system

Abstract

Provide a system. 【Solution means】 Means for collecting information from an external information source, Means for integrating information with a cloud-based management database, Means for analyzing the integrated data to identify appropriate targets, Means for generating optimal proposals for the identified targets, Means for providing the proposed content to a display device, Means for presenting products and services based on the characteristics of visitors, Means for recording the interaction with visitors and storing information for the next visit, A system including the above.

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 the chatbot's character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In corporate business, in order to make optimal proposals according to customer needs, it is necessary to quickly collect the latest industry information and technological trends and identify appropriate responsible persons. However, there is a problem that the burden of analyzing a huge amount of data is large and it is difficult to perform these efficiently. In addition, there is a problem that the failure to make timely proposals to appropriate responsible persons leads to the loss of business opportunities.

Means for Solving the Problems

[0005] This invention provides a means for collecting information from an external database and linking it with a cloud-based business card management system to centrally manage customer information. It then constructs a system that includes a means for analyzing the integrated data using natural language processing technology to identify the appropriate person within the organization. Furthermore, it aims to improve the efficiency and accuracy of the sales process by generating optimal product and service proposals for the identified person and providing these proposals through a user interface.

[0006] An "external database" refers to a collection of data located outside of a company, containing industry information, technological trends, and other relevant data.

[0007] A "business card management system" is a system that digitizes business card information and manages it on the cloud.

[0008] "Integrating information" refers to the process of combining information collected from different data sources to build a unified dataset.

[0009] "Natural language processing technology" is a technology that enables computers to understand and analyze human language.

[0010] "Identifying the person in charge" refers to selecting the appropriate individual within the organization to whom a proposal should be submitted.

[0011] "Generating a proposal" refers to creating the optimal product or service based on customer needs and compiling that information into a proposal document.

[0012] "User interface" refers to the screens and designs that allow users to visually confirm and interact with proposals and other important information. [Brief explanation of the drawing]

[0013] [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]It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Embodiments for Carrying Out the Invention

[0014] 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.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, the 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.

[0017] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0018] In the following embodiments, the 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.

[0019] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), etc.

[0020] 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."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] 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.

[0024] 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).

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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".

[0034] This corporate sales support system implements a series of automated processes to provide optimal proposals to customers. The system consists of a server, terminals, and users. Its main operations are described below.

[0035] The server collects academic presentations and industry news from external databases. The server uses APIs to access external information sources and retrieve information relevant to the customer's industry. For example, by collecting the latest technology announcements related to AI, the server ensures information that matches the customer's area of ​​interest.

[0036] Next, the server integrates with a cloud-based business card management system to retrieve customer information. This makes it possible to centralize information on the customer's organizational structure and relevant contacts. Past transaction data related to specific contacts is collected and used as foundational data to predict customer interests and needs.

[0037] The server uses this integrated data to perform analysis, leveraging natural language processing technology. This analysis identifies the appropriate personnel within the client company to propose solutions to. Relevance is assessed through text analysis and similarity comparisons to determine the most suitable candidates for proposals that meet the client's needs.

[0038] The server then generates product and service suggestions based on the customer's needs. These suggestions reflect the customer's past purchase history and current market trends, aiming to be the most effective within acceptable limits. For example, a customer interested in AI technology might be offered suggestions for new AI solutions.

[0039] Finally, the user receives the proposals provided by the server via their device. The user interface includes a dashboard where the user can visually review the details of the proposals. The user can also take actions based on the proposals (e.g., scheduling a meeting or requesting further information) directly from the interface.

[0040] In this way, the system efficiently manages and analyzes customer information and functions as a powerful support tool for sales representatives to quickly make appropriate proposals.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] The server accesses an external database to collect conference presentations and news articles. The server automatically retrieves the latest information from the external database's API using keywords related to specific industries and technologies. The retrieved information is stored in an internal database and tagged for later use.

[0044] Step 2:

[0045] The server uses the API of a cloud-based business card management system to retrieve business card information from client companies. The server normalizes the business card information and stores the client's organizational structure and contact person information in a database. During this process, data matching is performed to ensure there are no duplicates with existing data.

[0046] Step 3:

[0047] The server analyzes external information and business card data, and uses natural language processing technology to evaluate their relationships. Statistical models and machine learning algorithms are used to identify client company contacts who are highly relevant to conference presenters. This list of identified contacts is then sorted by priority.

[0048] Step 4:

[0049] The server generates product and service suggestions based on customer needs, taking into account historical transaction data and market trends. The generated suggestions are documented and prepared in a customized format for each customer.

[0050] Step 5:

[0051] Users view proposals generated by the server via their device on a dashboard. They can evaluate the proposals and take action as needed, including directly approaching customers or providing additional information.

[0052] (Example 1)

[0053] 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."

[0054] In traditional corporate sales, customer information gathering and proposal creation are performed separately, resulting in low efficiency. Furthermore, analyzing information to produce appropriate proposals is difficult, sometimes preventing proposals from achieving their full potential. Therefore, there is a need for a system that can quickly and reliably generate and deliver proposals that meet customer needs.

[0055] 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.

[0056] In this invention, the server includes means for extracting industry-related information from external sources, means for integrating customer information using a cloud-based data management system, and means for generating optimal suggestions for identified target individuals using generative AI technology. This enables the rapid and effective generation and presentation of suggestions.

[0057] "External information sources" refer to information providers such as databases and news sources that provide industry-related information and technical announcements.

[0058] A "cloud-based data management system" is an information processing platform that uses services provided over the internet to centrally manage, access, and manipulate data.

[0059] "Natural language processing technology" is a technology that enables computers to understand and analyze human language, and it involves analyzing text data and the semantic analysis of documents.

[0060] "Generative AI technology" is a technology that uses artificial intelligence to automatically generate new suggestions and information from data.

[0061] A "user interface" is the foundation of interaction that includes visual or manipulative elements for a user to directly interact with a system.

[0062] The implementation of this corporate sales support system invention will be structured as follows: The system mainly consists of three elements: a server, a terminal, and a user.

[0063] The server is responsible for extracting industry-related information from external sources. Specifically, it uses APIs to retrieve the latest industry information from databases and news sites on the internet. In this process, it can focus on collecting information on topics such as artificial intelligence and trending technologies.

[0064] Next, the server utilizes a cloud-based data management system. This system integrates with business card management software to consolidate data on customer organizations and contacts. This centralizes customer information management, making detailed data, such as past transaction history, available.

[0065] Furthermore, the server analyzes the collected data using natural language processing techniques. These techniques include text mining and cluster analysis, which are used to improve customer needs analysis and the accuracy of proposals. Based on this, the server utilizes generative AI technology to automatically generate optimal proposals. A specific example of a prompt is, "Generate proposals for customers interested in the new AI solution." The generated proposals are aligned with the customer's past activities and market trends.

[0066] Users receive suggestions from the server via a provided terminal. The user interface employs a dashboard, allowing users to visually review the suggestions. From this dashboard, users can directly take specific actions based on the suggestions, such as scheduling a meeting or requesting additional information.

[0067] In this way, this system significantly improves the efficiency of proposal creation in sales activities and functions as a powerful tool that enables the rapid delivery of appropriate proposals.

[0068] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0069] Step 1:

[0070] The server extracts industry-related information from external sources. In this process, the server uses a RESTful API to query multiple databases and collect data based on specific keywords and filter conditions. Inputs are keywords and industry categories, and the output is a set of collected news articles and technical reports. Specifically, the server periodically updates the data using automated scheduling.

[0071] Step 2:

[0072] The server integrates collected industry information with a cloud-based data management system to retrieve customer information. Specifically, the server accesses the API of business card management software to retrieve customer organizational structure and contact information, and then stores this information in a database. The input is the customer ID and organization name, and the output is a list of integrated customer information. The server matches the relationships between the data to centralize customer information.

[0073] Step 3:

[0074] The server performs analysis using natural language processing techniques with customer information and industry data. Input includes text data and customer history, and output includes identified potential clients and highly relevant keywords. In this process, the server utilizes text mining to extract frequently occurring words and important contexts. This allows it to predict customer needs and prioritize potential clients.

[0075] Step 4:

[0076] The server automatically generates proposals based on customer needs using a generative AI model. The input is the analysis results and prompt text obtained in the previous step. The output is specific and valuable proposal content. By inputting the prompt "Generate proposals for customers interested in new AI solutions," the AI ​​generates relevant proposals. The server manages this generation process and improves accuracy by incorporating appropriate feedback loops.

[0077] Step 5:

[0078] Users view proposal details via their terminal and take necessary actions. Input is the generated proposal sent from the server. Output is the specific action the user takes based on the presented proposal. Users can interact with the dashboard interface to visually check the proposal content and immediately schedule meetings or request further information. Optimizing user interaction is crucial at this stage.

[0079] (Application Example 1)

[0080] 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."

[0081] In today's commercial environment, there is a need to improve the quality of customer service in physical stores and to provide customers with the most suitable products and services. Traditional customer service methods rely heavily on the experience and skills of individual staff members, making it difficult to quickly provide personalized recommendations to individual customers. Furthermore, the lack of mechanisms to effectively utilize past customer service history and customer profiles makes improving customer satisfaction and increasing repeat visit rates a challenge.

[0082] 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.

[0083] In this invention, the server includes means for collecting information from external information sources, means for integrating the information with a cloud-based management database, means for analyzing the integrated data to identify appropriate targets, means for presenting products and services based on the characteristics of visitors, and means for recording interactions with visitors and saving the information for future visits. This makes it possible to quickly provide appropriate suggestions to customers who visit the store, based on their individual profiles.

[0084] An "external information source" refers to an external database or information platform used to obtain data.

[0085] A "cloud-based management database" is a database management system that stores customer information and business data on cloud servers on the internet, making them accessible anytime, anywhere.

[0086] "Means of integrating information" refers to methods of centralizing data collected from different sources and effectively combining and utilizing them.

[0087] "Methods for analyzing integrated data to identify appropriate targets" refers to methods of analyzing aggregated data and selecting the most suitable customers or target audience based on specific conditions and needs.

[0088] "Methods for presenting products and services based on visitor characteristics" refers to techniques that select and present the most suitable products and services based on customer preferences and past purchase history.

[0089] "A means of recording conversations with visitors and saving information for future visits" refers to a technology that records communication with customers and accumulates information useful for future sales activities and proposals.

[0090] This invention aims to realize a customer service support system using smart devices in physical stores. The server collects customer data from external sources and integrates it into a cloud-based management database. Smartphones and smart glasses are used as hardware, and customer information management APIs and Google® Cloud Natural Language API are used as software. Based on the integrated data, the server performs analysis using natural language processing and generates suggestions for appropriate products and services based on the characteristics of visitors.

[0091] These generated suggestions are displayed on smart glasses or devices worn by service staff and presented to customers. Furthermore, the server records customer interactions and saves the information for future visits. This functionality allows staff members to refer to past interaction history and suggestions to provide more personalized recommendations to customers.

[0092] As a concrete example, when a customer visits a store, the server analyzes past purchase history and preference information, and uses prompts to a generative AI model such as, "This customer has previously purchased AI-related products and has shown high customer satisfaction. Please generate product or service suggestions that will interest her," to derive the optimal suggestions for the customer. This system allows visitors to consistently enjoy a high level of service.

[0093] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0094] Step 1:

[0095] The server collects customer information from external sources. It takes information requests from external APIs as input and receives collected customer-related data as output. This data includes industry news and up-to-date information on areas of interest.

[0096] Step 2:

[0097] The server integrates customer information into a cloud-based management database. Inputs are customer information obtained from external sources and existing database data, while output is integrated customer profile data. This profile reflects the customer's organizational structure and past transaction history.

[0098] Step 3:

[0099] The server analyzes the integrated data using natural language processing to identify appropriate targets. In this process, the input is customer profile data, and the output is a suggested product or service. Data processing includes text analysis and similarity comparison.

[0100] Step 4:

[0101] The terminal displays the suggestions received from the server. The input is suggestion data from the server, and the output is the suggestion information displayed on the terminal's screen. The sales staff, as users, use this information to make appropriate suggestions to customers in person.

