latest company information and market trends," thereby supporting the user's sales activities.

[0375] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0376] Step 1:

[0377] The user enters the name of a company they are interested in via their device. The entered company name is sent to the server and functions as data that triggers information collection. This step prepares the necessary keywords to initiate information collection.

[0378] Step 2:

[0379] The server automatically collects relevant data from publicly available information sources on the internet using RESTful APIs and scraping techniques, based on the entered company name. Specifically, it retrieves the latest company trends, related news, and financial status from news sites and industry databases. The collected data is stored on the server in its raw state and becomes input data for the next analysis step.

[0380] Step 3:

[0381] The server analyzes the collected data using natural language processing techniques. The analysis utilizes open-source language analysis libraries to perform sentiment analysis, keyword extraction, and trend change detection on text data. This reveals market sentiment and potential challenges relevant to the company. The analyzed data is used as foundational information for generating commercial proposals.

[0382] Step 4:

[0383] The server automatically generates commercial proposals using an AI model based on the analysis results. The model proposes solutions to the assumed company's challenges and documents them. This process creates concrete proposals that serve as guidelines for sales negotiations provided by the user. The generated proposals are used as input data for creating sales negotiation materials.

[0384] Step 5:

[0385] The server automatically generates sales materials and anticipated questions and answers based on the generated commercial proposal using a document generation tool. These materials contain information that should be used during sales negotiations and serve as support materials to help users efficiently conduct negotiations. The generated materials are sent to the terminal and made available for the user to review.

[0386] Step 6:

[0387] During a business negotiation, the server uses speech recognition and facial expression analysis technologies to analyze the user's emotions in real time. The acquired emotional data is immediately reflected as a factor influencing the adjustment of the proposal and the flow of the negotiation. This enables dynamic feedback on the progress of the negotiation.

[0388] Step 7:

[0389] The server analyzes customer response data acquired during sales negotiations in real time and suggests appropriate actions to the user based on that analysis. This allows the user to take a flexible approach tailored to the progress of the negotiation. It serves as a basis for decision-making to maximize the outcome of the negotiation.

[0390] Step 8:

[0391] After a sales meeting, the server automatically generates a follow-up communication message and sends it to the terminal. The user can then review, edit, and quickly send this message to the customer. This process enables efficient post-sales follow-up.

[0392] Step 9:

[0393] The server monitors the sales representative's biometric data through a wearable device and sends notifications to the device prompting them to refresh themselves as needed, based on stress indicators. This supports the sales representative in continuing their work while taking their health into consideration.

[0394] (Application Example 2)

[0395] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0396] In today's business environment, security consultants and sales representatives are required to provide timely and accurate information during interactions with clients. However, collecting and analyzing useful data from vast amounts of information is time-consuming and laborious, and making appropriate proposals that take into account human emotions is a difficult challenge. Furthermore, efficient and effective processes are needed for follow-up after meetings and health management of the representatives.

[0397] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0398] In this invention, the server includes a device for receiving corporate information, a device for automatically collecting data from publicly available information sources based on the corporate information, and a device for analyzing the collected data and hypothesizing the company's challenges. This makes it possible to quickly grasp the company's security challenges and make appropriate proposals. Furthermore, by providing means for analyzing the user's emotional state during the interview and dynamically adjusting the content of the proposal, the quality of the response can be improved. In addition, by automatically generating follow-up communication messages after the interview and monitoring the user's biometric data, efficient follow-up and health management can be achieved.

[0399] "Corporate information" refers to data related to a specific corporation or organization, including its performance, social reputation, and the products and services it provides.

[0400] A "device" is a system that combines hardware and software for performing information processing.

[0401] "Publicly available information sources" refer to databases and media that are publicly accessible via the internet or other means.

[0402] "Data" refers to a unit of information that is collected and analyzed for a specific purpose.

[0403] "Challenges" refer to problems or challenges that a company or organization must overcome.

[0404] A "business proposal" refers to a recommended action plan or solution for improving specific business benefits.

[0405] "Dialogue" refers to the communication process that takes place between two or more parties.

[0406] "Materials" refers to documents or digital content provided for a specific purpose.

[0407] "Emotional state" refers to the state of emotions and moods experienced within a person's mind.

[0408] The system implementing this invention consists of multiple devices and software components. The server first receives company information entered by the user and automatically collects data from publicly available sources related to that company. This includes publicly available databases on the internet, news sites, etc. The server uses AWS SageMaker to analyze the collected data and hypothesize the company's challenges.

[0409] Next, the system generates optimal business proposals for the given challenges. To generate these proposals, the server compares the analysis results with the company's database and optimizes the proposals using TensorFlow. Based on these proposals, interactive materials, anticipated problems, and example answers are created. An automated script written in Python is used to generate these materials.

[0410] During the interview, the system uses Azure Cognitive Services to analyze the user's voice and facial expressions in real time to determine their emotional state. Based on this analysis, the server dynamically adjusts the suggestions to improve the quality of the conversation. Optimized information is displayed on the user's device in real time.

[0411] After the interview, the server automatically generates a follow-up communication message and sends it to the user's device. This message is customizable by the user and ready to be sent. In addition, the system monitors the user's biometric data through IoT devices and suggests breaks based on stress levels and heart rate.

[0412] For example, when a user prepares for a meeting with a new financial institution client, the latest information on specific financial risks is automatically collected and analyzed. During the meeting, the proposal is dynamically adjusted in response to the client's reactions, resulting in more effective communication.

[0413] An example of an input prompt statement for a generative AI model is as follows:

[0414] "Gather up-to-date information on security risks for specific clients and begin data analysis to propose appropriate solutions."

[0415] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0416] Step 1:

[0417] The server receives company information from the user. Based on this entered company information, the server automatically collects relevant data from publicly available data sources on the internet (e.g., databases, news sites). The collected data is stored on the server as primary data for analysis.

[0418] Step 2:

[0419] The server uses AWS SageMaker to analyze the collected primary data. The analysis process applies machine learning algorithms to the data to extract potential challenges the company may face. Based on these analysis results, hypotheses about the challenges are formulated. The output of this process is a list of specific challenges.

[0420] Step 3:

[0421] The server uses TensorFlow to generate optimal business proposals by matching the analyzed list of issues with the company's internal database. Here, it executes a data matching algorithm to optimize the proposal content to suit the company's needs. The output of this step is an optimized proposal document and supporting materials.

[0422] Step 4:

[0423] During the interview, the user's voice and facial expression data are collected in real time via Azure Cognitive Services on the user's device. Based on this data, the server analyzes the user's emotional state and dynamically adjusts the proposed content based on the analysis results. The output is information about the adjusted proposal.

[0424] Step 5:

[0425] After the interview, the server automatically generates a follow-up communication message using the analyzed information. The user receives this message and can customize its content. The generated message is designed to facilitate the next action.

[0426] Step 6:

[0427] The user's biometric data is sent to a server via an IoT device. The server analyzes this data and monitors stress levels and heart rate. If necessary, it sends a notification to the user's device suggesting a break. The output is a refresh suggestion at the appropriate time.

[0428] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0429] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0430] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0431] [Third Embodiment]

[0432] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0433] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0434] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0435] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0436] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0437] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0438] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0439] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0440] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0441] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0442] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0443] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0444] This invention is an AI-powered sales support system that efficiently supports sales activities by utilizing company information. When a user enters a company name via a terminal, the system automatically collects information related to that company from various publicly available data sources. This information includes investor relations (IR) information, financial results, stock price trends, press releases, and information from official websites and social media.

[0445] Next, the server analyzes the collected information and hypothesizes potential challenges for the company. Based on this analysis, the server refers to its own product database and generates appropriate commercial proposals. These proposals include materials for sales meetings, talk scripts, and anticipated questions and answers, all of which are automatically generated by the server.

[0446] Furthermore, the system supports the sales process by displaying materials provided from the server via the terminal when sales representatives conduct business negotiations. During negotiations, the server can acquire and analyze customer response data in real time. Based on this analysis, it also has a function to suggest appropriate actions to take during the negotiation. This enables immediate reactions and appropriate actions in the field of sales.

[0447] Furthermore, after the business meeting concludes, the server automatically generates a follow-up communication message. This message includes information about the next contact and a summary of the meeting's content, and is sent directly to the customer from the terminal.

[0448] This system also has a function to monitor the condition of sales representatives. The server periodically receives physical data from IoT devices and, depending on their stress levels and heart rate, can send notifications via the device prompting them to take a break if they need to refresh themselves.

