system

Abstract

We provide the system. [Solution] A means for collecting customer information and analyzing customer characteristics based on said customer information, A means for generating a proposal plan optimized for the characteristics of the sales representative based on the analysis results, A means of presenting the generated proposal plan to the sales representative, A means of automatically generating materials and tools to support sales activities, A means of analyzing customer transaction history and market trends related to electronic payment services to generate optimal payment solution proposals, A means for displaying the proposed content on a mobile communication device or visual aid, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In business activities, it is one problem that it takes time for a new salesperson to acquire a sales method suitable for customer characteristics and to effectively conduct activities. Also, there is a problem that the sales method tends to be personal, resulting in variations in sales performance and making it difficult to conduct efficient sales activities as an organization. For this reason, there is a demand for the development of a support system that can make optimal proposals according to customer characteristics without depending on the individual abilities of salespersons.

Means for Solving the Problems

[0005] This invention provides a system that collects and analyzes customer information and automatically generates proposal strategies based on customer characteristics. Specifically, it analyzes customer information, including recent sales activities and market data, and extracts customer preferences using natural language processing technology. Furthermore, it generates proposal strategies optimized for the characteristics of each sales representative and presents these proposals to the sales representatives, enabling sales representatives, including new recruits, to conduct sales activities efficiently. This system reduces the reliance on individual sales activities and enables high sales performance across the entire organization.

[0006] "Customer information" refers to data necessary for sales activities, such as the financial data of customer companies, industry trends, past transaction history, and communication history of customer representatives.

[0007] "Analysis" refers to technical methods used to analyze collected customer information and clarify customer characteristics, preferences, and needs.

[0008] A "sales representative" refers to a person whose role is to propose a company's products and services to customers and to handle contracts and sales.

[0009] A "proposal strategy" refers to a policy for selecting the most suitable products and sales methods based on the customer's characteristics and preferences, and then proposing them to the customer.

[0010] "Generative AI" refers to artificial intelligence that uses technologies such as machine learning and natural language processing to automatically generate proposal strategies and materials based on various data from sales activities.

[0011] "Natural language processing technology" refers to techniques that enable computers to understand human language, and it is a method used to extract customer intentions and preferences from text data.

[0012] "Automatic document generation" refers to the process by which a computer automatically creates proposal materials and meeting minutes necessary for sales activities in a specified format.

[0013] "Support tools" refer to software used by sales representatives to efficiently carry out sales activities, such as schedule management and follow-up assistance. [Brief explanation of the drawing]

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

Embodiments for Carrying Out the Invention

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

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

[0017] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

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

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

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

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

[0022] [First Embodiment]

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

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

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

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

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

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

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

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

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

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

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

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

[0035] This invention is a system that supports sales activities based on customer information, and consists of a server, a terminal, and user interaction. An example of the program is described below in natural language.

[0036] First, the server collects and stores customer-related information from the company's internal database and external APIs. This data includes the customer company's financial status, industry trends, and past transaction history. It also includes recent communication and visit history with customers entered by the user via their device.

[0037] Next, the server analyzes the collected data to identify customer preferences and characteristics. This analysis utilizes generative AI and natural language processing technologies to extract customer needs and preferred sales styles. Based on this analysis, a proposal strategy is generated to provide the optimal solution for each customer.

[0038] The terminal presents this generated proposal strategy to the user, who can select the best option from multiple proposals. At this stage, the proposals can be customized, allowing the user to choose proposals that match their sales skills.

[0039] Furthermore, the server automatically generates meeting minute templates and proposal documents as support tools to streamline sales activities. This allows users to conduct business negotiations quickly and effectively, and to follow up with customers without any delays.

[0040] As a concrete example, when a user makes a proposal to a new customer, the server analyzes the customer's past purchasing patterns and interests, and determines that they are interested in digital solutions. Based on this information, it generates a proposal style, such as a more assertive approach, and presents it to the user on their device. The user then uses this to prepare specific proposal content and use it during negotiations, thereby accurately meeting the customer's needs and closing the deal.

[0041] Thus, the present invention aims to improve the efficiency and results of sales activities by supporting customer management and proposal activities in a data-driven manner.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] The server begins collecting customer information. Specifically, it connects with the company's internal database to retrieve customer company information, past transaction history, and industry trend information, and stores this information in an integrated database. It also downloads market trends and industry-specific news via external APIs to supplement relevant information.

[0045] Step 2:

[0046] The terminal provides the user with an input interface. Here, the user provides the system with up-to-date data by entering information such as recent interactions with customers, key conversation points, and upcoming visit schedules. This allows the server to link the collected data with user input data to build a more detailed customer profile.

[0047] Step 3:

[0048] The server analyzes the collected data. Using generative AI, it analyzes customer preferences, interests, and past behavioral patterns to identify customer characteristics. In this process, natural language processing techniques are used to extract emotional and interest tendencies from text data, and together with numerical data, it forms an overall customer personality.

[0049] Step 4:

[0050] The server generates an optimal proposal strategy based on customer characteristics and considering the sales representative's strengths. This strategy includes customized products and sales tone to best meet customer needs and interests. The strategy also incorporates the order and focus items of each proposal presentation, tailored to its specific objectives.

[0051] Step 5:

[0052] The terminal presents the generated proposed strategies to the user in a dashboard format. The proposed strategies are visualized, and the user can select from multiple strategic options. If necessary, the user is provided with tools to edit the proposed strategies and make fine adjustments to suit the customer's situation.

[0053] Step 6:

[0054] The server provides sales support tools and automatically generates proposal documents and meeting minute templates based on the proposal strategy. These documents are immediately downloadable, allowing users to quickly prepare for sales activities. Follow-up planning is also supported at this stage.

[0055] Step 7:

[0056] Users efficiently conduct business negotiations with customers and aim to close deals using proposal materials and strategies supported by the system. After the negotiation, users record follow-up activities in the system and update the data for future proposals.

[0057] By connecting each step in this way, sales activities are made data-driven and efficient.

[0058] (Example 1)

[0059] 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."

[0060] In sales activities, providing optimal proposals to each customer requires collecting and analyzing large amounts of information and creating proposals in a format suitable for the individual salesperson. However, this process is extremely time-consuming and labor-intensive, and heavily relies on the individual capabilities of the salesperson. Therefore, there is a need for efficiency improvements and standardization.

[0061] 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.

[0062] In this invention, the server includes means for collecting information and analyzing attributes based on that information, means for generating a strategy optimized for the attributes of the person in charge based on the analysis results, and means for presenting the generated strategy to the person in charge. This enables the efficient and effective generation of data-driven proposals.

[0063] "Information" is a general term for a variety of data, including customer data, past performance, and industry trends.

[0064] "Attributes" refer to characteristic information that indicates the preferences, characteristics, and style of a customer or person in charge.

[0065] A "strategy" is a proposal or plan derived from gathered information to achieve a specific objective.

[0066] "Responsible person" refers to an individual or person who is responsible for conducting sales or business negotiations.

[0067] "Tools" refers to support materials such as automatically generated documents and templates used to assist with tasks.

[0068] "Generative AI technology" is a technology that uses artificial intelligence to analyze data and automatically generate new insights and suggestions.

[0069] A description of the embodiment for carrying out the invention will be provided.

[0070] This system is primarily composed of three elements: a server, terminals, and users. The server is responsible for collecting and storing necessary data from internal corporate databases and external information sources. Specifically, the server collects financial information of client companies, industry trend data, and historical business transaction records. Furthermore, it also collects recent communication history information with customers entered by users via terminals.

[0071] The server uses this collected data for analysis. This analysis employs advanced data analytics combining natural language processing and generative AI technologies. During this analysis, the server extracts customer needs and generates prompts to derive the optimal proposal for the sales representative.

[0072] The terminal plays a role in this process by presenting the generated proposal strategies to the user. The user compares multiple proposals displayed on the screen and selects the best one. Furthermore, the user can customize the proposal content by leveraging their own experience and knowledge. Through this process, the user can quickly and effectively create proposals that meet customer expectations.

[0073] Furthermore, the server automatically generates various documents and templates to support sales activities. Specific examples include meeting minute templates and proposal documents. This significantly reduces the time spent preparing proposals, enabling smoother negotiations and follow-ups.

[0074] Examples of prompts include instructions such as, "Based on customer information, generate proposals for customers interested in digital solutions. Consider past purchasing patterns and industry trends, and include an appropriate proposal style." Such prompts enable the generating AI model to produce highly accurate proposals.

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

[0076] Step 1:

[0077] The server collects data. Specifically, it retrieves customer financial information and transaction history from an internal database and collects industry trend information using external APIs. It also obtains the latest communication data with customers entered by users through terminals. Inputs include database queries and API requests, and the output is a structured dataset.

[0078] Step 2:

[0079] The server analyzes data using a generative AI model and natural language processing technology. It analyzes the input data and extracts customer needs and characteristics. Specifically, it inputs prompt sentences into the generative AI and obtains information about customer preferences and recommended suggestion styles as output.

[0080] Step 3:

[0081] The server generates the optimal proposal strategy based on the analysis results. Based on the analysis results provided by the generating AI, a sales model is generated using prompt messages, and this is then refined into a proposal strategy. The input is the analysis results from step 2, and the output is the proposal strategy presented to the user.

[0082] Step 4:

[0083] The terminal presents the generated proposed strategies to the user. The user reviews the proposed strategies on the terminal screen, selects a strategy based on their own judgment, and customizes it. The input is information about the strategies, and the output is the final proposed strategy selected and customized by the user.

[0084] Step 5:

[0085] The server automatically generates support materials and templates to improve the efficiency of sales activities. Specifically, it generates meeting minute templates and proposal materials based on the selected proposal content. The input is the proposal content chosen by the user, and the output is specific sales support tools.

[0086] (Application Example 1)

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

[0088] In modern society, with the widespread adoption of electronic payment services, there is a growing demand for swift and effective sales activities. However, accurately understanding customer needs and market trends, and providing optimal solutions accordingly, is a significant challenge for sales representatives. Furthermore, the preparation of proposals and the ability to respond quickly during negotiations are required, but traditional methods are insufficient to address these needs.

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

[0090] In this invention, the server includes means for collecting customer information and analyzing customer characteristics based on said customer information; means for generating a proposal plan optimized for the characteristics of the sales representative based on the analysis results; means for presenting the generated proposal plan to the sales representative; means for analyzing the customer's transaction history and market trends related to electronic payment services and generating an optimal payment solution proposal; and means for displaying the proposal content on a mobile communication device or visual aid device. This enables sales representatives to quickly propose payment solutions that meet customer needs and conduct effective sales activities.

[0091] "Customer information" refers to data such as attributes, behavioral history, purchase history, preferences, and transaction history related to a specific user.

[0092] "Means for analyzing characteristics" refers to a function that analyzes collected user information and processes it to identify the user's attributes and preferences.

[0093] "Means for generating proposal plans" refers to a system for creating optimal sales strategies and service plans based on the characteristics of the user.

[0094] "Means of presentation to sales representatives" refers to interfaces and functions for displaying generated sales strategies and proposals to sales representatives.

[0095] An "electronic payment service" is a system that allows payments for goods and services to be made using digital technology, without the use of cash or physical cards.

[0096] "Methods for analyzing transaction history and market trends" refer to technologies that identify needs and opportunities by analyzing users' past purchase and payment patterns as well as market trends.

[0097] "Methods for generating payment solution proposals" refer to methods for considering the most suitable payment system or method for the user and creating a proposal based on that.

[0098] "Portable communication devices or visual aids" refers to portable or wearable devices used to display or view information.

[0099] To implement this invention, a system consisting of a server, terminals, and users is used. The server collects customer information from the company's internal database and external APIs. The data collected includes the customer company's transaction history, market trends, and purchasing patterns. The server manages this information using a database management system (e.g., MySQL®, PostgreSQL).

[0100] The collected data is analyzed on the server, and customer characteristics are analyzed using generative AI models (e.g., OpenAI® GPT-3®) and natural language processing libraries (e.g., NLTK, spaCy). From this analysis, customer needs and market potential are derived. Furthermore, the transaction history related to electronic payments is analyzed, and the most appropriate payment solution is generated as a proposal. The generated proposal is presented to the user via mobile communication devices (e.g., iPhone®, Android® devices) or visual assistance devices (e.g., Google® Glass®, Vuzix).

[0101] Users access sales strategies and payment solution proposals displayed on their devices using mobile communication devices or visual aids. This interface operates via an API integration framework (e.g., RESTful API). The proposals can be further customized through user interaction, enhancing their effectiveness.

[0102] For example, if a sales representative proposes an electronic payment system to a new restaurant client, the server will analyze the restaurant's past payment data and trends to determine that proposing the latest QR code (registered trademark) payment system would be effective. Based on this information, the sales representative can make a proposal on the spot and pique the client's interest.

[0103] An example of a prompt for the generating AI model is, "Based on the customer's industry trends and past payment history, please propose the latest electronic payment solutions for the food and beverage industry." In this way, the server strongly supports sales activities by performing appropriate information analysis and proposal generation.

[0104] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0105] Step 1:

[0106] The server collects customer information from the company's internal database and external APIs. Inputs include customer company transaction history, market trends, and purchasing patterns, and this data is stored using a database management system. The output is a structured dataset.

[0107] Step 2:

[0108] The server analyzes the collected data. The input is the dataset accumulated in step 1. Natural language processing techniques are used to extract customer characteristics, and a generative AI model is used to analyze needs and market potential. The output is customer characteristics and insights for proposals.

[0109] Step 3:

[0110] The server generates a proposed plan for electronic payment services based on the analysis results. The input is the analysis results from step 2, and this is used to create a proposal for the optimal payment solution for the customer. The output is the generated proposed plan.

