system

Abstract

We provide the system. [Solution] A means of authenticating users, A means for initiating a dialogue between the user and the virtual sales representative, A means for receiving inquiries from the aforementioned user and analyzing their content, A means of selecting and proposing the optimal solution based on the aforementioned analysis results, A means of providing the aforementioned users with trend information on a regular basis, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a 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 conventional sales activities, there is a problem that communication with customers is not smoothly carried out. Specifically, there are frequent occurrences of insufficient transfer of customer information due to changes in sales staff, delays and quality variations in the responses of the staff, and furthermore, insufficient proposals for added value to customers. As a result, corporate and individual customers have difficulty receiving proposals that meet their needs, leading to a problem of decreased customer satisfaction.

Means for Solving the Problems

[0005] This invention solves the above problems by providing a system that allows direct interaction with customers through a virtual sales representative using a generated computer dialogue model. Specifically, it provides a means for users to initiate interaction with a virtual sales representative through a dedicated portal, including a means for user authentication. It also includes a means for analyzing received inquiries and selecting and proposing the optimal solution based on the results, thereby realizing effective solution proposals tailored to industry and performance information. Furthermore, it continuously provides value to customers by regularly providing trend information. This aims to improve the efficiency of sales activities and enhance customer satisfaction.

[0006] "User" refers to a corporation or individual that uses the system to interact with a virtual sales representative.

[0007] "Authentication" refers to the identity verification process necessary for users to securely access the system.

[0008] A "virtual sales representative" refers to a simulated sales representative on an artificial intelligence platform that interacts with users using a generated computer dialogue model.

[0009] "Dialogue" refers to the process of information exchange that takes place between the user and the virtual sales representative.

[0010] An "inquiry" refers to a request for information retrieval or other inquiries sent by a user to a system.

[0011] "Analysis" refers to the data processing process used to evaluate and interpret the content of an incoming inquiry.

[0012] A "solution" refers to a problem-solving method or a feasible proposal that is offered in accordance with the user's needs and challenges.

[0013] "Trend information" refers to data and knowledge about the latest trends and tendencies in an industry or market. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

[0022] [First Embodiment]

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

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

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

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

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

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

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

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

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

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

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

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

[0035] This invention provides a system that allows users to seamlessly conduct sales activities using virtual sales representatives. This system is supported by a server running multiple computer programs and databases.

[0036] The server first has a module for authenticating users. This module queries a database for authentication information entered by the user to verify their identity. Once authentication is complete, the server provides the user with a dashboard and displays an interface on the terminal to begin interacting with a virtual sales representative.

[0037] Users send inquiries and requests to virtual sales representatives using a chat window on their devices. The device sends this input to a server, where the inquiry is analyzed. The server uses natural language processing technology to understand the user's intent and generates the most appropriate response or suggestion based on that understanding.

[0038] For example, if a user is seeking solutions for a new project, the server utilizes historical data and customer performance information within the system to find the optimal proposal through an AI model. As a result, a virtual sales representative presents the user with specific solutions.

[0039] Furthermore, the server collects and regularly distributes trend information to continuously provide users with useful information. This trend information is individually customized based on the user's industry and interests.

[0040] In this way, users can optimize their sales activities in real time and achieve a smooth sales process without past constraints.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] The user enters their authentication information to log in to the portal site.

[0044] The terminal sends the entered authentication information to the server.

[0045] The server compares the received authentication information with the database and performs user authentication.

[0046] Step 2:

[0047] The server sends a screen displaying the dashboard to the user's device upon successful authentication.

[0048] The user selects the "Interact with a virtual sales representative" option on the dashboard.

[0049] The device displays a chat interface to the user.

[0050] Step 3:

[0051] Users enter their inquiries or requests into the chat window.

[0052] The terminal transmits the entered data to the server in real time.

[0053] The server analyzes the received data using natural language processing techniques.

[0054] Step 4:

[0055] The server uses an AI model to generate optimal solutions and suggestions based on the user's inquiry.

[0056] The server sends the generated response to the terminal.

[0057] The terminal displays a response to the user and waits for further input.

[0058] Step 5:

[0059] If the user requests multiple solutions, the server uses an AI model to select the best one based on the available data.

[0060] The server sends detailed information about the selected solution to the terminal.

[0061] The device displays the suggested content to the user.

[0062] Step 6:

[0063] The server generates trend information at pre-configured intervals and sends individual notifications based on the user's profile.

[0064] The server sends trend information to the terminal.

[0065] The device notifies the user of new information and events.

[0066] (Example 1)

[0067] 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."

[0068] Traditional sales activities have been time-consuming and labor-intensive, as sales representatives have to individually collect customer information and then make proposals based on that information. Furthermore, because there is no system in place to effectively utilize trend information and historical data, the quality of proposals is not always optimal. Solving these challenges is essential.

[0069] 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.

[0070] In this invention, the server includes means for receiving user authentication information and verifying the user's identity based on that information, means for providing the user with a display screen to initiate a dialogue with a virtual sales representative, and means for receiving the user's inquiry and analyzing its intent using natural language processing technology. This enables the user to receive personalized proposals quickly and accurately. Furthermore, the server can select and propose the optimal solution using the generated knowledge model, thereby improving the efficiency and effectiveness of sales activities.

[0071] "User authentication information" refers to the information provided by a user when requesting access to the system, and is used for verifying their identity.

[0072] A "virtual sales representative" is a software agent that simulates a sales representative on a computer, providing information and making proposals through interaction with the user.

[0073] "Natural language processing technology" is a technology that enables computers to understand and process human language, and is used to interpret user inquiries and commands.

[0074] A "generated knowledge model" is a data model built on knowledge learned from past data and experience, and is used to generate suggestions and solutions for users.

[0075] "Trend information" refers to information that indicates major current and future trends and developments in a specific industry or field, providing users with valuable insights.

[0076] A "personalized solution" is a solution tailored to the specific needs and circumstances of a user, where the proposed solution is structured in a way that is most suitable for each individual user.

[0077] This invention relates to a system that utilizes virtual sales representatives to streamline sales activities. This system is primarily server-centric and uses natural language processing technology and generative AI models to provide personalized proposals to users.

[0078] The server first receives the user's authentication information and verifies their identity by comparing it with the database. Once authentication is complete, the server displays an interface on the terminal to begin interacting with the virtual sales representative. This creates an environment where the user can efficiently interact with the virtual sales representative.

[0079] The terminal's role is to send user-entered inquiries and requests to the server. The natural language processing technology used in this process employs advanced AI technology to accurately understand the user's intent. For example, if a user enters the prompt "I want to know this month's industry trends," the server will refer to relevant data, use a generative AI model to generate the most relevant information, and provide it to the user.

[0080] The generative AI model has the ability to select the optimal solution based on past data and performance information. This makes it possible to propose personalized solutions to users, thereby improving the efficiency of sales activities. For example, if a user asks, "Please tell me about new product proposals," the server analyzes past product data and market trends to generate the most effective product proposals.

[0081] The distinctive hardware components of this system include a database server and user interface terminals, while the software comprises a natural language processing engine and a generative AI model. This enables the deployment of efficient and highly customized sales strategies.

[0082] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0083] Step 1:

[0084] The server receives authentication information from the user. This information includes the username and password and is encrypted to ensure security. The server checks this information against a database to verify the user's identity. If authentication is successful, the server generates a session key and sends an authentication completion response to the terminal.

[0085] Step 2:

[0086] The server sends information to the terminal of authenticated users to display a dashboard for interacting with a virtual sales representative. This dashboard is designed for easy user access and displays a list of past transaction data and recommended actions. The inputs used are the user ID and session key, and the output generates configuration data for the dashboard.

[0087] Step 3:

[0088] Users enter inquiries and requests to a virtual sales representative through a chat window on their device. This input is sent from the device to the server as text data. A specific example is the prompt, "I would like a proposal about a new product." The input data includes the inquiry and the user ID.

[0089] Step 4:

[0090] The server analyzes the received query text using natural language processing technology. A generative AI model analyzes the user's intent and selects the most appropriate response to the query. Data processing includes text decomposition, syntactic analysis, and semantic analysis, and the output is a list of actions corresponding to the user's request.

[0091] Step 5:

[0092] The server generates optimal suggestions based on the analysis results. Past performance data and customer information are utilized in this process. The generating AI model extracts relevant information from the database and constructs the most relevant suggestions. Specific examples include related product information and pricing options. The output suggestion data is prepared as a response to be provided to the user.

[0093] Step 6:

[0094] The server sends the generated response to the terminal, which displays it in the chat window. The user can review the suggestion and, if necessary, make further inquiries or take other actions. The input is the suggestion data, and the output is information adapted as a visual interface for the user.

[0095] (Application Example 1)

[0096] 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."

[0097] In today's online commercial environment, users are overwhelmed with information, making it difficult to efficiently select products. Furthermore, they lack the personalized customer experience offered in face-to-face interactions, resulting in a lack of personalized user experience in online stores. Therefore, there is a need for information tailored to user needs and methods to efficiently select appropriate products from a wide range of options.

[0098] 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.

[0099] In this invention, the server includes means for authenticating the user, means for initiating a dialogue with a virtual sales representative, and means for acquiring product information using gaze direction information. This enables the user to receive information based on their gaze direction in real time using a head-mounted display device, realizing an efficient and personalized user experience.

[0100] "User authentication" is a means of verifying the identity of users accessing the system.

[0101] A "virtual sales representative" is an agent that communicates with users using a computer-generated dialogue model.

[0102] "Means for initiating a dialogue" refers to a function that provides an interface for users to start communication with a virtual sales representative.

[0103] "Means for receiving inquiries and analyzing their content" refers to a function that receives questions and requests from users and analyzes them to understand the user's intentions.

[0104] "The means of selecting and proposing the optimal solution" refers to a function that provides the most suitable proposal to the user based on the analysis results.

[0105] "Means of providing trend information" refers to a function that provides the latest market trends and information based on the user's interests and industry.

[0106] A "head-mounted display device" is a device worn on the head by the user to obtain visual information.

[0107] "Means for acquiring product information using gaze direction information" refers to a function that detects the direction of the user's gaze and acquires information about products located in that direction.

[0108] This invention provides a system that enables users to effectively select products in a virtual store and receive personalized recommendations. This system uses a head-mounted display device to provide real-time information based on gaze direction.

[0109] The server first receives the user's authentication information and verifies their identity by referring to the database. Once authentication is complete, it displays an interface on the terminal to begin interacting with a virtual sales representative.

[0110] The gaze direction information generated by the terminal is acquired from the camera and sensors of the head-mounted display device. This allows information related to the product the user is looking at to be sent to the server. The server uses image recognition software (e.g., OpenCV) to identify the product and, based on that data, generates suggestions based on the user's interests using an AI model (e.g., GPT model).

[0111] The generated suggestions are provided to the user in real time via a head-mounted display device, providing visual and audible feedback. During this process, detailed information about the product the user is looking at and recommended promotional information are overlaid on their field of view, while a virtual sales representative provides suggestions via voice.

[0112] For example, when a user looks at a specific piece of clothing in a virtual shopping mall, its price, material, and review information are instantly displayed. Furthermore, a virtual sales representative can provide voice guidance such as, "This item is currently on sale. Please also check out these new arrivals," offering a more effective shopping experience.

[0113] An example of a prompt for a generative AI model is, "Please input the user's gaze data and product images, and generate recommendation comments for related products." In this way, a system that utilizes real-time gaze data and a generative AI model enables the provision of information tailored to the user's personal needs.

[0114] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0115] Step 1:

[0116] The user wears a head-mounted display device and fixes their gaze on a specific product. During this process, the device's camera and sensors capture information about the user's gaze direction. The input is the user's gaze direction data, and based on this, the camera acquires an image of the product.

[0117] Step 2:

[0118] The terminal sends gaze direction data and product images to the server. On the server side, the gaze data is analyzed and image recognition software (e.g., OpenCV) is used to identify the product. The output is the product ID and related information.

[0119] Step 3:

[0120] The server uses the identified product ID to retrieve detailed product information from the database. During this process, it extracts relevant data such as product category, price, reviews, and recommendation rating. The output is the detailed product data.

[0121] Step 4:

[0122] The server inputs a prompt message, such as "Generate recommended comments based on product ID," to the generating AI model (e.g., the GPT model). Based on this prompt, the generating AI model generates suggested comments tailored to the user's interests. The output is the suggested comments.

[0123] Step 5:

[0124] The generated suggestion comments and product details are transmitted to a head-mounted display device via a terminal. The terminal presents this information to the user visually and audibly. Specifically, the user is provided with information overlaid on their field of vision and audio guidance.