[0449] As a concrete example, when a sales representative is conducting business negotiations with a new company, "XYZ Corporation," using this system allows for the rapid collection and analysis of a vast amount of information related to XYZ Corporation in advance, automatically generating an optimal proposal document based on the results. During the negotiation, the system guides the sales representative to respond appropriately while observing the customer's reactions, enabling them to work efficiently and effectively.

[0450] Thus, the present invention contributes to improving the efficiency of sales activities and reducing the burden on sales representatives.

[0451] The following describes the processing flow.

[0452] Step 1:

[0453] The user uses their device to enter the name of the target company. The device then sends the entered company name to the server.

[0454] Step 2:

[0455] Based on the company name it receives, the server collects relevant information from data sources on the internet. Specifically, it starts the process of acquiring data from IR information, financial statements, stock price trends, press releases, the company's official website, and social media.

[0456] Step 3:

[0457] The server analyzes the collected data. Using information analysis algorithms, it identifies the company's current situation and potential challenges. Based on the analysis results, it hypothesizes the challenges the target company may be facing.

[0458] Step 4:

[0459] The server references its product database to generate the optimal commercial proposal for the assumed problem. It then concretizes the proposal and automatically generates sales materials, talk scripts, and anticipated questions and answers based on it.

[0460] Step 5:

[0461] The terminal displays sales materials provided by the server. The user reviews the generated materials through the terminal and prepares for the sales negotiation. These materials are used to effectively advance the negotiation.

[0462] Step 6:

[0463] During a business negotiation, the server receives and analyzes user and customer interaction data in real time. Specifically, it analyzes the customer's facial expressions and reactions and provides a function to suggest the next action to take.

[0464] Step 7:

[0465] After the business meeting concludes, the server automatically generates a follow-up communication message and sends it to the user's device. The user reviews and edits the message before sending it to the customer.

[0466] Step 8:

[0467] The server periodically checks data from IoT devices to monitor sales representatives' stress levels and heart rates. If certain thresholds are exceeded, it sends a notification to the sales representative via their device prompting them to take a break or refresh themselves.

[0468] (Example 1)

[0469] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0470] Traditional sales support systems faced challenges such as the enormous time and effort required for collecting and analyzing company information, as well as the difficulty for sales representatives to respond appropriately to customer reactions during negotiations. Furthermore, follow-up after negotiations and management of the sales representatives' own well-being were often insufficient. To address these issues, there was a need for a system that could support sales activities more efficiently and effectively.

[0471] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0472] In this invention, the server includes a device for receiving company names, a device for automatically collecting information from publicly available data sources based on the company names, a device for analyzing the collected information and hypothesizing organizational challenges, and a device for automatically generating commercial proposals and negotiation materials using a generative AI model. This enables the efficient execution of a series of sales support processes, from information collection and analysis to proposal material creation, responding to real-time reactions during business negotiations, and even follow-up and sales representative condition management, thereby maximizing the effectiveness of sales activities.

[0473] A "device for receiving company names" is an interface that acquires company name information entered by the user and incorporates it into the system.

[0474] A "device that automatically collects information from publicly available data sources" is part of a system that has the function of acquiring data from publicly available information sources on the internet based on predetermined search criteria.

[0475] A "device for analyzing information and hypothesizing organizational challenges" is a program component that analyzes collected data to estimate potential challenges an organization may face.

[0476] A "device for automatically generating commercial proposals and negotiation materials using a generative AI model" is a system component that utilizes AI technology to automatically create proposal content and materials necessary for business negotiations.

[0477] A "device for analyzing customer response data in real time" is part of a system that provides the functionality to immediately process and analyze data based on customer behavior and statements acquired during business negotiations.

[0478] A "device for automatically generating follow-up communication messages" is a system component that has the function of automatically creating and sending necessary follow-up emails and notifications after a business negotiation.

[0479] The "device that monitors physical data and sends notifications to encourage refreshment" is part of a system that monitors the biometric information of sales representatives and generates and delivers notifications to encourage rest as needed.

[0480] This invention is a system designed to support a company's sales activities, with a server, terminals, and users working together. The server first receives the company name entered by the user from the terminal. Based on this company name, the server automatically collects relevant company information using publicly available data sources on the internet, such as company information APIs and SNS APIs. The software used includes web scraping tools and API clients.

[0481] Next, the server uses generative AI models and natural language processing algorithms to analyze the collected data. This allows it to hypothesize potential challenges facing the company. Once the analysis is complete, the server refers to the company's product database and generates commercial proposals based on the analysis results. In the process of generating these proposal documents, the generative AI model is used to automatically create sales materials and talk scripts.

[0482] As a concrete example, when a user enters the name of a new client company, relevant information is quickly collected through this system. For instance, by entering a prompt message into the server such as, "Based on the latest market trends and competitive landscape related to this company, please create a proposal document for the next new product," the necessary proposal document is automatically generated.

[0483] As described above, this system supports the sales process and enables users to conduct efficient sales activities.

[0484] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0485] Step 1:

[0486] The user enters the company name into the input field on the terminal. The terminal sends this company name to the server. At this stage, the input is the company name, and the output is the company name information transferred to the server. In particular, the user is required to accurately enter the name of the new customer company.

[0487] Step 2:

[0488] Based on the received company name, the server uses corporate information APIs and web scraping tools to collect relevant information from publicly available data sources on the internet. Input includes company name and search criteria, while output includes corporate-related information such as IR information, financial statements, stock prices, and press releases. The server collects information accurately and quickly.

[0489] Step 3:

[0490] The server analyzes collected company-related information using natural language processing algorithms and generative AI models to hypothesize potential challenges facing the organization. It performs data analysis on the input company information and generates outputs that identify potential challenges the company may be facing and their hypotheses. This analysis makes it possible to understand the company's current situation and formulate appropriate strategies.

[0491] Step 4:

[0492] To address the analyzed issues, the server references the company's product database and utilizes a generation AI model to automatically generate commercial proposal materials and documents necessary for negotiations. Inputs include hypothetical issues and database information, while outputs include negotiation materials, talk scripts, and anticipated questions and answers. Specifically, the server aims to generate easily understandable documents.

[0493] Step 5:

[0494] During business negotiations, sales representatives use terminals to view negotiation materials provided by the server and proceed with the negotiation. The server acquires and analyzes customer response data through the terminals during the negotiation. Real-time response data is used as input, and instructions and suggestions based on customer responses are provided to the sales representatives as output. The terminals provide an intuitive interface to facilitate operation by the sales representatives.

[0495] Step 6:

[0496] After a sales meeting concludes, the server automatically generates a follow-up communication message and sends it to the customer via the terminal. Input includes the details and outcome of the meeting, and output generates a follow-up email or message. Furthermore, the server monitors the sales representative's condition via IoT devices and sends notifications for rest as needed. This ensures that customer support is maintained effectively even after sales activities have ended.

[0497] (Application Example 1)

[0498] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0499] In modern sales activities, there is a demand for quick and effective proposals that meet customer interests and needs. However, traditional methods make it difficult to respond in real time and adjust proposals based on customer feedback, which hinders maximizing sales efficiency. Furthermore, there is a need to increase sales while reducing the burden on sales representatives.

[0500] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0501] In this invention, the server includes means for receiving corporate information, means for automatically collecting information from publicly available data sources, and means for analyzing customer interests. This makes it possible to quickly and accurately grasp customer needs in sales activities and adjust proposals in real time accordingly.

[0502] "Means for receiving corporate information" refers to a function for electronically acquiring information about a company.

[0503] "Means for automatically collecting information" refers to a function that automatically collects necessary information from specified data sources.

[0504] "Means of analyzing information" refers to the function of analyzing a company's current situation and potential problems based on the collected information.

[0505] "Means for generating commercial proposals" refers to a function that automatically creates appropriate commercial strategies tailored to a company's challenges.

[0506] "Means for creating materials for business negotiations" refers to the function of preparing the necessary materials for business negotiations based on the generated commercial proposal.

[0507] "Means for analyzing customer interests" refers to a function that evaluates and analyzes customer interests and concerns based on customer response data.

[0508] "A means of adjusting proposals in real time" refers to a function that allows for the rapid adjustment and modification of commercial proposals in response to customer reactions during business negotiations.

[0509] This invention relates to an AI-powered sales support system that enables efficient and effective sales activities. At the heart of the system is a server that first receives company information and automatically collects detailed information about target companies from publicly available data sources. The collected information is analyzed using an AI model to hypothesize the company's challenges and potential needs.

