system

Abstract

We provide the system. [Solution] A means of collecting data related to sales and operations, A means of storing the collected data in a database, A means of receiving requests from users, A means of retrieving appropriate data from a database, analyzing it, and generating an answer, A means of notifying the user of the generated response, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional dissemination of information related to sales and business and provision of answers, it is often done manually, and there is a problem of low efficiency. Specifically, there is a problem that the collection, analysis, and generation and notification of answers based on the latest customer feedback and new product information are not carried out quickly, resulting in a decrease in business efficiency. Also, there may be a lack of consistency and accuracy of information, and there is also a risk that important decision-making in sales activities will be delayed.

Means for Solving the Problems

[0005] To solve the above problems, the present invention provides the following means: a system including means for collecting sales and business-related data, means for storing the collected data in a database, means for receiving requests from users, means for obtaining and analyzing appropriate data from the database to generate a response, and means for notifying the user of the generated response. Furthermore, the system includes means for collecting the data from the internet and internal systems, and is characterized by the use of artificial intelligence for analysis. As a result, information collection, storage, analysis, response generation, and notification are automated, improving business efficiency while maintaining the consistency and accuracy of the information.

[0006] "Sales and operational data" refers to all information related to commercial activities and business processes, including customer feedback, sales data, and new product information.

[0007] "Means of collection" refers to devices or programs that have the function of collecting necessary data from the internet and internal systems.

[0008] "Means of storing data in a database" refers to systems and software that efficiently store collected data and facilitate data retrieval and access.

[0009] "Means of receiving requests from users" refers to interfaces or programs that receive requests when a user requests specific information.

[0010] "Means for acquiring and analyzing appropriate data and generating responses" refers to systems or programs that have the function of extracting necessary information from a database, analyzing it, and creating responses that are suitable for the user's request.

[0011] "Means of notifying the user of the generated response" refers to communication methods and notification systems for conveying analysis results and generated information to the user.

[0012] "Internet and internal systems" refers to information systems used on external networks and within a company.

[0013] "Using artificial intelligence to analyze data and generate answers" refers to a function that uses AI technologies such as machine learning and natural language processing to process data and create appropriate answers for the user. [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]It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined.

Mode for Carrying Out the Invention

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

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

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

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

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

[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] In order to implement the present invention, it is desirable that the system be configured as follows. Specifically, the system consists of three components: a server, a terminal, and a user, and their roles and processing flow are described below.

[0036] Overall system configuration

[0037] The system consists of the following components:

[0038] 1. Data Acquisition Module

[0039] 2. Database

[0040] 3. User Interface

[0041] 4. AI Module

[0042] 5. Notification Module

[0043] Data Acquisition Module

[0044] The server uses a data collection module to gather sales and operational information from the internet and internal systems. For example, the server periodically calls a market research API to collect customer feedback information. It also extracts information about new products from the ERP system.

[0045] database

[0046] The collected data is stored in a database by the server. The data is organized by category and stored in the appropriate fields to enable quick searching and retrieval. For example, the server categorizes text-based feedback data (positive, negative, neutral) and stores it in the corresponding field.

[0047] User Interface

[0048] The user accesses the system through a terminal and requests specific information (e.g., promotional materials for a new product). The terminal then transmits the request to the server. For example, the user might type "Please provide detailed information about the new product" and press the submit button.

[0049] AI Module

[0050] The server receives a request and generates a query to retrieve relevant information from the database. The retrieved data is passed to an AI module for analysis. The AI ​​module processes the data using machine learning and natural language processing techniques to generate optimal information tailored to the user's needs. For example, the server might pass information about a new product to the AI ​​module, and the AI ​​might use that information to create promotional materials.

[0051] Notification module

[0052] The generated response (e.g., promotional materials) is sent from the server to the terminal. The terminal then sends a notification to the user, providing instructions on how to access the generated materials. For example, the terminal might notify the user, "New product promotional materials are ready. Please download them from here."