[0102] Step 5:

[0103] The server records customer interactions and stores information for future visits. Inputs include voice and text data based on the interaction, while output is structured historical data for future suggestions. This information is stored in the server's database and available for future interactions.

[0104] 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.

[0105] This corporate sales support system, which incorporates emotion recognition, is designed to make proposals to users more effective and personalized. The system consists of three components: a server, a terminal, and a user.

[0106] The server collects information from external databases and integrates the latest customer data. The server also integrates with a cloud-based business card management system to retrieve and centrally manage customer organizational and contact information. Using this data, it employs natural language processing to identify appropriate targets within the customer organization.

[0107] Furthermore, this system incorporates a process that utilizes an emotion engine to enhance the effectiveness of its suggestions. The server analyzes user reactions and past feedback data using the emotion engine to determine the user's current emotional state. Based on this, it personalizes suggestions to provide recommendations that resonate with the user's emotions.

[0108] Specifically, when a user receives a suggestion using their device, the emotion engine analyzes the user's facial recognition data and voice tone to evaluate whether they are in a positive emotional state or one that needs improvement. For example, if the server recognizes that the user is interested, it immediately presents detailed information about the suggestion to encourage purchase.

[0109] On the other hand, if a user expresses negative feelings towards a proposal, the server also has a function to support acceptance of the proposal by providing alternatives and supplementary information. Furthermore, user feedback is recorded sequentially within the system and reflected in the next proposal strategy through the sentiment engine.

[0110] This allows the system to flexibly respond to the user's emotional state, further enhancing the effectiveness of sales proposals. The proposals are optimized for the user, enabling more personalized responses.

[0111] The following describes the processing flow.

[0112] Step 1:

[0113] The server collects relevant information from an external database. Based on a specific algorithm, the server extracts keywords from industry news and academic presentations, collecting the information. This information is integrated with the client's organizational information and used for later analysis.

[0114] Step 2:

[0115] The server retrieves and integrates customer information from a cloud-based business card management system. Through the business card database, customer organizational structure and contact information are centralized and automatically entered into the database.

[0116] Step 3:

[0117] The server analyzes integrated data and uses natural language processing techniques to identify the appropriate personnel within the organization. Text mining and similarity analysis are performed to determine the priority of proposed targets.

[0118] Step 4:

[0119] The server uses an emotion engine to recognize the user's emotions. It analyzes the voice and facial expression data provided by the user in real time to determine the current emotional state. This uses facial recognition technology and voice analysis technology.

[0120] Step 5:

[0121] The server generates suggestions based on the user's emotional state and sends them to the user's device. If a positive emotion is detected, it provides more detailed information and makes suggestions to encourage purchases.

[0122] Step 6:

[0123] Users can review suggestions on their devices and provide feedback. Based on the suggestions, users can request more information about products or services that interest them, or schedule meetings.

[0124] Step 7:

[0125] The server records user feedback, which is then analyzed again by the emotion engine. This feedback is then incorporated into future proposal strategies, enabling more personalized sales activities.

[0126] (Example 2)

[0127] 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 will be referred to as the "terminal."

[0128] In corporate sales, the collection and integration of customer information is often insufficient, resulting in proposals that are not personalized. Furthermore, it is difficult to create proposals that consider customer emotions, hindering the maximization of sales effectiveness. Additionally, a lack of analytical technology leads to proposals that are less relevant.

[0129] 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.

[0130] In this invention, the server includes means for collecting information from external information sources, means for integrating the information with an electronic business card management structure, and means for detecting the user's emotional state using emotion analysis technology and adjusting the suggestions accordingly. This makes it possible to provide personalized and optimal suggestions to customers and maximize sales effectiveness.

[0131] "External information sources" refer to databases or information services that provide information that exists outside the system.

[0132] An "electronic business card management structure" is a system for storing, managing, and searching business card information in digital format.

[0133] "Integrating information" means combining data obtained from multiple sources into a single system and managing it while maintaining consistency.

[0134] "Emotion analysis technology" is a technology that analyzes a user's emotions from facial expressions and voice data and evaluates their emotional state.

[0135] "User interface" refers to the screens and methods of operation that allow users to directly interact with a system.

[0136] "Adjusting a proposal" means changing the content and approach of the proposal presented based on the user's situation and feelings.

[0137] "Personalized proposals" refer to the process of providing proposals tailored to the specific needs and interests of a particular user.

[0138] This invention provides a specific means for making personalized and optimal proposals to customers in a corporate sales support system. This system consists of three components: a server, a terminal, and a user.

[0139] server

[0140] The server is responsible for collecting customer and market information from external sources via APIs and integrating it with the electronic business card management structure. The integrated data is stored in a database (e.g., MySQL®). The server further analyzes the collected data using natural language processing technologies (e.g., Python's spaCy or TENSORFLOW®) to identify appropriate individuals within the organization. For sentiment analysis, Microsoft®'s Azure® Emotion API is used to adjust suggestions based on the user's emotional state.

[0141] terminal

[0142] The terminal functions as an interface for users to receive suggestions. In particular, it is responsible for capturing user facial expression data and voice using the built-in camera and microphone and sending them to the server in real time. By using OpenCV for facial recognition and Praat for voice tone analysis, the system accurately grasps the user's emotional state.

[0143] User

[0144] Users receive personalized suggestions presented through their devices and provide feedback accordingly. This feedback is then used by the server to improve future suggestions. For example, if a user shows interest in a suggestion and gives a positive response, the server can provide more detailed information to encourage purchase. If a negative attitude is observed, alternative options can be offered.

[0145] For example, if the proposal concerns the introduction of a new product, detailed information and success stories designed to pique the user's interest will be presented. On the other hand, if it is determined that the user's interest is low, alternative perspectives or supplementary explanations will be offered.

[0146] An example of a prompt for the generating AI model is: "When proposing new product XX, generate detailed information for when the customer shows interest. Also, consider alternative proposals for when they do not show interest."

[0147] This invention makes it possible to accurately understand the emotional state of customers and make proposals based on that understanding, thereby achieving high sales effectiveness.

[0148] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0149] Step 1:

[0150] The server retrieves customer information from external sources. It uses APIs as input to obtain basic customer information and market data, and integrates this data with an electronic business card management structure as output. Specifically, it periodically sends API requests to save the latest dataset to the database.

[0151] Step 2:

[0152] The server analyzes integrated data to identify appropriate individuals. It receives integrated data as input and analyzes it using natural language processing techniques. As output, it generates a list of individuals suitable for the suggestion. Specifically, it uses the Python spaCy library to extract and identify past transaction history and customer interests.

[0153] Step 3:

[0154] The device captures the user's facial expressions and voice when receiving suggestions. It takes real-time data via a webcam and microphone as input and sends that data to a server as output. Specifically, it uses OpenCV to capture facial data and Praat to analyze voice tone, thereby collecting the user's emotional state.

[0155] Step 4:

[0156] The server uses emotion analysis technology to determine the user's emotional state. It uses facial and audio data received from the device as input and performs analysis using the Azure Emotion API. The output identifies whether the user's emotional state is positive or negative. Specifically, it analyzes the data in real time and adjusts the suggested content based on the results.

[0157] Step 5:

[0158] The server generates suggestions that correspond to the user's emotional state and presents them to the user via the terminal. It uses the results of emotion analysis and user-specific information as input, and generates adjusted suggestions as output. Specifically, its operation involves quickly displaying detailed information and alternatives that are likely to interest the user through the user interface.

[0159] Step 6:

[0160] Users provide responses and feedback to the suggestions they receive. They evaluate the suggestions provided by the server as input and record their responses as output in the feedback system. Specifically, they provide feedback through choices and input fields provided on their terminal.

[0161] Step 7:

[0162] The server incorporates user feedback into future suggestions. It analyzes feedback data as input and updates the data to optimize the suggestion strategy as output. Specifically, it uses machine learning algorithms to analyze feedback data and incorporates the findings into subsequent suggestions.

[0163] (Application Example 2)

[0164] 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".

[0165] Traditional sales support systems have been unable to address the individual emotional states of users, resulting in uniform proposals and making it difficult to optimize customer approaches. Therefore, there is a need for systems that can provide real-time, personalized proposals based on the diverse emotional states of customers.

[0166] 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.

[0167] In this invention, the server includes means for collecting information from external information sources, means for integrating information with an information management system, means for analyzing the integrated data to identify relevant personnel within the organization, means for acquiring user language data and visual information to determine emotions, and means for modifying suggested content based on acquired emotions. This makes it possible to personalize and optimize suggested content according to the user's emotional state.

[0168] "External information sources" refer to data providers or information bases located outside the organization that are acquired for use by the system.

[0169] An "information management system" refers to a platform for collecting, managing, and integrating data such as business cards and customer information.

[0170] "Integrated data" refers to a set of information that is centrally managed by combining data from external sources and information management systems.

[0171] "Relevant personnel" refers to individuals within the organization who, based on integrated data, are deemed the most suitable to make specific proposals.

[0172] A "user input device" refers to a device used by a user to receive information and perform operations.

[0173] "Linguistic data" refers to linguistic information used for communication, such as user speech and text.

[0174] "Visual information" refers to visual data such as images and videos, and is used for purposes such as recognizing user facial expressions.

[0175] "Means of determining emotions" refers to technologies that analyze linguistic data and visual information obtained from users to infer the user's emotional state.

[0176] "Means of modifying suggested content" refers to a mechanism that adjusts suggested content to the user based on their analyzed emotional state, providing optimized information.

[0177] The system implementing this invention is built on a foundation of three components: a server, a terminal, and a user. The server collects data from external sources and integrates it with an information management system. This information management system includes customer information and business card data, thereby centralizing all customer-related information. The integrated data is analyzed using natural language processing technology to identify relevant personnel within the organization.

[0178] The terminal is provided to the user and connects to the information management system to display information in real time. The terminal also acquires the user's visual and linguistic data and analyzes it using a means to determine emotions. The emotion recognition engine, for example, uses Rekognition or the Google Cloud API to determine the user's emotional state from their facial expressions and tone of voice. As a result, the suggested content is modified to be optimal for the situation and presented on the terminal.

[0179] As a concrete example, when a customer is selecting products in a physical store, their facial expressions are captured by a camera, and their reactions to the products are collected using a microphone. Based on this data, related product information and special offers that are of interest to the user are displayed on the terminal, enabling personalized suggestions to increase their purchasing intent.

[0180] Examples of prompts for generative AI models:

[0181] "This customer is interested in the product but is hesitant to make a decision. We would like to offer additional information or incentives to help the customer feel more confident in making a purchase."

[0182] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0183] Step 1:

[0184] The server collects customer data from external sources. This input data consists of customer information residing in an external database, which is retrieved via the network. The output is an unintegrated, temporary dataset. The server performs initial data processing on this data, such as filtering and removing duplicates.

[0185] Step 2:

[0186] The server integrates the filtered data with the information management system. This process matches existing cloud-based information with newly collected data to update business card information and customer profiles. The input is filtered raw data, and the output is an integrated customer information database.

[0187] Step 3:

[0188] The server analyzes the integrated data and identifies relevant personnel within the organization. Using natural language processing techniques, it performs semantic analysis of the text data and evaluates the relationship between personnel and customer needs. The input is an integrated database, and the output is a list of relevant personnel. This list is used in the next proposal generation step.

[0189] Step 4:

[0190] The device collects the user's visual and audio data. Using the camera and microphone equipped on the device, it acquires the user's facial recognition data and voice tone in real time. The input is raw data from the device's sensors, while the output is processed data for sentiment analysis.

[0191] Step 5:

[0192] The server uses an emotion engine to analyze the acquired visual and audio data. The emotion engine classifies the data into emotional categories such as positive and negative. The input is processed visual and audio data transmitted from the terminal, and the output is data determining the emotional state.

[0193] Step 6:

[0194] The server generates suggestions based on the user's emotional state and sends them to the terminal. This process uses a generative AI model to generate prompts tailored to the customer's emotions and combine them with appropriate suggestions. The input is customer information integrated with emotional state determination data, and the output is a personalized suggestion message.