[0510] Next, the server automatically generates a commercial proposal and, based on this proposal, prepares materials for the business negotiation and answers to anticipated customer questions. Furthermore, it acquires customer response data through the terminal during the negotiation and analyzes it in real time to evaluate and analyze customer interest. Based on this evaluation, the server can adjust the content of the negotiation in real time and provide the optimal proposal.

[0511] To perform these processes, the server utilizes AI technologies such as TensorFlow, OpenCV, and Google Cloud AI, and employs libraries such as Google Speech-to-Text API for speech recognition and Microsoft Azure Face API for facial recognition and emotion analysis. For example, a robot could visit a home and propose smart home devices tailored to the lifestyle of the residents. This approach is expected to accurately meet customer needs and maximize the effectiveness of sales activities.

[0512] Examples of prompts for a generative AI model:

[0513] "Please generate a proposal document for smart home devices that the customer is likely to be interested in. His facial expression suggests he may be interested."

[0514] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0515] Step 1:

[0516] The server receives company information from the user. Based on the received company name, it automatically collects information from publicly available data sources. At this stage, the input is the company name, and the server uses Web APIs and scraping techniques to collect data such as IR information, financial statements, and stock price trends, and stores them in the database. The output is the collected company-related information.

[0517] Step 2:

[0518] The server uses an AI model to analyze the collected information. The input is company information collected in the previous step, and the AI ​​model (e.g., TensorFlow) is used to analyze data patterns and extract potential challenges and needs of the company. The output is a list of assumed company challenges and needs.

[0519] Step 3:

[0520] The server generates commercial proposals based on problems assumed by the AI ​​model. The input is a company's challenges and needs, which are then compared with the company's product database to create the most suitable proposal document. This proposal document includes information on specific products and services. The output is a commercial proposal document.

[0521] Step 4:

[0522] The user initiates a business negotiation with a customer and displays negotiation materials through their terminal. The server acquires the customer's voice and facial expression data during the negotiation and performs real-time analysis. The input is customer reaction data (voice and video), and based on this, the customer's emotional state is analyzed using an AI model (e.g., OpenCV, Microsoft Azure Face API). The output is the customer's interest and emotional state.

[0523] Step 5:

[0524] The server adjusts commercial proposals based on the customer's emotional state, which is acquired in real time. The input is the customer's emotional state, and a generative AI model (including the creation of prompts) is used to optimize the content and order of proposals and adjust the response during the sales negotiation. The output is the adjusted commercial proposal.

[0525] Step 6:

[0526] After a business meeting concludes, the server summarizes the meeting content and generates a follow-up message. The input is the meeting log data; natural language generation technology is used to create a summary and next contact information, preparing it for transmission. The output is the follow-up message.

[0527] Step 7:

[0528] The server periodically monitors the physical data of sales representatives to check their condition. Input is physical data obtained from IoT devices (e.g., heart rate, stress level). Based on the analysis results, it sends notifications to the terminal prompting refreshment as needed. Output is the refreshment notification.

[0529] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0530] This invention combines an AI-powered sales support system with an emotion engine, aiming to efficiently utilize company information and make sales activities more effective. In this system, the user inputs the company name via a terminal, and the server automatically collects relevant data from multiple sources on the internet based on that company information.

[0531] The collected data is analyzed on a server to hypothesize potential challenges for the company. The server then compares the analysis results with the company's product database to generate optimal commercial proposals. Based on these proposals, sales materials, talk scripts, and anticipated questions and answers are automatically created and delivered to the terminal.

[0532] During a business negotiation, the server uses an emotion engine to recognize the user's emotions in real time and optimize the commercial proposal. This engine analyzes the user's emotional state from voice and facial expression data and dynamically modifies the proposal according to the progress of the negotiation. This allows the user to flexibly adapt to the flow of the negotiation.

[0533] Furthermore, customer response data acquired during negotiations is analyzed in real time, and the system also has a function to suggest the next action based on the analysis results. This allows users to accurately grasp the customer's interests and reactions during negotiations and conduct effective negotiations.

[0534] After a sales meeting, the server automatically generates a follow-up communication message and sends it to the user's device. The user can then review and edit this message before sending it to the customer. This process allows for quick and efficient follow-up after a sales meeting has concluded.

[0535] Furthermore, the server monitors the sales representatives' physical data via IoT devices and sends notifications to their terminals prompting them to refresh themselves as needed. It also has a function that suggests taking breaks at appropriate times based on data such as stress levels and heart rate.

[0536] For example, when a user prepares for a business meeting with a new customer, "Ryuutsu Co., Ltd.," this system allows for the rapid collection of extensive information in advance, and the automatic generation of business meeting materials based on the analysis results. During the meeting, emotional data is acquired in real time, and commercial proposals are adjusted accordingly, enabling the user to respond flexibly to the customer's needs. In this way, the system of the present invention supports all stages of sales activities and improves operational efficiency.

[0537] The following describes the processing flow.

[0538] Step 1:

[0539] The user enters the name of the company they are negotiating with via their terminal. The terminal then sends the entered company name to the server.

[0540] Step 2:

[0541] Based on the received company name, the server automatically collects relevant information from various data sources on the internet (e.g., IR information, financial reports, stock price information, press releases, etc.). The server then stores the collected data in a database.

[0542] Step 3:

[0543] The server analyzes the accumulated data and performs an analysis to identify the company's challenges. Based on the results of this analysis, it hypothesizes the potential challenges the company faces.

[0544] Step 4:

[0545] The server, as a solution to the assumed problem, refers to the company's product database and generates the optimal commercial proposal. Based on this, it automatically generates sales materials, talk scripts, and anticipated questions and answers.

[0546] Step 5:

[0547] The terminal receives sales materials sent from the server and displays them to the user. The user reviews these materials and prepares for the sales meeting.

[0548] Step 6:

[0549] During business negotiations, the server uses an emotion engine to recognize the user's emotions in real time. It analyzes the user's voice and facial expression data and dynamically adjusts commercial proposals according to their emotional state.

[0550] Step 7:

[0551] The server analyzes customer response data acquired during the sales negotiation and, if necessary, suggests the next action to take for the user during the negotiation.

[0552] Step 8:

[0553] After the business meeting concludes, the server automatically generates a follow-up message and sends it to the user's device. The user then reviews and edits this message, preparing to send it to the customer.

[0554] Step 9:

[0555] The server monitors the sales representative's physical data from IoT devices and sends notifications to the devices prompting them to refresh themselves, depending on their stress levels and other conditions. Users receive these notifications and take breaks at appropriate times.

[0556] (Example 2)

[0557] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0558] In today's sales environment, competition is fierce, and there is a demand for efficient and effective proposals that meet customer needs. However, traditional methods require a great deal of time and effort for information gathering and proposal preparation, and real-time responses during negotiations are difficult. Furthermore, managing the stress levels of sales representatives is also a crucial issue.

[0559] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0560] In this invention, the server includes a device for receiving information, a device for automatically collecting data from multiple publicly available information sources based on the information, and a device for analyzing the collected data to hypothesize potential challenges for the company. This enables sales representatives to quickly gain a deep understanding of customers, make appropriate proposals, respond flexibly to customer reactions during negotiations, improve work efficiency, and manage stress.

[0561] A "device for receiving information" is a device that receives data entered by a user and begins processing it.

[0562] "Publicly available information sources" refer to a collection of data that is made available to anyone on the internet or other accessible media.

[0563] A "device that automatically collects data" is a device that automatically retrieves relevant information from the internet or databases based on specified conditions.

[0564] A "device that analyzes collected data to hypothesize potential challenges for a company" is a device that uses acquired data to predict future problems and areas for improvement in a specific company.

[0565] A "business proposal generation device" is a device that automatically creates proposals as solutions to a company's challenges based on analysis results.

[0566] A "device for creating materials for business negotiations and anticipated questions and answers" is a device for preparing materials and question-and-answer sets that will be useful during business negotiations based on a proposal.

[0567] A "device that recognizes emotions in real time from voice and facial expression data and dynamically optimizes proposals" is a device that analyzes the user's voice and facial expressions to understand their emotions and adjusts the proposal for the business negotiation in real time based on the results.

[0568] A "device for analyzing customer reaction data in real time" is a device that instantly processes customer behavior and speech data acquired during business negotiations and obtains evaluation results.

[0569] A "device that automatically generates follow-up communication messages" is a device that automatically creates messages to build appropriate ongoing relationships after a business negotiation.

[0570] A "device that monitors biometric data and sends notifications to encourage refreshment based on stress indicators" is a device that constantly monitors the health status and stress levels of sales representatives and sends notifications to suggest rest as needed.