[0111] Step 4:

[0112] The server prepares the generated proposed plan for display on a mobile communication device or visual aid. The input is the proposed plan created in step 3, which is sent to the terminal via the API integration framework. The output is digital information as a proposed plan that can be displayed on the terminal.

[0113] Step 5:

[0114] The user operates the terminal to review and customize the displayed proposal plan. The input is the proposal plan displayed on the terminal in step 4. The user edits the proposal content through the interface to create the optimal content tailored to the customer. The output is the proposal plan as a customized sales strategy.

[0115] Step 6:

[0116] The user presents a customized proposal plan to the customer during negotiations using a terminal or visual aid. The input is the customized proposal plan from step 5, which serves as the basis for the negotiation. The output is customer feedback and the outcome of the negotiation.

[0117] 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.

[0118] This invention provides a sales support system that incorporates an emotion engine to support sales activities based on customer information, thereby offering proposal strategies that are linked to the user's emotions. This system is implemented through interactions between a server, a terminal, and the user. An example of the program is described below in natural language.

[0119] First, the server collects customer-related information. This includes background data on the customer company, industry trends, and past transaction information. The terminal also provides an interface where the user can input details of their most recent interactions with the customer, which allows the server to build a more refined customer profile.

[0120] Next, the server analyzes customer information to identify customer characteristics and preferences. The emotion engine recognizes the user's emotional state in real time and incorporates that information into the analysis process. Natural language processing technology extracts the customer's latent emotions from text data, and the emotion engine analyzes the user's tone of voice and facial expressions to determine the user's emotional biases.

[0121] Based on the analysis results, the server generates a proposal strategy tailored to the characteristics of the sales representative. The emotion engine recognizes changes in the user's emotions and can dynamically adjust the proposal strategy based on the results. Specifically, it optimizes the presentation of proposal content at a timing that matches the customer's level of understanding and current interests, so that the user can make proposals smoothly.

[0122] The terminal presents the generated proposal strategy to the user. The strategy is visualized on a dashboard, allowing the user to choose the most suitable option from the different proposals. Based on the user's emotional state, as detected by the emotion engine, the proposal materials are automatically adjusted and optimized, allowing the user to proceed with negotiations with confidence.

[0123] For example, when a user shows positive emotions in response to customer feedback during a meeting, the server identifies this and automatically generates materials to support further exploration of the products the system proposes. Conversely, if the user feels stressed or anxious, the system switches to a strategy of temporarily refraining from making proposals, thereby supporting the smooth continuation of negotiations.

[0124] Therefore, the present invention provides a proposal strategy that takes into account the emotional state of sales representatives in real time, thereby improving the effectiveness of business negotiations and the closing rate.

[0125] The following describes the processing flow.

[0126] Step 1:

[0127] The server begins collecting customer information. It integrates internal databases with external sources to obtain data such as background information on the customer company, past transaction history, and industry trends. Furthermore, it stores this information in a database for centralized management.

[0128] Step 2:

[0129] The terminal provides the user with an input interface. The user directly inputs information such as the latest interactions with customers, conversation points, and scheduled visits. This enables real-time data updates, ensuring that customer information is always up-to-date.

[0130] Step 3:

[0131] The server analyzes collected customer information to identify customer characteristics and preferences. Generative AI is used to analyze past customer behavior patterns and text data to extract potential needs and interests. Furthermore, natural language processing techniques are used to infer customer emotional responses.

[0132] Step 4:

[0133] An emotion engine is installed on the device, recognizing the user's voice tone and facial expressions in real time. The emotion engine analyzes these emotional indicators to identify the user's emotional state. This analysis result is used to customize the proposed strategy.

[0134] Step 5:

[0135] The server generates a proposal strategy optimized for the sales representative's characteristics based on analysis results and data from the emotion engine. It adjusts the proposal content and materials to ensure the user can conduct the sales negotiation most effectively. This strategy is customized based on the timing and methods for engaging the customer.

[0136] Step 6:

[0137] The device presents the generated suggestion strategy to the user. It is visualized on a dashboard, allowing the user to choose the best option from multiple suggestion choices. Suggestion content is automatically adjusted based on feedback from the emotion engine, so the user can confidently implement the suggestions.

[0138] Step 7:

[0139] The server provides materials and tools to support sales activities. Specifically, it automatically generates presentation materials and meeting minute templates linked to the proposal strategy, supporting the user's sales activities. This allows users to quickly prepare for business negotiations and effectively pursue results.

[0140] By coordinating each step in this way, a comprehensive sales support system that takes into account the user's emotional state is built.

[0141] (Example 2)

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

[0143] In traditional sales activities, salespeople devise proposal strategies based on customer information, but there is a challenge in understanding customer emotions and preferences in real time and immediately reflecting them in sales strategies. Furthermore, there is a lack of dynamic adjustments that take into account the salesperson's past performance and current emotional state, which hinders improvements in deal closing rates and operational efficiency.

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

[0145] In this invention, the server includes means for collecting customer information and analyzing customer characteristics based on said customer information; means for generating a proposal strategy optimized for the characteristics of sales personnel based on the analysis results; and means for recognizing the emotional state of the user using emotion analysis technology and dynamically adjusting the proposal strategy to reflect that state. As a result, sales personnel can use proposal strategies that are immediately adjusted based on the customer's emotions and preferences, thereby improving the closing rate of deals and realizing effective sales activities.

[0146] "Customer information" refers to information that includes background data about customers, industry trends, and past transaction history.

[0147] "Customer characteristics" refer to the features related to a customer's purchasing tendencies, behavioral patterns, preferences, and needs.

[0148] "Analysis results" refer to information about customer characteristics and trends obtained by utilizing analytical technology based on customer data.

[0149] "Proposal strategy" refers to the content and strategy of proposals that sales personnel make to customers, and is optimized based on the characteristics of the customer and the characteristics of the sales personnel.

[0150] "Emotional analysis technology" refers to a technology that analyzes a user's voice tone, facial expressions, and language to understand their emotional state in real time.

[0151] "Dynamic adjustment" refers to changing and optimizing the proposed strategy in real time based on analysis results and information obtained from sentiment analysis technology.

[0152] "Visualization" is the process of representing information and data visually, using graphs, charts, and other tools to make it easy for users to understand.

[0153] A "sales professional" refers to an individual whose purpose is to propose products or services to customers and to complete transactions.

[0154] This system collects customer information, uses sentiment analysis technology to understand the user's emotional state, and generates proposal strategies to support sales activities. Specifically, the server collects data from the internet and corporate databases to gather customer-related information. This information includes customer background data, industry trends, and past transaction history. The data is cleaned and organized using the Python pandas library and stored in the database.

[0155] The terminal provides an interface for users to input customer interaction information. Users use this interface to input conversation details and needs, updating the information. Emotion analysis technology uses devices such as cameras and microphones to recognize the user's voice tone and facial expressions in real time, and the emotion engine incorporates this into the analysis process. A generative AI model then generates the optimal suggestion strategy from this information.

[0156] This system visualizes the generated proposed strategies on a dashboard and displays them on the user's device. Users can compare multiple proposed options and decide which strategy to adopt. In this process, Python libraries such as matplotlib and seaborn are used for data visualization.

[0157] For example, if a user shows positive emotions towards a customer's response during a meeting, the server will detect this and generate materials to support further exploration of relevant products. If the user feels stressed or anxious, the system will dynamically switch to a strategy that either refrains from making suggestions or mitigates them. An example of a prompt message might be: "Generate the optimal suggestion strategy based on the following customer data. The user has shown positive emotions towards the customer's response."

[0158] In this way, salespeople can make real-time proposals that are tailored to the customer's emotions and needs, improving the closing rate and effectiveness of sales negotiations.

[0159] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0160] Step 1:

[0161] The server collects customer information. It uses data obtained from the internet and corporate databases as input, and generates an integrated information set as output, including customer background data, industry trends, and past transaction history. Specifically, it automatically collects online information using web scraping techniques and cleans and organizes the data using the Python pandas library.

[0162] Step 2:

[0163] The terminal provides an interface for users to input the latest interaction information with customers. As input, users enter conversation details and needs in text format, and as output, it generates up-to-date information to be added to the customer profile. Specifically, information is recorded on the tablet or PC screen and sent directly to the server.

[0164] Step 3:

[0165] The server analyzes the collected customer information. It receives integrated customer data and the latest information from users as input, and generates profiles that reveal customer characteristics and preferences as output. Specifically, it utilizes machine learning algorithms to analyze customer purchasing trends and characteristics.

[0166] Step 4:

[0167] The server uses emotion analysis technology to recognize the user's emotional state. It takes the user's voice tone and facial expression data from sensors as input and generates data indicating the user's current emotional state as output. Specifically, it collects data through cameras and microphones and analyzes it using an emotion engine.

[0168] Step 5:

[0169] The server generates proposal strategies based on analysis results and sentiment information. Using customer profiles and user sentiment data as input, it generates optimized proposal strategies for sales professionals as output. Specifically, a generative AI model constructs proposal strategies from the data and creates a list of appropriate products and services.

[0170] Step 6:

[0171] The terminal presents the generated proposed policies to the user. Using the proposed policies received from the server as input, the output is displayed on a visualized dashboard, allowing the user to select the optimal proposal. Specifically, data visualization is performed using libraries such as Python's matplotlib and seaborn, providing an easy-to-use interface.

[0172] Step 7:

[0173] Users conduct actual sales activities based on the presented proposal strategy. Using visualized proposal information as input, the system outputs appropriate proposals to customers, aiming to close deals. Specifically, users make proposals in actual sales situations, provide feedback to the system based on customer reactions, and adjust proposals as needed.

[0174] (Application Example 2)

[0175] 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".

[0176] In modern sales activities, it is essential to tailor proposals to customers' needs and emotions through effective dialogue. Meeting these needs requires considering not only the customer's characteristics but also the salesperson's own emotional state, but there is a lack of efficient means to do so. Solving this problem and improving the closing rate of deals is urgently needed.

[0177] 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.

[0178] In this invention, the server includes means for collecting customer information and analyzing customer characteristics, means for generating a proposal strategy optimized for the characteristics of the sales representative, and means for recognizing the user's emotional state in real time and dynamically adjusting the proposal strategy based on that. This enables sales representatives to instantly take the optimal approach tailored to the customer's characteristics and current emotions, making sales negotiations more effective and smoother.

[0179] "Customer information" refers to data collected in order to understand customers during sales activities, and includes background data, industry trends, and past transaction information.

[0180] "Characteristics" refer to the unique attributes and tendencies of individual customers and sales representatives, and are important factors to consider when generating personalized proposal strategies.

[0181] "Emotional state" refers to a user's emotional response and psychological state, and is an indicator element used in the dynamic adjustment of the proposed strategy.

[0182] A "proposal strategy" is a systematically designed plan outlining the content and approach of proposals that sales representatives offer to customers, and it is optimized to reflect the customer's characteristics and the sales representative's emotional state.

[0183] "Dynamic adjustment" refers to the process of modifying the system's behavior in real time based on changes in circumstances and information in order to achieve optimal results.

[0184] "Materials" refer to informational media provided to support sales activities, including product details, past performance, and success stories.

[0185] "Equipment" refers to tools and devices used to facilitate sales activities, and is utilized in various business negotiation scenarios.

[0186] This invention constructs a system to support sales activities and optimize customer interactions. The server is responsible for collecting customer information and analyzing its characteristics. This information includes background data on the customer company, industry trends, and past transaction information. The terminal provides the user with an interface where they can input details of their interactions with customers. Based on this, the server constructs a more refined customer profile.

[0187] Recognizing the user's emotional state utilizes natural language processing and facial expression analysis technologies. Specifically, voice data is analyzed via the Google Cloud Natural Language API, and visual data is analyzed using Microsoft Azure's Face API. The results of this analysis allow us to determine the customer's potential emotions and the user's emotional biases, and incorporate this into our proposal strategy.

[0188] The optimized proposal strategy is delivered to sales representatives via their devices. A key feature of this process is that the system incorporates real-time emotional data from the sales representatives and makes dynamic adjustments to ensure smooth negotiations. Users are presented with visualized strategies on a dashboard, allowing them to select the most suitable proposal from a variety of options.

[0189] For example, when proposing security services, if a customer expresses concerns, the proposal can be immediately adjusted, and additional information can be provided to alleviate their worries. In this way, it is possible to support the progress of the sales negotiation and aim to improve the closing rate.

[0190] Examples of prompts to input into a generative AI model:

[0191] Regarding "dynamically adjusting proposals based on customer reactions," please explain how to assess the sales representative's emotions in real time and describe best practice proposals based on that assessment.

[0192] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0193] Step 1:

[0194] The server collects customer information and stores it in a database. Inputs include background data on the client company, industry trends, and historical transaction information, which are then analyzed to create customer profiles. This prepares the system to identify customer characteristics and preferences.

[0195] Step 2:

[0196] The terminal provides an interface for users to input details of their most recent interactions with customers. This input data, including voice and text data from the user, is sent to the server, improving the accuracy of the customer profile. Based on the input information, the server extracts even more detailed customer characteristics.

[0197] Step 3:

[0198] The server analyzes the user's emotional state in real time. This process uses emotional data acquired from sensors as input and utilizes natural language processing and facial recognition technologies. This allows the system to determine the user's emotional biases and process the data to integrate it with the customer profile.

[0199] Step 4:

[0200] The server generates a proposal strategy based on analyzed customer characteristics and user emotional states. This strategy is optimized for the characteristics of the sales representative, and the generated strategy is presented on the terminal as a dashboard. The output includes the proposal strategy and related materials, visualizing information useful for sales activities.