[0053] Specific example

[0054] 1. Data Collection: The server collects the latest customer feedback from the internet and stores it in a database.

[0055] 2. Data organization: The server stores feedback data categorized into positive, negative, and neutral.

[0056] 3. Processing user requests: A user requests promotional materials for a new product from their device, and the device sends that request to the server.

[0057] 4. Data analysis and response generation: The server passes the request to the AI ​​module, which generates promotional materials.

[0058] 5. Notification: The server sends the generated promotional materials to the terminal and notifies the user.

[0059] This system automates the entire process from collecting sales and business-related information to providing it to users, thereby improving operational efficiency.

[0060] The following describes the processing flow.

[0061] Step 1:

[0062] The server uses a data collection module to gather sales and operational data. Specifically, the server sends requests to a market research API to obtain the latest customer feedback data. It also extracts information about new products from the ERP system.

[0063] Step 2:

[0064] The server preprocesses the collected data and stores it in a database as structured data. For example, the server categorizes text-based feedback into positive, negative, and neutral categories and stores them in their respective fields.

[0065] Step 3:

[0066] The user uses the device to request specific information. For example, if a user wants to obtain promotional materials for a new product, they would type "Please provide details about the new product" on the device's interface and press the send button.

[0067] Step 4:

[0068] The terminal sends the user's request to the server. An HTTP request is used to communicate the user's request to the server.

[0069] Step 5:

[0070] The server receives the request and generates a query to retrieve relevant information from the database. For example, it generates an SQL query to search for new product information and sends it to the database.

[0071] Step 6:

[0072] The server passes the acquired data to the AI ​​module. The AI ​​module uses machine learning and natural language processing techniques to analyze the data and generate the most suitable response to the user's request. For example, it can create promotional materials based on the attributes and features of a new product.

[0073] Step 7:

[0074] The server receives the response generated by the AI ​​module and sends it to the terminal as a means of notifying the user. For example, it might send the generated promotional material in PDF format to the terminal as an HTTP response.

[0075] Step 8:

[0076] The device sends a notification to the user. For example, it might display a message to the user saying, "Promotional materials for our new product are ready. You can download them here."

[0077] Users download and view materials using their devices.

[0078] (Example 1)

[0079] 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."

[0080] The processes of collecting, organizing, analyzing, and providing sales and business-related information are often performed manually, which can result in decreased operational efficiency and the occurrence of data omissions and errors. Furthermore, traditional systems struggle to process large amounts of data quickly, making it difficult to provide users with optimal information. Solving these problems is therefore essential.

[0081] 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.

[0082] In this invention, the server includes means for collecting sales and business-related information using a data collection module, means for organizing the collected data by category and storing it in a database, means for receiving information requests from users via a user interface, means for retrieving appropriate data from the database, analyzing it with an AI module, and generating a response, and means for notifying the user of the generated response. This automates the process from information collection to provision, improves operational efficiency, and enables the rapid provision of optimal information to users.

[0083] A "data collection module" is software or hardware used to collect sales and business-related information from the internet or internal systems.

[0084] A "database" is an information management system that organizes and stores collected data by category, enabling rapid searching and retrieval of data.

[0085] A "user interface" is the interface that a user uses to access a system and request information, and includes operation screens and input forms.

[0086] An "AI module" is a component that includes artificial intelligence technology used to analyze data and generate appropriate responses based on user requests.

[0087] "Notification means" refers to a means of informing the user of the generated response, and includes a function to send a notification to the user's device.

[0088] "Machine learning" refers to algorithms and techniques for learning patterns and knowledge from data, and for performing analysis and decision-making.

[0089] "Natural language processing" is a technology that enables machines to understand and process human language, and is used for text analysis and generation.

[0090] Modes for carrying out the invention

[0091] The system for implementing the present invention consists of three components: a server, a terminal, and a user. Their roles and processing flow are as follows. The components of this system include a data collection module, a database, a user interface, an AI module, and a notification module. Each component is described in detail below.