[0571] This invention is a sales support system that efficiently collects and analyzes corporate information from multiple sources and automatically generates commercial proposals based on that information. The system integrates functions for information reception, data collection, data analysis, commercial proposal generation, document creation, sentiment recognition, action suggestions, follow-up message generation, and biometric data monitoring.

[0572] The user first enters the name of a company they are interested in using a terminal. This terminal functions as an interface and sends data to the server as an information receiving device. The server is connected to the internet via a network interface and automatically collects relevant data from public sources such as news sites and industry databases using RESTful APIs and scraping techniques.

[0573] The server then analyzes the collected data using natural language processing (NLP) techniques. Open-source language analysis libraries (e.g., NLTK, spaCy) and sentiment analysis tools are used for this analysis. Based on the analysis results, the server hypothesizes hidden challenges within the company and generates commercial proposals using data mining algorithms (e.g., Scikit-learn).

[0574] Based on the generated proposal, the server automatically creates materials for the business negotiation, as well as anticipated questions and their answers, using a document generation tool (e.g., LaTeX, Docx), and sends them to the terminal. The user can then prepare for the negotiation based on these materials.

[0575] During business negotiations, the server utilizes an emotion engine to analyze the user's emotions from their voice and facial expression data. Automatic speech recognition (ASR) software (e.g., Google Speech-to-Text) is used for speech recognition, and facial recognition technology (e.g., OpenCV) is used for facial expression analysis. Based on the analysis results, proposals are optimized in real time, and actions are suggested to streamline the business negotiation process.

[0576] After a business meeting, the server generates follow-up communication messages and displays them on the end user's device in an editable format. Furthermore, the server works with wearable devices to collect biometric data, such as the sales representative's stress level and heart rate. Based on the collected data, it issues notifications encouraging refreshment and breaks, supporting improved work efficiency and health management.

[0577] As a concrete example, the system quickly and accurately generates proposals based on an input prompt such as, "Create a sales proposal for a new customer. Customize the proposal based on the latest company information and market trends," thereby supporting the user's sales activities.

[0578] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0579] Step 1:

[0580] The user enters the name of a company they are interested in via their device. The entered company name is sent to the server and functions as data that triggers information collection. This step prepares the necessary keywords to initiate information collection.

[0581] Step 2:

[0582] The server automatically collects relevant data from publicly available information sources on the internet using RESTful APIs and scraping techniques, based on the entered company name. Specifically, it retrieves the latest company trends, related news, and financial status from news sites and industry databases. The collected data is stored on the server in its raw state and becomes input data for the next analysis step.

[0583] Step 3:

[0584] The server analyzes the collected data using natural language processing techniques. The analysis utilizes open-source language analysis libraries to perform sentiment analysis, keyword extraction, and trend change detection on text data. This reveals market sentiment and potential challenges relevant to the company. The analyzed data is used as foundational information for generating commercial proposals.

[0585] Step 4:

[0586] The server automatically generates commercial proposals using an AI model based on the analysis results. The model proposes solutions to the assumed company's challenges and documents them. This process creates concrete proposals that serve as guidelines for sales negotiations provided by the user. The generated proposals are used as input data for creating sales negotiation materials.

[0587] Step 5:

[0588] The server automatically generates sales materials and anticipated questions and answers based on the generated commercial proposal using a document generation tool. These materials contain information that should be used during sales negotiations and serve as support materials to help users efficiently conduct negotiations. The generated materials are sent to the terminal and made available for the user to review.

[0589] Step 6:

[0590] During a business negotiation, the server uses speech recognition and facial expression analysis technologies to analyze the user's emotions in real time. The acquired emotional data is immediately reflected as a factor influencing the adjustment of the proposal and the flow of the negotiation. This enables dynamic feedback on the progress of the negotiation.

[0591] Step 7:

[0592] The server analyzes customer response data acquired during sales negotiations in real time and suggests appropriate actions to the user based on that analysis. This allows the user to take a flexible approach tailored to the progress of the negotiation. It serves as a basis for decision-making to maximize the outcome of the negotiation.

[0593] Step 8:

[0594] After a sales meeting, the server automatically generates a follow-up communication message and sends it to the terminal. The user can then review, edit, and quickly send this message to the customer. This process enables efficient post-sales follow-up.

[0595] Step 9:

[0596] The server monitors the sales representative's biometric data through a wearable device and sends notifications to the device prompting them to refresh themselves as needed, based on stress indicators. This supports the sales representative in continuing their work while taking their health into consideration.

[0597] (Application Example 2)

[0598] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0599] In today's business environment, security consultants and sales representatives are required to provide timely and accurate information during interactions with clients. However, collecting and analyzing useful data from vast amounts of information is time-consuming and laborious, and making appropriate proposals that take into account human emotions is a difficult challenge. Furthermore, efficient and effective processes are needed for follow-up after meetings and health management of the representatives.

[0600] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0601] In this invention, the server includes a device for receiving corporate information, a device for automatically collecting data from publicly available information sources based on the corporate information, and a device for analyzing the collected data and hypothesizing the company's challenges. This makes it possible to quickly grasp the company's security challenges and make appropriate proposals. Furthermore, by providing means for analyzing the user's emotional state during the interview and dynamically adjusting the content of the proposal, the quality of the response can be improved. In addition, by automatically generating follow-up communication messages after the interview and monitoring the user's biometric data, efficient follow-up and health management can be achieved.

[0602] "Corporate information" refers to data related to a specific corporation or organization, including its performance, social reputation, and the products and services it provides.

[0603] A "device" is a system that combines hardware and software for performing information processing.

[0604] "Publicly available information sources" refer to databases and media that are publicly accessible via the internet or other means.

[0605] "Data" refers to a unit of information that is collected and analyzed for a specific purpose.

[0606] "Challenges" refer to problems or challenges that a company or organization must overcome.

[0607] A "business proposal" refers to a recommended action plan or solution for improving specific business benefits.

[0608] "Dialogue" refers to the communication process that takes place between two or more parties.

[0609] "Materials" refers to documents or digital content provided for a specific purpose.

[0610] "Emotional state" refers to the state of emotions and moods experienced within a person's mind.

[0611] The system implementing this invention consists of multiple devices and software components. The server first receives company information entered by the user and automatically collects data from publicly available sources related to that company. This includes publicly available databases on the internet, news sites, etc. The server uses AWS SageMaker to analyze the collected data and hypothesize the company's challenges.

[0612] Next, the system generates optimal business proposals for the given challenges. To generate these proposals, the server compares the analysis results with the company's database and optimizes the proposals using TensorFlow. Based on these proposals, interactive materials, anticipated problems, and example answers are created. An automated script written in Python is used to generate these materials.

[0613] During the interview, the system uses Azure Cognitive Services to analyze the user's voice and facial expressions in real time to determine their emotional state. Based on this analysis, the server dynamically adjusts the suggestions to improve the quality of the conversation. Optimized information is displayed on the user's device in real time.

[0614] After the interview, the server automatically generates a follow-up communication message and sends it to the user's device. This message is customizable by the user and ready to be sent. In addition, the system monitors the user's biometric data through IoT devices and suggests breaks based on stress levels and heart rate.

[0615] For example, when a user prepares for a meeting with a new financial institution client, the latest information on specific financial risks is automatically collected and analyzed. During the meeting, the proposal is dynamically adjusted in response to the client's reactions, resulting in more effective communication.

[0616] An example of an input prompt statement for a generative AI model is as follows:

[0617] "Gather up-to-date information on security risks for specific clients and begin data analysis to propose appropriate solutions."

[0618] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0619] Step 1:

[0620] The server receives company information from the user. Based on this entered company information, the server automatically collects relevant data from publicly available data sources on the internet (e.g., databases, news sites). The collected data is stored on the server as primary data for analysis.

[0621] Step 2:

[0622] The server uses AWS SageMaker to analyze the collected primary data. The analysis process applies machine learning algorithms to the data to extract potential challenges the company may face. Based on these analysis results, hypotheses about the challenges are formulated. The output of this process is a list of specific challenges.

[0623] Step 3:

[0624] The server uses TensorFlow to generate optimal business proposals by matching the analyzed list of issues with the company's internal database. Here, it executes a data matching algorithm to optimize the proposal content to suit the company's needs. The output of this step is an optimized proposal document and supporting materials.

[0625] Step 4:

[0626] During the interview, the user's voice and facial expression data are collected in real time via Azure Cognitive Services on the user's device. Based on this data, the server analyzes the user's emotional state and dynamically adjusts the proposed content based on the analysis results. The output is information about the adjusted proposal.