[0195] Step 7:

[0196] The terminal displays the generated suggestion messages in its user interface. This allows the user to directly consider their purchase intentions based on the suggestions. The input is the suggestion messages sent from the server, and the output is the marketing information displayed on the terminal's screen.

[0197] 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.

[0198] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0199] 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.

[0200] [Second Embodiment]

[0201] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0202] 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.

[0203] 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).

[0204] 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.

[0205] 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.

[0206] 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).

[0207] 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.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] 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".

[0213] This corporate sales support system implements a series of automated processes to provide optimal proposals to customers. The system consists of a server, terminals, and users. Its main operations are described below.

[0214] The server collects academic presentations and industry news from external databases. The server uses APIs to access external information sources and retrieve information relevant to the customer's industry. For example, by collecting the latest technology announcements related to AI, the server ensures information that matches the customer's area of ​​interest.

[0215] Next, the server integrates with a cloud-based business card management system to retrieve customer information. This makes it possible to centralize information on the customer's organizational structure and relevant contacts. Past transaction data related to specific contacts is collected and used as foundational data to predict customer interests and needs.

[0216] The server uses this integrated data to perform analysis, leveraging natural language processing technology. This analysis identifies the appropriate personnel within the client company to propose solutions to. Relevance is assessed through text analysis and similarity comparisons to determine the most suitable candidates for proposals that meet the client's needs.

[0217] The server then generates product and service suggestions based on the customer's needs. These suggestions reflect the customer's past purchase history and current market trends, aiming to be the most effective within acceptable limits. For example, a customer interested in AI technology might be offered suggestions for new AI solutions.

[0218] Finally, the user receives the proposals provided by the server via their device. The user interface includes a dashboard where the user can visually review the details of the proposals. The user can also take actions based on the proposals (e.g., scheduling a meeting or requesting further information) directly from the interface.

[0219] In this way, the system efficiently manages and analyzes customer information and functions as a powerful support tool for sales representatives to quickly make appropriate proposals.

[0220] The following describes the processing flow.

[0221] Step 1:

[0222] The server accesses an external database to collect conference presentations and news articles. The server automatically retrieves the latest information from the external database's API using keywords related to specific industries and technologies. The retrieved information is stored in an internal database and tagged for later use.

[0223] Step 2:

[0224] The server uses the API of a cloud-based business card management system to retrieve business card information from client companies. The server normalizes the business card information and stores the client's organizational structure and contact person information in a database. During this process, data matching is performed to ensure there are no duplicates with existing data.

[0225] Step 3:

[0226] The server analyzes external information and business card data, and uses natural language processing technology to evaluate their relationships. Statistical models and machine learning algorithms are used to identify client company contacts who are highly relevant to conference presenters. This list of identified contacts is then sorted by priority.

[0227] Step 4:

[0228] The server generates product and service suggestions based on customer needs, taking into account historical transaction data and market trends. The generated suggestions are documented and prepared in a customized format for each customer.

[0229] Step 5:

[0230] Users view proposals generated by the server via their device on a dashboard. They can evaluate the proposals and take action as needed, including directly approaching customers or providing additional information.

[0231] (Example 1)

[0232] 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."

[0233] In traditional corporate sales, customer information gathering and proposal creation are performed separately, resulting in low efficiency. Furthermore, analyzing information to produce appropriate proposals is difficult, sometimes preventing proposals from achieving their full potential. Therefore, there is a need for a system that can quickly and reliably generate and deliver proposals that meet customer needs.

[0234] 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.

[0235] In this invention, the server includes means for extracting industry-related information from external sources, means for integrating customer information using a cloud-based data management system, and means for generating optimal suggestions for identified target individuals using generative AI technology. This enables the rapid and effective generation and presentation of suggestions.

[0236] "External information sources" refer to information providers such as databases and news sources that provide industry-related information and technical announcements.

[0237] A "cloud-based data management system" is an information processing platform that uses services provided over the internet to centrally manage, access, and manipulate data.

[0238] "Natural language processing technology" is a technology that enables computers to understand and analyze human language, and it involves analyzing text data and the semantic analysis of documents.

[0239] "Generative AI technology" is a technology that uses artificial intelligence to automatically generate new suggestions and information from data.

[0240] A "user interface" is the foundation of interaction that includes visual or manipulative elements for a user to directly interact with a system.

[0241] The implementation of this corporate sales support system invention will be structured as follows: The system mainly consists of three elements: a server, a terminal, and a user.

[0242] The server is responsible for extracting industry-related information from external sources. Specifically, it uses APIs to retrieve the latest industry information from databases and news sites on the internet. In this process, it can focus on collecting information on topics such as artificial intelligence and trending technologies.

[0243] Next, the server utilizes a cloud-based data management system. This system integrates with business card management software to consolidate data on customer organizations and contacts. This centralizes customer information management, making detailed data, such as past transaction history, available.

[0244] Furthermore, the server analyzes the collected data using natural language processing techniques. These techniques include text mining and cluster analysis, which are used to improve customer needs analysis and the accuracy of proposals. Based on this, the server utilizes generative AI technology to automatically generate optimal proposals. A specific example of a prompt is, "Generate proposals for customers interested in the new AI solution." The generated proposals are aligned with the customer's past activities and market trends.

[0245] Users receive suggestions from the server via a provided terminal. The user interface employs a dashboard, allowing users to visually review the suggestions. From this dashboard, users can directly take specific actions based on the suggestions, such as scheduling a meeting or requesting additional information.

[0246] In this way, this system significantly improves the efficiency of proposal creation in sales activities and functions as a powerful tool that enables the rapid delivery of appropriate proposals.

[0247] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0248] Step 1:

[0249] The server extracts industry-related information from external sources. In this process, the server uses a RESTful API to query multiple databases and collect data based on specific keywords and filter conditions. Inputs are keywords and industry categories, and the output is a set of collected news articles and technical reports. Specifically, the server periodically updates the data using automated scheduling.

[0250] Step 2:

[0251] The server integrates collected industry information with a cloud-based data management system to retrieve customer information. Specifically, the server accesses the API of business card management software to retrieve customer organizational structure and contact information, and then stores this information in a database. The input is the customer ID and organization name, and the output is a list of integrated customer information. The server matches the relationships between the data to centralize customer information.

[0252] Step 3:

[0253] The server performs analysis using natural language processing techniques with customer information and industry data. Input includes text data and customer history, and output includes identified potential clients and highly relevant keywords. In this process, the server utilizes text mining to extract frequently occurring words and important contexts. This allows it to predict customer needs and prioritize potential clients.

[0254] Step 4:

[0255] The server automatically generates proposals based on customer needs using a generative AI model. The input is the analysis results and prompt text obtained in the previous step. The output is specific and valuable proposal content. By inputting the prompt "Generate proposals for customers interested in new AI solutions," the AI ​​generates relevant proposals. The server manages this generation process and improves accuracy by incorporating appropriate feedback loops.

[0256] Step 5:

[0257] Users view proposal details via their terminal and take necessary actions. Input is the generated proposal sent from the server. Output is the specific action the user takes based on the presented proposal. Users can interact with the dashboard interface to visually check the proposal content and immediately schedule meetings or request further information. Optimizing user interaction is crucial at this stage.

[0258] (Application Example 1)

[0259] 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."

[0260] In today's commercial environment, there is a need to improve the quality of customer service in physical stores and to provide customers with the most suitable products and services. Traditional customer service methods rely heavily on the experience and skills of individual staff members, making it difficult to quickly provide personalized recommendations to individual customers. Furthermore, the lack of mechanisms to effectively utilize past customer service history and customer profiles makes improving customer satisfaction and increasing repeat visit rates a challenge.

[0261] 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.

[0262] In this invention, the server includes means for collecting information from external information sources, means for integrating the information with a cloud-based management database, means for analyzing the integrated data to identify appropriate targets, means for presenting products and services based on the characteristics of visitors, and means for recording interactions with visitors and saving the information for future visits. This makes it possible to quickly provide appropriate suggestions to customers who visit the store, based on their individual profiles.

[0263] An "external information source" refers to an external database or information platform used to obtain data.

[0264] A "cloud-based management database" is a database management system that stores customer information and business data on cloud servers on the internet, making them accessible anytime, anywhere.

[0265] "Means of integrating information" refers to methods of centralizing data collected from different sources and effectively combining and utilizing them.

[0266] "Methods for analyzing integrated data to identify appropriate targets" refers to methods of analyzing aggregated data and selecting the most suitable customers or target audience based on specific conditions and needs.

[0267] "Methods for presenting products and services based on visitor characteristics" refers to techniques that select and present the most suitable products and services based on customer preferences and past purchase history.

[0268] "A means of recording conversations with visitors and saving information for future visits" refers to a technology that records communication with customers and accumulates information useful for future sales activities and proposals.

[0269] This invention aims to realize a customer service support system using smart devices in physical stores. The server collects customer data from external sources and integrates it into a cloud-based management database. Smartphones and smart glasses are used as hardware, and customer information management APIs and Google Cloud Natural Language APIs are used as software. Based on the integrated data, the server performs analysis using natural language processing and generates suggestions for appropriate products and services based on the characteristics of visitors.

[0270] These generated suggestions are displayed on smart glasses or devices worn by service staff and presented to customers. Furthermore, the server records customer interactions and saves the information for future visits. This functionality allows staff members to refer to past interaction history and suggestions to provide more personalized recommendations to customers.

[0271] As a concrete example, when a customer visits a store, the server analyzes past purchase history and preference information, and uses prompts to a generative AI model such as, "This customer has previously purchased AI-related products and has shown high customer satisfaction. Please generate product or service suggestions that will interest her," to derive the optimal suggestions for the customer. This system allows visitors to consistently enjoy a high level of service.

[0272] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0273] Step 1:

[0274] The server collects customer information from external sources. It takes information requests from external APIs as input and receives collected customer-related data as output. This data includes industry news and up-to-date information on areas of interest.

[0275] Step 2:

[0276] The server integrates customer information into a cloud-based management database. Inputs are customer information obtained from external sources and existing database data, while output is integrated customer profile data. This profile reflects the customer's organizational structure and past transaction history.

[0277] Step 3:

[0278] The server analyzes the integrated data using natural language processing to identify appropriate targets. In this process, the input is customer profile data, and the output is a suggested product or service. Data processing includes text analysis and similarity comparison.

[0279] Step 4:

[0280] The terminal displays the suggestions received from the server. The input is suggestion data from the server, and the output is the suggestion information displayed on the terminal's screen. The sales staff, as users, use this information to make appropriate suggestions to customers in person.

[0281] Step 5:

[0282] The server records customer interactions and stores information for future visits. Inputs include voice and text data based on the interaction, while output is structured historical data for future suggestions. This information is stored in the server's database and available for future interactions.

[0283] 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.

[0284] This corporate sales support system, which incorporates emotion recognition, is designed to make proposals to users more effective and personalized. The system consists of three components: a server, a terminal, and a user.

[0285] The server collects information from external databases and integrates the latest customer information. The server also collaborates with a cloud-based business card management system to obtain and centrally manage the customer's organizational information and contact information. Using this data, it makes full use of natural language processing to identify appropriate proposal recipients within the customer organization.

[0286] Furthermore, in this system, a process of using an emotion engine to enhance the effectiveness of proposals is added. The server analyzes the user's reactions and past feedback data with the emotion engine to determine the user's current emotional state. Based on this, by personalizing the proposal, it makes proposals that resonate with the user's emotions.

[0287] Specifically, when the user receives a proposal using the terminal, the emotion engine analyzes the user's facial recognition data and voice tone to evaluate whether the user is in a positive emotional state or an emotional state that needs improvement. For example, if the user is recognized as showing interest, the server immediately presents detailed information about the current proposal to promote the willingness to purchase.

[0288] On the other hand, when the user shows a negative emotion towards the proposal content, the server also has a function to support the acceptance of the proposal by providing alternatives or supplementary information. Furthermore, the user's feedback is sequentially recorded in the system and reflected in the next proposal strategy through the emotion engine.

[0289] As a result, this system can flexibly respond to the user's emotional state and further enhance the effectiveness of sales proposals. The proposal content is optimized for the user, enabling a more personalized response.

[0290] The following explains the processing flow.