[0125] Step 6:

[0126] The user reviews the proposal and continues the conversation with the virtual sales representative as needed. In this step, the user's feedback is input into the system as new eye-tracking information and requests, initiating a new information acquisition process. The output is the user's response, which becomes the input for the next cycle.

[0127] 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.

[0128] This invention relates to a system that enables more effective interaction in conventional sales systems by adding the ability to recognize the emotions of users to virtual sales representatives. This system operates through the interaction of a server, terminal, and user via an interface.

[0129] The server first verifies the user's identity through an authentication mechanism when the user accesses the dedicated portal site. After this authentication, the server provides the user with a dashboard on their device that allows them to interact with a virtual sales representative.

[0130] Users enter inquiries and requests using a chat window on their device and begin interacting with a virtual sales representative. Here, the emotion engine plays a particularly important role. The server analyzes the received text and voice data and uses the emotion engine to estimate the user's emotional state (e.g., satisfaction, dissatisfaction, expectations, etc.). This emotion recognition function allows the virtual sales representative to adjust the tone and content of their responses according to the user's emotions. For example, if the virtual sales representative is estimated to be dissatisfied, they are programmed to respond more carefully and empathetically.

[0131] The virtual sales representative further selects and provides the optimal solution based on the user's emotional state. To achieve this, the server considers historical data and industry information to generate automated suggestions.

[0132] As a concrete example, when a user feels anxious about adopting a new service, the emotion engine detects this anxiety, and a virtual sales representative provides reassurance by proposing an adoption plan with special benefits. Furthermore, in the regular provision of trend information, the server uses emotion data to select and deliver information that is likely to interest the user.

[0133] In this way, by making full use of an emotion engine, this system makes interactions with users more personal and effective, enabling strategic support for sales activities.

[0134] The following describes the processing flow.

[0135] Step 1:

[0136] The user accesses the portal site and enters their authentication information on the login page.

[0137] The device securely transmits this authentication information to the server.

[0138] The server authenticates the user by matching them against the database, and if successful, starts a session.

[0139] Step 2:

[0140] The server generates a dashboard screen for the authenticated user and sends it to their device.

[0141] The user selects the "Interact with a virtual sales representative" option on this dashboard.

[0142] The device displays a chat window and waits for user input.

[0143] Step 3:

[0144] Users enter their inquiries or requests into the chat window.

[0145] The terminal sends the entered text data to the server in real time.

[0146] The server uses an emotion engine to analyze the received data and recognize the user's emotional state.

[0147] Step 4:

[0148] The server generates optimal responses and suggestions using an AI model based on the recognized emotions.

[0149] The server adjusts the response by applying a tone appropriate to the emotion, and then sends it to the terminal.

[0150] The terminal displays this in the user's chat window.

[0151] Step 5:

[0152] If the user asks further questions or requests assistance, the server performs more detailed data analysis and provides answers based on industry information and past history.

[0153] The server adjusts its response style and next suggestions based on feedback from the emotion engine.

[0154] The device will continue to display updated information and continuously accept user input.

[0155] Step 6:

[0156] The server generates trend information and new suggestions according to a pre-configured schedule.

[0157] The emotion engine considers the user's emotional data when outputting this information and selects a presentation method that enhances the user's desire to view it.

[0158] The device will notify the user of new information and allow them to view the details.

[0159] (Example 2)

[0160] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0161] Traditional sales support systems often provide a uniform response without considering the user's feelings, which can lead to decreased user satisfaction and trust. Furthermore, because optimal solutions are not based on the user's individual emotional state or industry information, improving sales performance is difficult.

[0162] 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.

[0163] In this invention, the server includes means for analyzing the user's emotional state, means for adjusting the tone and content of the response, and means for selecting and proposing the optimal solution based on the adjusted response. This makes it possible to provide individualized responses that correspond to the user's emotions and improve satisfaction.

[0164] A "user" refers to an individual or organization that uses this system and interacts with a virtual sales representative through the system's authentication and inquiry functions.

[0165] "Authentication" is the process of verifying a user's identity and granting them access rights when they access a system.

[0166] A "virtual sales representative" is a software agent that interacts with users based on a computer-generated dialogue model and assists in sales activities.

[0167] "Analysis" is the process of processing data received from users and extracting and evaluating information.

[0168] "Emotional state" refers to the psychological state inferred from the text or audio expressed by the user, and includes emotions such as satisfaction, dissatisfaction, and expectation.

[0169] "Response tone" refers to the attitude and tone of voice used by the virtual sales representative when responding to the user, and is adjusted based on the user's emotional state.

[0170] A "solution" is the optimal solution or plan proposed based on the user's inquiries and needs.

[0171] "Trend information" refers to information provided to users about the latest developments and trends in their industry and market.

[0172] The term "computer" refers to a device that performs information processing, and generally means a computer.

[0173] "History information" refers to data about the user's past transactions and activities, and is used to customize suggestions.

[0174] This system provides users with a virtual sales representative accessible through an interface, and leverages sentiment recognition to enhance the user experience. First, the user accesses a dedicated portal site using a device. The device connects to the portal using a web browser and displays a front-end application provided by the server. This application is built using frameworks such as React and Angular.

[0175] When a user accesses the portal, the server authenticates the user using OAuth or OpenID Connect. Once authentication is complete, the server sends a dashboard to the user's device for interacting with a virtual sales representative. The dashboard includes a chat window that allows for text or voice input.

[0176] When a user enters an inquiry or request using the chat window, the device sends that data to the server. In the case of voice input, the device uses Azure® Speech Service to convert the speech to text. The server receives this data and uses the Google® Cloud Natural Language API to analyze the user's emotional state.

[0177] Based on the analysis results, the server adjusts the tone and content of the virtual sales representative's response. For example, if the user expresses dissatisfaction, it uses a natural language processing library such as NLTK to generate a more polite and empathetic response. The adjusted response is then sent from the server to the user's terminal.

[0178] Furthermore, the server automatically generates the optimal solution based on historical data and industry information. This solution is based on information obtained from databases such as Exadata. This suggestion is provided to the user and its contents are displayed in the chat window.

[0179] For example, if a user feels anxious about adopting a new service, the system will detect this anxiety and propose an introductory plan with added benefits. This reduces user anxiety and promotes a positive user experience.

[0180] Example of a prompt:

[0181] "Users are feeling anxious about the new service. To alleviate that anxiety, please come up with an introductory plan with special offers that a virtual sales representative can propose."

[0182] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0183] Step 1:

[0184] The user accesses a dedicated portal site using their device. The device connects to the portal site via a web browser and obtains a user ID and password. The server uses this authentication information to authenticate the user using the OAuth or OpenID Connect protocol. If authentication is successful, the server grants the user access to the dashboard.

[0185] Step 2:

[0186] Once the server verifies user authentication, it sends a dashboard to the device that allows interaction with a virtual sales representative. The device renders the received HTML, CSS, and JavaScript® files to display a visual dashboard to the user. The dashboard includes a chat window that allows input via text or voice.

[0187] Step 3:

[0188] Users enter inquiries and requests through a chat window. If the data is entered via a text form, it is used directly; if it's entered via voice input, it's converted from speech to text using Azure Speech Service. This data is then sent from the device to the server.

[0189] Step 4:

[0190] The server receives text data from the user and parses its content using the Google Cloud Natural Language API. This parsing process includes identifying the user's emotional state from the text. As a result, the emotional state (e.g., satisfied, dissatisfied, expectant) is output.

[0191] Step 5:

[0192] Based on the analysis results, the server generates a response from a virtual sales representative. Using natural language processing libraries such as NLTK, it adjusts the tone and content of the response according to the user's emotional state. For example, if the user is expressing dissatisfaction, it generates a careful and empathetic response. This adjusted response is then sent from the server to the terminal.

[0193] Step 6:

[0194] The server generates the optimal solution based on the user's emotional state and past history data. This includes searching and analyzing relevant data from the database, and the final selected suggestion is displayed on the terminal. A virtual sales representative provides specific suggestions tailored to the user and presents them to the user in a chat window on the dashboard.

[0195] Step 7:

[0196] The user reviews the provided suggestions and provides feedback as needed. The server collects this feedback and records it as data to improve the overall system performance. This improves the quality of future interactions and suggestions.

[0197] (Application Example 2)

[0198] 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 device 14 will be referred to as the "terminal."

[0199] This invention aims to improve the user experience by constructing a system that can accurately grasp the user's emotional state and provide a corresponding response, thereby enabling more efficient and personalized communication in a virtual environment.

[0200] 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.

[0201] In this invention, the server includes means for recognizing the user's emotional state and providing information in accordance with that emotion; means for adjusting responses using a computer-generated interaction model of a virtual sales representative; and means for optimizing proposals by utilizing emotional data while providing solutions adjusted by the proposal means based on the user's industry and performance information. This makes it possible to provide personalized proposals based on each user's emotions.

[0202] "Users" refer to end-users of this system, primarily individuals or organizations that receive services through a virtual environment.

[0203] "Authentication" is a process for verifying the user's identity and guaranteeing legitimate access to the system.

[0204] A "virtual sales representative" is a digital agent that operates using a generated computer model and is responsible for interacting with users.

[0205] "Dialogue" refers to the process by which users and virtual sales representatives exchange information in both directions, and includes communication via text or voice.

[0206] "Analysis" is the process by which a computer understands the content of a user's inquiry and analyzes the information in order to prepare an appropriate response or suggestion.

[0207] A "solution" refers to an appropriate solution or suggestion for a user's inquiry or problem.

[0208] "Trend information" refers to information that summarizes the latest trends and developments in a specific industry or market, and is provided to users on a regular basis.

[0209] "Emotional state" refers to the user's psychological and emotional state, including feelings such as satisfaction and dissatisfaction.

[0210] An "interaction model" is an algorithm or framework for realizing a simulated dialogue process using computers.

[0211] This invention is a system that provides personalized services by having a virtual sales representative recognize the user's emotions and adjust their response accordingly. The server authenticates the user and prepares the environment for initiating a conversation with the virtual sales representative via the terminal. Voice and text data received from the user are analyzed by the server, and the emotional state is estimated using an emotion engine.

[0212] The server uses OpenCV as facial recognition software and leverages the Google Cloud Natural Language API for sentiment analysis. Based on the sentiment data, the virtual sales representative forms and delivers a tailored response to the user via Dialogflow. In this process, the server uses a generative AI model to generate prompts appropriate to the sentiment.

[0213] As a concrete example, when a user inquires via their device, "I want to know more about a new product," the server detects the user's facial expressions from the camera feed and provides detailed information in a friendly tone based on the emotional data. An example of a prompt message would be, "If the user's emotional state is anxious, please provide the product description in a friendly and reassuring tone." This system configuration enables the provision of appropriate services tailored to each individual user.

[0214] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0215] Step 1:

[0216] The server authenticates the user.

[0217] The input is the user's authentication information, and the server uses this to compare and refer to data in the authentication database to verify that the user is legitimate. The output is information indicating whether authentication was successful or not. Specifically, the user ID and password are matched to determine access privileges.

[0218] Step 2:

[0219] The terminal receives an inquiry from the user and sends it to the server.

[0220] The input is voice or text data entered by the user into the terminal. The terminal relays this as digital data to the server. The output is query data sent to the server. Specifically, in the case of voice input, a conversion from voice to text is performed.

[0221] Step 3:

[0222] The server analyzes the user's inquiry.

[0223] The input is query data in text format, and the server analyzes the content using natural language processing techniques. The output is the analyzed semantic information. Specifically, this involves keyword extraction and contextual understanding.

[0224] Step 4:

[0225] The server retrieves the user's facial image and analyzes their emotional state.

[0226] The input is real-time facial image data sent from the terminal. The server uses OpenCV for facial recognition and an emotion analysis model to identify the emotional state. The output is the user's emotion information. Specifically, the emotion recognition algorithm performs facial expression analysis within the image.

[0227] Step 5:

[0228] The server uses a generative AI model based on the analysis results and sentiment information to generate prompt messages.

[0229] The input consists of analyzed semantic information and emotional states. The server inputs these into a generative AI model to generate appropriate prompt sentences. The output is the prompt sentence. Specifically, the sentence generation engine creates sentences while considering grammar and context.

[0230] Step 6:

[0231] The server passes a prompt message to a virtual sales representative, who then generates the most appropriate response for the user.

[0232] The input is a prompt, and the server generates a response to the user using a chatbot platform such as Dialogflow. The output is a customized response message. Specifically, a language generation model generates a context-aware sentence and sends it to the user via the terminal.

[0233] 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.