[0201] Step 5:

[0202] The user reviews the proposed strategy generated through their device and makes choices to ensure the smooth progress of the business negotiation. At this time, the user can respond flexibly to customer reactions based on the presented strategy. The final output supports the user's decision-making by providing optimal actions according to the progress of the business negotiation.

[0203] 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.

[0204] Data generation model 58 is a type of 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.

[0205] 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.

[0206] [Second Embodiment]

[0207] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0208] 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.

[0209] 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).

[0210] 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.

[0211] 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.

[0212] 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).

[0213] 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.

[0214] 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.

[0215] 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.

[0216] 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.

[0217] 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.

[0218] 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".

[0219] This invention is a system that supports sales activities based on customer information, and consists of a server, a terminal, and user interaction. An example of the program is described below in natural language.

[0220] First, the server collects and stores customer-related information from the company's internal database and external APIs. This data includes the customer company's financial status, industry trends, and past transaction history. It also includes recent communication and visit history with customers entered by the user via their device.

[0221] Next, the server analyzes the collected data to identify customer preferences and characteristics. This analysis utilizes generative AI and natural language processing technologies to extract customer needs and preferred sales styles. Based on this analysis, a proposal strategy is generated to provide the optimal solution for each customer.

[0222] The terminal presents this generated proposal strategy to the user, who can select the best option from multiple proposals. At this stage, the proposals can be customized, allowing the user to choose proposals that match their sales skills.

[0223] Furthermore, the server automatically generates meeting minute templates and proposal documents as support tools to streamline sales activities. This allows users to conduct business negotiations quickly and effectively, and to follow up with customers without any delays.

[0224] As a concrete example, when a user makes a proposal to a new customer, the server analyzes the customer's past purchasing patterns and interests, and determines that they are interested in digital solutions. Based on this information, it generates a proposal style, such as a more assertive approach, and presents it to the user on their device. The user then uses this to prepare specific proposal content and use it during negotiations, thereby accurately meeting the customer's needs and closing the deal.

[0225] Thus, the present invention aims to improve the efficiency and results of sales activities by supporting customer management and proposal activities in a data-driven manner.

[0226] The following describes the processing flow.

[0227] Step 1:

[0228] The server begins collecting customer information. Specifically, it connects with the company's internal database to retrieve customer company information, past transaction history, and industry trend information, and stores this information in an integrated database. It also downloads market trends and industry-specific news via external APIs to supplement relevant information.

[0229] Step 2:

[0230] The terminal provides the user with an input interface. Here, the user provides the system with up-to-date data by entering information such as recent interactions with customers, key conversation points, and upcoming visit schedules. This allows the server to link the collected data with user input data to build a more detailed customer profile.

[0231] Step 3:

[0232] The server analyzes the collected data. Using generative AI, it analyzes customer preferences, interests, and past behavioral patterns to identify customer characteristics. In this process, natural language processing techniques are used to extract emotional and interest tendencies from text data, and together with numerical data, it forms an overall customer personality.

[0233] Step 4:

[0234] The server generates an optimal proposal strategy based on customer characteristics and considering the sales representative's strengths. This strategy includes customized products and sales tone to best meet customer needs and interests. The strategy also incorporates the order and focus items of each proposal presentation, tailored to its specific objectives.

[0235] Step 5:

[0236] The terminal presents the generated proposed strategies to the user in a dashboard format. The proposed strategies are visualized, and the user can select from multiple strategic options. If necessary, the user is provided with tools to edit the proposed strategies and make fine adjustments to suit the customer's situation.

[0237] Step 6:

[0238] The server provides sales support tools and automatically generates proposal documents and meeting minute templates based on the proposal strategy. These documents are immediately downloadable, allowing users to quickly prepare for sales activities. Follow-up planning is also supported at this stage.

[0239] Step 7:

[0240] Users efficiently conduct business negotiations with customers and aim to close deals using proposal materials and strategies supported by the system. After the negotiation, users record follow-up activities in the system and update the data for future proposals.

[0241] By connecting each step in this way, sales activities are made data-driven and efficient.

[0242] (Example 1)

[0243] 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."

[0244] In sales activities, providing optimal proposals to each customer requires collecting and analyzing large amounts of information and creating proposals in a format suitable for the individual salesperson. However, this process is extremely time-consuming and labor-intensive, and heavily relies on the individual capabilities of the salesperson. Therefore, there is a need for efficiency improvements and standardization.

[0245] 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.

[0246] In this invention, the server includes means for collecting information and analyzing attributes based on that information, means for generating a strategy optimized for the attributes of the person in charge based on the analysis results, and means for presenting the generated strategy to the person in charge. This enables the efficient and effective generation of data-driven proposals.

[0247] "Information" is a general term for a variety of data, including customer data, past performance, and industry trends.

[0248] "Attributes" refer to characteristic information that indicates the preferences, characteristics, and style of a customer or person in charge.

[0249] A "strategy" is a proposal or plan derived from gathered information to achieve a specific objective.

[0250] "Responsible person" refers to an individual or person who is responsible for conducting sales or business negotiations.

[0251] "Tools" refers to support materials such as automatically generated documents and templates used to assist with tasks.

[0252] "Generative AI technology" is a technology that uses artificial intelligence to analyze data and automatically generate new insights and suggestions.

[0253] A description of the embodiment for carrying out the invention will be provided.

[0254] This system is primarily composed of three elements: a server, terminals, and users. The server is responsible for collecting and storing necessary data from internal corporate databases and external information sources. Specifically, the server collects financial information of client companies, industry trend data, and historical business transaction records. Furthermore, it also collects recent communication history information with customers entered by users via terminals.

[0255] The server uses this collected data for analysis. This analysis employs advanced data analytics combining natural language processing and generative AI technologies. During this analysis, the server extracts customer needs and generates prompts to derive the optimal proposal for the sales representative.

[0256] The terminal plays a role in this process by presenting the generated proposal strategies to the user. The user compares multiple proposals displayed on the screen and selects the best one. Furthermore, the user can customize the proposal content by leveraging their own experience and knowledge. Through this process, the user can quickly and effectively create proposals that meet customer expectations.

[0257] Furthermore, the server automatically generates various documents and templates to support sales activities. Specific examples include meeting minute templates and proposal documents. This significantly reduces the time spent preparing proposals, enabling smoother negotiations and follow-ups.

[0258] Examples of prompts include instructions such as, "Based on customer information, generate proposals for customers interested in digital solutions. Consider past purchasing patterns and industry trends, and include an appropriate proposal style." Such prompts enable the generating AI model to produce highly accurate proposals.

[0259] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0260] Step 1:

[0261] The server collects data. Specifically, it retrieves customer financial information and transaction history from an internal database and collects industry trend information using external APIs. It also obtains the latest communication data with customers entered by users through terminals. Inputs include database queries and API requests, and the output is a structured dataset.

[0262] Step 2:

[0263] The server analyzes data using a generative AI model and natural language processing technology. It analyzes the input data and extracts customer needs and characteristics. Specifically, it inputs prompt sentences into the generative AI and obtains information about customer preferences and recommended suggestion styles as output.

[0264] Step 3:

[0265] The server generates the optimal proposal strategy based on the analysis results. Based on the analysis results provided by the generating AI, a sales model is generated using prompt messages, and this is then refined into a proposal strategy. The input is the analysis results from step 2, and the output is the proposal strategy presented to the user.

[0266] Step 4:

[0267] The terminal presents the generated proposed strategies to the user. The user reviews the proposed strategies on the terminal screen, selects a strategy based on their own judgment, and customizes it. The input is information about the strategies, and the output is the final proposed strategy selected and customized by the user.

[0268] Step 5:

[0269] The server automatically generates support materials and templates to improve the efficiency of sales activities. Specifically, it generates meeting minute templates and proposal materials based on the selected proposal content. The input is the proposal content chosen by the user, and the output is specific sales support tools.

[0270] (Application Example 1)

[0271] 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."

[0272] In modern society, with the widespread adoption of electronic payment services, there is a growing demand for swift and effective sales activities. However, accurately understanding customer needs and market trends, and providing optimal solutions accordingly, is a significant challenge for sales representatives. Furthermore, the preparation of proposals and the ability to respond quickly during negotiations are required, but traditional methods are insufficient to address these needs.

[0273] 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.

[0274] In this invention, the server includes means for collecting customer information and analyzing customer characteristics based on said customer information; means for generating a proposal plan optimized for the characteristics of the sales representative based on the analysis results; means for presenting the generated proposal plan to the sales representative; means for analyzing the customer's transaction history and market trends related to electronic payment services and generating an optimal payment solution proposal; and means for displaying the proposal content on a mobile communication device or visual aid device. This enables sales representatives to quickly propose payment solutions that meet customer needs and conduct effective sales activities.

[0275] "Customer information" refers to data such as attributes, behavioral history, purchase history, preferences, and transaction history related to a specific user.

[0276] "Means for analyzing characteristics" refers to a function that analyzes collected user information and processes it to identify the user's attributes and preferences.

[0277] "Means for generating proposal plans" refers to a system for creating optimal sales strategies and service plans based on the characteristics of the user.

[0278] "Means of presentation to sales representatives" refers to interfaces and functions for displaying generated sales strategies and proposals to sales representatives.

[0279] "Electronic payment service" refers to a mechanism for paying for goods and services using digital technology without using cash or physical cards.

[0280] "Means for analyzing transaction history and market trends" refers to a technology for identifying needs and opportunities by analyzing users' past purchase and payment patterns and market trends.

[0281] "Means for generating payment solution proposals" refers to a method for considering the most suitable payment system and means for users and creating them as proposals.

[0282] "Mobile communication device or visual assistance device" refers to portable devices or wearable devices used for displaying or checking information.

[0283] To implement this invention, a system composed of a server, a terminal, and a user is used. The server collects customer-related information from the enterprise's internal database and external APIs. The data collected here includes the transaction history, market trends, purchase patterns, etc. of the customer enterprise. The server manages this information using a database management system (e.g., MySQL, PostgreSQL).

[0284] The collected data is analyzed within the server, and the characteristics of the customer are analyzed using a generated AI model (e.g., OpenAI GPT-3) and a natural language processing library (e.g., NLTK, spaCy). From this analysis, the needs of the customer and the potential in the market are derived. Furthermore, the transaction history related to electronic payment is analyzed, and the most appropriate payment solution is generated as a proposal. The generated proposal is presented to the user through a mobile communication device (e.g., iPhone, Android device) or a visual assistance device (e.g., Google Glass, Vuzix).

[0285] The user uses a mobile communication device or a visual assistance device to check the business strategies and payment solution proposals displayed on the terminal. This interface operates via an API integration framework (e.g., RESTful API). The content of the proposal can be further customized according to the user's operations, enhancing the effectiveness of the proposal.

[0286] As a specific example, when a salesperson proposes an electronic payment system to a restaurant, which is a new customer, the server analyzes the restaurant's past payment data and trends and determines that it is effective to propose the latest QR code payment system. Based on this information, the salesperson can make a proposal on the spot and attract the customer's interest.

[0287] An example of a prompt sentence for the generative AI model is "Please propose the latest electronic payment solutions for the food and beverage industry based on the customer's industry trends and past payment history." In this way, the server strongly supports sales activities by performing appropriate information analysis and proposal generation.

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

[0289] Step 1:

[0290] The server collects customer information from the enterprise's internal database and external APIs. The inputs are the customer enterprise's transaction history, market trends, purchase patterns, etc., and these data are accumulated using a database management system. The output is a structured dataset.

[0291] Step 2:

[0292] The server analyzes the collected data. The input is the dataset accumulated in Step 1. For this, natural language processing technology is used to extract customer characteristics, and a generative AI model is used to analyze needs and market potential. The output is customer characteristics and insights for proposals.

[0293] Step 3:

[0294] The server generates a proposed plan for electronic payment services based on the analysis results. The input is the analysis results from step 2, and this is used to create a proposal for the optimal payment solution for the customer. The output is the generated proposed plan.

[0295] Step 4:

[0296] The server prepares the generated proposed plan for display on a mobile communication device or visual aid. The input is the proposed plan created in step 3, which is sent to the terminal via the API integration framework. The output is digital information as a proposed plan that can be displayed on the terminal.

[0297] Step 5:

[0298] The user operates the terminal to review and customize the displayed proposal plan. The input is the proposal plan displayed on the terminal in step 4. The user edits the proposal content through the interface to create the optimal content tailored to the customer. The output is the proposal plan as a customized sales strategy.

[0299] Step 6:

[0300] The user presents a customized proposal plan to the customer during negotiations using a terminal or visual aid. The input is the customized proposal plan from step 5, which serves as the basis for the negotiation. The output is customer feedback and the outcome of the negotiation.

[0301] 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.

[0302] This invention provides a sales support system that incorporates an emotion engine to support sales activities based on customer information, thereby offering proposal strategies that are linked to the user's emotions. This system is implemented through interactions between a server, a terminal, and the user. An example of the program is described below in natural language.

[0303] First, the server collects customer-related information. This includes background data on the customer company, industry trends, and past transaction information. The terminal also provides an interface where the user can input details of their most recent interactions with the customer, which allows the server to build a more refined customer profile.

[0304] Next, the server analyzes customer information to identify customer characteristics and preferences. The emotion engine recognizes the user's emotional state in real time and incorporates that information into the analysis process. Natural language processing technology extracts the customer's latent emotions from text data, and the emotion engine analyzes the user's tone of voice and facial expressions to determine the user's emotional biases.