[0291] Step 1:

[0292] The server collects relevant information from an external database. Based on a specific algorithm, the server extracts keywords from industry news and academic presentations, collecting the information. This information is integrated with the client's organizational information and used for later analysis.

[0293] Step 2:

[0294] The server retrieves and integrates customer information from a cloud-based business card management system. Through the business card database, customer organizational structure and contact information are centralized and automatically entered into the database.

[0295] Step 3:

[0296] The server analyzes integrated data and uses natural language processing techniques to identify the appropriate personnel within the organization. Text mining and similarity analysis are performed to determine the priority of proposed targets.

[0297] Step 4:

[0298] The server uses an emotion engine to recognize the user's emotions. It analyzes the voice and facial expression data provided by the user in real time to determine the current emotional state. This uses facial recognition technology and voice analysis technology.

[0299] Step 5:

[0300] The server generates suggestions based on the user's emotional state and sends them to the user's device. If a positive emotion is detected, it provides more detailed information and makes suggestions to encourage purchases.

[0301] Step 6:

[0302] Users can review suggestions on their devices and provide feedback. Based on the suggestions, users can request more information about products or services that interest them, or schedule meetings.

[0303] Step 7:

[0304] The server records the feedback from the user and analyzes it again with the emotion engine. This feedback is reflected in the proposed strategies after the next time, enabling more personalized sales activities.

[0305] (Example 2)

[0306] Next, Example 2 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0307] In the corporate sales field, the collection and integration of customer information are often insufficient, and proposals are often not individualized. In addition, it is difficult to make proposals considering the emotions of customers, and there is a problem that the sales effect cannot be maximized. Furthermore, due to the lack of analysis technology, there is a problem that the relevance of proposals is low.

[0308] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0309] In this invention, the server includes means for collecting information from an external information source, means for integrating the electronic business card management structure and information, and means for detecting the emotional state of the user using emotion analysis technology and adjusting the proposal. Thereby, it becomes possible to make an optimal proposal individualized for the customer and maximize the sales effect.

[0310] The "external information source" refers to a database or information providing service that provides information existing outside the system.

[0311] The "electronic business card management structure" is a system for storing, managing, and searching business card information in digital form.

[0312] "Integrating information" means collecting data obtained from multiple information sources into one system and managing it while maintaining consistency.

[0313] "Emotion analysis technology" is a technology that analyzes a user's emotions from facial expressions and voice data and evaluates their emotional state.

[0314] "User interface" refers to the screens and methods of operation that allow users to directly interact with a system.

[0315] "Adjusting a proposal" means changing the content and approach of the proposal presented based on the user's situation and feelings.

[0316] "Personalized proposals" refer to the process of providing proposals tailored to the specific needs and interests of a particular user.

[0317] This invention provides a specific means for making personalized and optimal proposals to customers in a corporate sales support system. This system consists of three components: a server, a terminal, and a user.

[0318] server

[0319] The server is responsible for collecting customer and market information from external sources via APIs and integrating it with the electronic business card management structure. The integrated data is stored in a database (e.g., MySQL). The server further analyzes the collected data using natural language processing techniques (e.g., Python's spaCy or TensorFlow) to identify appropriate individuals within the organization. For sentiment analysis, Microsoft's Azure Emotion API is used to tailor suggestions based on the user's emotional state.

[0320] terminal

[0321] The terminal functions as an interface for users to receive suggestions. In particular, it is responsible for capturing user facial expression data and voice using the built-in camera and microphone and sending them to the server in real time. By using OpenCV for facial recognition and Praat for voice tone analysis, the system accurately grasps the user's emotional state.

[0322] User

[0323] Users receive personalized suggestions presented through their devices and provide feedback accordingly. This feedback is then used by the server to improve future suggestions. For example, if a user shows interest in a suggestion and gives a positive response, the server can provide more detailed information to encourage purchase. If a negative attitude is observed, alternative options can be offered.

[0324] For example, if the proposal concerns the introduction of a new product, detailed information and success stories designed to pique the user's interest will be presented. On the other hand, if it is determined that the user's interest is low, alternative perspectives or supplementary explanations will be offered.

[0325] An example of a prompt for the generating AI model is: "When proposing new product XX, generate detailed information for when the customer shows interest. Also, consider alternative proposals for when they do not show interest."

[0326] This invention makes it possible to accurately understand the emotional state of customers and make proposals based on that understanding, thereby achieving high sales effectiveness.

[0327] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0328] Step 1:

[0329] The server retrieves customer information from external sources. It uses APIs as input to obtain basic customer information and market data, and integrates this data with an electronic business card management structure as output. Specifically, it periodically sends API requests to save the latest dataset to the database.

[0330] Step 2:

[0331] The server analyzes integrated data to identify appropriate individuals. It receives integrated data as input and analyzes it using natural language processing techniques. As output, it generates a list of individuals suitable for the suggestion. Specifically, it uses the Python spaCy library to extract and identify past transaction history and customer interests.

[0332] Step 3:

[0333] The device captures the user's facial expressions and voice when receiving suggestions. It takes real-time data via a webcam and microphone as input and sends that data to a server as output. Specifically, it uses OpenCV to capture facial data and Praat to analyze voice tone, thereby collecting the user's emotional state.

[0334] Step 4:

[0335] The server uses emotion analysis technology to determine the user's emotional state. It uses facial and audio data received from the device as input and performs analysis using the Azure Emotion API. The output identifies whether the user's emotional state is positive or negative. Specifically, it analyzes the data in real time and adjusts the suggested content based on the results.

[0336] Step 5:

[0337] The server generates suggestions that correspond to the user's emotional state and presents them to the user via the terminal. It uses the results of emotion analysis and user-specific information as input, and generates adjusted suggestions as output. Specifically, its operation involves quickly displaying detailed information and alternatives that are likely to interest the user through the user interface.

[0338] Step 6:

[0339] Users provide responses and feedback to the suggestions they receive. They evaluate the suggestions provided by the server as input and record their responses as output in the feedback system. Specifically, they provide feedback through choices and input fields provided on their terminal.

[0340] Step 7:

[0341] The server incorporates user feedback into future suggestions. It analyzes feedback data as input and updates the data to optimize the suggestion strategy as output. Specifically, it uses machine learning algorithms to analyze feedback data and incorporates the findings into subsequent suggestions.

[0342] (Application Example 2)

[0343] 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."

[0344] Traditional sales support systems have been unable to address the individual emotional states of users, resulting in uniform proposals and making it difficult to optimize customer approaches. Therefore, there is a need for systems that can provide real-time, personalized proposals based on the diverse emotional states of customers.

[0345] 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.

[0346] In this invention, the server includes means for collecting information from external information sources, means for integrating information with an information management system, means for analyzing the integrated data to identify relevant personnel within the organization, means for acquiring user language data and visual information to determine emotions, and means for modifying suggested content based on acquired emotions. This makes it possible to personalize and optimize suggested content according to the user's emotional state.

[0347] "External information sources" refer to data providers or information bases located outside the organization that are acquired for use by the system.

[0348] An "information management system" refers to a platform for collecting, managing, and integrating data such as business cards and customer information.

[0349] "Integrated data" refers to a set of information that is centrally managed by combining data from external sources and information management systems.

[0350] "Relevant personnel" refers to individuals within the organization who, based on integrated data, are deemed the most suitable to make specific proposals.

[0351] A "user input device" refers to a device used by a user to receive information and perform operations.

[0352] "Linguistic data" refers to linguistic information used for communication, such as user speech and text.

[0353] "Visual information" refers to visual data such as images and videos, and is used for purposes such as recognizing user facial expressions.

[0354] "Means of determining emotions" refers to technologies that analyze linguistic data and visual information obtained from users to infer the user's emotional state.

[0355] "Means of modifying suggested content" refers to a mechanism that adjusts suggested content to the user based on their analyzed emotional state, providing optimized information.

[0356] The system implementing this invention is built on a foundation of three components: a server, a terminal, and a user. The server collects data from external sources and integrates it with an information management system. This information management system includes customer information and business card data, thereby centralizing all customer-related information. The integrated data is analyzed using natural language processing technology to identify relevant personnel within the organization.

[0357] The terminal is provided to the user and connects to the information management system to display information in real time. The terminal also acquires the user's visual and linguistic data and analyzes it using a means to determine emotions. The emotion recognition engine, for example, uses Rekognition or the Google Cloud API to determine the user's emotional state from their facial expressions and tone of voice. As a result, the suggested content is modified to be optimal for the situation and presented on the terminal.

[0358] As a concrete example, when a customer is selecting products in a physical store, their facial expressions are captured by a camera, and their reactions to the products are collected using a microphone. Based on this data, related product information and special offers that are of interest to the user are displayed on the terminal, enabling personalized suggestions to increase their purchasing intent.

[0359] Examples of prompts for generative AI models:

[0360] "This customer is interested in the product but is hesitant to make a decision. We would like to offer additional information or incentives to help the customer feel more confident in making a purchase."

[0361] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0362] Step 1:

[0363] The server collects customer data from external sources. This input data consists of customer information residing in an external database, which is retrieved via the network. The output is an unintegrated, temporary dataset. The server performs initial data processing on this data, such as filtering and removing duplicates.

[0364] Step 2:

[0365] The server integrates the filtered data with the information management system. This process matches existing cloud-based information with newly collected data to update business card information and customer profiles. The input is filtered raw data, and the output is an integrated customer information database.

[0366] Step 3:

[0367] The server analyzes the integrated data and identifies relevant personnel within the organization. Using natural language processing techniques, it performs semantic analysis of the text data and evaluates the relationship between personnel and customer needs. The input is an integrated database, and the output is a list of relevant personnel. This list is used in the next proposal generation step.

[0368] Step 4:

[0369] The device collects the user's visual and audio data. Using the camera and microphone equipped on the device, it acquires the user's facial recognition data and voice tone in real time. The input is raw data from the device's sensors, while the output is processed data for sentiment analysis.

[0370] Step 5:

[0371] The server uses an emotion engine to analyze the acquired visual and audio data. The emotion engine classifies the data into emotional categories such as positive and negative. The input is processed visual and audio data transmitted from the terminal, and the output is data determining the emotional state.

[0372] Step 6:

[0373] The server generates suggestions based on the user's emotional state and sends them to the terminal. This process uses a generative AI model to generate prompts tailored to the customer's emotions and combine them with appropriate suggestions. The input is customer information integrated with emotional state determination data, and the output is a personalized suggestion message.

[0374] Step 7:

[0375] The terminal displays the generated suggestion messages in its user interface. This allows the user to directly consider their purchase intentions based on the suggestions. The input is the suggestion messages sent from the server, and the output is the marketing information displayed on the terminal's screen.

[0376] 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.

[0377] 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.

[0378] 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.

[0379] [Third Embodiment]

[0380] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0381] 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.

[0382] 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).

[0383] 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.

[0384] 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.

[0385] 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).

[0386] 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.

[0387] 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.

[0388] 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.

[0389] 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.

[0390] 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.

[0391] 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".

[0392] This corporate sales support system implements a series of automated processes to provide optimal proposals to customers. The system consists of a server, terminals, and users. Its main operations are described below.

[0393] The server collects academic presentations and industry news from external databases. The server uses APIs to access external information sources and retrieve information relevant to the customer's industry. For example, by collecting the latest technology announcements related to AI, the server ensures information that matches the customer's area of ​​interest.

[0394] Next, the server integrates with a cloud-based business card management system to retrieve customer information. This makes it possible to centralize information on the customer's organizational structure and relevant contacts. Past transaction data related to specific contacts is collected and used as foundational data to predict customer interests and needs.

[0395] The server uses this integrated data to perform analysis, leveraging natural language processing technology. This analysis identifies the appropriate personnel within the client company to propose solutions to. Relevance is assessed through text analysis and similarity comparisons to determine the most suitable candidates for proposals that meet the client's needs.

[0396] The server then generates product and service suggestions based on the customer's needs. These suggestions reflect the customer's past purchase history and current market trends, aiming to be the most effective within acceptable limits. For example, a customer interested in AI technology might be offered suggestions for new AI solutions.