[0092] Data Acquisition Module

[0093] The server uses a data collection module to gather sales and operational information. This module has the capability to retrieve data from the internet and internal systems. For example, the server periodically calls a market research API to collect customer feedback information and accesses the ERP system to extract new product information.

[0094] database

[0095] The server stores the collected data in a database. The database is an information management system for organizing and storing the collected data by category, enabling quick searching and retrieval of data. Specifically, the server stores text-based feedback data, categorized into positive, negative, and neutral, and generates a search index.

[0096] User Interface

[0097] Users access the system through their terminals and use the user interface to request specific information. Users can make information requests using the terminal's operation screen and input forms. For example, if a user types "Please provide detailed information about the new product" and presses the submit button, the request is sent to the server.

[0098] AI Module

[0099] The server receives requests from users, retrieves relevant information from the database, and passes it to the AI ​​module. The AI ​​module analyzes the data using machine learning and natural language processing techniques to generate the optimal response. For example, the server retrieves information about a new product, and the AI ​​module analyzes that information to create promotional materials.

[0100] Notification module

[0101] The server sends the response generated by the AI ​​module to the terminal and notifies the user. The terminal sends a notification to the user and provides instructions on how to access the generated materials. For example, the terminal might notify the user, "Promotional materials for the new product are ready. Please download them from here."

[0102] Specific example

[0103] For example, here's a specific example of what to do when requesting promotional materials for a new product:

[0104] 1. Data Collection: The server uses a market research API to collect recent customer feedback and retrieves new product information from the ERP system.

[0105] 2. Data organization: The feedback data collected by the server is sorted into positive, negative, and neutral categories and stored in a database.

[0106] 3. Processing the user request: The user enters "Please provide promotional materials for the new product" into the device's UI and submits it.

[0107] 4. Data Analysis and Response Generation: The server analyzes the request, generates a data acquisition query, retrieves the necessary data from the database, passes it to the AI ​​module, and generates optimal promotional materials.

[0108] 5. Notification: The server sends the generated promotional materials to the terminal, and the terminal notifies the user that the materials are ready.

[0109] Example of a prompt

[0110] "Please create promotional materials for this new product."

[0111] "Analyze the latest customer feedback and generate a report."

[0112] "Please extract and provide information on new products from the ERP system."

[0113] The system according to the present invention automates the above processes, streamlining the entire process from information collection to provision. This improves operational efficiency and enables the rapid provision of optimal information to users.

[0114] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0115] Step 1: Data Collection

[0116] The server uses a data collection module to gather sales and operational information from the internet and internal systems. Inputs include market research APIs and data from ERP systems. The server periodically calls APIs to retrieve customer feedback information. It also accesses the ERP system to extract new product information. The output is the collected raw data.

[0117] Step 2: Saving and organizing in the database

[0118] The server organizes and stores the collected data in a database. The input is the raw data collected in step 1. The server categorizes the data into positive, negative, and neutral categories and stores them in the appropriate fields. Data organization and indexing enable rapid searching and retrieval. The output is database entries organized by category.

[0119] Step 3: Requesting information via the user interface

[0120] The user accesses the system using their device and requests specific information. The input is the request content entered by the user in the device's UI form (e.g., "Please provide details about the new product"). When the user clicks the submit button, the request is sent to the server via the device. The output is the information request sent to the server.

[0121] Step 4: Data analysis and response generation

[0122] The server receives information requests from users, retrieves relevant information from the database, and passes it to the AI ​​module. The input consists of the information requests received in step 3 and the data stored in step 2. The server generates appropriate queries and retrieves the necessary data. The AI ​​module analyzes the data using machine learning and natural language processing techniques to generate the optimal response. The output consists of reports and promotional materials generated by the AI ​​module.

[0123] Step 5: Notification and Information Provision

[0124] The server sends the generated responses to the terminal, and the terminal notifies the user that the materials are ready. The input is the response data generated in step 4. The server sends the generated materials to the terminal and notifies the user, "New product promotional materials are ready. Please download them from here." The user receives the notification and clicks the link on the terminal to download the materials. The output is the materials provided to the user and the notification.