[0305] Based on the analysis results, the server generates a proposal strategy tailored to the characteristics of the sales representative. The emotion engine recognizes changes in the user's emotions and can dynamically adjust the proposal strategy based on the results. Specifically, it optimizes the presentation of proposal content at a timing that matches the customer's level of understanding and current interests, so that the user can make proposals smoothly.

[0306] The terminal presents the generated proposal strategy to the user. The strategy is visualized on a dashboard, allowing the user to choose the most suitable option from the different proposals. Based on the user's emotional state, as detected by the emotion engine, the proposal materials are automatically adjusted and optimized, allowing the user to proceed with negotiations with confidence.

[0307] As a specific example, when the user shows positive feelings towards the customer's reaction during a meeting, the server identifies this and automatically generates materials to support the in-depth exploration of the products or services proposed by the system. On the contrary, when the user feels stressed or anxious, the system switches to a strategy of temporarily withholding the proposal to provide support for the smooth continuation of the negotiation.

[0308] Therefore, the present invention provides a proposal strategy that takes into account the emotional state of the salesperson in real time, thereby improving the effectiveness of business negotiations and the contract signing rate.

[0309] The following describes the processing flow.

[0310] Step 1:

[0311] The server starts collecting customer information. It integrates the internal database and external information sources to obtain data such as the background information of the customer company, past transaction history, and industry trends. Furthermore, it saves these information in the database for unified management.

[0312] Step 2:

[0313] The terminal provides the user with an input interface. The user directly inputs information such as the latest interactions with the customer, key points of the conversation, and visit schedules. This enables real-time data updates and keeps the customer information in an up-to-date state at all times.

[0314] Step 3:

[0315] The server analyzes the customer information collected to identify the characteristics and preferences of the customer. It uses generative AI to analyze the customer's past behavior patterns and text data to extract potential needs and interests. Furthermore, it utilizes natural language processing technology to infer the customer's emotional reactions.

[0316] Step 4:

[0317] An emotion engine is installed on the device, recognizing the user's voice tone and facial expressions in real time. The emotion engine analyzes these emotional indicators to identify the user's emotional state. This analysis result is used to customize the proposed strategy.

[0318] Step 5:

[0319] The server generates a proposal strategy optimized for the sales representative's characteristics based on analysis results and data from the emotion engine. It adjusts the proposal content and materials to ensure the user can conduct the sales negotiation most effectively. This strategy is customized based on the timing and methods for engaging the customer.

[0320] Step 6:

[0321] The device presents the generated suggestion strategy to the user. It is visualized on a dashboard, allowing the user to choose the best option from multiple suggestion choices. Suggestion content is automatically adjusted based on feedback from the emotion engine, so the user can confidently implement the suggestions.

[0322] Step 7:

[0323] The server provides materials and tools to support sales activities. Specifically, it automatically generates presentation materials and meeting minute templates linked to the proposal strategy, supporting the user's sales activities. This allows users to quickly prepare for business negotiations and effectively pursue results.

[0324] By coordinating each step in this way, a comprehensive sales support system that takes into account the user's emotional state is built.

[0325] (Example 2)

[0326] 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".

[0327] In traditional sales activities, salespeople devise proposal strategies based on customer information, but there is a challenge in understanding customer emotions and preferences in real time and immediately reflecting them in sales strategies. Furthermore, there is a lack of dynamic adjustments that take into account the salesperson's past performance and current emotional state, which hinders improvements in deal closing rates and operational efficiency.

[0328] 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.

[0329] In this invention, the server includes means for collecting customer information and analyzing customer characteristics based on said customer information; means for generating a proposal strategy optimized for the characteristics of sales personnel based on the analysis results; and means for recognizing the emotional state of the user using emotion analysis technology and dynamically adjusting the proposal strategy to reflect that state. As a result, sales personnel can use proposal strategies that are immediately adjusted based on the customer's emotions and preferences, thereby improving the closing rate of deals and realizing effective sales activities.

[0330] "Customer information" refers to information that includes background data about customers, industry trends, and past transaction history.

[0331] "Customer characteristics" refer to the features related to a customer's purchasing tendencies, behavioral patterns, preferences, and needs.

[0332] "Analysis results" refer to information about customer characteristics and trends obtained by utilizing analytical technology based on customer data.

[0333] "Proposal strategy" refers to the content and strategy of proposals that sales personnel make to customers, and is optimized based on the characteristics of the customer and the characteristics of the sales personnel.

[0334] "Emotional analysis technology" refers to a technology that analyzes a user's voice tone, facial expressions, and language to understand their emotional state in real time.

[0335] "Dynamic adjustment" refers to changing and optimizing the proposed strategy in real time based on analysis results and information obtained from sentiment analysis technology.

[0336] "Visualization" is the process of representing information and data visually, using graphs, charts, and other tools to make it easy for users to understand.

[0337] A "sales professional" refers to an individual whose purpose is to propose products or services to customers and to complete transactions.

[0338] This system collects customer information, uses sentiment analysis technology to understand the user's emotional state, and generates proposal strategies to support sales activities. Specifically, the server collects data from the internet and corporate databases to gather customer-related information. This information includes customer background data, industry trends, and past transaction history. The data is cleaned and organized using the Python pandas library and stored in the database.

[0339] The terminal provides an interface for users to input customer interaction information. Users use this interface to input conversation details and needs, updating the information. Emotion analysis technology uses devices such as cameras and microphones to recognize the user's voice tone and facial expressions in real time, and the emotion engine incorporates this into the analysis process. A generative AI model then generates the optimal suggestion strategy from this information.

[0340] This system visualizes the generated proposed strategies on a dashboard and displays them on the user's device. Users can compare multiple proposed options and decide which strategy to adopt. In this process, Python libraries such as matplotlib and seaborn are used for data visualization.

[0341] For example, if a user shows positive emotions towards a customer's response during a meeting, the server will detect this and generate materials to support further exploration of relevant products. If the user feels stressed or anxious, the system will dynamically switch to a strategy that either refrains from making suggestions or mitigates them. An example of a prompt message might be: "Generate the optimal suggestion strategy based on the following customer data. The user has shown positive emotions towards the customer's response."

[0342] In this way, salespeople can make real-time proposals that are tailored to the customer's emotions and needs, improving the closing rate and effectiveness of sales negotiations.

[0343] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0344] Step 1:

[0345] The server collects customer information. It uses data obtained from the internet and corporate databases as input, and generates an integrated information set as output, including customer background data, industry trends, and past transaction history. Specifically, it automatically collects online information using web scraping techniques and cleans and organizes the data using the Python pandas library.

[0346] Step 2:

[0347] The terminal provides an interface for users to input the latest interaction information with customers. As input, users enter conversation details and needs in text format, and as output, it generates up-to-date information to be added to the customer profile. Specifically, information is recorded on the tablet or PC screen and sent directly to the server.

[0348] Step 3:

[0349] The server analyzes the collected customer information. It receives integrated customer data and the latest information from users as input, and generates profiles that reveal customer characteristics and preferences as output. Specifically, it utilizes machine learning algorithms to analyze customer purchasing trends and characteristics.

[0350] Step 4:

[0351] The server uses emotion analysis technology to recognize the user's emotional state. It takes the user's voice tone and facial expression data from sensors as input and generates data indicating the user's current emotional state as output. Specifically, it collects data through cameras and microphones and analyzes it using an emotion engine.

[0352] Step 5:

[0353] The server generates proposal strategies based on analysis results and sentiment information. Using customer profiles and user sentiment data as input, it generates optimized proposal strategies for sales professionals as output. Specifically, a generative AI model constructs proposal strategies from the data and creates a list of appropriate products and services.

[0354] Step 6:

[0355] The terminal presents the generated proposed policies to the user. Using the proposed policies received from the server as input, the output is displayed on a visualized dashboard, allowing the user to select the optimal proposal. Specifically, data visualization is performed using libraries such as Python's matplotlib and seaborn, providing an easy-to-use interface.

[0356] Step 7:

[0357] Users conduct actual sales activities based on the presented proposal strategy. Using visualized proposal information as input, the system outputs appropriate proposals to customers, aiming to close deals. Specifically, users make proposals in actual sales situations, provide feedback to the system based on customer reactions, and adjust proposals as needed.

[0358] (Application Example 2)

[0359] 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."

[0360] In modern sales activities, it is essential to tailor proposals to customers' needs and emotions through effective dialogue. Meeting these needs requires considering not only the customer's characteristics but also the salesperson's own emotional state, but there is a lack of efficient means to do so. Solving this problem and improving the closing rate of deals is urgently needed.

[0361] 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.

[0362] In this invention, the server includes means for collecting customer information and analyzing customer characteristics, means for generating a proposal strategy optimized for the characteristics of the sales representative, and means for recognizing the user's emotional state in real time and dynamically adjusting the proposal strategy based on that. This enables sales representatives to instantly take the optimal approach tailored to the customer's characteristics and current emotions, making sales negotiations more effective and smoother.

[0363] "Customer information" refers to data collected in order to understand customers during sales activities, and includes background data, industry trends, and past transaction information.

[0364] "Characteristics" refer to the unique attributes and tendencies of individual customers and sales representatives, and are important factors to consider when generating personalized proposal strategies.

[0365] "Emotional state" refers to a user's emotional response and psychological state, and is an indicator element used in the dynamic adjustment of the proposed strategy.

[0366] A "proposal strategy" is a systematically designed plan outlining the content and approach of proposals that sales representatives offer to customers, and it is optimized to reflect the customer's characteristics and the sales representative's emotional state.

[0367] "Dynamic adjustment" refers to the process of modifying the system's behavior in real time based on changes in circumstances and information in order to achieve optimal results.

[0368] "Materials" refer to informational media provided to support sales activities, including product details, past performance, and success stories.

[0369] "Equipment" refers to tools and devices used to facilitate sales activities, and is utilized in various business negotiation scenarios.

[0370] This invention constructs a system to support sales activities and optimize customer interactions. The server is responsible for collecting customer information and analyzing its characteristics. This information includes background data on the customer company, industry trends, and past transaction information. The terminal provides the user with an interface where they can input details of their interactions with customers. Based on this, the server constructs a more refined customer profile.

[0371] Recognizing the user's emotional state utilizes natural language processing and facial expression analysis technologies. Specifically, voice data is analyzed via the Google Cloud Natural Language API, and visual data is analyzed using the Microsoft Azure Face API. The results of this analysis allow us to determine the customer's potential emotions and the user's emotional biases, and incorporate this into our proposal strategy.

[0372] The optimized proposal strategy is delivered to sales representatives via their devices. A key feature of this process is that the system incorporates real-time emotional data from the sales representatives and makes dynamic adjustments to ensure smooth negotiations. Users are presented with visualized strategies on a dashboard, allowing them to select the most suitable proposal from a variety of options.

[0373] For example, when proposing security services, if a customer expresses concerns, the proposal can be immediately adjusted, and additional information can be provided to alleviate their worries. In this way, it is possible to support the progress of the sales negotiation and aim to improve the closing rate.

[0374] Examples of prompts to input into a generative AI model:

[0375] Regarding "dynamically adjusting proposals based on customer reactions," please explain how to assess the sales representative's emotions in real time and describe best practice proposals based on that assessment.

[0376] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0377] Step 1:

[0378] The server collects customer information and stores it in a database. Inputs include background data on the client company, industry trends, and historical transaction information, which are then analyzed to create customer profiles. This prepares the server to identify customer characteristics and preferences.

[0379] Step 2:

[0380] The terminal provides an interface for users to input details of their most recent interactions with customers. This input data, including voice and text data from the user, is sent to the server, improving the accuracy of the customer profile. Based on the input information, the server extracts even more detailed customer characteristics.

[0381] Step 3:

[0382] The server analyzes the user's emotional state in real time. This process uses emotional data acquired from sensors as input and utilizes natural language processing and facial recognition technologies. This allows the system to determine the user's emotional biases and process the data to integrate it with the customer profile.

[0383] Step 4:

[0384] The server generates a proposal strategy based on analyzed customer characteristics and user emotional states. This strategy is optimized for the characteristics of the sales representative, and the generated strategy is presented on the terminal as a dashboard. The output includes the proposal strategy and related materials, visualizing information useful for sales activities.

[0385] Step 5:

[0386] The user reviews the proposed strategy generated through their device and makes choices to ensure the smooth progress of the business negotiation. At this time, the user can respond flexibly to customer reactions based on the presented strategy. The final output supports the user's decision-making by providing optimal actions according to the progress of the business negotiation.

[0387] 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.

[0388] 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.

[0389] 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.

[0390] [Third Embodiment]

[0391] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0392] 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.

[0393] 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).

[0394] 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.

[0395] 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.

[0396] 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).

[0397] 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.

[0398] 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.

[0399] 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.

[0400] 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.

[0401] 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.

[0402] 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".

[0403] This invention is a system that supports sales activities based on customer information, and consists of a server, a terminal, and user interaction. An example of the program is described below in natural language.

[0404] First, the server collects and stores customer-related information from the company's internal database and external APIs. This data includes the customer company's financial status, industry trends, and past transaction history. It also includes recent communication and visit history with customers entered by the user via their device.

[0405] Next, the server analyzes the collected data to identify customer preferences and characteristics. This analysis utilizes generative AI and natural language processing technologies to extract customer needs and preferred sales styles. Based on this analysis, a proposal strategy is generated to provide the optimal solution for each customer.

[0406] The terminal presents this generated proposal strategy to the user, who can select the best option from multiple proposals. At this stage, the proposals can be customized, allowing the user to choose proposals that match their sales skills.

[0407] Furthermore, the server automatically generates meeting minute templates and proposal documents as support tools to streamline sales activities. This allows users to conduct business negotiations quickly and effectively, and to follow up with customers without any delays.