[0234] 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.

[0235] 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.

[0236] [Second Embodiment]

[0237] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0238] 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.

[0239] 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).

[0240] 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.

[0241] 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.

[0242] 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).

[0243] 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.

[0244] 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.

[0245] 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.

[0246] 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.

[0247] 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.

[0248] 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".

[0249] This invention provides a system that allows users to seamlessly conduct sales activities using virtual sales representatives. This system is supported by a server running multiple computer programs and databases.

[0250] The server first has a module for authenticating users. This module queries a database for authentication information entered by the user to verify their identity. Once authentication is complete, the server provides the user with a dashboard and displays an interface on the terminal to begin interacting with a virtual sales representative.

[0251] Users send inquiries and requests to virtual sales representatives using a chat window on their devices. The device sends this input to a server, where the inquiry is analyzed. The server uses natural language processing technology to understand the user's intent and generates the most appropriate response or suggestion based on that understanding.

[0252] For example, if a user is seeking solutions for a new project, the server utilizes historical data and customer performance information within the system to find the optimal proposal through an AI model. As a result, a virtual sales representative presents the user with specific solutions.

[0253] Furthermore, the server collects and regularly distributes trend information to continuously provide users with useful information. This trend information is individually customized based on the user's industry and interests.

[0254] In this way, users can optimize their sales activities in real time and achieve a smooth sales process without past constraints.

[0255] The following describes the processing flow.

[0256] Step 1:

[0257] The user enters their authentication information to log in to the portal site.

[0258] The terminal sends the entered authentication information to the server.

[0259] The server compares the received authentication information with the database and performs user authentication.

[0260] Step 2:

[0261] The server sends a screen displaying the dashboard to the user's device upon successful authentication.

[0262] The user selects the "Interact with a virtual sales representative" option on the dashboard.

[0263] The device displays a chat interface to the user.

[0264] Step 3:

[0265] Users enter their inquiries or requests into the chat window.

[0266] The terminal transmits the entered data to the server in real time.

[0267] The server analyzes the received data using natural language processing techniques.

[0268] Step 4:

[0269] The server uses an AI model to generate optimal solutions and suggestions based on the user's inquiry.

[0270] The server sends the generated response to the terminal.

[0271] The terminal displays a response to the user and waits for further input.

[0272] Step 5:

[0273] If the user requests multiple solutions, the server uses an AI model to select the best one based on the available data.

[0274] The server sends detailed information about the selected solution to the terminal.

[0275] The device displays the suggested content to the user.

[0276] Step 6:

[0277] The server generates trend information at preset intervals and notifies users individually based on their user profiles.

[0278] The server sends the trend information to the terminal.

[0279] The terminal notifies the user of new information and events.

[0280] (Example 1)

[0281] Next, Example 1 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".

[0282] In conventional sales activities, there was a problem that sales representatives had to collect customer information individually and make proposals based on it, which was time-consuming and labor-intensive. Also, since there was no mechanism for effectively utilizing trend information and past data, the quality of the proposals was not always optimal. There is a need to solve these problems.

[0283] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0284] In this invention, the server includes means for receiving the user's authentication information and performing identity verification based on this information, means for providing a display screen for starting a conversation by a virtual sales representative to the user, and means for receiving the user's inquiry and analyzing the intention using natural language processing technology. As a result, the user can receive individualized proposals quickly and accurately. Also, since the server can select and propose an optimal solution using the generated knowledge model, the efficiency and effectiveness of sales activities can be improved.

[0285] The "user's authentication information" is information provided when the user requests access to the system and is used for identity verification.

[0286] A "virtual salesperson" refers to a salesperson simulated on a computer, which is a software agent that provides information and makes proposals through interaction with users.

[0287] "Natural language processing technology" refers to the technology by which a computer understands and processes human language, and is used to interpret inquiries and commands from users.

[0288] A "generated knowledge model" is a data model constructed based on knowledge learned from past data and experiences, and is used when generating proposals and solutions for users.

[0289] "Trend information" refers to information indicating the current and future major trends and developments in a specific industry or field, and provides useful insights to users.

[0290] An "individualized solution" refers to a solution adjusted according to the specific needs and situations of users, and the proposed content is configured in the most suitable form for individual users.

[0291] The present invention is a system that uses a virtual salesperson to improve the efficiency of sales activities. This system mainly centers around a server, utilizes natural language processing technology and a generative AI model, and makes individualized proposals to users.

[0292] First, the server receives the user's authentication information and verifies it against the database to confirm the identity of the user. When the authentication is completed, the server displays an interface for starting a conversation with the virtual salesperson on the terminal. As a result, an environment is created in which the user can efficiently interact with the virtual salesperson.

[0293] The terminal's role is to send user-entered inquiries and requests to the server. The natural language processing technology used in this process employs advanced AI technology to accurately understand the user's intent. For example, if a user enters the prompt "I want to know this month's industry trends," the server will refer to relevant data, use a generative AI model to generate the most relevant information, and provide it to the user.

[0294] The generative AI model has the ability to select the optimal solution based on past data and performance information. This makes it possible to propose personalized solutions to users, thereby improving the efficiency of sales activities. For example, if a user asks, "Please tell me about new product proposals," the server analyzes past product data and market trends to generate the most effective product proposals.

[0295] The distinctive hardware components of this system include a database server and user interface terminals, while the software comprises a natural language processing engine and a generative AI model. This enables the deployment of efficient and highly customized sales strategies.

[0296] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0297] Step 1:

[0298] The server receives authentication information from the user. This information includes the username and password and is encrypted to ensure security. The server checks this information against a database to verify the user's identity. If authentication is successful, the server generates a session key and sends an authentication completion response to the terminal.

[0299] Step 2:

[0300] The server sends information to the terminal for displaying a dashboard for an authenticated user to interact with a virtual salesperson. This dashboard is designed to be easily accessible to the user and displays past transaction data and a list of recommended actions. As inputs, a user ID and a session key are used, and as output, dashboard configuration data is generated.

[0301] Step 3:

[0302] The user enters inquiries and requests to the virtual salesperson through the chat window on the terminal. This input is sent from the terminal to the server as text data. As a specific example, there is a prompt such as "I hope to receive a proposal for a new product." The input data includes the content of the inquiry and the user ID.

[0303] Step 4:

[0304] The server analyzes the received inquiry text using natural language processing technology. The generative AI model analyzes the user's intention and selects the most suitable response for the inquiry content. As data processing, text decomposition, syntax analysis, and semantic analysis are performed, and as output, an action list corresponding to the user's request is generated.

[0305] Step 5:

[0306] Based on the analysis results, the server generates an optimal proposal. In this process, past performance data and customer information are utilized. The generative AI model extracts relevant information from the database and constructs the most relevant proposal. Specific examples include related product information and price options. The output proposal data is prepared as a response to be provided to the user.

[0307] Step 6:

[0308] The server sends the generated response to the terminal, which displays it in the chat window. The user can review the suggestion and, if necessary, make further inquiries or take other actions. The input is the suggestion data, and the output is information adapted as a visual interface for the user.

[0309] (Application Example 1)

[0310] 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."

[0311] In today's online commercial environment, users are overwhelmed with information, making it difficult to efficiently select products. Furthermore, they lack the personalized customer experience offered in face-to-face interactions, resulting in a lack of personalized user experience in online stores. Therefore, there is a need for information tailored to user needs and methods to efficiently select appropriate products from a wide range of options.

[0312] 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.

[0313] In this invention, the server includes means for authenticating the user, means for initiating a dialogue with a virtual sales representative, and means for acquiring product information using gaze direction information. This enables the user to receive information based on their gaze direction in real time using a head-mounted display device, realizing an efficient and personalized user experience.

[0314] "User authentication" is a means of verifying the identity of users accessing the system.

[0315] A "virtual sales representative" is an agent that communicates with users using a computer-generated dialogue model.

[0316] "Means for initiating a dialogue" refers to a function that provides an interface for users to start communication with a virtual sales representative.

[0317] "Means for receiving inquiries and analyzing their content" refers to a function that receives questions and requests from users and analyzes them to understand the user's intentions.

[0318] "The means of selecting and proposing the optimal solution" refers to a function that provides the most suitable proposal to the user based on the analysis results.

[0319] "Means of providing trend information" refers to a function that provides the latest market trends and information based on the user's interests and industry.

[0320] A "head-mounted display device" is a device worn on the head by the user to obtain visual information.

[0321] "Means for acquiring product information using gaze direction information" refers to a function that detects the direction of the user's gaze and acquires information about products located in that direction.

[0322] This invention provides a system that enables users to effectively select products in a virtual store and receive personalized recommendations. This system uses a head-mounted display device to provide real-time information based on gaze direction.

[0323] The server first receives the user's authentication information and verifies their identity by referring to the database. Once authentication is complete, it displays an interface on the terminal to begin interacting with a virtual sales representative.

[0324] The gaze direction information generated by the terminal is acquired from the camera and sensors of the head-mounted display device. This allows information related to the product the user is looking at to be sent to the server. The server uses image recognition software (e.g., OpenCV) to identify the product and, based on that data, generates suggestions based on the user's interests using an AI model (e.g., GPT model).

[0325] The generated suggestions are provided to the user in real time via a head-mounted display device, providing visual and audible feedback. During this process, detailed information about the product the user is looking at and recommended promotional information are overlaid on their field of view, while a virtual sales representative provides suggestions via voice.

[0326] For example, when a user looks at a specific piece of clothing in a virtual shopping mall, its price, material, and review information are instantly displayed. Furthermore, a virtual sales representative can provide voice guidance such as, "This item is currently on sale. Please also check out these new arrivals," offering a more effective shopping experience.

[0327] An example of a prompt for a generative AI model is, "Please input the user's gaze data and product images, and generate recommendation comments for related products." In this way, a system that utilizes real-time gaze data and a generative AI model enables the provision of information tailored to the user's personal needs.

[0328] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0329] Step 1:

[0330] The user wears a head-mounted display device and fixes their gaze on a specific product. During this process, the device's camera and sensors capture information about the user's gaze direction. The input is the user's gaze direction data, and based on this, the camera acquires an image of the product.

[0331] Step 2:

[0332] The terminal sends gaze direction data and product images to the server. On the server side, the gaze data is analyzed and image recognition software (e.g., OpenCV) is used to identify the product. The output is the product ID and related information.

[0333] Step 3:

[0334] The server uses the identified product ID to retrieve detailed product information from the database. During this process, it extracts relevant data such as product category, price, reviews, and recommendation rating. The output is the detailed product data.

[0335] Step 4:

[0336] The server inputs a prompt message, such as "Generate recommended comments based on product ID," to the generating AI model (e.g., the GPT model). Based on this prompt, the generating AI model generates suggested comments tailored to the user's interests. The output is the suggested comments.

[0337] Step 5:

[0338] The generated suggestion comments and product details are transmitted to a head-mounted display device via a terminal. The terminal presents this information to the user visually and audibly. Specifically, the user is provided with information overlaid on their field of vision and audio guidance.

[0339] Step 6:

[0340] The user reviews the proposal and continues the conversation with the virtual sales representative as needed. In this step, the user's feedback is input into the system as new eye-tracking information and requests, initiating a new information acquisition process. The output is the user's response, which becomes the input for the next cycle.

[0341] 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.

[0342] This invention relates to a system that enables more effective interaction in conventional sales systems by adding the ability to recognize the emotions of users to virtual sales representatives. This system operates through the interaction of a server, terminal, and user via an interface.

[0343] The server first verifies the user's identity through an authentication mechanism when the user accesses the dedicated portal site. After this authentication, the server provides the user with a dashboard on their device that allows them to interact with a virtual sales representative.

[0344] Users enter inquiries and requests using a chat window on their device and begin interacting with a virtual sales representative. Here, the emotion engine plays a particularly important role. The server analyzes the received text and voice data and uses the emotion engine to estimate the user's emotional state (e.g., satisfaction, dissatisfaction, expectations, etc.). This emotion recognition function allows the virtual sales representative to adjust the tone and content of their responses according to the user's emotions. For example, if the virtual sales representative is estimated to be dissatisfied, they are programmed to respond more carefully and empathetically.

[0345] The virtual sales representative further selects and provides the optimal solution based on the user's emotional state. To achieve this, the server considers historical data and industry information to generate automated suggestions.

[0346] As a concrete example, when a user feels anxious about adopting a new service, the emotion engine detects this anxiety, and a virtual sales representative provides reassurance by proposing an adoption plan with special benefits. Furthermore, in the regular provision of trend information, the server uses emotion data to select and deliver information that is likely to interest the user.