[0397] Finally, the user receives the proposals provided by the server via their device. The user interface includes a dashboard where the user can visually review the details of the proposals. The user can also take actions based on the proposals (e.g., scheduling a meeting or requesting further information) directly from the interface.

[0398] In this way, the system efficiently manages and analyzes customer information and functions as a powerful support tool for sales representatives to quickly make appropriate proposals.

[0399] The following describes the processing flow.

[0400] Step 1:

[0401] The server accesses an external database to collect conference presentations and news articles. The server automatically retrieves the latest information from the external database's API using keywords related to specific industries and technologies. The retrieved information is stored in an internal database and tagged for later use.

[0402] Step 2:

[0403] The server uses the API of a cloud-based business card management system to retrieve business card information from client companies. The server normalizes the business card information and stores the client's organizational structure and contact person information in a database. During this process, data matching is performed to ensure there are no duplicates with existing data.

[0404] Step 3:

[0405] The server analyzes external information and business card data, and uses natural language processing technology to evaluate their relationships. Statistical models and machine learning algorithms are used to identify client company contacts who are highly relevant to conference presenters. This list of identified contacts is then sorted by priority.

[0406] Step 4:

[0407] The server generates product and service suggestions based on customer needs, taking into account historical transaction data and market trends. The generated suggestions are documented and prepared in a customized format for each customer.

[0408] Step 5:

[0409] Users view proposals generated by the server via their device on a dashboard. They can evaluate the proposals and take action as needed, including directly approaching customers or providing additional information.

[0410] (Example 1)

[0411] 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."

[0412] In traditional corporate sales, customer information gathering and proposal creation are performed separately, resulting in low efficiency. Furthermore, analyzing information to produce appropriate proposals is difficult, sometimes preventing proposals from achieving their full potential. Therefore, there is a need for a system that can quickly and reliably generate and deliver proposals that meet customer needs.

[0413] 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.

[0414] In this invention, the server includes means for extracting industry-related information from external sources, means for integrating customer information using a cloud-based data management system, and means for generating optimal suggestions for identified target individuals using generative AI technology. This enables the rapid and effective generation and presentation of suggestions.

[0415] "External information sources" refer to information providers such as databases and news sources that provide industry-related information and technical announcements.

[0416] A "cloud-based data management system" is an information processing platform that uses services provided over the internet to centrally manage, access, and manipulate data.

[0417] "Natural language processing technology" is a technology that enables computers to understand and analyze human language, and it involves analyzing text data and the semantic analysis of documents.

[0418] "Generative AI technology" is a technology that uses artificial intelligence to automatically generate new suggestions and information from data.

[0419] A "user interface" is the foundation of interaction that includes visual or manipulative elements for a user to directly interact with a system.

[0420] The implementation of this corporate sales support system invention will be structured as follows: The system mainly consists of three elements: a server, a terminal, and a user.

[0421] The server is responsible for extracting industry-related information from external sources. Specifically, it uses APIs to retrieve the latest industry information from databases and news sites on the internet. In this process, it can focus on collecting information on topics such as artificial intelligence and trending technologies.

[0422] Next, the server utilizes a cloud-based data management system. This system integrates with business card management software to consolidate data on customer organizations and contacts. This centralizes customer information management, making detailed data, such as past transaction history, available.

[0423] Furthermore, the server analyzes the collected data using natural language processing techniques. These techniques include text mining and cluster analysis, which are used to improve customer needs analysis and the accuracy of proposals. Based on this, the server utilizes generative AI technology to automatically generate optimal proposals. A specific example of a prompt is, "Generate proposals for customers interested in the new AI solution." The generated proposals are aligned with the customer's past activities and market trends.

[0424] Users receive suggestions from the server via a provided terminal. The user interface employs a dashboard, allowing users to visually review the suggestions. From this dashboard, users can directly take specific actions based on the suggestions, such as scheduling a meeting or requesting additional information.

[0425] In this way, this system significantly improves the efficiency of proposal creation in sales activities and functions as a powerful tool that enables the rapid delivery of appropriate proposals.

[0426] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0427] Step 1:

[0428] The server extracts industry-related information from external sources. In this process, the server uses a RESTful API to query multiple databases and collect data based on specific keywords and filter conditions. Inputs are keywords and industry categories, and the output is a set of collected news articles and technical reports. Specifically, the server periodically updates the data using automated scheduling.

[0429] Step 2:

[0430] The server integrates collected industry information with a cloud-based data management system to retrieve customer information. Specifically, the server accesses the API of business card management software to retrieve customer organizational structure and contact information, and then stores this information in a database. The input is the customer ID and organization name, and the output is a list of integrated customer information. The server matches the relationships between the data to centralize customer information.

[0431] Step 3:

[0432] The server performs analysis using natural language processing techniques with customer information and industry data. Input includes text data and customer history, and output includes identified potential clients and highly relevant keywords. In this process, the server utilizes text mining to extract frequently occurring words and important contexts. This allows it to predict customer needs and prioritize potential clients.

[0433] Step 4:

[0434] The server automatically generates proposals based on customer needs using a generative AI model. The input is the analysis results and prompt text obtained in the previous step. The output is specific and valuable proposal content. By inputting the prompt "Generate proposals for customers interested in new AI solutions," the AI ​​generates relevant proposals. The server manages this generation process and improves accuracy by incorporating appropriate feedback loops.

[0435] Step 5:

[0436] Users view proposal details via their terminal and take necessary actions. Input is the generated proposal sent from the server. Output is the specific action the user takes based on the presented proposal. Users can interact with the dashboard interface to visually check the proposal content and immediately schedule meetings or request further information. Optimizing user interaction is crucial at this stage.

[0437] (Application Example 1)

[0438] 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."

[0439] In today's commercial environment, there is a need to improve the quality of customer service in physical stores and to provide customers with the most suitable products and services. Traditional customer service methods rely heavily on the experience and skills of individual staff members, making it difficult to quickly provide personalized recommendations to individual customers. Furthermore, the lack of mechanisms to effectively utilize past customer service history and customer profiles makes improving customer satisfaction and increasing repeat visit rates a challenge.

[0440] 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.

[0441] In this invention, the server includes means for collecting information from external information sources, means for integrating the information with a cloud-based management database, means for analyzing the integrated data to identify appropriate targets, means for presenting products and services based on the characteristics of visitors, and means for recording interactions with visitors and saving the information for future visits. This makes it possible to quickly provide appropriate suggestions to customers who visit the store, based on their individual profiles.

[0442] An "external information source" refers to an external database or information platform used to obtain data.

[0443] A "cloud-based management database" is a database management system that stores customer information and business data on cloud servers on the internet, making them accessible anytime, anywhere.

[0444] "Means of integrating information" refers to methods of centralizing data collected from different sources and effectively combining and utilizing them.

[0445] "Methods for analyzing integrated data to identify appropriate targets" refers to methods of analyzing aggregated data and selecting the most suitable customers or target audience based on specific conditions and needs.

[0446] "Methods for presenting products and services based on visitor characteristics" refers to techniques that select and present the most suitable products and services based on customer preferences and past purchase history.

[0447] "A means of recording conversations with visitors and saving information for future visits" refers to a technology that records communication with customers and accumulates information useful for future sales activities and proposals.

[0448] This invention aims to realize a customer service support system using smart devices in physical stores. The server collects customer data from external sources and integrates it into a cloud-based management database. Smartphones and smart glasses are used as hardware, and customer information management APIs and Google Cloud Natural Language APIs are used as software. Based on the integrated data, the server performs analysis using natural language processing and generates suggestions for appropriate products and services based on the characteristics of visitors.

[0449] These generated suggestions are displayed on smart glasses or devices worn by service staff and presented to customers. Furthermore, the server records customer interactions and saves the information for future visits. This functionality allows staff members to refer to past interaction history and suggestions to provide more personalized recommendations to customers.

[0450] As a concrete example, when a customer visits a store, the server analyzes past purchase history and preference information, and uses prompts to a generative AI model such as, "This customer has previously purchased AI-related products and has shown high customer satisfaction. Please generate product or service suggestions that will interest her," to derive the optimal suggestions for the customer. This system allows visitors to consistently enjoy a high level of service.

[0451] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0452] Step 1:

[0453] The server collects customer information from external sources. It takes information requests from external APIs as input and receives collected customer-related data as output. This data includes industry news and up-to-date information on areas of interest.

[0454] Step 2:

[0455] The server integrates customer information into a cloud-based management database. Inputs are customer information obtained from external sources and existing database data, while output is integrated customer profile data. This profile reflects the customer's organizational structure and past transaction history.

[0456] Step 3:

[0457] The server analyzes the integrated data using natural language processing to identify appropriate targets. In this process, the input is customer profile data, and the output is a suggested product or service. Data processing includes text analysis and similarity comparison.

[0458] Step 4:

[0459] The terminal displays the suggestions received from the server. The input is suggestion data from the server, and the output is the suggestion information displayed on the terminal's screen. The sales staff, as users, use this information to make appropriate suggestions to customers in person.

[0460] Step 5:

[0461] The server records customer interactions and stores information for future visits. Inputs include voice and text data based on the interaction, while output is structured historical data for future suggestions. This information is stored in the server's database and available for future interactions.

[0462] 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.

[0463] This corporate sales support system, which incorporates emotion recognition, is designed to make proposals to users more effective and personalized. The system consists of three components: a server, a terminal, and a user.

[0464] The server collects information from external databases and integrates the latest customer data. The server also integrates with a cloud-based business card management system to retrieve and centrally manage customer organizational and contact information. Using this data, it employs natural language processing to identify appropriate targets within the customer organization.

[0465] Furthermore, this system incorporates a process that utilizes an emotion engine to enhance the effectiveness of its suggestions. The server analyzes user reactions and past feedback data using the emotion engine to determine the user's current emotional state. Based on this, it personalizes suggestions to provide recommendations that resonate with the user's emotions.

[0466] Specifically, when a user receives a suggestion using their device, the emotion engine analyzes the user's facial recognition data and voice tone to evaluate whether they are in a positive emotional state or one that needs improvement. For example, if the server recognizes that the user is interested, it immediately presents detailed information about the suggestion to encourage purchase.

[0467] On the other hand, if a user expresses negative feelings towards a proposal, the server also has a function to support acceptance of the proposal by providing alternatives and supplementary information. Furthermore, user feedback is recorded sequentially within the system and reflected in the next proposal strategy through the sentiment engine.

[0468] This allows the system to flexibly respond to the user's emotional state, further enhancing the effectiveness of sales proposals. The proposals are optimized for the user, enabling more personalized responses.

[0469] The following describes the processing flow.

[0470] Step 1:

[0471] The server collects relevant information from an external database. Based on a specific algorithm, the server extracts keywords from industry news and academic presentations, collecting the information. This information is integrated with the client's organizational information and used for later analysis.

[0472] Step 2:

[0473] The server retrieves and integrates customer information from a cloud-based business card management system. Through the business card database, customer organizational structure and contact information are centralized and automatically entered into the database.

[0474] Step 3:

[0475] The server analyzes integrated data and uses natural language processing techniques to identify the appropriate personnel within the organization. Text mining and similarity analysis are performed to determine the priority of proposed targets.

[0476] Step 4:

[0477] The server uses an emotion engine to recognize the user's emotions. It analyzes the voice and facial expression data provided by the user in real time to determine the current emotional state. This uses facial recognition technology and voice analysis technology.

[0478] Step 5:

[0479] The server generates suggestions based on the user's emotional state and sends them to the user's device. If a positive emotion is detected, it provides more detailed information and makes suggestions to encourage purchases.

[0480] Step 6:

[0481] Users can review suggestions on their devices and provide feedback. Based on the suggestions, users can request more information about products or services that interest them, or schedule meetings.

[0482] Step 7:

[0483] The server records user feedback, which is then analyzed again by the emotion engine. This feedback is then incorporated into future proposal strategies, enabling more personalized sales activities.

[0484] (Example 2)

[0485] 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."

[0486] In corporate sales, the collection and integration of customer information is often insufficient, resulting in proposals that are not personalized. Furthermore, it is difficult to create proposals that consider customer emotions, hindering the maximization of sales effectiveness. Additionally, a lack of analytical technology leads to proposals that are less relevant.