[0408] As a concrete example, when a user makes a proposal to a new customer, the server analyzes the customer's past purchasing patterns and interests, and determines that they are interested in digital solutions. Based on this information, it generates a proposal style, such as a more assertive approach, and presents it to the user on their device. The user then uses this to prepare specific proposal content and use it during negotiations, thereby accurately meeting the customer's needs and closing the deal.

[0409] Thus, the present invention aims to improve the efficiency and results of sales activities by supporting customer management and proposal activities in a data-driven manner.

[0410] The following describes the processing flow.

[0411] Step 1:

[0412] The server begins collecting customer information. Specifically, it connects with the company's internal database to retrieve customer company information, past transaction history, and industry trend information, and stores this information in an integrated database. It also downloads market trends and industry-specific news via external APIs to supplement relevant information.

[0413] Step 2:

[0414] The terminal provides the user with an input interface. Here, the user provides the system with up-to-date data by entering information such as recent interactions with customers, key conversation points, and upcoming visit schedules. This allows the server to link the collected data with user input data to build a more detailed customer profile.

[0415] Step 3:

[0416] The server analyzes the collected data. Using generative AI, it analyzes customer preferences, interests, and past behavioral patterns to identify customer characteristics. In this process, natural language processing techniques are used to extract emotional and interest tendencies from text data, and together with numerical data, it forms an overall customer personality.

[0417] Step 4:

[0418] The server generates an optimal proposal strategy based on customer characteristics and considering the sales representative's strengths. This strategy includes customized products and sales tone to best meet customer needs and interests. The strategy also incorporates the order and focus items of each proposal presentation, tailored to its specific objectives.

[0419] Step 5:

[0420] The terminal presents the generated proposed strategies to the user in a dashboard format. The proposed strategies are visualized, and the user can select from multiple strategic options. If necessary, the user is provided with tools to edit the proposed strategies and make fine adjustments to suit the customer's situation.

[0421] Step 6:

[0422] The server provides sales support tools and automatically generates proposal documents and meeting minute templates based on the proposal strategy. These documents are immediately downloadable, allowing users to quickly prepare for sales activities. Follow-up planning is also supported at this stage.

[0423] Step 7:

[0424] Users efficiently conduct business negotiations with customers and aim to close deals using proposal materials and strategies supported by the system. After the negotiation, users record follow-up activities in the system and update the data for future proposals.

[0425] By connecting each step in this way, sales activities are made data-driven and efficient.

[0426] (Example 1)

[0427] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0428] In sales activities, providing optimal proposals to each customer requires collecting and analyzing large amounts of information and creating proposals in a format suitable for the individual salesperson. However, this process is extremely time-consuming and labor-intensive, and heavily relies on the individual capabilities of the salesperson. Therefore, there is a need for efficiency improvements and standardization.

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

[0430] In this invention, the server includes means for collecting information and analyzing attributes based on that information, means for generating a strategy optimized for the attributes of the person in charge based on the analysis results, and means for presenting the generated strategy to the person in charge. This enables the efficient and effective generation of data-driven proposals.

[0431] "Information" is a general term for a variety of data, including customer data, past performance, and industry trends.

[0432] "Attributes" refer to characteristic information that indicates the preferences, characteristics, and style of a customer or person in charge.

[0433] A "strategy" is a proposal or plan derived from gathered information to achieve a specific objective.

[0434] "Responsible person" refers to an individual or person who is responsible for conducting sales or business negotiations.

[0435] "Tools" refers to support materials such as automatically generated documents and templates used to assist with tasks.

[0436] "Generative AI technology" is a technology that uses artificial intelligence to analyze data and automatically generate new insights and suggestions.

[0437] A description of the embodiment for carrying out the invention will be provided.

[0438] This system is primarily composed of three elements: a server, terminals, and users. The server is responsible for collecting and storing necessary data from internal corporate databases and external information sources. Specifically, the server collects financial information of client companies, industry trend data, and historical business transaction records. Furthermore, it also collects recent communication history information with customers entered by users via terminals.

[0439] The server uses this collected data for analysis. This analysis employs advanced data analytics combining natural language processing and generative AI technologies. During this analysis, the server extracts customer needs and generates prompts to derive the optimal proposal for the sales representative.

[0440] The terminal plays a role in this process by presenting the generated proposal strategies to the user. The user compares multiple proposals displayed on the screen and selects the best one. Furthermore, the user can customize the proposal content by leveraging their own experience and knowledge. Through this process, the user can quickly and effectively create proposals that meet customer expectations.

[0441] Furthermore, the server automatically generates various documents and templates to support sales activities. Specific examples include meeting minute templates and proposal documents. This significantly reduces the time spent preparing proposals, enabling smoother negotiations and follow-ups.

[0442] Examples of prompts include instructions such as, "Based on customer information, generate proposals for customers interested in digital solutions. Consider past purchasing patterns and industry trends, and include an appropriate proposal style." Such prompts enable the generating AI model to produce highly accurate proposals.

[0443] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0444] Step 1:

[0445] The server collects data. Specifically, it retrieves customer financial information and transaction history from an internal database and collects industry trend information using external APIs. It also obtains the latest communication data with customers entered by users through terminals. Inputs include database queries and API requests, and the output is a structured dataset.

[0446] Step 2:

[0447] The server analyzes data using a generative AI model and natural language processing technology. It analyzes the input data and extracts customer needs and characteristics. Specifically, it inputs prompt sentences into the generative AI and obtains information about customer preferences and recommended suggestion styles as output.

[0448] Step 3:

[0449] The server generates the optimal proposal strategy based on the analysis results. Based on the analysis results provided by the generating AI, a sales model is generated using prompt messages, and this is then refined into a proposal strategy. The input is the analysis results from step 2, and the output is the proposal strategy presented to the user.

[0450] Step 4:

[0451] The terminal presents the generated proposed strategies to the user. The user reviews the proposed strategies on the terminal screen, selects a strategy based on their own judgment, and customizes it. The input is information about the strategies, and the output is the final proposed strategy selected and customized by the user.

[0452] Step 5:

[0453] The server automatically generates support materials and templates to improve the efficiency of sales activities. Specifically, it generates meeting minute templates and proposal materials based on the selected proposal content. The input is the proposal content chosen by the user, and the output is specific sales support tools.

[0454] (Application Example 1)

[0455] 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."

[0456] In modern society, with the widespread adoption of electronic payment services, there is a growing demand for swift and effective sales activities. However, accurately understanding customer needs and market trends, and providing optimal solutions accordingly, is a significant challenge for sales representatives. Furthermore, the preparation of proposals and the ability to respond quickly during negotiations are required, but traditional methods are insufficient to address these needs.

[0457] 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.

[0458] In this invention, the server includes means for collecting customer information and analyzing customer characteristics based on said customer information; means for generating a proposal plan optimized for the characteristics of the sales representative based on the analysis results; means for presenting the generated proposal plan to the sales representative; means for analyzing the customer's transaction history and market trends related to electronic payment services and generating an optimal payment solution proposal; and means for displaying the proposal content on a mobile communication device or visual aid device. This enables sales representatives to quickly propose payment solutions that meet customer needs and conduct effective sales activities.

[0459] "Customer information" refers to data such as attributes, behavioral history, purchase history, preferences, and transaction history related to a specific user.

[0460] "Means for analyzing characteristics" refers to a function that analyzes collected user information and processes it to identify the user's attributes and preferences.

[0461] "Means for generating proposal plans" refers to a system for creating optimal sales strategies and service plans based on the characteristics of the user.

[0462] "Means of presentation to sales representatives" refers to interfaces and functions for displaying generated sales strategies and proposals to sales representatives.

[0463] An "electronic payment service" is a system that allows payments for goods and services to be made using digital technology, without the use of cash or physical cards.

[0464] "Methods for analyzing transaction history and market trends" refer to technologies that identify needs and opportunities by analyzing users' past purchase and payment patterns as well as market trends.

[0465] "Methods for generating payment solution proposals" refer to methods for considering the most suitable payment system or method for the user and creating a proposal based on that.

[0466] "Portable communication devices or visual aids" refers to portable or wearable devices used to display or view information.

[0467] To implement this invention, a system consisting of a server, terminals, and users is used. The server collects customer information from the company's internal database and external APIs. The data collected includes the customer company's transaction history, market trends, and purchasing patterns. The server manages this information using a database management system (e.g., MySQL, PostgreSQL).

[0468] The collected data is analyzed on the server, and customer characteristics are analyzed using generative AI models (e.g., OpenAI GPT-3) and natural language processing libraries (e.g., NLTK, spaCy). From this analysis, customer needs and market potential are derived. Furthermore, the transaction history related to electronic payments is analyzed, and the most appropriate payment solution is generated as a proposal. The generated proposal is presented to the user through mobile communication devices (e.g., iPhone, Android devices) or visual assistance devices (e.g., Google Glass, Vuzix).

[0469] Users access sales strategies and payment solution proposals displayed on their devices using mobile communication devices or visual aids. This interface operates via an API integration framework (e.g., RESTful API). The proposals can be further customized through user interaction, enhancing their effectiveness.

[0470] For example, if a sales representative proposes an electronic payment system to a new restaurant client, the server will analyze the restaurant's past payment data and trends to determine that proposing the latest QR code payment system would be effective. Based on this information, the sales representative can make a proposal on the spot and pique the client's interest.

[0471] An example of a prompt for the generating AI model is, "Based on the customer's industry trends and past payment history, please propose the latest electronic payment solutions for the food and beverage industry." In this way, the server strongly supports sales activities by performing appropriate information analysis and proposal generation.

[0472] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0473] Step 1:

[0474] The server collects customer information from the company's internal database and external APIs. Inputs include customer company transaction history, market trends, and purchasing patterns, and this data is stored using a database management system. The output is a structured dataset.

[0475] Step 2:

[0476] The server analyzes the collected data. The input is the dataset accumulated in step 1. Natural language processing techniques are used to extract customer characteristics, and a generative AI model is used to analyze needs and market potential. The output is customer characteristics and insights for proposals.

[0477] Step 3:

[0478] The server generates a proposed plan for electronic payment services based on the analysis results. The input is the analysis results from step 2, and this is used to create a proposal for the optimal payment solution for the customer. The output is the generated proposed plan.

[0479] Step 4:

[0480] The server prepares the generated proposed plan for display on a mobile communication device or visual aid. The input is the proposed plan created in step 3, which is sent to the terminal via the API integration framework. The output is digital information as a proposed plan that can be displayed on the terminal.

[0481] Step 5:

[0482] The user operates the terminal to review and customize the displayed proposal plan. The input is the proposal plan displayed on the terminal in step 4. The user edits the proposal content through the interface to create the optimal content tailored to the customer. The output is the proposal plan as a customized sales strategy.

[0483] Step 6:

[0484] The user presents a customized proposal plan to the customer during negotiations using a terminal or visual aid. The input is the customized proposal plan from step 5, which serves as the basis for the negotiation. The output is customer feedback and the outcome of the negotiation.

[0485] 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.

[0486] This invention provides a sales support system that incorporates an emotion engine to support sales activities based on customer information, thereby offering proposal strategies that are linked to the user's emotions. This system is implemented through interactions between a server, a terminal, and the user. An example of the program is described below in natural language.

[0487] First, the server collects customer-related information. This includes background data on the customer company, industry trends, and past transaction information. The terminal also provides an interface where the user can input details of their most recent interactions with the customer, which allows the server to build a more refined customer profile.

[0488] Next, the server analyzes customer information to identify customer characteristics and preferences. The emotion engine recognizes the user's emotional state in real time and incorporates that information into the analysis process. Natural language processing technology extracts the customer's latent emotions from text data, and the emotion engine analyzes the user's tone of voice and facial expressions to determine the user's emotional biases.

[0489] Based on the analysis results, the server generates a proposal strategy tailored to the characteristics of the sales representative. The emotion engine recognizes changes in the user's emotions and can dynamically adjust the proposal strategy based on the results. Specifically, it optimizes the presentation of proposal content at a timing that matches the customer's level of understanding and current interests, so that the user can make proposals smoothly.

[0490] The terminal presents the generated proposal strategy to the user. The strategy is visualized on a dashboard, allowing the user to choose the most suitable option from the different proposals. Based on the user's emotional state, as detected by the emotion engine, the proposal materials are automatically adjusted and optimized, allowing the user to proceed with negotiations with confidence.

[0491] For example, when a user shows positive emotions in response to customer feedback during a meeting, the server identifies this and automatically generates materials to support further exploration of the products the system proposes. Conversely, if the user feels stressed or anxious, the system switches to a strategy of temporarily refraining from making proposals, thereby supporting the smooth continuation of negotiations.

[0492] Therefore, the present invention provides a proposal strategy that takes into account the emotional state of sales representatives in real time, thereby improving the effectiveness of business negotiations and the closing rate.

[0493] The following describes the processing flow.

[0494] Step 1:

[0495] The server begins collecting customer information. It integrates internal databases with external sources to obtain data such as background information on the customer company, past transaction history, and industry trends. Furthermore, it stores this information in a database for centralized management.

[0496] Step 2:

[0497] The terminal provides the user with an input interface. The user directly inputs information such as the latest interactions with customers, conversation points, and scheduled visits. This enables real-time data updates, ensuring that customer information is always up-to-date.

[0498] Step 3:

[0499] The server analyzes collected customer information to identify customer characteristics and preferences. Generative AI is used to analyze past customer behavior patterns and text data to extract potential needs and interests. Furthermore, natural language processing techniques are used to infer customer emotional responses.

[0500] Step 4:

[0501] An emotion engine is installed on the device, recognizing the user's voice tone and facial expressions in real time. The emotion engine analyzes these emotional indicators to identify the user's emotional state. This analysis result is used to customize the proposed strategy.