[0347] In this way, by making full use of an emotion engine, this system makes interactions with users more personal and effective, enabling strategic support for sales activities.

[0348] The following describes the processing flow.

[0349] Step 1:

[0350] The user accesses the portal site and enters their authentication information on the login page.

[0351] The device securely transmits this authentication information to the server.

[0352] The server authenticates the user by matching them against the database, and if successful, starts a session.

[0353] Step 2:

[0354] The server generates a dashboard screen for the authenticated user and sends it to their device.

[0355] The user selects the "Interact with a virtual sales representative" option on this dashboard.

[0356] The device displays a chat window and waits for user input.

[0357] Step 3:

[0358] Users enter their inquiries or requests into the chat window.

[0359] The terminal sends the entered text data to the server in real time.

[0360] The server uses an emotion engine to analyze the received data and recognize the user's emotional state.

[0361] Step 4:

[0362] The server generates optimal responses and suggestions using an AI model based on the recognized emotions.

[0363] The server adjusts the response by applying a tone appropriate to the emotion, and then sends it to the terminal.

[0364] The terminal displays this in the user's chat window.

[0365] Step 5:

[0366] If the user asks further questions or requests assistance, the server performs more detailed data analysis and provides answers based on industry information and past history.

[0367] The server adjusts its response style and next suggestions based on feedback from the emotion engine.

[0368] The device will continue to display updated information and continuously accept user input.

[0369] Step 6:

[0370] The server generates trend information and new suggestions according to a pre-configured schedule.

[0371] The emotion engine considers the user's emotional data when outputting this information and selects a presentation method that enhances the user's desire to view it.

[0372] The device will notify the user of new information and allow them to view the details.

[0373] (Example 2)

[0374] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0375] Traditional sales support systems often provide a uniform response without considering the user's feelings, which can lead to decreased user satisfaction and trust. Furthermore, because optimal solutions are not based on the user's individual emotional state or industry information, improving sales performance is difficult.

[0376] 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.

[0377] In this invention, the server includes means for analyzing the user's emotional state, means for adjusting the tone and content of the response, and means for selecting and proposing the optimal solution based on the adjusted response. This makes it possible to provide individualized responses that correspond to the user's emotions and improve satisfaction.

[0378] A "user" refers to an individual or organization that uses this system and interacts with a virtual sales representative through the system's authentication and inquiry functions.

[0379] "Authentication" is the process of verifying a user's identity and granting them access rights when they access a system.

[0380] A "virtual sales representative" is a software agent that interacts with users based on a computer-generated dialogue model and assists in sales activities.

[0381] "Analysis" is the process of processing data received from users and extracting and evaluating information.

[0382] "Emotional state" refers to the psychological state inferred from the text or audio expressed by the user, and includes emotions such as satisfaction, dissatisfaction, and expectation.

[0383] "Response tone" refers to the attitude and tone of voice used by the virtual sales representative when responding to the user, and is adjusted based on the user's emotional state.

[0384] A "solution" is the optimal solution or plan proposed based on the user's inquiries and needs.

[0385] "Trend information" refers to information provided to users about the latest developments and trends in their industry and market.

[0386] The term "computer" refers to a device that performs information processing, and generally means a computer.

[0387] "History information" refers to data about the user's past transactions and activities, and is used to customize suggestions.

[0388] This system provides users with a virtual sales representative accessible through an interface, and leverages sentiment recognition to enhance the user experience. First, the user accesses a dedicated portal site using a device. The device connects to the portal using a web browser and displays a front-end application provided by the server. This application is built using frameworks such as React and Angular.

[0389] When a user accesses the portal, the server authenticates the user using OAuth or OpenID Connect. Once authentication is complete, the server sends a dashboard to the user's device for interacting with a virtual sales representative. The dashboard includes a chat window that allows for text or voice input.

[0390] When a user enters an inquiry or request using the chat window, the device sends that data to the server. In the case of voice input, the device uses Azure Speech Service to convert the speech to text. The server receives this data and uses the Google Cloud Natural Language API to analyze the user's emotional state.

[0391] Based on the analysis results, the server adjusts the tone and content of the virtual sales representative's response. For example, if the user expresses dissatisfaction, it uses a natural language processing library such as NLTK to generate a more polite and empathetic response. The adjusted response is then sent from the server to the user's terminal.

[0392] Furthermore, the server automatically generates the optimal solution based on historical data and industry information. This solution is based on information obtained from databases such as Exadata. This suggestion is provided to the user and its contents are displayed in the chat window.

[0393] For example, if a user feels anxious about adopting a new service, the system will detect this anxiety and propose an introductory plan with added benefits. This reduces user anxiety and promotes a positive user experience.

[0394] Example of a prompt:

[0395] "Users are feeling anxious about the new service. To alleviate that anxiety, please come up with an introductory plan with special offers that a virtual sales representative can propose."

[0396] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0397] Step 1:

[0398] The user accesses a dedicated portal site using their device. The device connects to the portal site via a web browser and obtains a user ID and password. The server uses this authentication information to authenticate the user using the OAuth or OpenID Connect protocol. If authentication is successful, the server grants the user access to the dashboard.

[0399] Step 2:

[0400] Once the server verifies user authentication, it sends a dashboard to the device that allows interaction with a virtual sales representative. The device renders the received HTML, CSS, and JavaScript files to display a visual dashboard to the user. The dashboard provides a chat window that allows input via text or voice.

[0401] Step 3:

[0402] Users enter inquiries and requests through a chat window. If the data is entered via a text form, it is used directly; if it's entered via voice input, it's converted from speech to text using Azure Speech Service. This data is then sent from the device to the server.

[0403] Step 4:

[0404] The server receives text data from the user and parses its content using the Google Cloud Natural Language API. This parsing process includes identifying the user's emotional state from the text. As a result, the emotional state (e.g., satisfied, dissatisfied, expectant) is output.

[0405] Step 5:

[0406] Based on the analysis results, the server generates a response from a virtual sales representative. Using natural language processing libraries such as NLTK, it adjusts the tone and content of the response according to the user's emotional state. For example, if the user is expressing dissatisfaction, it generates a careful and empathetic response. This adjusted response is then sent from the server to the terminal.

[0407] Step 6:

[0408] The server generates the optimal solution based on the user's emotional state and past history data. This includes searching and analyzing relevant data from the database, and the final selected suggestion is displayed on the terminal. A virtual sales representative provides specific suggestions tailored to the user and presents them to the user in a chat window on the dashboard.

[0409] Step 7:

[0410] The user reviews the provided suggestions and provides feedback as needed. The server collects this feedback and records it as data to improve the overall system performance. This improves the quality of future interactions and suggestions.

[0411] (Application Example 2)

[0412] 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 as the "terminal".

[0413] This invention aims to improve the user experience by constructing a system that can accurately grasp the user's emotional state and provide a corresponding response, thereby enabling more efficient and personalized communication in a virtual environment.

[0414] 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.

[0415] In this invention, the server includes means for recognizing the user's emotional state and providing information in accordance with that emotion; means for adjusting responses using a computer-generated interaction model of a virtual sales representative; and means for optimizing proposals by utilizing emotional data while providing solutions adjusted by the proposal means based on the user's industry and performance information. This makes it possible to provide personalized proposals based on each user's emotions.

[0416] "Users" refer to end-users of this system, primarily individuals or organizations that receive services through a virtual environment.

[0417] "Authentication" is a process for verifying the user's identity and guaranteeing legitimate access to the system.

[0418] A "virtual sales representative" is a digital agent that operates using a generated computer model and is responsible for interacting with users.

[0419] "Dialogue" refers to the process by which users and virtual sales representatives exchange information in both directions, and includes communication via text or voice.

[0420] "Analysis" is the process by which a computer understands the content of a user's inquiry and analyzes the information in order to prepare an appropriate response or suggestion.

[0421] A "solution" refers to an appropriate solution or suggestion for a user's inquiry or problem.

[0422] "Trend information" refers to information that summarizes the latest trends and developments in a specific industry or market, and is provided to users on a regular basis.

[0423] "Emotional state" refers to the user's psychological and emotional state, including feelings such as satisfaction and dissatisfaction.

[0424] An "interaction model" is an algorithm or framework for realizing a simulated dialogue process using computers.

[0425] This invention is a system that provides personalized services by having a virtual sales representative recognize the user's emotions and adjust their response accordingly. The server authenticates the user and prepares the environment for initiating a conversation with the virtual sales representative via the terminal. Voice and text data received from the user are analyzed by the server, and the emotional state is estimated using an emotion engine.

[0426] The server uses OpenCV as facial recognition software and leverages the Google Cloud Natural Language API for sentiment analysis. Based on the sentiment data, the virtual sales representative forms and delivers a tailored response to the user via Dialogflow. In this process, the server uses a generative AI model to generate prompts appropriate to the sentiment.

[0427] As a concrete example, when a user inquires via their device, "I want to know more about a new product," the server detects the user's facial expressions from the camera feed and provides detailed information in a friendly tone based on the emotional data. An example of a prompt message would be, "If the user's emotional state is anxious, please provide the product description in a friendly and reassuring tone." This system configuration enables the provision of appropriate services tailored to each individual user.

[0428] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0429] Step 1:

[0430] The server authenticates the user.

[0431] The input is the user's authentication information, and the server uses this to compare and refer to data in the authentication database to verify that the user is legitimate. The output is information indicating whether authentication was successful or not. Specifically, the user ID and password are matched to determine access privileges.

[0432] Step 2:

[0433] The terminal receives an inquiry from the user and sends it to the server.

[0434] The input is voice or text data entered by the user into the terminal. The terminal relays this as digital data to the server. The output is query data sent to the server. Specifically, in the case of voice input, a conversion from voice to text is performed.

[0435] Step 3:

[0436] The server analyzes the user's inquiry.

[0437] The input is query data in text format, and the server analyzes the content using natural language processing techniques. The output is the analyzed semantic information. Specifically, this involves keyword extraction and contextual understanding.

[0438] Step 4:

[0439] The server retrieves the user's facial image and analyzes their emotional state.

[0440] The input is real-time facial image data sent from the terminal. The server uses OpenCV for facial recognition and an emotion analysis model to identify the emotional state. The output is the user's emotion information. Specifically, the emotion recognition algorithm performs facial expression analysis within the image.

[0441] Step 5:

[0442] The server uses a generative AI model based on the analysis results and sentiment information to generate prompt messages.

[0443] The input consists of analyzed semantic information and emotional states. The server inputs these into a generative AI model to generate appropriate prompt sentences. The output is the prompt sentence. Specifically, the sentence generation engine creates sentences while considering grammar and context.

[0444] Step 6:

[0445] The server passes a prompt message to a virtual sales representative, who then generates the most appropriate response for the user.

[0446] The input is a prompt, and the server generates a response to the user using a chatbot platform such as Dialogflow. The output is a customized response message. Specifically, a language generation model generates a context-aware sentence and sends it to the user via the terminal.

[0447] 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.

[0448] 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.

[0449] 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.

[0450] [Third Embodiment]

[0451] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0452] 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.

[0453] 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).

[0454] 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.

[0455] 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.

[0456] 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).

[0457] 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.

[0458] 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.

[0459] 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.

[0460] 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.

[0461] 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.

[0462] 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".

[0463] This invention provides a system that allows users to seamlessly conduct sales activities using virtual sales representatives. This system is supported by a server running multiple computer programs and databases.

[0464] The server first has a module for authenticating users. This module queries a database for authentication information entered by the user to verify their identity. Once authentication is complete, the server provides the user with a dashboard and displays an interface on the terminal to begin interacting with a virtual sales representative.

[0465] Users send inquiries and requests to virtual sales representatives using a chat window on their devices. The device sends this input to a server, where the inquiry is analyzed. The server uses natural language processing technology to understand the user's intent and generates the most appropriate response or suggestion based on that understanding.

[0466] For example, if a user is seeking solutions for a new project, the server utilizes historical data and customer performance information within the system to find the optimal proposal through an AI model. As a result, a virtual sales representative presents the user with specific solutions.

[0467] Furthermore, the server collects and regularly distributes trend information to continuously provide users with useful information. This trend information is individually customized based on the user's industry and interests.

[0468] In this way, users can optimize their sales activities in real time and achieve a smooth sales process without past constraints.

[0469] The following describes the processing flow.

[0470] Step 1:

[0471] The user enters their authentication information to log in to the portal site.

[0472] The terminal sends the entered authentication information to the server.

[0473] The server compares the received authentication information with the database and performs user authentication.

[0474] Step 2:

[0475] The server sends a screen displaying the dashboard to the user's device upon successful authentication.