[0627] Step 5:

[0628] After the interview, the server automatically generates a follow-up communication message using the analyzed information. The user receives this message and can customize its content. The generated message is designed to facilitate the next action.

[0629] Step 6:

[0630] The user's biometric data is sent to a server via an IoT device. The server analyzes this data and monitors stress levels and heart rate. If necessary, it sends a notification to the user's device suggesting a break. The output is a refresh suggestion at the appropriate time.

[0631] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0632] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0633] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0634] [Fourth Embodiment]

[0635] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0636] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0637] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0638] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0639] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0640] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0641] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0642] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0643] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0644] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0645] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0646] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0647] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0648] This invention is an AI-powered sales support system that efficiently supports sales activities by utilizing company information. When a user enters a company name via a terminal, the system automatically collects information related to that company from various publicly available data sources. This information includes investor relations (IR) information, financial results, stock price trends, press releases, and information from official websites and social media.

[0649] Next, the server analyzes the collected information and hypothesizes potential challenges for the company. Based on this analysis, the server refers to its own product database and generates appropriate commercial proposals. These proposals include materials for sales meetings, talk scripts, and anticipated questions and answers, all of which are automatically generated by the server.

[0650] Furthermore, the system supports the sales process by displaying materials provided from the server via the terminal when sales representatives conduct business negotiations. During negotiations, the server can acquire and analyze customer response data in real time. Based on this analysis, it also has a function to suggest appropriate actions to take during the negotiation. This enables immediate reactions and appropriate actions in the field of sales.

[0651] Furthermore, after the business meeting concludes, the server automatically generates a follow-up communication message. This message includes information about the next contact and a summary of the meeting's content, and is sent directly to the customer from the terminal.

[0652] This system also has a function to monitor the condition of sales representatives. The server periodically receives physical data from IoT devices and, depending on their stress levels and heart rate, can send notifications via the device prompting them to take a break if they need to refresh themselves.

[0653] As a concrete example, when a sales representative is conducting business negotiations with a new company, "XYZ Corporation," using this system allows for the rapid collection and analysis of a vast amount of information related to XYZ Corporation in advance, automatically generating an optimal proposal document based on the results. During the negotiation, the system guides the sales representative to respond appropriately while observing the customer's reactions, enabling them to work efficiently and effectively.

[0654] Thus, the present invention contributes to improving the efficiency of sales activities and reducing the burden on sales representatives.

[0655] The following describes the processing flow.

[0656] Step 1:

[0657] The user uses their device to enter the name of the target company. The device then sends the entered company name to the server.

[0658] Step 2:

[0659] Based on the company name it receives, the server collects relevant information from data sources on the internet. Specifically, it starts the process of acquiring data from IR information, financial statements, stock price trends, press releases, the company's official website, and social media.

[0660] Step 3:

[0661] The server analyzes the collected data. Using information analysis algorithms, it identifies the company's current situation and potential challenges. Based on the analysis results, it hypothesizes the challenges the target company may be facing.

[0662] Step 4:

[0663] The server references its product database to generate the optimal commercial proposal for the assumed problem. It then concretizes the proposal and automatically generates sales materials, talk scripts, and anticipated questions and answers based on it.

[0664] Step 5:

[0665] The terminal displays sales materials provided by the server. The user reviews the generated materials through the terminal and prepares for the sales negotiation. These materials are used to effectively advance the negotiation.

[0666] Step 6:

[0667] During a business negotiation, the server receives and analyzes user and customer interaction data in real time. Specifically, it analyzes the customer's facial expressions and reactions and provides a function to suggest the next action to take.

[0668] Step 7:

[0669] After the business meeting concludes, the server automatically generates a follow-up communication message and sends it to the user's device. The user reviews and edits the message before sending it to the customer.

[0670] Step 8:

[0671] The server periodically checks data from IoT devices to monitor sales representatives' stress levels and heart rates. If certain thresholds are exceeded, it sends a notification to the sales representative via their device prompting them to take a break or refresh themselves.

[0672] (Example 1)

[0673] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0674] Traditional sales support systems faced challenges such as the enormous time and effort required for collecting and analyzing company information, as well as the difficulty for sales representatives to respond appropriately to customer reactions during negotiations. Furthermore, follow-up after negotiations and management of the sales representatives' own well-being were often insufficient. To address these issues, there was a need for a system that could support sales activities more efficiently and effectively.

[0675] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0676] In this invention, the server includes a device for receiving company names, a device for automatically collecting information from publicly available data sources based on the company names, a device for analyzing the collected information and hypothesizing organizational challenges, and a device for automatically generating commercial proposals and negotiation materials using a generative AI model. This enables the efficient execution of a series of sales support processes, from information collection and analysis to proposal material creation, responding to real-time reactions during business negotiations, and even follow-up and sales representative condition management, thereby maximizing the effectiveness of sales activities.

[0677] A "device for receiving company names" is an interface that acquires company name information entered by the user and incorporates it into the system.

[0678] A "device that automatically collects information from publicly available data sources" is part of a system that has the function of acquiring data from publicly available information sources on the internet based on predetermined search criteria.

[0679] A "device for analyzing information and hypothesizing organizational challenges" is a program component that analyzes collected data to estimate potential challenges an organization may face.

[0680] A "device for automatically generating commercial proposals and negotiation materials using a generative AI model" is a system component that utilizes AI technology to automatically create proposal content and materials necessary for business negotiations.

[0681] A "device for analyzing customer response data in real time" is part of a system that provides the functionality to immediately process and analyze data based on customer behavior and statements acquired during business negotiations.

[0682] A "device for automatically generating follow-up communication messages" is a system component that has the function of automatically creating and sending necessary follow-up emails and notifications after a business negotiation.

[0683] The "device that monitors physical data and sends notifications to encourage refreshment" is part of a system that monitors the biometric information of sales representatives and generates and delivers notifications to encourage rest as needed.

[0684] This invention is a system designed to support a company's sales activities, with a server, terminals, and users working together. The server first receives the company name entered by the user from the terminal. Based on this company name, the server automatically collects relevant company information using publicly available data sources on the internet, such as company information APIs and SNS APIs. The software used includes web scraping tools and API clients.

[0685] Next, the server uses generative AI models and natural language processing algorithms to analyze the collected data. This allows it to hypothesize potential challenges facing the company. Once the analysis is complete, the server refers to the company's product database and generates commercial proposals based on the analysis results. In the process of generating these proposal documents, the generative AI model is used to automatically create sales materials and talk scripts.

[0686] As a concrete example, when a user enters the name of a new client company, relevant information is quickly collected through this system. For instance, by entering a prompt message into the server such as, "Based on the latest market trends and competitive landscape related to this company, please create a proposal document for the next new product," the necessary proposal document is automatically generated.

[0687] As described above, this system supports the sales process and enables users to conduct efficient sales activities.

[0688] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0689] Step 1:

[0690] The user enters the company name into the input field on the terminal. The terminal sends this company name to the server. At this stage, the input is the company name, and the output is the company name information transferred to the server. In particular, the user is required to accurately enter the name of the new customer company.

[0691] Step 2:

[0692] Based on the received company name, the server uses corporate information APIs and web scraping tools to collect relevant information from publicly available data sources on the internet. Input includes company name and search criteria, while output includes corporate-related information such as IR information, financial statements, stock prices, and press releases. The server collects information accurately and quickly.

[0693] Step 3:

[0694] The server analyzes collected company-related information using natural language processing algorithms and generative AI models to hypothesize potential challenges facing the organization. It performs data analysis on the input company information and generates outputs that identify potential challenges the company may be facing and their hypotheses. This analysis makes it possible to understand the company's current situation and formulate appropriate strategies.

[0695] Step 4:

[0696] To address the analyzed issues, the server references the company's product database and utilizes a generation AI model to automatically generate commercial proposal materials and documents necessary for negotiations. Inputs include hypothetical issues and database information, while outputs include negotiation materials, talk scripts, and anticipated questions and answers. Specifically, the server aims to generate easily understandable documents.

[0697] Step 5:

[0698] During business negotiations, sales representatives use terminals to view negotiation materials provided by the server and proceed with the negotiation. The server acquires and analyzes customer response data through the terminals during the negotiation. Real-time response data is used as input, and instructions and suggestions based on customer responses are provided to the sales representatives as output. The terminals provide an intuitive interface to facilitate operation by the sales representatives.