[0502] Step 5:

[0503] The server generates a proposal strategy optimized for the sales representative's characteristics based on analysis results and data from the emotion engine. It adjusts the proposal content and materials to ensure the user can conduct the sales negotiation most effectively. This strategy is customized based on the timing and methods for engaging the customer.

[0504] Step 6:

[0505] The device presents the generated suggestion strategy to the user. It is visualized on a dashboard, allowing the user to choose the best option from multiple suggestion choices. Suggestion content is automatically adjusted based on feedback from the emotion engine, so the user can confidently implement the suggestions.

[0506] Step 7:

[0507] The server provides materials and tools to support sales activities. Specifically, it automatically generates presentation materials and meeting minute templates linked to the proposal strategy, supporting the user's sales activities. This allows users to quickly prepare for business negotiations and effectively pursue results.

[0508] By coordinating each step in this way, a comprehensive sales support system that takes into account the user's emotional state is built.

[0509] (Example 2)

[0510] 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."

[0511] In traditional sales activities, salespeople devise proposal strategies based on customer information, but there is a challenge in understanding customer emotions and preferences in real time and immediately reflecting them in sales strategies. Furthermore, there is a lack of dynamic adjustments that take into account the salesperson's past performance and current emotional state, which hinders improvements in deal closing rates and operational efficiency.

[0512] 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.

[0513] In this invention, the server includes means for collecting customer information and analyzing customer characteristics based on said customer information; means for generating a proposal strategy optimized for the characteristics of sales personnel based on the analysis results; and means for recognizing the emotional state of the user using emotion analysis technology and dynamically adjusting the proposal strategy to reflect that state. As a result, sales personnel can use proposal strategies that are immediately adjusted based on the customer's emotions and preferences, thereby improving the closing rate of deals and realizing effective sales activities.

[0514] "Customer information" refers to information that includes background data about customers, industry trends, and past transaction history.

[0515] "Customer characteristics" refer to the features related to a customer's purchasing tendencies, behavioral patterns, preferences, and needs.

[0516] "Analysis results" refer to information about customer characteristics and trends obtained by utilizing analytical technology based on customer data.

[0517] "Proposal strategy" refers to the content and strategy of proposals that sales personnel make to customers, and is optimized based on the characteristics of the customer and the characteristics of the sales personnel.

[0518] "Emotional analysis technology" refers to a technology that analyzes a user's voice tone, facial expressions, and language to understand their emotional state in real time.

[0519] "Dynamic adjustment" refers to changing and optimizing the proposed strategy in real time based on analysis results and information obtained from sentiment analysis technology.

[0520] "Visualization" is the process of representing information and data visually, using graphs, charts, and other tools to make it easy for users to understand.

[0521] A "sales professional" refers to an individual whose purpose is to propose products or services to customers and to complete transactions.

[0522] This system collects customer information, uses sentiment analysis technology to understand the user's emotional state, and generates proposal strategies to support sales activities. Specifically, the server collects data from the internet and corporate databases to gather customer-related information. This information includes customer background data, industry trends, and past transaction history. The data is cleaned and organized using the Python pandas library and stored in the database.

[0523] The terminal provides an interface for users to input customer interaction information. Users use this interface to input conversation details and needs, updating the information. Emotion analysis technology uses devices such as cameras and microphones to recognize the user's voice tone and facial expressions in real time, and the emotion engine incorporates this into the analysis process. A generative AI model then generates the optimal suggestion strategy from this information.

[0524] This system visualizes the generated proposed strategies on a dashboard and displays them on the user's device. Users can compare multiple proposed options and decide which strategy to adopt. In this process, Python libraries such as matplotlib and seaborn are used for data visualization.

[0525] For example, if a user shows positive emotions towards a customer's response during a meeting, the server will detect this and generate materials to support further exploration of relevant products. If the user feels stressed or anxious, the system will dynamically switch to a strategy that either refrains from making suggestions or mitigates them. An example of a prompt message might be: "Generate the optimal suggestion strategy based on the following customer data. The user has shown positive emotions towards the customer's response."

[0526] In this way, salespeople can make real-time proposals that are tailored to the customer's emotions and needs, improving the closing rate and effectiveness of sales negotiations.

[0527] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0528] Step 1:

[0529] The server collects customer information. It uses data obtained from the internet and corporate databases as input, and generates an integrated information set as output, including customer background data, industry trends, and past transaction history. Specifically, it automatically collects online information using web scraping techniques and cleans and organizes the data using the Python pandas library.

[0530] Step 2:

[0531] The terminal provides an interface for users to input the latest interaction information with customers. As input, users enter conversation details and needs in text format, and as output, it generates up-to-date information to be added to the customer profile. Specifically, information is recorded on the tablet or PC screen and sent directly to the server.

[0532] Step 3:

[0533] The server analyzes the collected customer information. It receives integrated customer data and the latest information from users as input, and generates profiles that reveal customer characteristics and preferences as output. Specifically, it utilizes machine learning algorithms to analyze customer purchasing trends and characteristics.

[0534] Step 4:

[0535] The server uses emotion analysis technology to recognize the user's emotional state. It takes the user's voice tone and facial expression data from sensors as input and generates data indicating the user's current emotional state as output. Specifically, it collects data through cameras and microphones and analyzes it using an emotion engine.

[0536] Step 5:

[0537] The server generates proposal strategies based on analysis results and sentiment information. Using customer profiles and user sentiment data as input, it generates optimized proposal strategies for sales professionals as output. Specifically, a generative AI model constructs proposal strategies from the data and creates a list of appropriate products and services.

[0538] Step 6:

[0539] The terminal presents the generated proposed policies to the user. Using the proposed policies received from the server as input, the output is displayed on a visualized dashboard, allowing the user to select the optimal proposal. Specifically, data visualization is performed using libraries such as Python's matplotlib and seaborn, providing an easy-to-use interface.

[0540] Step 7:

[0541] Users conduct actual sales activities based on the presented proposal strategy. Using visualized proposal information as input, the system outputs appropriate proposals to customers, aiming to close deals. Specifically, users make proposals in actual sales situations, provide feedback to the system based on customer reactions, and adjust proposals as needed.

[0542] (Application Example 2)

[0543] 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."

[0544] In modern sales activities, it is essential to tailor proposals to customers' needs and emotions through effective dialogue. Meeting these needs requires considering not only the customer's characteristics but also the salesperson's own emotional state, but there is a lack of efficient means to do so. Solving this problem and improving the closing rate of deals is urgently needed.

[0545] 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.

[0546] In this invention, the server includes means for collecting customer information and analyzing customer characteristics, means for generating a proposal strategy optimized for the characteristics of the sales representative, and means for recognizing the user's emotional state in real time and dynamically adjusting the proposal strategy based on that. This enables sales representatives to instantly take the optimal approach tailored to the customer's characteristics and current emotions, making sales negotiations more effective and smoother.

[0547] "Customer information" refers to data collected in order to understand customers during sales activities, and includes background data, industry trends, and past transaction information.

[0548] "Characteristics" refer to the unique attributes and tendencies of individual customers and sales representatives, and are important factors to consider when generating personalized proposal strategies.

[0549] "Emotional state" refers to a user's emotional response and psychological state, and is an indicator element used in the dynamic adjustment of the proposed strategy.

[0550] A "proposal strategy" is a systematically designed plan outlining the content and approach of proposals that sales representatives offer to customers, and it is optimized to reflect the customer's characteristics and the sales representative's emotional state.

[0551] "Dynamic adjustment" refers to the process of modifying the system's behavior in real time based on changes in circumstances and information in order to achieve optimal results.

[0552] "Materials" refer to informational media provided to support sales activities, including product details, past performance, and success stories.

[0553] "Equipment" refers to tools and devices used to facilitate sales activities, and is utilized in various business negotiation scenarios.

[0554] This invention constructs a system to support sales activities and optimize customer interactions. The server is responsible for collecting customer information and analyzing its characteristics. This information includes background data on the customer company, industry trends, and past transaction information. The terminal provides the user with an interface where they can input details of their interactions with customers. Based on this, the server constructs a more refined customer profile.

[0555] Recognizing the user's emotional state utilizes natural language processing and facial expression analysis technologies. Specifically, voice data is analyzed via the Google Cloud Natural Language API, and visual data is analyzed using the Microsoft Azure Face API. The results of this analysis allow us to determine the customer's potential emotions and the user's emotional biases, and incorporate this into our proposal strategy.

[0556] The optimized proposal strategy is delivered to sales representatives via their devices. A key feature of this process is that the system incorporates real-time emotional data from the sales representatives and makes dynamic adjustments to ensure smooth negotiations. Users are presented with visualized strategies on a dashboard, allowing them to select the most suitable proposal from a variety of options.

[0557] For example, when proposing security services, if a customer expresses concerns, the proposal can be immediately adjusted, and additional information can be provided to alleviate their worries. In this way, it is possible to support the progress of the sales negotiation and aim to improve the closing rate.

[0558] Examples of prompts to input into a generative AI model:

[0559] Regarding "dynamically adjusting proposals based on customer reactions," please explain how to assess the sales representative's emotions in real time and describe best practice proposals based on that assessment.

[0560] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0561] Step 1:

[0562] The server collects customer information and stores it in a database. Inputs include background data on the client company, industry trends, and historical transaction information, which are then analyzed to create customer profiles. This prepares the server to identify customer characteristics and preferences.

[0563] Step 2:

[0564] The terminal provides an interface for users to input details of their most recent interactions with customers. This input data, including voice and text data from the user, is sent to the server, improving the accuracy of the customer profile. Based on the input information, the server extracts even more detailed customer characteristics.

[0565] Step 3:

[0566] The server analyzes the user's emotional state in real time. This process uses emotional data acquired from sensors as input and utilizes natural language processing and facial recognition technologies. This allows the system to determine the user's emotional biases and process the data to integrate it with the customer profile.

[0567] Step 4:

[0568] The server generates a proposal strategy based on analyzed customer characteristics and user emotional states. This strategy is optimized for the characteristics of the sales representative, and the generated strategy is presented on the terminal as a dashboard. The output includes the proposal strategy and related materials, visualizing information useful for sales activities.

[0569] Step 5:

[0570] The user reviews the proposed strategy generated through their device and makes choices to ensure the smooth progress of the business negotiation. At this time, the user can respond flexibly to customer reactions based on the presented strategy. The final output supports the user's decision-making by providing optimal actions according to the progress of the business negotiation.

[0571] 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.

[0572] 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.

[0573] 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.

[0574] [Fourth Embodiment]

[0575] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0576] 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.

[0577] 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).

[0578] 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.

[0579] 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.

[0580] 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).

[0581] 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.

[0582] 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.

[0583] 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.

[0584] 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.

[0585] 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.

[0586] 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.

[0587] 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".

[0588] This invention is a system that supports sales activities based on customer information, and consists of a server, a terminal, and user interaction. An example of the program is described below in natural language.

[0589] First, the server collects and stores customer-related information from the company's internal database and external APIs. This data includes the customer company's financial status, industry trends, and past transaction history. It also includes recent communication and visit history with customers entered by the user via their device.

[0590] Next, the server analyzes the collected data to identify customer preferences and characteristics. This analysis utilizes generative AI and natural language processing technologies to extract customer needs and preferred sales styles. Based on this analysis, a proposal strategy is generated to provide the optimal solution for each customer.

[0591] The terminal presents this generated proposal strategy to the user, who can select the best option from multiple proposals. At this stage, the proposals can be customized, allowing the user to choose proposals that match their sales skills.

[0592] Furthermore, the server automatically generates meeting minute templates and proposal documents as support tools to streamline sales activities. This allows users to conduct business negotiations quickly and effectively, and to follow up with customers without any delays.

[0593] As a concrete example, when a user makes a proposal to a new customer, the server analyzes the customer's past purchasing patterns and interests, and determines that they are interested in digital solutions. Based on this information, it generates a proposal style, such as a more assertive approach, and presents it to the user on their device. The user then uses this to prepare specific proposal content and use it during negotiations, thereby accurately meeting the customer's needs and closing the deal.

[0594] Thus, the present invention aims to improve the efficiency and results of sales activities by supporting customer management and proposal activities in a data-driven manner.

[0595] The following describes the processing flow.

[0596] Step 1:

[0597] The server begins collecting customer information. Specifically, it connects with the company's internal database to retrieve customer company information, past transaction history, and industry trend information, and stores this information in an integrated database. It also downloads market trends and industry-specific news via external APIs to supplement relevant information.

[0598] Step 2:

[0599] The terminal provides the user with an input interface. Here, the user provides the system with up-to-date data by entering information such as recent interactions with customers, key conversation points, and upcoming visit schedules. This allows the server to link the collected data with user input data to build a more detailed customer profile.

[0600] Step 3:

[0601] The server analyzes the collected data. Using generative AI, it analyzes customer preferences, interests, and past behavioral patterns to identify customer characteristics. In this process, natural language processing techniques are used to extract emotional and interest tendencies from text data, and together with numerical data, it forms an overall customer personality.

[0602] Step 4:

[0603] The server generates an optimal proposal strategy based on customer characteristics and considering the sales representative's strengths. This strategy includes customized products and sales tone to best meet customer needs and interests. The strategy also incorporates the order and focus items of each proposal presentation, tailored to its specific objectives.

[0604] Step 5:

[0605] The terminal presents the generated proposed strategies to the user in a dashboard format. The proposed strategies are visualized, and the user can select from multiple strategic options. If necessary, the user is provided with tools to edit the proposed strategies and make fine adjustments to suit the customer's situation.