[0476] The user selects the "Interact with a virtual sales representative" option on the dashboard.

[0477] The device displays a chat interface to the user.

[0478] Step 3:

[0479] Users enter their inquiries or requests into the chat window.

[0480] The terminal transmits the entered data to the server in real time.

[0481] The server analyzes the received data using natural language processing techniques.

[0482] Step 4:

[0483] The server uses an AI model to generate optimal solutions and suggestions based on the user's inquiry.

[0484] The server sends the generated response to the terminal.

[0485] The terminal displays a response to the user and waits for further input.

[0486] Step 5:

[0487] If the user requests multiple solutions, the server uses an AI model to select the best one based on the available data.

[0488] The server sends detailed information about the selected solution to the terminal.

[0489] The device displays the suggested content to the user.

[0490] Step 6:

[0491] The server generates trend information at pre-configured intervals and sends individual notifications based on the user's profile.

[0492] The server sends trend information to the terminal.

[0493] The device notifies the user of new information and events.

[0494] (Example 1)

[0495] 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."

[0496] Traditional sales activities have been time-consuming and labor-intensive, as sales representatives have to individually collect customer information and then make proposals based on that information. Furthermore, because there is no system in place to effectively utilize trend information and historical data, the quality of proposals is not always optimal. Solving these challenges is essential.

[0497] 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.

[0498] In this invention, the server includes means for receiving user authentication information and verifying the user's identity based on that information, means for providing the user with a display screen to initiate a dialogue with a virtual sales representative, and means for receiving the user's inquiry and analyzing its intent using natural language processing technology. This enables the user to receive personalized proposals quickly and accurately. Furthermore, the server can select and propose the optimal solution using the generated knowledge model, thereby improving the efficiency and effectiveness of sales activities.

[0499] "User authentication information" refers to the information provided by a user when requesting access to the system, and is used for verifying their identity.

[0500] A "virtual sales representative" is a software agent that simulates a sales representative on a computer, providing information and making proposals through interaction with the user.

[0501] "Natural language processing technology" is a technology that enables computers to understand and process human language, and is used to interpret user inquiries and commands.

[0502] A "generated knowledge model" is a data model built on knowledge learned from past data and experience, and is used to generate suggestions and solutions for users.

[0503] "Trend information" refers to information that indicates major current and future trends and developments in a specific industry or field, providing users with valuable insights.

[0504] A "personalized solution" is a solution tailored to the specific needs and circumstances of a user, where the proposed solution is structured in a way that is most suitable for each individual user.

[0505] This invention relates to a system that utilizes virtual sales representatives to streamline sales activities. This system is primarily server-centric and uses natural language processing technology and generative AI models to provide personalized proposals to users.

[0506] The server first receives the user's authentication information and verifies their identity by comparing it with the database. Once authentication is complete, the server displays an interface on the terminal to begin interacting with the virtual sales representative. This creates an environment where the user can efficiently interact with the virtual sales representative.

[0507] The terminal's role is to send user-entered inquiries and requests to the server. The natural language processing technology used in this process employs advanced AI technology to accurately understand the user's intent. For example, if a user enters the prompt "I want to know this month's industry trends," the server will refer to relevant data, use a generative AI model to generate the most relevant information, and provide it to the user.

[0508] The generative AI model has the ability to select the optimal solution based on past data and performance information. This makes it possible to propose personalized solutions to users, thereby improving the efficiency of sales activities. For example, if a user asks, "Please tell me about new product proposals," the server analyzes past product data and market trends to generate the most effective product proposals.

[0509] The distinctive hardware components of this system include a database server and user interface terminals, while the software comprises a natural language processing engine and a generative AI model. This enables the deployment of efficient and highly customized sales strategies.

[0510] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0511] Step 1:

[0512] The server receives authentication information from the user. This information includes the username and password and is encrypted to ensure security. The server checks this information against a database to verify the user's identity. If authentication is successful, the server generates a session key and sends an authentication completion response to the terminal.

[0513] Step 2:

[0514] The server sends information to the terminal of authenticated users to display a dashboard for interacting with a virtual sales representative. This dashboard is designed for easy user access and displays a list of past transaction data and recommended actions. The inputs used are the user ID and session key, and the output generates configuration data for the dashboard.

[0515] Step 3:

[0516] Users enter inquiries and requests to a virtual sales representative through a chat window on their device. This input is sent from the device to the server as text data. A specific example is the prompt, "I would like a proposal about a new product." The input data includes the inquiry and the user ID.

[0517] Step 4:

[0518] The server analyzes the received query text using natural language processing technology. A generative AI model analyzes the user's intent and selects the most appropriate response to the query. Data processing includes text decomposition, syntactic analysis, and semantic analysis, and the output is a list of actions corresponding to the user's request.

[0519] Step 5:

[0520] The server generates optimal suggestions based on the analysis results. Past performance data and customer information are utilized in this process. The generating AI model extracts relevant information from the database and constructs the most relevant suggestions. Specific examples include related product information and pricing options. The output suggestion data is prepared as a response to be provided to the user.

[0521] Step 6:

[0522] The server sends the generated response to the terminal, which displays it in the chat window. The user can review the suggestion and, if necessary, make further inquiries or take other actions. The input is the suggestion data, and the output is information adapted as a visual interface for the user.

[0523] (Application Example 1)

[0524] 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."

[0525] In today's online commercial environment, users are overwhelmed with information, making it difficult to efficiently select products. Furthermore, they lack the personalized customer experience offered in face-to-face interactions, resulting in a lack of personalized user experience in online stores. Therefore, there is a need for information tailored to user needs and methods to efficiently select appropriate products from a wide range of options.

[0526] 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.

[0527] In this invention, the server includes means for authenticating the user, means for initiating a dialogue with a virtual sales representative, and means for acquiring product information using gaze direction information. This enables the user to receive information based on their gaze direction in real time using a head-mounted display device, realizing an efficient and personalized user experience.

[0528] "User authentication" is a means of verifying the identity of users accessing the system.

[0529] A "virtual sales representative" is an agent that communicates with users using a computer-generated dialogue model.

[0530] "Means for initiating a dialogue" refers to a function that provides an interface for users to start communication with a virtual sales representative.

[0531] "Means for receiving inquiries and analyzing their content" refers to a function that receives questions and requests from users and analyzes them to understand the user's intentions.

[0532] "The means of selecting and proposing the optimal solution" refers to a function that provides the most suitable proposal to the user based on the analysis results.

[0533] "Means of providing trend information" refers to a function that provides the latest market trends and information based on the user's interests and industry.

[0534] A "head-mounted display device" is a device worn on the head by the user to obtain visual information.

[0535] "Means for acquiring product information using gaze direction information" refers to a function that detects the direction of the user's gaze and acquires information about products located in that direction.

[0536] This invention provides a system that enables users to effectively select products in a virtual store and receive personalized recommendations. This system uses a head-mounted display device to provide real-time information based on gaze direction.

[0537] The server first receives the user's authentication information and verifies their identity by referring to the database. Once authentication is complete, it displays an interface on the terminal to begin interacting with a virtual sales representative.

[0538] The gaze direction information generated by the terminal is acquired from the camera and sensors of the head-mounted display device. This allows information related to the product the user is looking at to be sent to the server. The server uses image recognition software (e.g., OpenCV) to identify the product and, based on that data, generates suggestions based on the user's interests using an AI model (e.g., GPT model).

[0539] The generated suggestions are provided to the user in real time via a head-mounted display device, providing visual and audible feedback. During this process, detailed information about the product the user is looking at and recommended promotional information are overlaid on their field of view, while a virtual sales representative provides suggestions via voice.

[0540] For example, when a user looks at a specific piece of clothing in a virtual shopping mall, its price, material, and review information are instantly displayed. Furthermore, a virtual sales representative can provide voice guidance such as, "This item is currently on sale. Please also check out these new arrivals," offering a more effective shopping experience.

[0541] An example of a prompt for a generative AI model is, "Please input the user's gaze data and product images, and generate recommendation comments for related products." In this way, a system that utilizes real-time gaze data and a generative AI model enables the provision of information tailored to the user's personal needs.

[0542] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0543] Step 1:

[0544] The user wears a head-mounted display device and fixes their gaze on a specific product. During this process, the device's camera and sensors capture information about the user's gaze direction. The input is the user's gaze direction data, and based on this, the camera acquires an image of the product.

[0545] Step 2:

[0546] The terminal sends gaze direction data and product images to the server. On the server side, the gaze data is analyzed and image recognition software (e.g., OpenCV) is used to identify the product. The output is the product ID and related information.

[0547] Step 3:

[0548] The server uses the identified product ID to retrieve detailed product information from the database. During this process, it extracts relevant data such as product category, price, reviews, and recommendation rating. The output is the detailed product data.

[0549] Step 4:

[0550] The server inputs a prompt message, such as "Generate recommended comments based on product ID," to the generating AI model (e.g., the GPT model). Based on this prompt, the generating AI model generates suggested comments tailored to the user's interests. The output is the suggested comments.

[0551] Step 5:

[0552] The generated suggestion comments and product details are transmitted to a head-mounted display device via a terminal. The terminal presents this information to the user visually and audibly. Specifically, the user is provided with information overlaid on their field of vision and audio guidance.

[0553] Step 6:

[0554] The user reviews the proposal and continues the conversation with the virtual sales representative as needed. In this step, the user's feedback is input into the system as new eye-tracking information and requests, initiating a new information acquisition process. The output is the user's response, which becomes the input for the next cycle.

[0555] 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.

[0556] This invention relates to a system that enables more effective interaction in conventional sales systems by adding the ability to recognize the emotions of users to virtual sales representatives. This system operates through the interaction of a server, terminal, and user via an interface.

[0557] The server first verifies the user's identity through an authentication mechanism when the user accesses the dedicated portal site. After this authentication, the server provides the user with a dashboard on their device that allows them to interact with a virtual sales representative.

[0558] Users enter inquiries and requests using a chat window on their device and begin interacting with a virtual sales representative. Here, the emotion engine plays a particularly important role. The server analyzes the received text and voice data and uses the emotion engine to estimate the user's emotional state (e.g., satisfaction, dissatisfaction, expectations, etc.). This emotion recognition function allows the virtual sales representative to adjust the tone and content of their responses according to the user's emotions. For example, if the virtual sales representative is estimated to be dissatisfied, they are programmed to respond more carefully and empathetically.

[0559] The virtual sales representative further selects and provides the optimal solution based on the user's emotional state. To achieve this, the server considers historical data and industry information to generate automated suggestions.

[0560] As a concrete example, when a user feels anxious about adopting a new service, the emotion engine detects this anxiety, and a virtual sales representative provides reassurance by proposing an adoption plan with special benefits. Furthermore, in the regular provision of trend information, the server uses emotion data to select and deliver information that is likely to interest the user.

[0561] In this way, by making full use of an emotion engine, this system makes interactions with users more personal and effective, enabling strategic support for sales activities.

[0562] The following describes the processing flow.

[0563] Step 1:

[0564] The user accesses the portal site and enters their authentication information on the login page.

[0565] The device securely transmits this authentication information to the server.

[0566] The server authenticates the user by matching them against the database, and if successful, starts a session.

[0567] Step 2:

[0568] The server generates a dashboard screen for the authenticated user and sends it to their device.

[0569] The user selects the "Interact with a virtual sales representative" option on this dashboard.

[0570] The device displays a chat window and waits for user input.

[0571] Step 3:

[0572] Users enter their inquiries or requests into the chat window.

[0573] The terminal sends the entered text data to the server in real time.

[0574] The server uses an emotion engine to analyze the received data and recognize the user's emotional state.

[0575] Step 4:

[0576] The server generates optimal responses and suggestions using an AI model based on the recognized emotions.

[0577] The server adjusts the response by applying a tone appropriate to the emotion, and then sends it to the terminal.

[0578] The terminal displays this in the user's chat window.

[0579] Step 5:

[0580] If the user asks further questions or requests assistance, the server performs more detailed data analysis and provides answers based on industry information and past history.

[0581] The server adjusts its response style and next suggestions based on feedback from the emotion engine.

[0582] The device will continue to display updated information and continuously accept user input.

[0583] Step 6:

[0584] The server generates trend information and new suggestions according to a pre-configured schedule.

[0585] The emotion engine considers the user's emotional data when outputting this information and selects a presentation method that enhances the user's desire to view it.

[0586] The device will notify the user of new information and allow them to view the details.

[0587] (Example 2)

[0588] 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."

[0589] Traditional sales support systems often provide a uniform response without considering the user's feelings, which can lead to decreased user satisfaction and trust. Furthermore, because optimal solutions are not based on the user's individual emotional state or industry information, improving sales performance is difficult.