[0487] 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.

[0488] In this invention, the server includes means for collecting information from external information sources, means for integrating the information with an electronic business card management structure, and means for detecting the user's emotional state using emotion analysis technology and adjusting the suggestions accordingly. This makes it possible to provide personalized and optimal suggestions to customers and maximize sales effectiveness.

[0489] "External information sources" refer to databases or information services that provide information that exists outside the system.

[0490] An "electronic business card management structure" is a system for storing, managing, and searching business card information in digital format.

[0491] "Integrating information" means combining data obtained from multiple sources into a single system and managing it while maintaining consistency.

[0492] "Emotion analysis technology" is a technology that analyzes a user's emotions from facial expressions and voice data and evaluates their emotional state.

[0493] "User interface" refers to the screens and methods of operation that allow users to directly interact with a system.

[0494] "Adjusting a proposal" means changing the content and approach of the proposal presented based on the user's situation and feelings.

[0495] "Personalized proposals" refer to the process of providing proposals tailored to the specific needs and interests of a particular user.

[0496] This invention provides a specific means for making personalized and optimal proposals to customers in a corporate sales support system. This system consists of three components: a server, a terminal, and a user.

[0497] server

[0498] The server is responsible for collecting customer and market information from external sources via APIs and integrating it with the electronic business card management structure. The integrated data is stored in a database (e.g., MySQL). The server further analyzes the collected data using natural language processing techniques (e.g., Python's spaCy or TensorFlow) to identify appropriate individuals within the organization. For sentiment analysis, Microsoft's Azure Emotion API is used to tailor suggestions based on the user's emotional state.

[0499] terminal

[0500] The terminal functions as an interface for users to receive suggestions. In particular, it is responsible for capturing user facial expression data and voice using the built-in camera and microphone and sending them to the server in real time. By using OpenCV for facial recognition and Praat for voice tone analysis, the system accurately grasps the user's emotional state.

[0501] User

[0502] Users receive personalized suggestions presented through their devices and provide feedback accordingly. This feedback is then used by the server to improve future suggestions. For example, if a user shows interest in a suggestion and gives a positive response, the server can provide more detailed information to encourage purchase. If a negative attitude is observed, alternative options can be offered.

[0503] For example, if the proposal concerns the introduction of a new product, detailed information and success stories designed to pique the user's interest will be presented. On the other hand, if it is determined that the user's interest is low, alternative perspectives or supplementary explanations will be offered.

[0504] An example of a prompt for the generating AI model is: "When proposing new product XX, generate detailed information for when the customer shows interest. Also, consider alternative proposals for when they do not show interest."

[0505] This invention makes it possible to accurately understand the emotional state of customers and make proposals based on that understanding, thereby achieving high sales effectiveness.

[0506] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0507] Step 1:

[0508] The server retrieves customer information from external sources. It uses APIs as input to obtain basic customer information and market data, and integrates this data with an electronic business card management structure as output. Specifically, it periodically sends API requests to save the latest dataset to the database.

[0509] Step 2:

[0510] The server analyzes integrated data to identify appropriate individuals. It receives integrated data as input and analyzes it using natural language processing techniques. As output, it generates a list of individuals suitable for the suggestion. Specifically, it uses the Python spaCy library to extract and identify past transaction history and customer interests.

[0511] Step 3:

[0512] The device captures the user's facial expressions and voice when receiving suggestions. It takes real-time data via a webcam and microphone as input and sends that data to a server as output. Specifically, it uses OpenCV to capture facial data and Praat to analyze voice tone, thereby collecting the user's emotional state.

[0513] Step 4:

[0514] The server uses emotion analysis technology to determine the user's emotional state. It uses facial and audio data received from the device as input and performs analysis using the Azure Emotion API. The output identifies whether the user's emotional state is positive or negative. Specifically, it analyzes the data in real time and adjusts the suggested content based on the results.

[0515] Step 5:

[0516] The server generates suggestions that correspond to the user's emotional state and presents them to the user via the terminal. It uses the results of emotion analysis and user-specific information as input, and generates adjusted suggestions as output. Specifically, its operation involves quickly displaying detailed information and alternatives that are likely to interest the user through the user interface.

[0517] Step 6:

[0518] Users provide responses and feedback to the suggestions they receive. They evaluate the suggestions provided by the server as input and record their responses as output in the feedback system. Specifically, they provide feedback through choices and input fields provided on their terminal.

[0519] Step 7:

[0520] The server incorporates user feedback into future suggestions. It analyzes feedback data as input and updates the data to optimize the suggestion strategy as output. Specifically, it uses machine learning algorithms to analyze feedback data and incorporates the findings into subsequent suggestions.

[0521] (Application Example 2)

[0522] 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."

[0523] Traditional sales support systems have been unable to address the individual emotional states of users, resulting in uniform proposals and making it difficult to optimize customer approaches. Therefore, there is a need for systems that can provide real-time, personalized proposals based on the diverse emotional states of customers.

[0524] 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.

[0525] In this invention, the server includes means for collecting information from external information sources, means for integrating information with an information management system, means for analyzing the integrated data to identify relevant personnel within the organization, means for acquiring user language data and visual information to determine emotions, and means for modifying suggested content based on acquired emotions. This makes it possible to personalize and optimize suggested content according to the user's emotional state.

[0526] "External information sources" refer to data providers or information bases located outside the organization that are acquired for use by the system.

[0527] An "information management system" refers to a platform for collecting, managing, and integrating data such as business cards and customer information.

[0528] "Integrated data" refers to a set of information that is centrally managed by combining data from external sources and information management systems.

[0529] "Relevant personnel" refers to individuals within the organization who, based on integrated data, are deemed the most suitable to make specific proposals.

[0530] A "user input device" refers to a device used by a user to receive information and perform operations.

[0531] "Linguistic data" refers to linguistic information used for communication, such as user speech and text.

[0532] "Visual information" refers to visual data such as images and videos, and is used for purposes such as recognizing user facial expressions.

[0533] "Means of determining emotions" refers to technologies that analyze linguistic data and visual information obtained from users to infer the user's emotional state.

[0534] "Means of modifying suggested content" refers to a mechanism that adjusts suggested content to the user based on their analyzed emotional state, providing optimized information.

[0535] The system implementing this invention is built on a foundation of three components: a server, a terminal, and a user. The server collects data from external sources and integrates it with an information management system. This information management system includes customer information and business card data, thereby centralizing all customer-related information. The integrated data is analyzed using natural language processing technology to identify relevant personnel within the organization.

[0536] The terminal is provided to the user and connects to the information management system to display information in real time. The terminal also acquires the user's visual and linguistic data and analyzes it using a means to determine emotions. The emotion recognition engine, for example, uses Rekognition or the Google Cloud API to determine the user's emotional state from their facial expressions and tone of voice. As a result, the suggested content is modified to be optimal for the situation and presented on the terminal.

[0537] As a concrete example, when a customer is selecting products in a physical store, their facial expressions are captured by a camera, and their reactions to the products are collected using a microphone. Based on this data, related product information and special offers that are of interest to the user are displayed on the terminal, enabling personalized suggestions to increase their purchasing intent.

[0538] Examples of prompts for generative AI models:

[0539] "This customer is interested in the product but is hesitant to make a decision. We would like to offer additional information or incentives to help the customer feel more confident in making a purchase."

[0540] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0541] Step 1:

[0542] The server collects customer data from external sources. This input data consists of customer information residing in an external database, which is retrieved via the network. The output is an unintegrated, temporary dataset. The server performs initial data processing on this data, such as filtering and removing duplicates.

[0543] Step 2:

[0544] The server integrates the filtered data with the information management system. This process matches existing cloud-based information with newly collected data to update business card information and customer profiles. The input is filtered raw data, and the output is an integrated customer information database.

[0545] Step 3:

[0546] The server analyzes the integrated data and identifies relevant personnel within the organization. Using natural language processing techniques, it performs semantic analysis of the text data and evaluates the relationship between personnel and customer needs. The input is an integrated database, and the output is a list of relevant personnel. This list is used in the next proposal generation step.

[0547] Step 4:

[0548] The device collects the user's visual and audio data. Using the camera and microphone equipped on the device, it acquires the user's facial recognition data and voice tone in real time. The input is raw data from the device's sensors, while the output is processed data for sentiment analysis.

[0549] Step 5:

[0550] The server uses an emotion engine to analyze the acquired visual and audio data. The emotion engine classifies the data into emotional categories such as positive and negative. The input is processed visual and audio data transmitted from the terminal, and the output is data determining the emotional state.

[0551] Step 6:

[0552] The server generates suggestions based on the user's emotional state and sends them to the terminal. This process uses a generative AI model to generate prompts tailored to the customer's emotions and combine them with appropriate suggestions. The input is customer information integrated with emotional state determination data, and the output is a personalized suggestion message.

[0553] Step 7:

[0554] The terminal displays the generated suggestion messages in its user interface. This allows the user to directly consider their purchase intentions based on the suggestions. The input is the suggestion messages sent from the server, and the output is the marketing information displayed on the terminal's screen.

[0555] 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.

[0556] 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.

[0557] 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.

[0558] [Fourth Embodiment]

[0559] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0560] 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.

[0561] 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).

[0562] 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.

[0563] 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.

[0564] 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).

[0565] 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.

[0566] 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.

[0567] 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.

[0568] 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.

[0569] 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.

[0570] 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.

[0571] 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".

[0572] This corporate sales support system implements a series of automated processes to provide optimal proposals to customers. The system consists of a server, terminals, and users. Its main operations are described below.

[0573] The server collects academic presentations and industry news from external databases. The server uses APIs to access external information sources and retrieve information relevant to the customer's industry. For example, by collecting the latest technology announcements related to AI, the server ensures information that matches the customer's area of ​​interest.

[0574] Next, the server integrates with a cloud-based business card management system to retrieve customer information. This makes it possible to centralize information on the customer's organizational structure and relevant contacts. Past transaction data related to specific contacts is collected and used as foundational data to predict customer interests and needs.

[0575] The server uses this integrated data to perform analysis, leveraging natural language processing technology. This analysis identifies the appropriate personnel within the client company to propose solutions to. Relevance is assessed through text analysis and similarity comparisons to determine the most suitable candidates for proposals that meet the client's needs.

[0576] The server then generates product and service suggestions based on the customer's needs. These suggestions reflect the customer's past purchase history and current market trends, aiming to be the most effective within acceptable limits. For example, a customer interested in AI technology might be offered suggestions for new AI solutions.

[0577] Finally, the user receives the proposals provided by the server via their device. The user interface includes a dashboard where the user can visually review the details of the proposals. The user can also take actions based on the proposals (e.g., scheduling a meeting or requesting further information) directly from the interface.

[0578] In this way, the system efficiently manages and analyzes customer information and functions as a powerful support tool for sales representatives to quickly make appropriate proposals.

[0579] The following describes the processing flow.

[0580] Step 1:

[0581] The server accesses an external database to collect conference presentations and news articles. The server automatically retrieves the latest information from the external database's API using keywords related to specific industries and technologies. The retrieved information is stored in an internal database and tagged for later use.

[0582] Step 2:

[0583] The server uses the API of a cloud-based business card management system to retrieve business card information from client companies. The server normalizes the business card information and stores the client's organizational structure and contact person information in a database. During this process, data matching is performed to ensure there are no duplicates with existing data.

[0584] Step 3:

[0585] The server analyzes external information and business card data, and uses natural language processing technology to evaluate their relationships. Statistical models and machine learning algorithms are used to identify client company contacts who are highly relevant to conference presenters. This list of identified contacts is then sorted by priority.

[0586] Step 4:

[0587] The server generates product and service suggestions based on customer needs, taking into account historical transaction data and market trends. The generated suggestions are documented and prepared in a customized format for each customer.

[0588] Step 5:

[0589] Users view proposals generated by the server via their device on a dashboard. They can evaluate the proposals and take action as needed, including directly approaching customers or providing additional information.

[0590] (Example 1)

[0591] 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".