[0606] Step 6:

[0607] The server provides sales support tools and automatically generates proposal documents and meeting minute templates based on the proposal strategy. These documents are immediately downloadable, allowing users to quickly prepare for sales activities. Follow-up planning is also supported at this stage.

[0608] Step 7:

[0609] Users efficiently conduct business negotiations with customers and aim to close deals using proposal materials and strategies supported by the system. After the negotiation, users record follow-up activities in the system and update the data for future proposals.

[0610] By connecting each step in this way, sales activities are made data-driven and efficient.

[0611] (Example 1)

[0612] 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".

[0613] In sales activities, providing optimal proposals to each customer requires collecting and analyzing large amounts of information and creating proposals in a format suitable for the individual salesperson. However, this process is extremely time-consuming and labor-intensive, and heavily relies on the individual capabilities of the salesperson. Therefore, there is a need for efficiency improvements and standardization.

[0614] 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.

[0615] In this invention, the server includes means for collecting information and analyzing attributes based on that information, means for generating a strategy optimized for the attributes of the person in charge based on the analysis results, and means for presenting the generated strategy to the person in charge. This enables the efficient and effective generation of data-driven proposals.

[0616] "Information" is a general term for a variety of data, including customer data, past performance, and industry trends.

[0617] "Attributes" refer to characteristic information that indicates the preferences, characteristics, and style of a customer or person in charge.

[0618] A "strategy" is a proposal or plan derived from gathered information to achieve a specific objective.

[0619] "Responsible person" refers to an individual or person who is responsible for conducting sales or business negotiations.

[0620] "Tools" refers to support materials such as automatically generated documents and templates used to assist with tasks.

[0621] "Generative AI technology" is a technology that uses artificial intelligence to analyze data and automatically generate new insights and suggestions.

[0622] A description of the embodiment for carrying out the invention will be provided.

[0623] This system is primarily composed of three elements: a server, terminals, and users. The server is responsible for collecting and storing necessary data from internal corporate databases and external information sources. Specifically, the server collects financial information of client companies, industry trend data, and historical business transaction records. Furthermore, it also collects recent communication history information with customers entered by users via terminals.

[0624] The server uses this collected data for analysis. This analysis employs advanced data analytics combining natural language processing and generative AI technologies. During this analysis, the server extracts customer needs and generates prompts to derive the optimal proposal for the sales representative.

[0625] The terminal plays a role in this process by presenting the generated proposal strategies to the user. The user compares multiple proposals displayed on the screen and selects the best one. Furthermore, the user can customize the proposal content by leveraging their own experience and knowledge. Through this process, the user can quickly and effectively create proposals that meet customer expectations.

[0626] Furthermore, the server automatically generates various documents and templates to support sales activities. Specific examples include meeting minute templates and proposal documents. This significantly reduces the time spent preparing proposals, enabling smoother negotiations and follow-ups.

[0627] Examples of prompts include instructions such as, "Based on customer information, generate proposals for customers interested in digital solutions. Consider past purchasing patterns and industry trends, and include an appropriate proposal style." Such prompts enable the generating AI model to produce highly accurate proposals.

[0628] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0629] Step 1:

[0630] The server collects data. Specifically, it retrieves customer financial information and transaction history from an internal database and collects industry trend information using external APIs. It also obtains the latest communication data with customers entered by users through terminals. Inputs include database queries and API requests, and the output is a structured dataset.

[0631] Step 2:

[0632] The server analyzes data using a generative AI model and natural language processing technology. It analyzes the input data and extracts customer needs and characteristics. Specifically, it inputs prompt sentences into the generative AI and obtains information about customer preferences and recommended suggestion styles as output.

[0633] Step 3:

[0634] The server generates the optimal proposal strategy based on the analysis results. Based on the analysis results provided by the generating AI, a sales model is generated using prompt messages, and this is then refined into a proposal strategy. The input is the analysis results from step 2, and the output is the proposal strategy presented to the user.

[0635] Step 4:

[0636] The terminal presents the generated proposed strategies to the user. The user reviews the proposed strategies on the terminal screen, selects a strategy based on their own judgment, and customizes it. The input is information about the strategies, and the output is the final proposed strategy selected and customized by the user.

[0637] Step 5:

[0638] The server automatically generates support materials and templates to improve the efficiency of sales activities. Specifically, it generates meeting minute templates and proposal materials based on the selected proposal content. The input is the proposal content chosen by the user, and the output is specific sales support tools.

[0639] (Application Example 1)

[0640] 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".

[0641] In modern society, with the widespread adoption of electronic payment services, there is a growing demand for swift and effective sales activities. However, accurately understanding customer needs and market trends, and providing optimal solutions accordingly, is a significant challenge for sales representatives. Furthermore, the preparation of proposals and the ability to respond quickly during negotiations are required, but traditional methods are insufficient to address these needs.

[0642] 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.

[0643] In this invention, the server includes means for collecting customer information and analyzing customer characteristics based on said customer information; means for generating a proposal plan optimized for the characteristics of the sales representative based on the analysis results; means for presenting the generated proposal plan to the sales representative; means for analyzing the customer's transaction history and market trends related to electronic payment services and generating an optimal payment solution proposal; and means for displaying the proposal content on a mobile communication device or visual aid device. This enables sales representatives to quickly propose payment solutions that meet customer needs and conduct effective sales activities.

[0644] "Customer information" refers to data such as attributes, behavioral history, purchase history, preferences, and transaction history related to a specific user.

[0645] "Means for analyzing characteristics" refers to a function that analyzes collected user information and processes it to identify the user's attributes and preferences.

[0646] "Means for generating proposal plans" refers to a system for creating optimal sales strategies and service plans based on the characteristics of the user.

[0647] "Means of presentation to sales representatives" refers to interfaces and functions for displaying generated sales strategies and proposals to sales representatives.

[0648] An "electronic payment service" is a system that allows payments for goods and services to be made using digital technology, without the use of cash or physical cards.

[0649] "Methods for analyzing transaction history and market trends" refer to technologies that identify needs and opportunities by analyzing users' past purchase and payment patterns as well as market trends.

[0650] "Methods for generating payment solution proposals" refer to methods for considering the most suitable payment system or method for the user and creating a proposal based on that.

[0651] "Portable communication devices or visual aids" refers to portable or wearable devices used to display or view information.

[0652] To implement this invention, a system consisting of a server, terminals, and users is used. The server collects customer information from the company's internal database and external APIs. The data collected includes the customer company's transaction history, market trends, and purchasing patterns. The server manages this information using a database management system (e.g., MySQL, PostgreSQL).

[0653] The collected data is analyzed on the server, and customer characteristics are analyzed using generative AI models (e.g., OpenAI GPT-3) and natural language processing libraries (e.g., NLTK, spaCy). From this analysis, customer needs and market potential are derived. Furthermore, the transaction history related to electronic payments is analyzed, and the most appropriate payment solution is generated as a proposal. The generated proposal is presented to the user through mobile communication devices (e.g., iPhone, Android devices) or visual assistance devices (e.g., Google Glass, Vuzix).

[0654] Users access sales strategies and payment solution proposals displayed on their devices using mobile communication devices or visual aids. This interface operates via an API integration framework (e.g., RESTful API). The proposals can be further customized through user interaction, enhancing their effectiveness.

[0655] For example, if a sales representative proposes an electronic payment system to a new restaurant client, the server will analyze the restaurant's past payment data and trends to determine that proposing the latest QR code payment system would be effective. Based on this information, the sales representative can make a proposal on the spot and pique the client's interest.

[0656] An example of a prompt for the generating AI model is, "Based on the customer's industry trends and past payment history, please propose the latest electronic payment solutions for the food and beverage industry." In this way, the server strongly supports sales activities by performing appropriate information analysis and proposal generation.

[0657] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0658] Step 1:

[0659] The server collects customer information from the company's internal database and external APIs. Inputs include customer company transaction history, market trends, and purchasing patterns, and this data is stored using a database management system. The output is a structured dataset.

[0660] Step 2:

[0661] The server analyzes the collected data. The input is the dataset accumulated in step 1. Natural language processing techniques are used to extract customer characteristics, and a generative AI model is used to analyze needs and market potential. The output is customer characteristics and insights for proposals.

[0662] Step 3:

[0663] The server generates a proposed plan for electronic payment services based on the analysis results. The input is the analysis results from step 2, and this is used to create a proposal for the optimal payment solution for the customer. The output is the generated proposed plan.

[0664] Step 4:

[0665] The server prepares the generated proposed plan for display on a mobile communication device or visual aid. The input is the proposed plan created in step 3, which is sent to the terminal via the API integration framework. The output is digital information as a proposed plan that can be displayed on the terminal.

[0666] Step 5:

[0667] The user operates the terminal to review and customize the displayed proposal plan. The input is the proposal plan displayed on the terminal in step 4. The user edits the proposal content through the interface to create the optimal content tailored to the customer. The output is the proposal plan as a customized sales strategy.

[0668] Step 6:

[0669] The user presents a customized proposal plan to the customer during negotiations using a terminal or visual aid. The input is the customized proposal plan from step 5, which serves as the basis for the negotiation. The output is customer feedback and the outcome of the negotiation.

[0670] 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.

[0671] This invention provides a sales support system that incorporates an emotion engine to support sales activities based on customer information, thereby offering proposal strategies that are linked to the user's emotions. This system is implemented through interactions between a server, a terminal, and the user. An example of the program is described below in natural language.

[0672] First, the server collects customer-related information. This includes background data on the customer company, industry trends, and past transaction information. The terminal also provides an interface where the user can input details of their most recent interactions with the customer, which allows the server to build a more refined customer profile.

[0673] Next, the server analyzes customer information to identify customer characteristics and preferences. The emotion engine recognizes the user's emotional state in real time and incorporates that information into the analysis process. Natural language processing technology extracts the customer's latent emotions from text data, and the emotion engine analyzes the user's tone of voice and facial expressions to determine the user's emotional biases.

[0674] Based on the analysis results, the server generates a proposal strategy tailored to the characteristics of the sales representative. The emotion engine recognizes changes in the user's emotions and can dynamically adjust the proposal strategy based on the results. Specifically, it optimizes the presentation of proposal content at a timing that matches the customer's level of understanding and current interests, so that the user can make proposals smoothly.

[0675] The terminal presents the generated proposal strategy to the user. The strategy is visualized on a dashboard, allowing the user to choose the most suitable option from the different proposals. Based on the user's emotional state, as detected by the emotion engine, the proposal materials are automatically adjusted and optimized, allowing the user to proceed with negotiations with confidence.

[0676] For example, when a user shows positive emotions in response to customer feedback during a meeting, the server identifies this and automatically generates materials to support further exploration of the products the system proposes. Conversely, if the user feels stressed or anxious, the system switches to a strategy of temporarily refraining from making proposals, thereby supporting the smooth continuation of negotiations.

[0677] Therefore, the present invention provides a proposal strategy that takes into account the emotional state of sales representatives in real time, thereby improving the effectiveness of business negotiations and the closing rate.

[0678] The following describes the processing flow.

[0679] Step 1:

[0680] The server begins collecting customer information. It integrates internal databases with external sources to obtain data such as background information on the customer company, past transaction history, and industry trends. Furthermore, it stores this information in a database for centralized management.

[0681] Step 2:

[0682] The terminal provides the user with an input interface. The user directly inputs information such as the latest interactions with customers, conversation points, and scheduled visits. This enables real-time data updates, ensuring that customer information is always up-to-date.

[0683] Step 3:

[0684] The server analyzes collected customer information to identify customer characteristics and preferences. Generative AI is used to analyze past customer behavior patterns and text data to extract potential needs and interests. Furthermore, natural language processing techniques are used to infer customer emotional responses.

[0685] Step 4:

[0686] An emotion engine is installed on the device, recognizing the user's voice tone and facial expressions in real time. The emotion engine analyzes these emotional indicators to identify the user's emotional state. This analysis result is used to customize the proposed strategy.

[0687] Step 5:

[0688] The server generates a proposal strategy optimized for the sales representative's characteristics based on analysis results and data from the emotion engine. It adjusts the proposal content and materials to ensure the user can conduct the sales negotiation most effectively. This strategy is customized based on the timing and methods for engaging the customer.

[0689] Step 6:

[0690] The device presents the generated suggestion strategy to the user. It is visualized on a dashboard, allowing the user to choose the best option from multiple suggestion choices. Suggestion content is automatically adjusted based on feedback from the emotion engine, so the user can confidently implement the suggestions.

[0691] Step 7:

[0692] The server provides materials and tools to support sales activities. Specifically, it automatically generates presentation materials and meeting minute templates linked to the proposal strategy, supporting the user's sales activities. This allows users to quickly prepare for business negotiations and effectively pursue results.

[0693] By coordinating each step in this way, a comprehensive sales support system that takes into account the user's emotional state is built.

[0694] (Example 2)

[0695] 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".

[0696] In traditional sales activities, salespeople devise proposal strategies based on customer information, but there is a challenge in understanding customer emotions and preferences in real time and immediately reflecting them in sales strategies. Furthermore, there is a lack of dynamic adjustments that take into account the salesperson's past performance and current emotional state, which hinders improvements in deal closing rates and operational efficiency.

[0697] 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.

[0698] In this invention, the server includes means for collecting customer information and analyzing customer characteristics based on said customer information; means for generating a proposal strategy optimized for the characteristics of sales personnel based on the analysis results; and means for recognizing the emotional state of the user using emotion analysis technology and dynamically adjusting the proposal strategy to reflect that state. As a result, sales personnel can use proposal strategies that are immediately adjusted based on the customer's emotions and preferences, thereby improving the closing rate of deals and realizing effective sales activities.

[0699] "Customer information" refers to information that includes background data about customers, industry trends, and past transaction history.