[0590] 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.

[0591] In this invention, the server includes means for analyzing the user's emotional state, means for adjusting the tone and content of the response, and means for selecting and proposing the optimal solution based on the adjusted response. This makes it possible to provide individualized responses that correspond to the user's emotions and improve satisfaction.

[0592] A "user" refers to an individual or organization that uses this system and interacts with a virtual sales representative through the system's authentication and inquiry functions.

[0593] "Authentication" is the process of verifying a user's identity and granting them access rights when they access a system.

[0594] A "virtual sales representative" is a software agent that interacts with users based on a computer-generated dialogue model and assists in sales activities.

[0595] "Analysis" is the process of processing data received from users and extracting and evaluating information.

[0596] "Emotional state" refers to the psychological state inferred from the text or audio expressed by the user, and includes emotions such as satisfaction, dissatisfaction, and expectation.

[0597] "Response tone" refers to the attitude and tone of voice used by the virtual sales representative when responding to the user, and is adjusted based on the user's emotional state.

[0598] A "solution" is the optimal solution or plan proposed based on the user's inquiries and needs.

[0599] "Trend information" refers to information provided to users about the latest developments and trends in their industry and market.

[0600] The term "computer" refers to a device that performs information processing, and generally means a computer.

[0601] "History information" refers to data about the user's past transactions and activities, and is used to customize suggestions.

[0602] This system provides users with a virtual sales representative accessible through an interface, and leverages sentiment recognition to enhance the user experience. First, the user accesses a dedicated portal site using a device. The device connects to the portal using a web browser and displays a front-end application provided by the server. This application is built using frameworks such as React and Angular.

[0603] When a user accesses the portal, the server authenticates the user using OAuth or OpenID Connect. Once authentication is complete, the server sends a dashboard to the user's device for interacting with a virtual sales representative. The dashboard includes a chat window that allows for text or voice input.

[0604] When a user enters an inquiry or request using the chat window, the device sends that data to the server. In the case of voice input, the device uses Azure Speech Service to convert the speech to text. The server receives this data and uses the Google Cloud Natural Language API to analyze the user's emotional state.

[0605] Based on the analysis results, the server adjusts the tone and content of the virtual sales representative's response. For example, if the user expresses dissatisfaction, it uses a natural language processing library such as NLTK to generate a more polite and empathetic response. The adjusted response is then sent from the server to the user's terminal.

[0606] Furthermore, the server automatically generates the optimal solution based on historical data and industry information. This solution is based on information obtained from databases such as Exadata. This suggestion is provided to the user and its contents are displayed in the chat window.

[0607] For example, if a user feels anxious about adopting a new service, the system will detect this anxiety and propose an introductory plan with added benefits. This reduces user anxiety and promotes a positive user experience.

[0608] Example of a prompt:

[0609] "Users are feeling anxious about the new service. To alleviate that anxiety, please come up with an introductory plan with special offers that a virtual sales representative can propose."

[0610] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0611] Step 1:

[0612] The user accesses a dedicated portal site using their device. The device connects to the portal site via a web browser and obtains a user ID and password. The server uses this authentication information to authenticate the user using the OAuth or OpenID Connect protocol. If authentication is successful, the server grants the user access to the dashboard.

[0613] Step 2:

[0614] Once the server verifies user authentication, it sends a dashboard to the device that allows interaction with a virtual sales representative. The device renders the received HTML, CSS, and JavaScript files to display a visual dashboard to the user. The dashboard provides a chat window that allows input via text or voice.

[0615] Step 3:

[0616] Users enter inquiries and requests through a chat window. If the data is entered via a text form, it is used directly; if it's entered via voice input, it's converted from speech to text using Azure Speech Service. This data is then sent from the device to the server.

[0617] Step 4:

[0618] The server receives text data from the user and parses its content using the Google Cloud Natural Language API. This parsing process includes identifying the user's emotional state from the text. As a result, the emotional state (e.g., satisfied, dissatisfied, expectant) is output.

[0619] Step 5:

[0620] Based on the analysis results, the server generates a response from a virtual sales representative. Using natural language processing libraries such as NLTK, it adjusts the tone and content of the response according to the user's emotional state. For example, if the user is expressing dissatisfaction, it generates a careful and empathetic response. This adjusted response is then sent from the server to the terminal.

[0621] Step 6:

[0622] The server generates the optimal solution based on the user's emotional state and past history data. This includes searching and analyzing relevant data from the database, and the final selected suggestion is displayed on the terminal. A virtual sales representative provides specific suggestions tailored to the user and presents them to the user in a chat window on the dashboard.

[0623] Step 7:

[0624] The user reviews the provided suggestions and provides feedback as needed. The server collects this feedback and records it as data to improve the overall system performance. This improves the quality of future interactions and suggestions.

[0625] (Application Example 2)

[0626] 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."

[0627] This invention aims to improve the user experience by constructing a system that can accurately grasp the user's emotional state and provide a corresponding response, thereby enabling more efficient and personalized communication in a virtual environment.

[0628] 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.

[0629] In this invention, the server includes means for recognizing the user's emotional state and providing information in accordance with that emotion; means for adjusting responses using a computer-generated interaction model of a virtual sales representative; and means for optimizing proposals by utilizing emotional data while providing solutions adjusted by the proposal means based on the user's industry and performance information. This makes it possible to provide personalized proposals based on each user's emotions.

[0630] "Users" refer to end-users of this system, primarily individuals or organizations that receive services through a virtual environment.

[0631] "Authentication" is a process for verifying the user's identity and guaranteeing legitimate access to the system.

[0632] A "virtual sales representative" is a digital agent that operates using a generated computer model and is responsible for interacting with users.

[0633] "Dialogue" refers to the process by which users and virtual sales representatives exchange information in both directions, and includes communication via text or voice.

[0634] "Analysis" is the process by which a computer understands the content of a user's inquiry and analyzes the information in order to prepare an appropriate response or suggestion.

[0635] A "solution" refers to an appropriate solution or suggestion for a user's inquiry or problem.

[0636] "Trend information" refers to information that summarizes the latest trends and developments in a specific industry or market, and is provided to users on a regular basis.

[0637] "Emotional state" refers to the user's psychological and emotional state, including feelings such as satisfaction and dissatisfaction.

[0638] An "interaction model" is an algorithm or framework for realizing a simulated dialogue process using computers.

[0639] This invention is a system that provides personalized services by having a virtual sales representative recognize the user's emotions and adjust their response accordingly. The server authenticates the user and prepares the environment for initiating a conversation with the virtual sales representative via the terminal. Voice and text data received from the user are analyzed by the server, and the emotional state is estimated using an emotion engine.

[0640] The server uses OpenCV as facial recognition software and leverages the Google Cloud Natural Language API for sentiment analysis. Based on the sentiment data, the virtual sales representative forms and delivers a tailored response to the user via Dialogflow. In this process, the server uses a generative AI model to generate prompts appropriate to the sentiment.

[0641] As a concrete example, when a user inquires via their device, "I want to know more about a new product," the server detects the user's facial expressions from the camera feed and provides detailed information in a friendly tone based on the emotional data. An example of a prompt message would be, "If the user's emotional state is anxious, please provide the product description in a friendly and reassuring tone." This system configuration enables the provision of appropriate services tailored to each individual user.

[0642] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0643] Step 1:

[0644] The server authenticates the user.

[0645] The input is the user's authentication information, and the server uses this to compare and refer to data in the authentication database to verify that the user is legitimate. The output is information indicating whether authentication was successful or not. Specifically, the user ID and password are matched to determine access privileges.

[0646] Step 2:

[0647] The terminal receives an inquiry from the user and sends it to the server.

[0648] The input is voice or text data entered by the user into the terminal. The terminal relays this as digital data to the server. The output is query data sent to the server. Specifically, in the case of voice input, a conversion from voice to text is performed.

[0649] Step 3:

[0650] The server analyzes the user's inquiry.

[0651] The input is query data in text format, and the server analyzes the content using natural language processing techniques. The output is the analyzed semantic information. Specifically, this involves keyword extraction and contextual understanding.

[0652] Step 4:

[0653] The server retrieves the user's facial image and analyzes their emotional state.

[0654] The input is real-time facial image data sent from the terminal. The server uses OpenCV for facial recognition and an emotion analysis model to identify the emotional state. The output is the user's emotion information. Specifically, the emotion recognition algorithm performs facial expression analysis within the image.

[0655] Step 5:

[0656] The server uses a generative AI model based on the analysis results and sentiment information to generate prompt messages.

[0657] The input consists of analyzed semantic information and emotional states. The server inputs these into a generative AI model to generate appropriate prompt sentences. The output is the prompt sentence. Specifically, the sentence generation engine creates sentences while considering grammar and context.

[0658] Step 6:

[0659] The server passes a prompt message to a virtual sales representative, who then generates the most appropriate response for the user.

[0660] The input is a prompt, and the server generates a response to the user using a chatbot platform such as Dialogflow. The output is a customized response message. Specifically, a language generation model generates a context-aware sentence and sends it to the user via the terminal.

[0661] 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.

[0662] 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.

[0663] 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.

[0664] [Fourth Embodiment]

[0665] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0666] 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.

[0667] 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).

[0668] 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.

[0669] 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.

[0670] 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).

[0671] 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.

[0672] 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.

[0673] 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.

[0674] 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.

[0675] 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.

[0676] 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.

[0677] 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".

[0678] This invention provides a system that allows users to seamlessly conduct sales activities using virtual sales representatives. This system is supported by a server running multiple computer programs and databases.

[0679] The server first has a module for authenticating users. This module queries a database for authentication information entered by the user to verify their identity. Once authentication is complete, the server provides the user with a dashboard and displays an interface on the terminal to begin interacting with a virtual sales representative.

[0680] Users send inquiries and requests to virtual sales representatives using a chat window on their devices. The device sends this input to a server, where the inquiry is analyzed. The server uses natural language processing technology to understand the user's intent and generates the most appropriate response or suggestion based on that understanding.

[0681] For example, if a user is seeking solutions for a new project, the server utilizes historical data and customer performance information within the system to find the optimal proposal through an AI model. As a result, a virtual sales representative presents the user with specific solutions.

[0682] Furthermore, the server collects and regularly distributes trend information to continuously provide users with useful information. This trend information is individually customized based on the user's industry and interests.

[0683] In this way, users can optimize their sales activities in real time and achieve a smooth sales process without past constraints.

[0684] The following describes the processing flow.

[0685] Step 1:

[0686] The user enters their authentication information to log in to the portal site.

[0687] The terminal sends the entered authentication information to the server.

[0688] The server compares the received authentication information with the database and performs user authentication.

[0689] Step 2:

[0690] The server sends a screen displaying the dashboard to the user's device upon successful authentication.

[0691] The user selects the "Interact with a virtual sales representative" option on the dashboard.

[0692] The device displays a chat interface to the user.

[0693] Step 3:

[0694] Users enter their inquiries or requests into the chat window.

[0695] The terminal transmits the entered data to the server in real time.

[0696] The server analyzes the received data using natural language processing techniques.

[0697] Step 4:

[0698] The server uses an AI model to generate optimal solutions and suggestions based on the user's inquiry.

[0699] The server sends the generated response to the terminal.

[0700] The terminal displays a response to the user and waits for further input.

[0701] Step 5:

[0702] If the user requests multiple solutions, the server uses an AI model to select the best one based on the available data.

[0703] The server sends detailed information about the selected solution to the terminal.

[0704] The device displays the suggested content to the user.

[0705] Step 6:

[0706] The server generates trend information at pre-configured intervals and sends individual notifications based on the user's profile.

[0707] The server sends trend information to the terminal.

[0708] The device notifies the user of new information and events.

[0709] (Example 1)

[0710] 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".

[0711] Traditional sales activities have been time-consuming and labor-intensive, as sales representatives have to individually collect customer information and then make proposals based on that information. Furthermore, because there is no system in place to effectively utilize trend information and historical data, the quality of proposals is not always optimal. Solving these challenges is essential.

[0712] 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.

[0713] In this invention, the server includes means for receiving user authentication information and verifying the user's identity based on that information, means for providing the user with a display screen to initiate a dialogue with a virtual sales representative, and means for receiving the user's inquiry and analyzing its intent using natural language processing technology. This enables the user to receive personalized proposals quickly and accurately. Furthermore, the server can select and propose the optimal solution using the generated knowledge model, thereby improving the efficiency and effectiveness of sales activities.

[0714] "User authentication information" refers to the information provided by a user when requesting access to the system, and is used for verifying their identity.