[0699] Step 6:

[0700] After a sales meeting concludes, the server automatically generates a follow-up communication message and sends it to the customer via the terminal. Input includes the details and outcome of the meeting, and output generates a follow-up email or message. Furthermore, the server monitors the sales representative's condition via IoT devices and sends notifications for rest as needed. This ensures that customer support is maintained effectively even after sales activities have ended.

[0701] (Application Example 1)

[0702] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0703] In modern sales activities, there is a demand for quick and effective proposals that meet customer interests and needs. However, traditional methods make it difficult to respond in real time and adjust proposals based on customer feedback, which hinders maximizing sales efficiency. Furthermore, there is a need to increase sales while reducing the burden on sales representatives.

[0704] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0705] In this invention, the server includes means for receiving corporate information, means for automatically collecting information from publicly available data sources, and means for analyzing customer interests. This makes it possible to quickly and accurately grasp customer needs in sales activities and adjust proposals in real time accordingly.

[0706] "Means for receiving corporate information" refers to a function for electronically acquiring information about a company.

[0707] "Means for automatically collecting information" refers to a function that automatically collects necessary information from specified data sources.

[0708] "Means of analyzing information" refers to the function of analyzing a company's current situation and potential problems based on the collected information.

[0709] "Means for generating commercial proposals" refers to a function that automatically creates appropriate commercial strategies tailored to a company's challenges.

[0710] "Means for creating materials for business negotiations" refers to the function of preparing the necessary materials for business negotiations based on the generated commercial proposal.

[0711] "Means for analyzing customer interests" refers to a function that evaluates and analyzes customer interests and concerns based on customer response data.

[0712] "A means of adjusting proposals in real time" refers to a function that allows for the rapid adjustment and modification of commercial proposals in response to customer reactions during business negotiations.

[0713] This invention relates to an AI-powered sales support system that enables efficient and effective sales activities. At the heart of the system is a server that first receives company information and automatically collects detailed information about target companies from publicly available data sources. The collected information is analyzed using an AI model to hypothesize the company's challenges and potential needs.

[0714] Next, the server automatically generates a commercial proposal and, based on this proposal, prepares materials for the business negotiation and answers to anticipated customer questions. Furthermore, it acquires customer response data through the terminal during the negotiation and analyzes it in real time to evaluate and analyze customer interest. Based on this evaluation, the server can adjust the content of the negotiation in real time and provide the optimal proposal.

[0715] To perform these processes, the server utilizes AI technologies such as TensorFlow, OpenCV, and Google Cloud AI, and employs libraries such as Google Speech-to-Text API for speech recognition and Microsoft Azure Face API for facial recognition and emotion analysis. For example, a robot could visit a home and propose smart home devices tailored to the lifestyle of the residents. This approach is expected to accurately meet customer needs and maximize the effectiveness of sales activities.

[0716] Examples of prompts for a generative AI model:

[0717] "Please generate a proposal document for smart home devices that the customer is likely to be interested in. His facial expression suggests he may be interested."

[0718] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0719] Step 1:

[0720] The server receives company information from the user. Based on the received company name, it automatically collects information from publicly available data sources. At this stage, the input is the company name, and the server uses Web APIs and scraping techniques to collect data such as IR information, financial statements, and stock price trends, and stores them in the database. The output is the collected company-related information.

[0721] Step 2:

[0722] The server uses an AI model to analyze the collected information. The input is company information collected in the previous step, and the AI ​​model (e.g., TensorFlow) is used to analyze data patterns and extract potential challenges and needs of the company. The output is a list of assumed company challenges and needs.

[0723] Step 3:

[0724] The server generates commercial proposals based on problems assumed by the AI ​​model. The input is a company's challenges and needs, which are then compared with the company's product database to create the most suitable proposal document. This proposal document includes information on specific products and services. The output is a commercial proposal document.

[0725] Step 4:

[0726] The user initiates a business negotiation with a customer and displays negotiation materials through their terminal. The server acquires the customer's voice and facial expression data during the negotiation and performs real-time analysis. The input is customer reaction data (voice and video), and based on this, the customer's emotional state is analyzed using an AI model (e.g., OpenCV, Microsoft Azure Face API). The output is the customer's interest and emotional state.

[0727] Step 5:

[0728] The server adjusts commercial proposals based on the customer's emotional state, which is acquired in real time. The input is the customer's emotional state, and a generative AI model (including the creation of prompts) is used to optimize the content and order of proposals and adjust the response during the sales negotiation. The output is the adjusted commercial proposal.

[0729] Step 6:

[0730] After a business meeting concludes, the server summarizes the meeting content and generates a follow-up message. The input is the meeting log data; natural language generation technology is used to create a summary and next contact information, preparing it for transmission. The output is the follow-up message.

[0731] Step 7:

[0732] The server periodically monitors the physical data of sales representatives to check their condition. Input is physical data obtained from IoT devices (e.g., heart rate, stress level). Based on the analysis results, it sends notifications to the terminal prompting refreshment as needed. Output is the refreshment notification.

[0733] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0734] This invention combines an AI-powered sales support system with an emotion engine, aiming to efficiently utilize company information and make sales activities more effective. In this system, the user inputs the company name via a terminal, and the server automatically collects relevant data from multiple sources on the internet based on that company information.

[0735] The collected data is analyzed on a server to hypothesize potential challenges for the company. The server then compares the analysis results with the company's product database to generate optimal commercial proposals. Based on these proposals, sales materials, talk scripts, and anticipated questions and answers are automatically created and delivered to the terminal.

[0736] During a business negotiation, the server uses an emotion engine to recognize the user's emotions in real time and optimize the commercial proposal. This engine analyzes the user's emotional state from voice and facial expression data and dynamically modifies the proposal according to the progress of the negotiation. This allows the user to flexibly adapt to the flow of the negotiation.

[0737] Furthermore, customer response data acquired during negotiations is analyzed in real time, and the system also has a function to suggest the next action based on the analysis results. This allows users to accurately grasp the customer's interests and reactions during negotiations and conduct effective negotiations.

[0738] After a sales meeting, the server automatically generates a follow-up communication message and sends it to the user's device. The user can then review and edit this message before sending it to the customer. This process allows for quick and efficient follow-up after a sales meeting has concluded.

[0739] Furthermore, the server monitors the sales representatives' physical data via IoT devices and sends notifications to their terminals prompting them to refresh themselves as needed. It also has a function that suggests taking breaks at appropriate times based on data such as stress levels and heart rate.

[0740] For example, when a user prepares for a business meeting with a new customer, "Ryuutsu Co., Ltd.," this system allows for the rapid collection of extensive information in advance, and the automatic generation of business meeting materials based on the analysis results. During the meeting, emotional data is acquired in real time, and commercial proposals are adjusted accordingly, enabling the user to respond flexibly to the customer's needs. In this way, the system of the present invention supports all stages of sales activities and improves operational efficiency.

[0741] The following describes the processing flow.

[0742] Step 1:

[0743] The user enters the name of the company they are negotiating with via their terminal. The terminal then sends the entered company name to the server.

[0744] Step 2:

[0745] Based on the received company name, the server automatically collects relevant information from various data sources on the internet (e.g., IR information, financial reports, stock price information, press releases, etc.). The server then stores the collected data in a database.

[0746] Step 3:

[0747] The server analyzes the accumulated data and performs an analysis to identify the company's challenges. Based on the results of this analysis, it hypothesizes the potential challenges the company faces.

[0748] Step 4:

[0749] The server, as a solution to the assumed problem, refers to the company's product database and generates the optimal commercial proposal. Based on this, it automatically generates sales materials, talk scripts, and anticipated questions and answers.

[0750] Step 5:

[0751] The terminal receives sales materials sent from the server and displays them to the user. The user reviews these materials and prepares for the sales meeting.

[0752] Step 6:

[0753] During business negotiations, the server uses an emotion engine to recognize the user's emotions in real time. It analyzes the user's voice and facial expression data and dynamically adjusts commercial proposals according to their emotional state.

[0754] Step 7:

[0755] The server analyzes customer response data acquired during the sales negotiation and, if necessary, suggests the next action to take for the user during the negotiation.

[0756] Step 8:

[0757] After the business meeting concludes, the server automatically generates a follow-up message and sends it to the user's device. The user then reviews and edits this message, preparing to send it to the customer.

[0758] Step 9:

[0759] The server monitors the sales representative's physical data from IoT devices and sends notifications to the devices prompting them to refresh themselves, depending on their stress levels and other conditions. Users receive these notifications and take breaks at appropriate times.