[0125] (Specific examples of actions)

[0126] For example, the specific steps involved in requesting promotional materials for a new product are as follows:

[0127] 1. Data Collection: The server uses a market research API to collect customer feedback data and retrieves new product information from the ERP system.

[0128] 2. Data organization: The data collected by the server is classified into positive, negative, and neutral categories and stored in the database.

[0129] 3. Processing user requests: The user enters "Please provide promotional materials for the new product" into the device's UI and submits the request.

[0130] 4. Data Analysis and Response Generation: The server analyzes the request, generates a data retrieval query to obtain the necessary data from the database, and then the AI ​​module analyzes it to generate promotional materials.

[0131] 5. Notification and Information Provision: The server sends the generated promotional materials to the terminal, and the terminal notifies the user that the materials are ready. The user clicks the link to download the materials.

[0132] (Application Example 1)

[0133] 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."

[0134] In traditional brick-and-mortar stores, customers must directly ask store staff for detailed product information and promotional materials. This manual search and provision of information makes rapid service difficult. Furthermore, real-time responses to questions are challenging, contributing to decreased customer satisfaction. Additionally, inability to provide adequate product information can lead to missed sales opportunities. To address this, a system is needed that allows both staff and customers to access and verify information in real time.

[0135] 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.

[0136] In this invention, the server includes means for collecting sales and business-related data, means for storing the collected data in a database, means for receiving requests from users, means for retrieving and analyzing appropriate data from the database and generating responses, means for notifying the user of the generated responses, and means for users to check the information in real time via a smart device. This enables customers to obtain detailed product information and promotional materials in real time, and allows for the rapid and efficient provision of services.

[0137] "Means of collecting data" refers to a system for obtaining information related to sales and operations from multiple sources, such as the internet and internal systems.

[0138] "Means of storing data in a database" refers to a system that has the function of appropriately classifying and organizing collected information and storing it in a database.

[0139] "Means of receiving requests from users" refers to a mechanism that provides an interface for users to communicate their requests for information to the system.

[0140] "A means of retrieving and analyzing appropriate data from a database to generate an answer" refers to a system that searches for information stored in a database, analyzes it using artificial intelligence or other means, and generates the information the user is looking for.

[0141] "Means of notifying users of generated responses" refers to a system equipped with a notification function to convey generated information or documents to users.

[0142] "Means that allow users to check information in real time via smart devices" refers to a system that enables users to quickly acquire and check information via digital devices such as smart glasses and smartphones.

[0143] To implement the present invention, the system is preferably configured as follows. Specifically, it is a system that collects data related to sales and operations and generates and provides appropriate information according to user requests. The main components and their roles are described below.

[0144] Overall system configuration

[0145] The system consists of the following components:

[0146] 1. Data Acquisition Module

[0147] 2. Database

[0148] 3. User Interface

[0149] 4. AI Module

[0150] 5. Notification Module

[0151] 6. Smart devices

[0152] Data Acquisition Module

[0153] First, the server uses a data collection module to gather sales and operational information from the internet and internal systems. The server also periodically retrieves data from market research APIs and ERP systems to collect customer feedback and new product information.

[0154] database

[0155] The collected data is stored in a database by the server. The data is organized by category and stored in appropriate fields to enable quick searching and retrieval. For example, feedback data is stored in positive, negative, and neutral categories.

[0156] User Interface

[0157] Users access the system through a device (e.g., smart glasses or a smartphone) and request specific information. Requests can be made via text input or voice input. For example, a user might type "Please provide detailed information about the new product" and send it.

[0158] AI Module

[0159] Next, the server receives the request and generates a query to retrieve relevant information from the database. The AI ​​module analyzes the data using machine learning and natural language processing techniques to generate optimal information tailored to the user's request. For example, it might create promotional materials based on information about a new product.