[0715] A "virtual sales representative" is a software agent that simulates a sales representative on a computer, providing information and making proposals through interaction with the user.

[0716] "Natural language processing technology" is a technology that enables computers to understand and process human language, and is used to interpret user inquiries and commands.

[0717] A "generated knowledge model" is a data model built on knowledge learned from past data and experience, and is used to generate suggestions and solutions for users.

[0718] "Trend information" refers to information that indicates major current and future trends and developments in a specific industry or field, providing users with valuable insights.

[0719] A "personalized solution" is a solution tailored to the specific needs and circumstances of a user, where the proposed solution is structured in a way that is most suitable for each individual user.

[0720] This invention relates to a system that utilizes virtual sales representatives to streamline sales activities. This system is primarily server-centric and uses natural language processing technology and generative AI models to provide personalized proposals to users.

[0721] The server first receives the user's authentication information and verifies their identity by comparing it with the database. Once authentication is complete, the server displays an interface on the terminal to begin interacting with the virtual sales representative. This creates an environment where the user can efficiently interact with the virtual sales representative.

[0722] The terminal's role is to send user-entered inquiries and requests to the server. The natural language processing technology used in this process employs advanced AI technology to accurately understand the user's intent. For example, if a user enters the prompt "I want to know this month's industry trends," the server will refer to relevant data, use a generative AI model to generate the most relevant information, and provide it to the user.

[0723] The generative AI model has the ability to select the optimal solution based on past data and performance information. This makes it possible to propose personalized solutions to users, thereby improving the efficiency of sales activities. For example, if a user asks, "Please tell me about new product proposals," the server analyzes past product data and market trends to generate the most effective product proposals.

[0724] The distinctive hardware components of this system include a database server and user interface terminals, while the software comprises a natural language processing engine and a generative AI model. This enables the deployment of efficient and highly customized sales strategies.

[0725] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0726] Step 1:

[0727] The server receives authentication information from the user. This information includes the username and password and is encrypted to ensure security. The server checks this information against a database to verify the user's identity. If authentication is successful, the server generates a session key and sends an authentication completion response to the terminal.

[0728] Step 2:

[0729] The server sends information to the terminal of authenticated users to display a dashboard for interacting with a virtual sales representative. This dashboard is designed for easy user access and displays a list of past transaction data and recommended actions. The inputs used are the user ID and session key, and the output generates configuration data for the dashboard.

[0730] Step 3:

[0731] Users enter inquiries and requests to a virtual sales representative through a chat window on their device. This input is sent from the device to the server as text data. A specific example is the prompt, "I would like a proposal about a new product." The input data includes the inquiry and the user ID.

[0732] Step 4:

[0733] The server analyzes the received query text using natural language processing technology. A generative AI model analyzes the user's intent and selects the most appropriate response to the query. Data processing includes text decomposition, syntactic analysis, and semantic analysis, and the output is a list of actions corresponding to the user's request.

[0734] Step 5:

[0735] The server generates optimal suggestions based on the analysis results. Past performance data and customer information are utilized in this process. The generating AI model extracts relevant information from the database and constructs the most relevant suggestions. Specific examples include related product information and pricing options. The output suggestion data is prepared as a response to be provided to the user.

[0736] Step 6:

[0737] The server sends the generated response to the terminal, which displays it in the chat window. The user can review the suggestion and, if necessary, make further inquiries or take other actions. The input is the suggestion data, and the output is information adapted as a visual interface for the user.

[0738] (Application Example 1)

[0739] 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".

[0740] In today's online commercial environment, users are overwhelmed with information, making it difficult to efficiently select products. Furthermore, they lack the personalized customer experience offered in face-to-face interactions, resulting in a lack of personalized user experience in online stores. Therefore, there is a need for information tailored to user needs and methods to efficiently select appropriate products from a wide range of options.

[0741] 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.

[0742] In this invention, the server includes means for authenticating the user, means for initiating a dialogue with a virtual sales representative, and means for acquiring product information using gaze direction information. This enables the user to receive information based on their gaze direction in real time using a head-mounted display device, realizing an efficient and personalized user experience.

[0743] "User authentication" is a means of verifying the identity of users accessing the system.

[0744] A "virtual sales representative" is an agent that communicates with users using a computer-generated dialogue model.

[0745] "Means for initiating a dialogue" refers to a function that provides an interface for users to start communication with a virtual sales representative.

[0746] "Means for receiving inquiries and analyzing their content" refers to a function that receives questions and requests from users and analyzes them to understand the user's intentions.

[0747] "The means of selecting and proposing the optimal solution" refers to a function that provides the most suitable proposal to the user based on the analysis results.

[0748] "Means of providing trend information" refers to a function that provides the latest market trends and information based on the user's interests and industry.

[0749] A "head-mounted display device" is a device worn on the head by the user to obtain visual information.

[0750] "Means for acquiring product information using gaze direction information" refers to a function that detects the direction of the user's gaze and acquires information about products located in that direction.

[0751] This invention provides a system that enables users to effectively select products in a virtual store and receive personalized recommendations. This system uses a head-mounted display device to provide real-time information based on gaze direction.

[0752] The server first receives the user's authentication information and verifies their identity by referring to the database. Once authentication is complete, it displays an interface on the terminal to begin interacting with a virtual sales representative.

[0753] The gaze direction information generated by the terminal is acquired from the camera and sensors of the head-mounted display device. This allows information related to the product the user is looking at to be sent to the server. The server uses image recognition software (e.g., OpenCV) to identify the product and, based on that data, generates suggestions based on the user's interests using an AI model (e.g., GPT model).

[0754] The generated suggestions are provided to the user in real time via a head-mounted display device, providing visual and audible feedback. During this process, detailed information about the product the user is looking at and recommended promotional information are overlaid on their field of view, while a virtual sales representative provides suggestions via voice.

[0755] For example, when a user looks at a specific piece of clothing in a virtual shopping mall, its price, material, and review information are instantly displayed. Furthermore, a virtual sales representative can provide voice guidance such as, "This item is currently on sale. Please also check out these new arrivals," offering a more effective shopping experience.

[0756] An example of a prompt for a generative AI model is, "Please input the user's gaze data and product images, and generate recommendation comments for related products." In this way, a system that utilizes real-time gaze data and a generative AI model enables the provision of information tailored to the user's personal needs.

[0757] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0758] Step 1:

[0759] The user wears a head-mounted display device and fixes their gaze on a specific product. During this process, the device's camera and sensors capture information about the user's gaze direction. The input is the user's gaze direction data, and based on this, the camera acquires an image of the product.

[0760] Step 2:

[0761] The terminal sends gaze direction data and product images to the server. On the server side, the gaze data is analyzed and image recognition software (e.g., OpenCV) is used to identify the product. The output is the product ID and related information.

[0762] Step 3:

[0763] The server uses the identified product ID to retrieve detailed product information from the database. During this process, it extracts relevant data such as product category, price, reviews, and recommendation rating. The output is the detailed product data.

[0764] Step 4:

[0765] The server inputs a prompt message, such as "Generate recommended comments based on product ID," to the generating AI model (e.g., the GPT model). Based on this prompt, the generating AI model generates suggested comments tailored to the user's interests. The output is the suggested comments.

[0766] Step 5:

[0767] The generated suggestion comments and product details are transmitted to a head-mounted display device via a terminal. The terminal presents this information to the user visually and audibly. Specifically, the user is provided with information overlaid on their field of vision and audio guidance.

[0768] Step 6:

[0769] The user reviews the proposal and continues the conversation with the virtual sales representative as needed. In this step, the user's feedback is input into the system as new eye-tracking information and requests, initiating a new information acquisition process. The output is the user's response, which becomes the input for the next cycle.

[0770] 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.

[0771] This invention relates to a system that enables more effective interaction in conventional sales systems by adding the ability to recognize the emotions of users to virtual sales representatives. This system operates through the interaction of a server, terminal, and user via an interface.

[0772] The server first verifies the user's identity through an authentication mechanism when the user accesses the dedicated portal site. After this authentication, the server provides the user with a dashboard on their device that allows them to interact with a virtual sales representative.

[0773] Users enter inquiries and requests using a chat window on their device and begin interacting with a virtual sales representative. Here, the emotion engine plays a particularly important role. The server analyzes the received text and voice data and uses the emotion engine to estimate the user's emotional state (e.g., satisfaction, dissatisfaction, expectations, etc.). This emotion recognition function allows the virtual sales representative to adjust the tone and content of their responses according to the user's emotions. For example, if the virtual sales representative is estimated to be dissatisfied, they are programmed to respond more carefully and empathetically.

[0774] The virtual sales representative further selects and provides the optimal solution based on the user's emotional state. To achieve this, the server considers historical data and industry information to generate automated suggestions.

[0775] As a concrete example, when a user feels anxious about adopting a new service, the emotion engine detects this anxiety, and a virtual sales representative provides reassurance by proposing an adoption plan with special benefits. Furthermore, in the regular provision of trend information, the server uses emotion data to select and deliver information that is likely to interest the user.

[0776] In this way, by making full use of an emotion engine, this system makes interactions with users more personal and effective, enabling strategic support for sales activities.

[0777] The following describes the processing flow.

[0778] Step 1:

[0779] The user accesses the portal site and enters their authentication information on the login page.

[0780] The device securely transmits this authentication information to the server.

[0781] The server authenticates the user by matching them against the database, and if successful, starts a session.

[0782] Step 2:

[0783] The server generates a dashboard screen for the authenticated user and sends it to their device.

[0784] The user selects the "Interact with a virtual sales representative" option on this dashboard.

[0785] The device displays a chat window and waits for user input.

[0786] Step 3:

[0787] Users enter their inquiries or requests into the chat window.

[0788] The terminal sends the entered text data to the server in real time.

[0789] The server uses an emotion engine to analyze the received data and recognize the user's emotional state.

[0790] Step 4:

[0791] The server generates optimal responses and suggestions using an AI model based on the recognized emotions.

[0792] The server adjusts the response by applying a tone appropriate to the emotion, and then sends it to the terminal.

[0793] The terminal displays this in the user's chat window.

[0794] Step 5:

[0795] If the user asks further questions or requests assistance, the server performs more detailed data analysis and provides answers based on industry information and past history.

[0796] The server adjusts its response style and next suggestions based on feedback from the emotion engine.

[0797] The device will continue to display updated information and continuously accept user input.

[0798] Step 6:

[0799] The server generates trend information and new suggestions according to a pre-configured schedule.

[0800] The emotion engine considers the user's emotional data when outputting this information and selects a presentation method that enhances the user's desire to view it.

[0801] The device will notify the user of new information and allow them to view the details.

[0802] (Example 2)

[0803] 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".

[0804] Traditional sales support systems often provide a uniform response without considering the user's feelings, which can lead to decreased user satisfaction and trust. Furthermore, because optimal solutions are not based on the user's individual emotional state or industry information, improving sales performance is difficult.

[0805] 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.

[0806] In this invention, the server includes means for analyzing the user's emotional state, means for adjusting the tone and content of the response, and means for selecting and proposing the optimal solution based on the adjusted response. This makes it possible to provide individualized responses that correspond to the user's emotions and improve satisfaction.

[0807] A "user" refers to an individual or organization that uses this system and interacts with a virtual sales representative through the system's authentication and inquiry functions.

[0808] "Authentication" is the process of verifying a user's identity and granting them access rights when they access a system.

[0809] A "virtual sales representative" is a software agent that interacts with users based on a computer-generated dialogue model and assists in sales activities.

[0810] "Analysis" is the process of processing data received from users and extracting and evaluating information.

[0811] "Emotional state" refers to the psychological state inferred from the text or audio expressed by the user, and includes emotions such as satisfaction, dissatisfaction, and expectation.

[0812] "Response tone" refers to the attitude and tone of voice used by the virtual sales representative when responding to the user, and is adjusted based on the user's emotional state.

[0813] A "solution" is the optimal solution or plan proposed based on the user's inquiries and needs.

[0814] "Trend information" refers to information provided to users about the latest developments and trends in their industry and market.

[0815] The term "computer" refers to a device that performs information processing, and generally means a computer.

[0816] "History information" refers to data about the user's past transactions and activities, and is used to customize suggestions.

[0817] This system provides users with a virtual sales representative accessible through an interface, and leverages sentiment recognition to enhance the user experience. First, the user accesses a dedicated portal site using a device. The device connects to the portal using a web browser and displays a front-end application provided by the server. This application is built using frameworks such as React and Angular.