[0592] In traditional corporate sales, customer information gathering and proposal creation are performed separately, resulting in low efficiency. Furthermore, analyzing information to produce appropriate proposals is difficult, sometimes preventing proposals from achieving their full potential. Therefore, there is a need for a system that can quickly and reliably generate and deliver proposals that meet customer needs.

[0593] 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.

[0594] In this invention, the server includes means for extracting industry-related information from external sources, means for integrating customer information using a cloud-based data management system, and means for generating optimal suggestions for identified target individuals using generative AI technology. This enables the rapid and effective generation and presentation of suggestions.

[0595] "External information sources" refer to information providers such as databases and news sources that provide industry-related information and technical announcements.

[0596] A "cloud-based data management system" is an information processing platform that uses services provided over the internet to centrally manage, access, and manipulate data.

[0597] "Natural language processing technology" is a technology that enables computers to understand and analyze human language, and it involves analyzing text data and the semantic analysis of documents.

[0598] "Generative AI technology" is a technology that uses artificial intelligence to automatically generate new suggestions and information from data.

[0599] A "user interface" is the foundation of interaction that includes visual or manipulative elements for a user to directly interact with a system.

[0600] The implementation of this corporate sales support system invention will be structured as follows: The system mainly consists of three elements: a server, a terminal, and a user.

[0601] The server is responsible for extracting industry-related information from external sources. Specifically, it uses APIs to retrieve the latest industry information from databases and news sites on the internet. In this process, it can focus on collecting information on topics such as artificial intelligence and trending technologies.

[0602] Next, the server utilizes a cloud-based data management system. This system integrates with business card management software to consolidate data on customer organizations and contacts. This centralizes customer information management, making detailed data, such as past transaction history, available.

[0603] Furthermore, the server analyzes the collected data using natural language processing techniques. These techniques include text mining and cluster analysis, which are used to improve customer needs analysis and the accuracy of proposals. Based on this, the server utilizes generative AI technology to automatically generate optimal proposals. A specific example of a prompt is, "Generate proposals for customers interested in the new AI solution." The generated proposals are aligned with the customer's past activities and market trends.

[0604] Users receive suggestions from the server via a provided terminal. The user interface employs a dashboard, allowing users to visually review the suggestions. From this dashboard, users can directly take specific actions based on the suggestions, such as scheduling a meeting or requesting additional information.

[0605] In this way, this system significantly improves the efficiency of proposal creation in sales activities and functions as a powerful tool that enables the rapid delivery of appropriate proposals.

[0606] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0607] Step 1:

[0608] The server extracts industry-related information from external sources. In this process, the server uses a RESTful API to query multiple databases and collect data based on specific keywords and filter conditions. Inputs are keywords and industry categories, and the output is a set of collected news articles and technical reports. Specifically, the server periodically updates the data using automated scheduling.

[0609] Step 2:

[0610] The server integrates collected industry information with a cloud-based data management system to retrieve customer information. Specifically, the server accesses the API of business card management software to retrieve customer organizational structure and contact information, and then stores this information in a database. The input is the customer ID and organization name, and the output is a list of integrated customer information. The server matches the relationships between the data to centralize customer information.

[0611] Step 3:

[0612] The server performs analysis using natural language processing techniques with customer information and industry data. Input includes text data and customer history, and output includes identified potential clients and highly relevant keywords. In this process, the server utilizes text mining to extract frequently occurring words and important contexts. This allows it to predict customer needs and prioritize potential clients.

[0613] Step 4:

[0614] The server automatically generates proposals based on customer needs using a generative AI model. The input is the analysis results and prompt text obtained in the previous step. The output is specific and valuable proposal content. By inputting the prompt "Generate proposals for customers interested in new AI solutions," the AI ​​generates relevant proposals. The server manages this generation process and improves accuracy by incorporating appropriate feedback loops.

[0615] Step 5:

[0616] Users view proposal details via their terminal and take necessary actions. Input is the generated proposal sent from the server. Output is the specific action the user takes based on the presented proposal. Users can interact with the dashboard interface to visually check the proposal content and immediately schedule meetings or request further information. Optimizing user interaction is crucial at this stage.

[0617] (Application Example 1)

[0618] 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".

[0619] In today's commercial environment, there is a need to improve the quality of customer service in physical stores and to provide customers with the most suitable products and services. Traditional customer service methods rely heavily on the experience and skills of individual staff members, making it difficult to quickly provide personalized recommendations to individual customers. Furthermore, the lack of mechanisms to effectively utilize past customer service history and customer profiles makes improving customer satisfaction and increasing repeat visit rates a challenge.

[0620] 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.

[0621] In this invention, the server includes means for collecting information from external information sources, means for integrating the information with a cloud-based management database, means for analyzing the integrated data to identify appropriate targets, means for presenting products and services based on the characteristics of visitors, and means for recording interactions with visitors and saving the information for future visits. This makes it possible to quickly provide appropriate suggestions to customers who visit the store, based on their individual profiles.

[0622] An "external information source" refers to an external database or information platform used to obtain data.

[0623] A "cloud-based management database" is a database management system that stores customer information and business data on cloud servers on the internet, making them accessible anytime, anywhere.

[0624] "Means of integrating information" refers to methods of centralizing data collected from different sources and effectively combining and utilizing them.

[0625] "Methods for analyzing integrated data to identify appropriate targets" refers to methods of analyzing aggregated data and selecting the most suitable customers or target audience based on specific conditions and needs.

[0626] "Methods for presenting products and services based on visitor characteristics" refers to techniques that select and present the most suitable products and services based on customer preferences and past purchase history.

[0627] "A means of recording conversations with visitors and saving information for future visits" refers to a technology that records communication with customers and accumulates information useful for future sales activities and proposals.

[0628] This invention aims to realize a customer service support system using smart devices in physical stores. The server collects customer data from external sources and integrates it into a cloud-based management database. Smartphones and smart glasses are used as hardware, and customer information management APIs and Google Cloud Natural Language APIs are used as software. Based on the integrated data, the server performs analysis using natural language processing and generates suggestions for appropriate products and services based on the characteristics of visitors.

[0629] These generated suggestions are displayed on smart glasses or devices worn by service staff and presented to customers. Furthermore, the server records customer interactions and saves the information for future visits. This functionality allows staff members to refer to past interaction history and suggestions to provide more personalized recommendations to customers.

[0630] As a concrete example, when a customer visits a store, the server analyzes past purchase history and preference information, and uses prompts to a generative AI model such as, "This customer has previously purchased AI-related products and has shown high customer satisfaction. Please generate product or service suggestions that will interest her," to derive the optimal suggestions for the customer. This system allows visitors to consistently enjoy a high level of service.

[0631] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0632] Step 1:

[0633] The server collects customer information from external sources. It takes information requests from external APIs as input and receives collected customer-related data as output. This data includes industry news and up-to-date information on areas of interest.

[0634] Step 2:

[0635] The server integrates customer information into a cloud-based management database. Inputs are customer information obtained from external sources and existing database data, while output is integrated customer profile data. This profile reflects the customer's organizational structure and past transaction history.

[0636] Step 3:

[0637] The server analyzes the integrated data using natural language processing to identify appropriate targets. In this process, the input is customer profile data, and the output is a suggested product or service. Data processing includes text analysis and similarity comparison.

[0638] Step 4:

[0639] The terminal displays the suggestions received from the server. The input is suggestion data from the server, and the output is the suggestion information displayed on the terminal's screen. The sales staff, as users, use this information to make appropriate suggestions to customers in person.

[0640] Step 5:

[0641] The server records customer interactions and stores information for future visits. Inputs include voice and text data based on the interaction, while output is structured historical data for future suggestions. This information is stored in the server's database and available for future interactions.

[0642] 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.

[0643] This corporate sales support system, which incorporates emotion recognition, is designed to make proposals to users more effective and personalized. The system consists of three components: a server, a terminal, and a user.

[0644] The server collects information from external databases and integrates the latest customer data. The server also integrates with a cloud-based business card management system to retrieve and centrally manage customer organizational and contact information. Using this data, it employs natural language processing to identify appropriate targets within the customer organization.

[0645] Furthermore, this system incorporates a process that utilizes an emotion engine to enhance the effectiveness of its suggestions. The server analyzes user reactions and past feedback data using the emotion engine to determine the user's current emotional state. Based on this, it personalizes suggestions to provide recommendations that resonate with the user's emotions.

[0646] Specifically, when a user receives a suggestion using their device, the emotion engine analyzes the user's facial recognition data and voice tone to evaluate whether they are in a positive emotional state or one that needs improvement. For example, if the server recognizes that the user is interested, it immediately presents detailed information about the suggestion to encourage purchase.

[0647] On the other hand, if a user expresses negative feelings towards a proposal, the server also has a function to support acceptance of the proposal by providing alternatives and supplementary information. Furthermore, user feedback is recorded sequentially within the system and reflected in the next proposal strategy through the sentiment engine.

[0648] This allows the system to flexibly respond to the user's emotional state, further enhancing the effectiveness of sales proposals. The proposals are optimized for the user, enabling more personalized responses.

[0649] The following describes the processing flow.

[0650] Step 1:

[0651] The server collects relevant information from an external database. Based on a specific algorithm, the server extracts keywords from industry news and academic presentations, collecting the information. This information is integrated with the client's organizational information and used for later analysis.

[0652] Step 2:

[0653] The server retrieves and integrates customer information from a cloud-based business card management system. Through the business card database, customer organizational structure and contact information are centralized and automatically entered into the database.

[0654] Step 3:

[0655] The server analyzes integrated data and uses natural language processing techniques to identify the appropriate personnel within the organization. Text mining and similarity analysis are performed to determine the priority of proposed targets.

[0656] Step 4:

[0657] The server uses an emotion engine to recognize the user's emotions. It analyzes the voice and facial expression data provided by the user in real time to determine the current emotional state. This uses facial recognition technology and voice analysis technology.

[0658] Step 5:

[0659] The server generates suggestions based on the user's emotional state and sends them to the user's device. If a positive emotion is detected, it provides more detailed information and makes suggestions to encourage purchases.

[0660] Step 6:

[0661] Users can review suggestions on their devices and provide feedback. Based on the suggestions, users can request more information about products or services that interest them, or schedule meetings.

[0662] Step 7:

[0663] The server records user feedback, which is then analyzed again by the emotion engine. This feedback is then incorporated into future proposal strategies, enabling more personalized sales activities.

[0664] (Example 2)

[0665] 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".

[0666] In corporate sales, the collection and integration of customer information is often insufficient, resulting in proposals that are not personalized. Furthermore, it is difficult to create proposals that consider customer emotions, hindering the maximization of sales effectiveness. Additionally, a lack of analytical technology leads to proposals that are less relevant.

[0667] 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.

[0668] In this invention, the server includes means for collecting information from external information sources, means for integrating the information with an electronic business card management structure, and means for detecting the user's emotional state using emotion analysis technology and adjusting the suggestions accordingly. This makes it possible to provide personalized and optimal suggestions to customers and maximize sales effectiveness.

[0669] "External information sources" refer to databases or information services that provide information that exists outside the system.

[0670] An "electronic business card management structure" is a system for storing, managing, and searching business card information in digital format.

[0671] "Integrating information" means combining data obtained from multiple sources into a single system and managing it while maintaining consistency.

[0672] "Emotion analysis technology" is a technology that analyzes a user's emotions from facial expressions and voice data and evaluates their emotional state.

[0673] "User interface" refers to the screens and methods of operation that allow users to directly interact with a system.

[0674] "Adjusting a proposal" means changing the content and approach of the proposal presented based on the user's situation and feelings.

[0675] "Personalized proposals" refer to the process of providing proposals tailored to the specific needs and interests of a particular user.

[0676] This invention provides a specific means for making personalized and optimal proposals to customers in a corporate sales support system. This system consists of three components: a server, a terminal, and a user.

[0677] server

[0678] The server is responsible for collecting customer and market information from external sources via APIs and integrating it with the electronic business card management structure. The integrated data is stored in a database (e.g., MySQL). The server further analyzes the collected data using natural language processing techniques (e.g., Python's spaCy or TensorFlow) to identify appropriate individuals within the organization. For sentiment analysis, Microsoft's Azure Emotion API is used to tailor suggestions based on the user's emotional state.