[0700] "Customer characteristics" refer to the features related to a customer's purchasing tendencies, behavioral patterns, preferences, and needs.

[0701] "Analysis results" refer to information about customer characteristics and trends obtained by utilizing analytical technology based on customer data.

[0702] "Proposal strategy" refers to the content and strategy of proposals that sales personnel make to customers, and is optimized based on the characteristics of the customer and the characteristics of the sales personnel.

[0703] "Emotional analysis technology" refers to a technology that analyzes a user's voice tone, facial expressions, and language to understand their emotional state in real time.

[0704] "Dynamic adjustment" refers to changing and optimizing the proposed strategy in real time based on analysis results and information obtained from sentiment analysis technology.

[0705] "Visualization" is the process of representing information and data visually, using graphs, charts, and other tools to make it easy for users to understand.

[0706] A "sales professional" refers to an individual whose purpose is to propose products or services to customers and to complete transactions.

[0707] This system collects customer information, uses sentiment analysis technology to understand the user's emotional state, and generates proposal strategies to support sales activities. Specifically, the server collects data from the internet and corporate databases to gather customer-related information. This information includes customer background data, industry trends, and past transaction history. The data is cleaned and organized using the Python pandas library and stored in the database.

[0708] The terminal provides an interface for users to input customer interaction information. Users use this interface to input conversation details and needs, updating the information. Emotion analysis technology uses devices such as cameras and microphones to recognize the user's voice tone and facial expressions in real time, and the emotion engine incorporates this into the analysis process. A generative AI model then generates the optimal suggestion strategy from this information.

[0709] This system visualizes the generated proposed strategies on a dashboard and displays them on the user's device. Users can compare multiple proposed options and decide which strategy to adopt. In this process, Python libraries such as matplotlib and seaborn are used for data visualization.

[0710] For example, if a user shows positive emotions towards a customer's response during a meeting, the server will detect this and generate materials to support further exploration of relevant products. If the user feels stressed or anxious, the system will dynamically switch to a strategy that either refrains from making suggestions or mitigates them. An example of a prompt message might be: "Generate the optimal suggestion strategy based on the following customer data. The user has shown positive emotions towards the customer's response."

[0711] In this way, salespeople can make real-time proposals that are tailored to the customer's emotions and needs, improving the closing rate and effectiveness of sales negotiations.

[0712] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0713] Step 1:

[0714] The server collects customer information. It uses data obtained from the internet and corporate databases as input, and generates an integrated information set as output, including customer background data, industry trends, and past transaction history. Specifically, it automatically collects online information using web scraping techniques and cleans and organizes the data using the Python pandas library.

[0715] Step 2:

[0716] The terminal provides an interface for users to input the latest interaction information with customers. As input, users enter conversation details and needs in text format, and as output, it generates up-to-date information to be added to the customer profile. Specifically, information is recorded on the tablet or PC screen and sent directly to the server.

[0717] Step 3:

[0718] The server analyzes the collected customer information. It receives integrated customer data and the latest information from users as input, and generates profiles that reveal customer characteristics and preferences as output. Specifically, it utilizes machine learning algorithms to analyze customer purchasing trends and characteristics.

[0719] Step 4:

[0720] The server uses emotion analysis technology to recognize the user's emotional state. It takes the user's voice tone and facial expression data from sensors as input and generates data indicating the user's current emotional state as output. Specifically, it collects data through cameras and microphones and analyzes it using an emotion engine.

[0721] Step 5:

[0722] The server generates proposal strategies based on analysis results and sentiment information. Using customer profiles and user sentiment data as input, it generates optimized proposal strategies for sales professionals as output. Specifically, a generative AI model constructs proposal strategies from the data and creates a list of appropriate products and services.

[0723] Step 6:

[0724] The terminal presents the generated proposed policies to the user. Using the proposed policies received from the server as input, the output is displayed on a visualized dashboard, allowing the user to select the optimal proposal. Specifically, data visualization is performed using libraries such as Python's matplotlib and seaborn, providing an easy-to-use interface.

[0725] Step 7:

[0726] Users conduct actual sales activities based on the presented proposal strategy. Using visualized proposal information as input, the system outputs appropriate proposals to customers, aiming to close deals. Specifically, users make proposals in actual sales situations, provide feedback to the system based on customer reactions, and adjust proposals as needed.

[0727] (Application Example 2)

[0728] 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".

[0729] In modern sales activities, it is essential to tailor proposals to customers' needs and emotions through effective dialogue. Meeting these needs requires considering not only the customer's characteristics but also the salesperson's own emotional state, but there is a lack of efficient means to do so. Solving this problem and improving the closing rate of deals is urgently needed.

[0730] 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.

[0731] In this invention, the server includes means for collecting customer information and analyzing customer characteristics, means for generating a proposal strategy optimized for the characteristics of the sales representative, and means for recognizing the user's emotional state in real time and dynamically adjusting the proposal strategy based on that. This enables sales representatives to instantly take the optimal approach tailored to the customer's characteristics and current emotions, making sales negotiations more effective and smoother.

[0732] "Customer information" refers to data collected in order to understand customers during sales activities, and includes background data, industry trends, and past transaction information.

[0733] "Characteristics" refer to the unique attributes and tendencies of individual customers and sales representatives, and are important factors to consider when generating personalized proposal strategies.

[0734] "Emotional state" refers to a user's emotional response and psychological state, and is an indicator element used in the dynamic adjustment of the proposed strategy.

[0735] A "proposal strategy" is a systematically designed plan outlining the content and approach of proposals that sales representatives offer to customers, and it is optimized to reflect the customer's characteristics and the sales representative's emotional state.

[0736] "Dynamic adjustment" refers to the process of modifying the system's behavior in real time based on changes in circumstances and information in order to achieve optimal results.

[0737] "Materials" refer to informational media provided to support sales activities, including product details, past performance, and success stories.

[0738] "Equipment" refers to tools and devices used to facilitate sales activities, and is utilized in various business negotiation scenarios.

[0739] This invention constructs a system to support sales activities and optimize customer interactions. The server is responsible for collecting customer information and analyzing its characteristics. This information includes background data on the customer company, industry trends, and past transaction information. The terminal provides the user with an interface where they can input details of their interactions with customers. Based on this, the server constructs a more refined customer profile.

[0740] Recognizing the user's emotional state utilizes natural language processing and facial expression analysis technologies. Specifically, voice data is analyzed via the Google Cloud Natural Language API, and visual data is analyzed using the Microsoft Azure Face API. The results of this analysis allow us to determine the customer's potential emotions and the user's emotional biases, and incorporate this into our proposal strategy.

[0741] The optimized proposal strategy is delivered to sales representatives via their devices. A key feature of this process is that the system incorporates real-time emotional data from the sales representatives and makes dynamic adjustments to ensure smooth negotiations. Users are presented with visualized strategies on a dashboard, allowing them to select the most suitable proposal from a variety of options.

[0742] For example, when proposing security services, if a customer expresses concerns, the proposal can be immediately adjusted, and additional information can be provided to alleviate their worries. In this way, it is possible to support the progress of the sales negotiation and aim to improve the closing rate.

[0743] Examples of prompts to input into a generative AI model:

[0744] Regarding "dynamically adjusting proposals based on customer reactions," please explain how to assess the sales representative's emotions in real time and describe best practice proposals based on that assessment.

[0745] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0746] Step 1:

[0747] The server collects customer information and stores it in a database. Inputs include background data on the client company, industry trends, and historical transaction information, which are then analyzed to create customer profiles. This prepares the server to identify customer characteristics and preferences.

[0748] Step 2:

[0749] The terminal provides an interface for users to input details of their most recent interactions with customers. This input data, including voice and text data from the user, is sent to the server, improving the accuracy of the customer profile. Based on the input information, the server extracts even more detailed customer characteristics.

[0750] Step 3:

[0751] The server analyzes the user's emotional state in real time. This process uses emotional data acquired from sensors as input and utilizes natural language processing and facial recognition technologies. This allows the system to determine the user's emotional biases and process the data to integrate it with the customer profile.

[0752] Step 4:

[0753] The server generates a proposal strategy based on analyzed customer characteristics and user emotional states. This strategy is optimized for the characteristics of the sales representative, and the generated strategy is presented on the terminal as a dashboard. The output includes the proposal strategy and related materials, visualizing information useful for sales activities.

[0754] Step 5:

[0755] The user reviews the proposed strategy generated through their device and makes choices to ensure the smooth progress of the business negotiation. At this time, the user can respond flexibly to customer reactions based on the presented strategy. The final output supports the user's decision-making by providing optimal actions according to the progress of the business negotiation.

[0756] 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.

[0757] 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.

[0758] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0759] 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.

[0760] 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.

[0761] 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.

[0762] 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.

[0763] 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.

[0764] 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."

[0765] 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.

[0766] 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.

[0767] 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.

[0768] 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.

[0769] 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.

[0770] 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.

[0771] 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.

[0772] 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.

[0773] 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.

[0774] 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.

[0775] 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.

[0776] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0777] The following is further disclosed regarding the embodiments described above.

[0778] (Claim 1)

[0779] A means for collecting customer information and analyzing customer characteristics based on said customer information,

[0780] A means for generating a proposal strategy optimized for the characteristics of sales representatives based on the analysis results,

[0781] A means of presenting the generated proposal strategy to the sales representative,

[0782] A system that includes means for automatically generating materials and tools to support sales activities.

[0783] (Claim 2)

[0784] The system according to claim 1, which extracts customer preferences using natural language processing technology and reflects them in the generation of sales strategies.

[0785] (Claim 3)

[0786] The system described in claim 1, which customizes and ranks proposal strategies based on the past performance data of sales representatives.

[0787] "Example 1"

[0788] (Claim 1)

[0789] A means for collecting information and analyzing attributes based on that information,

[0790] A means for generating a strategy optimized for the attributes of the person in charge, based on the analysis results,

[0791] A means of presenting the generated strategy to the person in charge,

[0792] A means of automatically generating materials and tools to support the activities,

[0793] A means of identifying attributes using generative AI technology in data analysis and deriving the optimal strategy,

[0794] A means of providing the person in charge with multiple options via a terminal, enabling selection and customization,

[0795] ...

[0796] A system that includes this.

[0797] (Claim 2)

[0798] The system according to claim 1, which extracts information preferences based on processing technology and reflects them in strategy generation.

[0799] (Claim 3)

[0800] The system according to claim 1, which uses the past performance data of the person in charge to adjust and prioritize strategies.

[0801] "Application Example 1"

[0802] (Claim 1)

[0803] A means for collecting customer information and analyzing customer characteristics based on said customer information,

[0804] A means for generating a proposal plan optimized for the characteristics of the sales representative based on the analysis results,

[0805] A means of presenting the generated proposal plan to the sales representative,

[0806] A means of automatically generating materials and tools to support sales activities,

[0807] A means of analyzing customer transaction history and market trends related to electronic payment services to generate optimal payment solution proposals,

[0808] A means for displaying the proposed content on a mobile communication device or visual aid,

[0809] A system that includes this.

[0810] (Claim 2)

[0811] The system according to claim 1, which extracts customer preferences using natural language processing technology and reflects them in the generation of sales strategies.

[0812] (Claim 3)

[0813] The system according to claim 1, which customizes and ranks proposal plans based on the past performance data of sales representatives.

[0814] "Example 2 of combining an emotion engine"

[0815] (Claim 1)

[0816] A means for collecting customer information and analyzing customer characteristics based on said customer information,

[0817] A means for generating a proposal strategy optimized for the characteristics of sales personnel based on the analysis results,

[0818] A means of presenting the generated proposal policy to sales personnel,

[0819] A means of recognizing the user's emotional state using emotion analysis technology and dynamically adjusting the proposed policy to reflect that state,

[0820] A visual dashboard displays proposal strategies and provides a means to encourage the selection of the best option from different proposals.

[0821] A system that includes means for automatically generating materials and tools to support sales activities.

[0822] (Claim 2)

[0823] The system according to claim 1, which extracts customer preferences using natural language processing technology and incorporates the analyzed emotional information into the generation of sales policies.

[0824] (Claim 3)

[0825] The system described in claim 1, which customizes and ranks proposal strategies based on past performance data and emotional changes of sales personnel.

[0826] "Application example 2 when combining with an emotional engine"

[0827] (Claim 1)

[0828] A means for collecting customer information and analyzing customer characteristics based on said customer information,

[0829] A means for generating a proposal strategy optimized for the characteristics of sales representatives based on the analysis results,

[0830] A means to recognize the user's emotional state in real time and dynamically adjust the proposed strategy based on that,

[0831] A means of presenting the generated proposal strategy to the sales representative,

[0832] A system that includes means for automatically generating materials and tools to support sales activities.

[0833] (Claim 2)

[0834] The system according to claim 1, which extracts customer preferences using natural language processing techniques and reflects them in the generation of sales strategies.

[0835] (Claim 3)

[0836] The system according to claim 1, which customizes and ranks proposal strategies based on the past performance records of sales representatives. [Explanation of Symbols]

[0837] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for collecting customer information and analyzing customer characteristics based on said customer information, A means for generating a proposal plan optimized for the characteristics of the sales representative based on the analysis results, A means of presenting the generated proposal plan to the sales representative, A means of automatically generating materials and tools to support sales activities, A means of analyzing customer transaction history and market trends related to electronic payment services and generating optimal payment solution proposals, A means for displaying the proposed content on a mobile communication device or visual aid, A system that includes this.

2. The system according to claim 1, which extracts customer preferences using natural language processing technology and reflects them in the generation of sales strategies.

3. The system according to claim 1, which customizes and ranks proposal plans based on the past performance data of sales representatives.

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