[0818] When a user accesses the portal, the server authenticates the user using OAuth or OpenID Connect. Once authentication is complete, the server sends a dashboard to the user's device for interacting with a virtual sales representative. The dashboard includes a chat window that allows for text or voice input.

[0819] When a user enters an inquiry or request using the chat window, the device sends that data to the server. In the case of voice input, the device uses Azure Speech Service to convert the speech to text. The server receives this data and uses the Google Cloud Natural Language API to analyze the user's emotional state.

[0820] Based on the analysis results, the server adjusts the tone and content of the virtual sales representative's response. For example, if the user expresses dissatisfaction, it uses a natural language processing library such as NLTK to generate a more polite and empathetic response. The adjusted response is then sent from the server to the user's terminal.

[0821] Furthermore, the server automatically generates the optimal solution based on historical data and industry information. This solution is based on information obtained from databases such as Exadata. This suggestion is provided to the user and its contents are displayed in the chat window.

[0822] For example, if a user feels anxious about adopting a new service, the system will detect this anxiety and propose an introductory plan with added benefits. This reduces user anxiety and promotes a positive user experience.

[0823] Example of a prompt:

[0824] "Users are feeling anxious about the new service. To alleviate that anxiety, please come up with an introductory plan with special offers that a virtual sales representative can propose."

[0825] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0826] Step 1:

[0827] The user accesses a dedicated portal site using their device. The device connects to the portal site via a web browser and obtains a user ID and password. The server uses this authentication information to authenticate the user using the OAuth or OpenID Connect protocol. If authentication is successful, the server grants the user access to the dashboard.

[0828] Step 2:

[0829] Once the server verifies user authentication, it sends a dashboard to the device that allows interaction with a virtual sales representative. The device renders the received HTML, CSS, and JavaScript files to display a visual dashboard to the user. The dashboard provides a chat window that allows input via text or voice.

[0830] Step 3:

[0831] Users enter inquiries and requests through a chat window. If the data is entered via a text form, it is used directly; if it's entered via voice input, it's converted from speech to text using Azure Speech Service. This data is then sent from the device to the server.

[0832] Step 4:

[0833] The server receives text data from the user and parses its content using the Google Cloud Natural Language API. This parsing process includes identifying the user's emotional state from the text. As a result, the emotional state (e.g., satisfied, dissatisfied, expectant) is output.

[0834] Step 5:

[0835] Based on the analysis results, the server generates a response from a virtual sales representative. Using natural language processing libraries such as NLTK, it adjusts the tone and content of the response according to the user's emotional state. For example, if the user is expressing dissatisfaction, it generates a careful and empathetic response. This adjusted response is then sent from the server to the terminal.

[0836] Step 6:

[0837] The server generates the optimal solution based on the user's emotional state and past history data. This includes searching and analyzing relevant data from the database, and the final selected suggestion is displayed on the terminal. A virtual sales representative provides specific suggestions tailored to the user and presents them to the user in a chat window on the dashboard.

[0838] Step 7:

[0839] The user reviews the provided suggestions and provides feedback as needed. The server collects this feedback and records it as data to improve the overall system performance. This improves the quality of future interactions and suggestions.

[0840] (Application Example 2)

[0841] 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".

[0842] This invention aims to improve the user experience by constructing a system that can accurately grasp the user's emotional state and provide a corresponding response, thereby enabling more efficient and personalized communication in a virtual environment.

[0843] 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.

[0844] In this invention, the server includes means for recognizing the user's emotional state and providing information in accordance with that emotion; means for adjusting responses using a computer-generated interaction model of a virtual sales representative; and means for optimizing proposals by utilizing emotional data while providing solutions adjusted by the proposal means based on the user's industry and performance information. This makes it possible to provide personalized proposals based on each user's emotions.

[0845] "Users" refer to end-users of this system, primarily individuals or organizations that receive services through a virtual environment.

[0846] "Authentication" is a process for verifying the user's identity and guaranteeing legitimate access to the system.

[0847] A "virtual sales representative" is a digital agent that operates using a generated computer model and is responsible for interacting with users.

[0848] "Dialogue" refers to the process by which users and virtual sales representatives exchange information in both directions, and includes communication via text or voice.

[0849] "Analysis" is the process by which a computer understands the content of a user's inquiry and analyzes the information in order to prepare an appropriate response or suggestion.

[0850] A "solution" refers to an appropriate solution or suggestion for a user's inquiry or problem.

[0851] "Trend information" refers to information that summarizes the latest trends and developments in a specific industry or market, and is provided to users on a regular basis.

[0852] "Emotional state" refers to the user's psychological and emotional state, including feelings such as satisfaction and dissatisfaction.

[0853] An "interaction model" is an algorithm or framework for realizing a simulated dialogue process using computers.

[0854] This invention is a system that provides personalized services by having a virtual sales representative recognize the user's emotions and adjust their response accordingly. The server authenticates the user and prepares the environment for initiating a conversation with the virtual sales representative via the terminal. Voice and text data received from the user are analyzed by the server, and the emotional state is estimated using an emotion engine.

[0855] The server uses OpenCV as facial recognition software and leverages the Google Cloud Natural Language API for sentiment analysis. Based on the sentiment data, the virtual sales representative forms and delivers a tailored response to the user via Dialogflow. In this process, the server uses a generative AI model to generate prompts appropriate to the sentiment.

[0856] As a concrete example, when a user inquires via their device, "I want to know more about a new product," the server detects the user's facial expressions from the camera feed and provides detailed information in a friendly tone based on the emotional data. An example of a prompt message would be, "If the user's emotional state is anxious, please provide the product description in a friendly and reassuring tone." This system configuration enables the provision of appropriate services tailored to each individual user.

[0857] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0858] Step 1:

[0859] The server authenticates the user.

[0860] The input is the user's authentication information, and the server uses this to compare and refer to data in the authentication database to verify that the user is legitimate. The output is information indicating whether authentication was successful or not. Specifically, the user ID and password are matched to determine access privileges.

[0861] Step 2:

[0862] The terminal receives an inquiry from the user and sends it to the server.

[0863] The input is voice or text data entered by the user into the terminal. The terminal relays this as digital data to the server. The output is query data sent to the server. Specifically, in the case of voice input, a conversion from voice to text is performed.

[0864] Step 3:

[0865] The server analyzes the user's inquiry.

[0866] The input is query data in text format, and the server analyzes the content using natural language processing techniques. The output is the analyzed semantic information. Specifically, this involves keyword extraction and contextual understanding.

[0867] Step 4:

[0868] The server retrieves the user's facial image and analyzes their emotional state.

[0869] The input is real-time facial image data sent from the terminal. The server uses OpenCV for facial recognition and an emotion analysis model to identify the emotional state. The output is the user's emotion information. Specifically, the emotion recognition algorithm performs facial expression analysis within the image.

[0870] Step 5:

[0871] The server uses a generative AI model based on the analysis results and sentiment information to generate prompt messages.

[0872] The input consists of analyzed semantic information and emotional states. The server inputs these into a generative AI model to generate appropriate prompt sentences. The output is the prompt sentence. Specifically, the sentence generation engine creates sentences while considering grammar and context.

[0873] Step 6:

[0874] The server passes a prompt message to a virtual sales representative, who then generates the most appropriate response for the user.

[0875] The input is a prompt, and the server generates a response to the user using a chatbot platform such as Dialogflow. The output is a customized response message. Specifically, a language generation model generates a context-aware sentence and sends it to the user via the terminal.

[0876] 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.

[0877] 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.

[0878] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0879] 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.

[0880] 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.

[0881] 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.

[0882] 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.

[0883] 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.

[0884] 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."

[0885] 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.

[0886] 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.

[0887] 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.

[0888] 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.

[0889] 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.

[0890] 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.

[0891] 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.

[0892] 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.

[0893] 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.

[0894] 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.

[0895] 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.

[0896] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[0897] The following is further disclosed regarding the embodiments described above.

[0898] (Claim 1)

[0899] A means of authenticating users,

[0900] A means for initiating a dialogue between the user and the virtual sales representative,

[0901] A means for receiving inquiries from the aforementioned user and analyzing their content,

[0902] A means of selecting and proposing the optimal solution based on the aforementioned analysis results,

[0903] A means of providing the aforementioned users with trend information on a regular basis,

[0904] A system that includes this.

[0905] (Claim 2)

[0906] The aforementioned virtual sales representative uses a computer-generated dialogue model.

[0907] The system according to claim 1.

[0908] (Claim 3)

[0909] The proposed means provides a solution tailored using the user's industry and performance information.

[0910] The system according to claim 1.

[0911] "Example 1"

[0912] (Claim 1)

[0913] A means of receiving user authentication information and verifying the user's identity based on that information,

[0914] A means for providing the user with a display screen to initiate a conversation with a virtual sales representative,

[0915] A means for receiving the user's inquiry and analyzing its intent using natural language processing technology,

[0916] Based on the aforementioned analysis results, a means of selecting and proposing the optimal solution using the generated knowledge model,

[0917] A means of adjusting the aforementioned proposal by utilizing past data and user performance information,

[0918] A means of regularly providing the aforementioned users with industry-specific trend information,

[0919] A system that includes this.

[0920] (Claim 2)

[0921] The virtual sales representative generates a natural language response using the generated dialogue model.

[0922] The system according to claim 1.

[0923] (Claim 3)

[0924] The proposed means provides personalized solutions using artificial intelligence technology based on the user's industry and performance information.

[0925] The system according to claim 1.

[0926] "Application Example 1"

[0927] (Claim 1)

[0928] A means of authenticating users,

[0929] A means for initiating a dialogue between the user and the virtual sales representative,

[0930] A means for receiving inquiries from the aforementioned user and analyzing their content,

[0931] A means of selecting and proposing the optimal solution based on the aforementioned analysis results,

[0932] A means of providing the aforementioned users with trend information on a regular basis,

[0933] A means for displaying visual and auditory information using a head-mounted display device,

[0934] A means for acquiring product information using gaze direction information from the head-mounted display device,

[0935] A system that includes this.

[0936] (Claim 2)

[0937] The aforementioned virtual sales representative uses a computer-generated dialogue model,

[0938] The means include providing product information based on the aforementioned gaze direction information.

[0939] The system according to claim 1.

[0940] (Claim 3)

[0941] The proposed means provides a solution tailored using the user's industry and performance information,

[0942] To display information in real time via the head-mounted display device,

[0943] The system according to claim 1.

[0944] "Example 2 of combining an emotion engine"

[0945] (Claim 1)

[0946] A means of authenticating users,

[0947] A means for initiating a dialogue between the user and the virtual sales representative,

[0948] A means for receiving inquiries from the aforementioned user and analyzing their content,

[0949] Based on the aforementioned analysis results, a means for analyzing the user's emotional state,

[0950] Means for adjusting the tone and content of the response based on the aforementioned emotional state,

[0951] A means for selecting and proposing the optimal solution based on the aforementioned adjusted response,

[0952] A means of providing the aforementioned users with trend information on a regular basis,

[0953] A system that includes this.

[0954] (Claim 2)

[0955] The aforementioned virtual sales representative uses a computer-generated dialogue model.

[0956] The system according to claim 1.

[0957] (Claim 3)

[0958] The proposed means provides a solution tailored using the user's industry and historical information.

[0959] The system according to claim 1.

[0960] "Application example 2 of combining emotional engines"

[0961] (Claim 1)

[0962] A means of authenticating users,

[0963] A means for initiating a dialogue between the user and the virtual sales representative,

[0964] A means for receiving inquiries from the aforementioned user and analyzing their content,

[0965] A means of selecting and proposing the optimal solution based on the aforementioned analysis results,

[0966] A means of providing the aforementioned user with trend information on a regular basis,

[0967] A means for recognizing the emotional state of the user and providing information in accordance with that emotion,

[0968] A system that includes this.

[0969] (Claim 2)

[0970] The system according to claim 1, further comprising means for the virtual sales representative to use a computer-generated interaction model and for adjusting the response based on the user's emotional state.

[0971] (Claim 3)

[0972] The system according to claim 1, wherein the proposed means provides a solution tailored using the user's industry and performance information, and optimizes the proposal by utilizing the user's emotional data. [Explanation of Symbols]

[0973] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of authenticating users, A means for initiating a dialogue between the user and the virtual sales representative, A means for receiving inquiries from the aforementioned user and analyzing their content, A means of selecting and proposing the optimal solution based on the aforementioned analysis results, A means of providing the aforementioned users with trend information on a regular basis, A system that includes this.

2. The aforementioned virtual sales representative uses a computer-generated dialogue model. The system according to claim 1.

3. The proposed means provides a solution tailored using the user's industry and performance information. The system according to claim 1.

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