[0679] terminal

[0680] The terminal functions as an interface for users to receive suggestions. In particular, it is responsible for capturing user facial expression data and voice using the built-in camera and microphone and sending them to the server in real time. By using OpenCV for facial recognition and Praat for voice tone analysis, the system accurately grasps the user's emotional state.

[0681] User

[0682] Users receive personalized suggestions presented through their devices and provide feedback accordingly. This feedback is then used by the server to improve future suggestions. For example, if a user shows interest in a suggestion and gives a positive response, the server can provide more detailed information to encourage purchase. If a negative attitude is observed, alternative options can be offered.

[0683] For example, if the proposal concerns the introduction of a new product, detailed information and success stories designed to pique the user's interest will be presented. On the other hand, if it is determined that the user's interest is low, alternative perspectives or supplementary explanations will be offered.

[0684] An example of a prompt for the generating AI model is: "When proposing new product XX, generate detailed information for when the customer shows interest. Also, consider alternative proposals for when they do not show interest."

[0685] This invention makes it possible to accurately understand the emotional state of customers and make proposals based on that understanding, thereby achieving high sales effectiveness.

[0686] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0687] Step 1:

[0688] The server retrieves customer information from external sources. It uses APIs as input to obtain basic customer information and market data, and integrates this data with an electronic business card management structure as output. Specifically, it periodically sends API requests to save the latest dataset to the database.

[0689] Step 2:

[0690] The server analyzes integrated data to identify appropriate individuals. It receives integrated data as input and analyzes it using natural language processing techniques. As output, it generates a list of individuals suitable for the suggestion. Specifically, it uses the Python spaCy library to extract and identify past transaction history and customer interests.

[0691] Step 3:

[0692] The device captures the user's facial expressions and voice when receiving suggestions. It takes real-time data via a webcam and microphone as input and sends that data to a server as output. Specifically, it uses OpenCV to capture facial data and Praat to analyze voice tone, thereby collecting the user's emotional state.

[0693] Step 4:

[0694] The server uses emotion analysis technology to determine the user's emotional state. It uses facial and audio data received from the device as input and performs analysis using the Azure Emotion API. The output identifies whether the user's emotional state is positive or negative. Specifically, it analyzes the data in real time and adjusts the suggested content based on the results.

[0695] Step 5:

[0696] The server generates suggestions that correspond to the user's emotional state and presents them to the user via the terminal. It uses the results of emotion analysis and user-specific information as input, and generates adjusted suggestions as output. Specifically, its operation involves quickly displaying detailed information and alternatives that are likely to interest the user through the user interface.

[0697] Step 6:

[0698] Users provide responses and feedback to the suggestions they receive. They evaluate the suggestions provided by the server as input and record their responses as output in the feedback system. Specifically, they provide feedback through choices and input fields provided on their terminal.

[0699] Step 7:

[0700] The server incorporates user feedback into future suggestions. It analyzes feedback data as input and updates the data to optimize the suggestion strategy as output. Specifically, it uses machine learning algorithms to analyze feedback data and incorporates the findings into subsequent suggestions.

[0701] (Application Example 2)

[0702] 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".

[0703] Traditional sales support systems have been unable to address the individual emotional states of users, resulting in uniform proposals and making it difficult to optimize customer approaches. Therefore, there is a need for systems that can provide real-time, personalized proposals based on the diverse emotional states of customers.

[0704] 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.

[0705] In this invention, the server includes means for collecting information from external information sources, means for integrating information with an information management system, means for analyzing the integrated data to identify relevant personnel within the organization, means for acquiring user language data and visual information to determine emotions, and means for modifying suggested content based on acquired emotions. This makes it possible to personalize and optimize suggested content according to the user's emotional state.

[0706] "External information sources" refer to data providers or information bases located outside the organization that are acquired for use by the system.

[0707] An "information management system" refers to a platform for collecting, managing, and integrating data such as business cards and customer information.

[0708] "Integrated data" refers to a set of information that is centrally managed by combining data from external sources and information management systems.

[0709] "Relevant personnel" refers to individuals within the organization who, based on integrated data, are deemed the most suitable to make specific proposals.

[0710] A "user input device" refers to a device used by a user to receive information and perform operations.

[0711] "Linguistic data" refers to linguistic information used for communication, such as user speech and text.

[0712] "Visual information" refers to visual data such as images and videos, and is used for purposes such as recognizing user facial expressions.

[0713] "Means of determining emotions" refers to technologies that analyze linguistic data and visual information obtained from users to infer the user's emotional state.

[0714] "Means of modifying suggested content" refers to a mechanism that adjusts suggested content to the user based on their analyzed emotional state, providing optimized information.

[0715] The system implementing this invention is built on a foundation of three components: a server, a terminal, and a user. The server collects data from external sources and integrates it with an information management system. This information management system includes customer information and business card data, thereby centralizing all customer-related information. The integrated data is analyzed using natural language processing technology to identify relevant personnel within the organization.

[0716] The terminal is provided to the user and connects to the information management system to display information in real time. The terminal also acquires the user's visual and linguistic data and analyzes it using a means to determine emotions. The emotion recognition engine, for example, uses Rekognition or the Google Cloud API to determine the user's emotional state from their facial expressions and tone of voice. As a result, the suggested content is modified to be optimal for the situation and presented on the terminal.

[0717] As a concrete example, when a customer is selecting products in a physical store, their facial expressions are captured by a camera, and their reactions to the products are collected using a microphone. Based on this data, related product information and special offers that are of interest to the user are displayed on the terminal, enabling personalized suggestions to increase their purchasing intent.

[0718] Examples of prompts for generative AI models:

[0719] "This customer is interested in the product but is hesitant to make a decision. We would like to offer additional information or incentives to help the customer feel more confident in making a purchase."

[0720] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0721] Step 1:

[0722] The server collects customer data from external sources. This input data consists of customer information residing in an external database, which is retrieved via the network. The output is an unintegrated, temporary dataset. The server performs initial data processing on this data, such as filtering and removing duplicates.

[0723] Step 2:

[0724] The server integrates the filtered data with the information management system. This process matches existing cloud-based information with newly collected data to update business card information and customer profiles. The input is filtered raw data, and the output is an integrated customer information database.

[0725] Step 3:

[0726] The server analyzes the integrated data and identifies relevant personnel within the organization. Using natural language processing techniques, it performs semantic analysis of the text data and evaluates the relationship between personnel and customer needs. The input is an integrated database, and the output is a list of relevant personnel. This list is used in the next proposal generation step.

[0727] Step 4:

[0728] The device collects the user's visual and audio data. Using the camera and microphone equipped on the device, it acquires the user's facial recognition data and voice tone in real time. The input is raw data from the device's sensors, while the output is processed data for sentiment analysis.

[0729] Step 5:

[0730] The server uses an emotion engine to analyze the acquired visual and audio data. The emotion engine classifies the data into emotional categories such as positive and negative. The input is processed visual and audio data transmitted from the terminal, and the output is data determining the emotional state.

[0731] Step 6:

[0732] The server generates suggestions based on the user's emotional state and sends them to the terminal. This process uses a generative AI model to generate prompts tailored to the customer's emotions and combine them with appropriate suggestions. The input is customer information integrated with emotional state determination data, and the output is a personalized suggestion message.

[0733] Step 7:

[0734] The terminal displays the generated suggestion messages in its user interface. This allows the user to directly consider their purchase intentions based on the suggestions. The input is the suggestion messages sent from the server, and the output is the marketing information displayed on the terminal's screen.

[0735] 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.

[0736] 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.

[0737] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0738] 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.

[0739] 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.

[0740] 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.

[0741] 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.

[0742] 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.

[0743] 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."

[0744] 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.

[0745] 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.

[0746] 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.

[0747] 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.

[0748] 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.

[0749] 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.

[0750] 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.

[0751] 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.

[0752] 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.

[0753] 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.

[0754] 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.

[0755] 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.

[0756] The following is further disclosed regarding the embodiments described above.

[0757] (Claim 1)

[0758] Means of collecting information from external databases,

[0759] A means of integrating information with a cloud-based business card management system,

[0760] A means of analyzing integrated data to identify the appropriate person within the organization,

[0761] A means of generating the optimal proposal for the designated person in charge,

[0762] A means of providing the proposed content to the user interface,

[0763] A system that includes this.

[0764] (Claim 2)

[0765] The system according to claim 1, having a function that allows the user to take action based on the generated suggestions.

[0766] (Claim 3)

[0767] The system according to claim 1, comprising means for analyzing information using natural language processing technology and evaluating its relevance.

[0768] "Example 1"

[0769] (Claim 1)

[0770] A means of extracting industry-related information from external sources,

[0771] A means of integrating customer information using a cloud-based data management system,

[0772] A means of evaluating and identifying subjects using natural language processing technology with integrated information,

[0773] A means of generating optimal suggestions for identified individuals using generative AI technology,

[0774] A means of providing the generated proposals visually on a user interface,

[0775] A system that includes this.

[0776] (Claim 2)

[0777] The system according to claim 1, which has a function that allows users to take immediate action based on the generated suggestions.

[0778] (Claim 3)

[0779] The system according to claim 1, comprising means for highly analyzing the relationships between information acquired using a learning model and improving the accuracy of proposals.

[0780] "Application Example 1"

[0781] (Claim 1)

[0782] Means of collecting information from external sources,

[0783] A means of integrating information with a cloud-based management database,

[0784] A means of analyzing integrated data to identify the appropriate target,

[0785] A means for generating the optimal proposal for a specified target,

[0786] Means for providing the proposed content to a display device,

[0787] A means of presenting products and services based on the characteristics of visitors,

[0788] A means of recording conversations with visitors and saving information for future visits,

[0789] A system that includes this.

[0790] (Claim 2)

[0791] The system according to claim 1, having a function that allows operations to be performed based on the generated proposal content.

[0792] (Claim 3)

[0793] The system according to claim 1, which includes means for analyzing information using natural language processing technology and evaluating relevance, and which makes product suggestions based on the perception and characteristics of visitors.

[0794] "Example 2 of combining an emotion engine"

[0795] (Claim 1)

[0796] Means of collecting information from external sources,

[0797] A means of integrating an electronic business card management structure and information,

[0798] A means of analyzing integrated information to identify the appropriate individuals within an organization,

[0799] A means of creating the most suitable proposal for a specific individual,

[0800] A means of providing the proposed content to the user interface,

[0801] A means of detecting the user's emotional state using emotion analysis technology and adjusting suggestions accordingly,

[0802] A system that includes this.

[0803] (Claim 2)

[0804] The system according to claim 1, having a function that allows the user to take action based on the generated suggestions.

[0805] (Claim 3)

[0806] The system according to claim 1, comprising means for analyzing information using language processing technology and evaluating relationships.

[0807] "Application example 2 when combining with an emotional engine"

[0808] (Claim 1)

[0809] Means of collecting information from external sources,

[0810] Information management systems and means of integrating information,

[0811] A means of analyzing integrated data to identify relevant personnel within the organization,

[0812] A means of generating the optimal proposal for the designated person in charge,

[0813] A means of providing the proposed content to the user input device,

[0814] A means of acquiring user language data and visual information to determine emotions,

[0815] A means of modifying the proposal based on the emotions obtained,

[0816] A system that includes this.

[0817] (Claim 2)

[0818] The system according to claim 1, having a function that allows the user to perform an action based on the generated proposal.

[0819] (Claim 3)

[0820] The system according to claim 1, comprising means for analyzing information and evaluating its relevance using natural language processing technology and visual analysis. [Explanation of Symbols]

[0821] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. Means of collecting information from external sources, A means of integrating information with a cloud-based management database, A means of analyzing integrated data to identify the appropriate target, A means for generating the optimal proposal for a specified target, Means for providing the proposed content to a display device, A means of presenting products and services based on the characteristics of visitors, A means of recording conversations with visitors and saving information for future visits, A system that includes this.

2. The system according to claim 1, having a function that allows operations to be performed based on the generated proposal content.

3. The system according to claim 1, which includes means for analyzing information using natural language processing technology and evaluating relevance, and which makes product suggestions based on the perception and characteristics of visitors.

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