[0760] (Example 2)

[0761] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0762] In today's sales environment, competition is fierce, and there is a demand for efficient and effective proposals that meet customer needs. However, traditional methods require a great deal of time and effort for information gathering and proposal preparation, and real-time responses during negotiations are difficult. Furthermore, managing the stress levels of sales representatives is also a crucial issue.

[0763] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0764] In this invention, the server includes a device for receiving information, a device for automatically collecting data from multiple publicly available information sources based on the information, and a device for analyzing the collected data to hypothesize potential challenges for the company. This enables sales representatives to quickly gain a deep understanding of customers, make appropriate proposals, respond flexibly to customer reactions during negotiations, improve work efficiency, and manage stress.

[0765] A "device for receiving information" is a device that receives data entered by a user and begins processing it.

[0766] "Publicly available information sources" refer to a collection of data that is made available to anyone on the internet or other accessible media.

[0767] A "device that automatically collects data" is a device that automatically retrieves relevant information from the internet or databases based on specified conditions.

[0768] A "device that analyzes collected data to hypothesize potential challenges for a company" is a device that uses acquired data to predict future problems and areas for improvement in a specific company.

[0769] A "business proposal generation device" is a device that automatically creates proposals as solutions to a company's challenges based on analysis results.

[0770] A "device for creating materials for business negotiations and anticipated questions and answers" is a device for preparing materials and question-and-answer sets that will be useful during business negotiations based on a proposal.

[0771] A "device that recognizes emotions in real time from voice and facial expression data and dynamically optimizes proposals" is a device that analyzes the user's voice and facial expressions to understand their emotions and adjusts the proposal for the business negotiation in real time based on the results.

[0772] A "device for analyzing customer reaction data in real time" is a device that instantly processes customer behavior and speech data acquired during business negotiations and obtains evaluation results.

[0773] A "device that automatically generates follow-up communication messages" is a device that automatically creates messages to build appropriate ongoing relationships after a business negotiation.

[0774] A "device that monitors biometric data and sends notifications to encourage refreshment based on stress indicators" is a device that constantly monitors the health status and stress levels of sales representatives and sends notifications to suggest rest as needed.

[0775] This invention is a sales support system that efficiently collects and analyzes corporate information from multiple sources and automatically generates commercial proposals based on that information. The system integrates functions for information reception, data collection, data analysis, commercial proposal generation, document creation, sentiment recognition, action suggestions, follow-up message generation, and biometric data monitoring.

[0776] The user first enters the name of a company they are interested in using a terminal. This terminal functions as an interface and sends data to the server as an information receiving device. The server is connected to the internet via a network interface and automatically collects relevant data from public sources such as news sites and industry databases using RESTful APIs and scraping techniques.

[0777] The server then analyzes the collected data using natural language processing (NLP) techniques. Open-source language analysis libraries (e.g., NLTK, spaCy) and sentiment analysis tools are used for this analysis. Based on the analysis results, the server hypothesizes hidden challenges within the company and generates commercial proposals using data mining algorithms (e.g., Scikit-learn).

[0778] Based on the generated proposal, the server automatically creates materials for the business negotiation, as well as anticipated questions and their answers, using a document generation tool (e.g., LaTeX, Docx), and sends them to the terminal. The user can then prepare for the negotiation based on these materials.

[0779] During business negotiations, the server utilizes an emotion engine to analyze the user's emotions from their voice and facial expression data. Automatic speech recognition (ASR) software (e.g., Google Speech-to-Text) is used for speech recognition, and facial recognition technology (e.g., OpenCV) is used for facial expression analysis. Based on the analysis results, proposals are optimized in real time, and actions are suggested to streamline the business negotiation process.

[0780] After a business meeting, the server generates follow-up communication messages and displays them on the end user's device in an editable format. Furthermore, the server works with wearable devices to collect biometric data, such as the sales representative's stress level and heart rate. Based on the collected data, it issues notifications encouraging refreshment and breaks, supporting improved work efficiency and health management.

[0781] As a concrete example, the system quickly and accurately generates proposals based on an input prompt such as, "Create a sales proposal for a new customer. Customize the proposal based on the latest company information and market trends," thereby supporting the user's sales activities.

[0782] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0783] Step 1:

[0784] The user enters the name of a company they are interested in via their device. The entered company name is sent to the server and functions as data that triggers information collection. This step prepares the necessary keywords to initiate information collection.

[0785] Step 2:

[0786] The server automatically collects relevant data from publicly available information sources on the internet using RESTful APIs and scraping techniques, based on the entered company name. Specifically, it retrieves the latest company trends, related news, and financial status from news sites and industry databases. The collected data is stored on the server in its raw state and becomes input data for the next analysis step.

[0787] Step 3:

[0788] The server analyzes the collected data using natural language processing techniques. The analysis utilizes open-source language analysis libraries to perform sentiment analysis, keyword extraction, and trend change detection on text data. This reveals market sentiment and potential challenges relevant to the company. The analyzed data is used as foundational information for generating commercial proposals.

[0789] Step 4:

[0790] The server automatically generates commercial proposals using an AI model based on the analysis results. The model proposes solutions to the assumed company's challenges and documents them. This process creates concrete proposals that serve as guidelines for sales negotiations provided by the user. The generated proposals are used as input data for creating sales negotiation materials.

[0791] Step 5:

[0792] The server automatically generates sales materials and anticipated questions and answers based on the generated commercial proposal using a document generation tool. These materials contain information that should be used during sales negotiations and serve as support materials to help users efficiently conduct negotiations. The generated materials are sent to the terminal and made available for the user to review.

[0793] Step 6:

[0794] During a business negotiation, the server uses speech recognition and facial expression analysis technologies to analyze the user's emotions in real time. The acquired emotional data is immediately reflected as a factor influencing the adjustment of the proposal and the flow of the negotiation. This enables dynamic feedback on the progress of the negotiation.

[0795] Step 7:

[0796] The server analyzes customer response data acquired during sales negotiations in real time and suggests appropriate actions to the user based on that analysis. This allows the user to take a flexible approach tailored to the progress of the negotiation. It serves as a basis for decision-making to maximize the outcome of the negotiation.

[0797] Step 8:

[0798] After a sales meeting, the server automatically generates a follow-up communication message and sends it to the terminal. The user can then review, edit, and quickly send this message to the customer. This process enables efficient post-sales follow-up.

[0799] Step 9:

[0800] The server monitors the sales representative's biometric data through a wearable device and sends notifications to the device prompting them to refresh themselves as needed, based on stress indicators. This supports the sales representative in continuing their work while taking their health into consideration.

[0801] (Application Example 2)

[0802] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0803] In today's business environment, security consultants and sales representatives are required to provide timely and accurate information during interactions with clients. However, collecting and analyzing useful data from vast amounts of information is time-consuming and laborious, and making appropriate proposals that take into account human emotions is a difficult challenge. Furthermore, efficient and effective processes are needed for follow-up after meetings and health management of the representatives.

[0804] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0805] In this invention, the server includes a device for receiving corporate information, a device for automatically collecting data from publicly available information sources based on the corporate information, and a device for analyzing the collected data and hypothesizing the company's challenges. This makes it possible to quickly grasp the company's security challenges and make appropriate proposals. Furthermore, by providing means for analyzing the user's emotional state during the interview and dynamically adjusting the content of the proposal, the quality of the response can be improved. In addition, by automatically generating follow-up communication messages after the interview and monitoring the user's biometric data, efficient follow-up and health management can be achieved.

[0806] "Corporate information" refers to data related to a specific corporation or organization, including its performance, social reputation, and the products and services it provides.

[0807] A "device" is a system that combines hardware and software for performing information processing.

[0808] "Publicly available information sources" refer to databases and media that are publicly accessible via the internet or other means.

[0809] "Data" refers to a unit of information that is collected and analyzed for a specific purpose.

[0810] "Challenges" refer to problems or challenges that a company or organization must overcome.

[0811] A "business proposal" refers to a recommended action plan or solution for improving specific business benefits.

[0812] "Dialogue" refers to the communication process that takes place between two or more parties.

[0813] "Materials" refers to documents or digital content provided for a specific purpose.

[0814] "Emotional state" refers to the state of emotions and moods experienced within a person's mind.

[0815] The system implementing this invention consists of multiple devices and software components. The server first receives company information entered by the user and automatically collects data from publicly available sources related to that company. This includes publicly available databases on the internet, news sites, etc. The server uses AWS SageMaker to analyze the collected data and hypothesize the company's challenges.

[0816] Next, the system generates optimal business proposals for the given challenges. To generate these proposals, the server compares the analysis results with the company's database and optimizes the proposals using TensorFlow. Based on these proposals, interactive materials, anticipated problems, and example answers are created. An automated script written in Python is used to generate these materials.

[0817] During the interview, the system uses Azure Cognitive Services to analyze the user's voice and facial expressions in real time to determine their emotional state. Based on this analysis, the server dynamically adjusts the suggestions to improve the quality of the conversation. Optimized information is displayed on the user's device in real time.

[0818] After the interview, the server automatically generates a follow-up communication message and sends it to the user's device. This message is customizable by the user and ready to be sent. In addition, the system monitors the user's biometric data through IoT devices and suggests breaks based on stress levels and heart rate.

[0819] For example, when a user prepares for a meeting with a new financial institution client, the latest information on specific financial risks is automatically collected and analyzed. During the meeting, the proposal is dynamically adjusted in response to the client's reactions, resulting in more effective communication.

[0820] An example of an input prompt statement for a generative AI model is as follows:

[0821] "Gather up-to-date information on security risks for specific clients and begin data analysis to propose appropriate solutions."

[0822] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0823] Step 1:

[0824] The server receives company information from the user. Based on this entered company information, the server automatically collects relevant data from publicly available data sources on the internet (e.g., databases, news sites). The collected data is stored on the server as primary data for analysis.

[0825] Step 2:

[0826] The server uses AWS SageMaker to analyze the collected primary data. The analysis process applies machine learning algorithms to the data to extract potential challenges the company may face. Based on these analysis results, hypotheses about the challenges are formulated. The output of this process is a list of specific challenges.

[0827] Step 3:

[0828] The server uses TensorFlow to generate optimal business proposals by matching the analyzed list of issues with the company's internal database. Here, it executes a data matching algorithm to optimize the proposal content to suit the company's needs. The output of this step is an optimized proposal document and supporting materials.

[0829] Step 4:

[0830] During the interview, the user's voice and facial expression data are collected in real time via Azure Cognitive Services on the user's device. Based on this data, the server analyzes the user's emotional state and dynamically adjusts the proposed content based on the analysis results. The output is information about the adjusted proposal.

[0831] Step 5:

[0832] After the interview, the server automatically generates a follow-up communication message using the analyzed information. The user receives this message and can customize its content. The generated message is designed to facilitate the next action.

[0833] Step 6:

[0834] The user's biometric data is sent to a server via an IoT device. The server analyzes this data and monitors stress levels and heart rate. If necessary, it sends a notification to the user's device suggesting a break. The output is a refresh suggestion at the appropriate time.

[0835] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0836] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0837] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0838] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0839] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0840] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0841] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0842] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0843] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0844] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0845] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0846] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0847] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0848] 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.

[0849] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0850] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0851] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0852] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0853] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0854] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0855] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[0856] The following is further disclosed regarding the embodiments described above.

[0857] (Claim 1)

[0858] Means of receiving company information,

[0859] A means for automatically collecting information from publicly available data sources based on the aforementioned company information,

[0860] A means of analyzing collected information to hypothesize the company's challenges,

[0861] A means for generating the optimal commercial proposal for the aforementioned problem,

[0862] A means of creating sales materials and anticipated questions and answers based on the generated proposals,

[0863] A system that includes this.

[0864] (Claim 2)

[0865] A means of analyzing customer response data acquired during business negotiations in real time,

[0866] A means of proposing actions during negotiations based on the aforementioned analysis,

[0867] The system according to claim 1, further comprising:

[0868] (Claim 3)

[0869] A method for automatically generating follow-up communication messages after a business negotiation,

[0870] A means of monitoring the physical data of sales representatives and sending notifications to encourage them to refresh themselves,

[0871] The system according to claim 1, further comprising:

[0872] "Example 1"

[0873] (Claim 1)

[0874] A device that receives company names,

[0875] A device that automatically collects information from publicly available data sources based on the aforementioned company name,

[0876] A device that analyzes collected information to hypothesize organizational challenges,

[0877] A device for generating the optimal commercial proposal for the aforementioned problem,

[0878] A device for creating negotiation materials and anticipated questions and answers based on the generated proposals,

[0879] A device that automatically generates commercial proposals and negotiation materials using a generative AI model,

[0880] A system that includes this.

[0881] (Claim 2)

[0882] The system according to claim 1, comprising a device that analyzes customer response data acquired during a business negotiation in real time and proposes actions to take during the negotiation based on the analysis results.

[0883] (Claim 3)

[0884] A device that automatically generates follow-up communication messages after a business negotiation,

[0885] The system according to claim 1, comprising a device that monitors the physical data of sales representatives and sends notifications to encourage them to refresh themselves.

[0886] "Application Example 1"

[0887] (Claim 1)

[0888] Means of receiving company information,

[0889] A means for automatically collecting information from publicly available data sources based on the aforementioned company information,

[0890] A means of analyzing collected information to hypothesize the company's challenges,

[0891] A means for generating the optimal commercial proposal for the aforementioned problem,

[0892] A means of creating sales materials and anticipated questions and answers based on the generated proposals,

[0893] Means for analyzing customer interests,

[0894] A means of adjusting proposals in real time in response to customer feedback,

[0895] A system that includes this.

[0896] (Claim 2)

[0897] A means of analyzing customer response data acquired during business negotiations in real time,

[0898] A means of proposing actions during negotiations based on the aforementioned analysis,

[0899] A means of adjusting proposals in real time,

[0900] The system according to claim 1, further comprising:

[0901] (Claim 3)

[0902] A method for automatically generating follow-up communication messages after a business negotiation,

[0903] A means of monitoring the physical data of sales representatives and sending notifications to encourage them to refresh themselves,

[0904] A means of analyzing customer interests in real time,

[0905] The system according to claim 1, further comprising:

[0906] "Example 2 of combining an emotion engine"

[0907] (Claim 1)

[0908] A device that receives information,

[0909] A device that automatically collects data from multiple publicly available sources based on the aforementioned information,

[0910] A device that analyzes collected data to hypothesize potential challenges for a company,

[0911] A device for generating the optimal business proposal for the aforementioned problem,

[0912] A device that generates materials for business negotiations and anticipated questions and answers based on the generated proposals,

[0913] A device that recognizes user emotions in real time from voice and facial expression data and dynamically optimizes suggestions,

[0914] A system that includes this.

[0915] (Claim 2)

[0916] A device that analyzes customer reaction data acquired during business negotiations in real time,

[0917] A device that proposes actions during a business negotiation based on the aforementioned results,

[0918] The system according to claim 1.

[0919] (Claim 3)

[0920] A device that automatically generates follow-up communication messages after a business negotiation,

[0921] A device that monitors the biometric data of sales representatives and sends notifications to encourage them to refresh based on stress indicators,

[0922] The system according to claim 1.

[0923] "Application example 2 when combining with an emotional engine"

[0924] (Claim 1)

[0925] A device for receiving corporate information,

[0926] A device that automatically collects data from publicly available information sources based on the aforementioned company information,

[0927] A device that analyzes collected data to hypothesize the company's challenges,

[0928] A device for generating the optimal business proposal for the aforementioned problem,

[0929] A device that generates dialogue materials and hypothetical questions and answers based on the generated proposals,

[0930] A device that analyzes the user's emotional state during an interview and dynamically adjusts the proposed solutions,

[0931] A system that includes this.

[0932] (Claim 2)

[0933] A device that analyzes user response data acquired during interviews in real time,

[0934] A device that proposes responses during an interview based on the aforementioned analysis,

[0935] The system according to claim 1, further comprising:

[0936] (Claim 3)

[0937] A device that automatically generates follow-up communication messages after an interview,

[0938] A device that monitors the user's biometric data and sends notifications suggesting appropriate rest,

[0939] The system according to claim 1, further comprising: [Explanation of Symbols]

[0940] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. Means of receiving company information, A means for automatically collecting information from publicly available data sources based on the aforementioned company information, A means of analyzing collected information to hypothesize the company's challenges, A means for generating the optimal commercial proposal for the aforementioned problem, A means of creating sales materials and anticipated questions and answers based on the generated proposals, A system that includes this.

2. A means of analyzing customer response data acquired during business negotiations in real time, A means of proposing actions during negotiations based on the aforementioned analysis, The system according to claim 1, further comprising:

3. A method for automatically generating follow-up communication messages after a business negotiation, A means of monitoring the physical data of sales representatives and sending notifications to encourage them to refresh themselves, The system according to claim 1, further comprising:

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