[0160] Notification module

[0161] The generated response is sent from the server to the terminal. The terminal sends a notification to the user, providing instructions on how to access the generated materials. For example, the terminal might notify, "Promotional materials for the new product are ready. Please download them from here."

[0162] Smart devices

[0163] Smart devices such as smart glasses and smartphones provide users with the ability to access information in real time. This allows customers to quickly obtain detailed product information and promotional materials.

[0164] Specific example

[0165] For example, a customer enters the following prompt through smart glasses in a store:

[0166] "Please provide detailed information about the new product."

[0167] "Please display customer feedback for specific products."

[0168] "Please generate promotional materials."

[0169] This allows the system to provide timely and appropriate information, not only improving customer satisfaction but also maximizing sales opportunities.

[0170] Hardware and software to be used

[0171] Smart glasses: Google® Glass®, etc.

[0172] Smartphones: General Android® and iOS devices

[0173] Server: Cloud server (Amazon Web Services, Google Cloud Platform, etc.)

[0174] Natural language processing libraries: spaCy, NLTK

[0175] Databases: MySQL (registered trademark), PostgreSQL, etc.

[0176] This invention will streamline in-store services and facilitate smooth two-way communication between customers and staff.

[0177] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0178] Step 1:

[0179] Data collection

[0180] The server collects sales and operational data from the internet and internal systems. Specifically, it uses market research APIs to obtain the latest customer feedback and retrieves new product information from the ERP system. Once this data is collected, the server stores it in a database.

[0181] Input: Market research API, ERP system

[0182] Output: Feedback data, new product information (stored in database)

[0183] Step 2:

[0184] Data categorization

[0185] The server analyzes the collected customer feedback data using natural language processing (NLP) techniques and classifies it into positive, negative, and neutral categories. The server takes in the text of the acquired feedback data, estimates the category through the NLP model, and stores the results in a database.

[0186] Input: Feedback data (text)

[0187] Output: Category-specific feedback data (saved in database)

[0188] Step 3:

[0189] Receiving user requests

[0190] A user uses a smart device (such as smart glasses or a smartphone) to request specific information from the system. For example, they might enter a prompt like, "Please provide detailed information about the new product." The device receives this request and sends it to the server.

[0191] Input: User request (prompt text)

[0192] Output: Request data (sent to the server)

[0193] Step 4:

[0194] Acquisition and analysis of related information

[0195] The server analyzes the received request and generates a query to retrieve relevant information from the database. The retrieved data is passed to an AI module, where it is analyzed using machine learning and natural language processing techniques. Specifically, it generates appropriate promotional materials based on the content of the request.

[0196] Input: Request data, database

[0197] Output: Analysis results (generated promotional materials)

[0198] Step 5:

[0199] Information notification

[0200] The generated promotional materials and other responses are sent from the server to the terminal. The terminal sends a notification to the user, providing instructions on how to access the generated materials. For example, it might notify, "New product promotional materials are ready. Please download them from here."

[0201] Input: Analysis results (promotional materials)

[0202] Output: Notification message (user terminal)

[0203] Step 6:

[0204] Real-time information verification

[0205] Users can use smart devices to check notified information in real time. Information is displayed on smart glasses or smartphone screens, which users can then refer to. For example, smart glasses might display detailed information about a new product.

[0206] Input: Notification message

[0207] Output: Displayed information (smart device)

[0208] These steps enable the system to provide customers with quick and appropriate information, thereby improving the efficiency of services in physical stores.

[0209] 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.

[0210] To implement the present invention, it is desirable that the system be configured as follows. Specifically, the system consists of three components: a server, a terminal, and a user, and their roles and processing flow are described below.

[0211] Overall system configuration

[0212] The system consists of the following components:

[0213] 1. Data Acquisition Module

[0214] 2. Database

[0215] 3. User Interface

[0216] 4. AI Module

[0217] 5. Emotional Engine

[0218] 6. Notification Module

[0219] Data Acquisition Module

[0220] The server uses a data collection module to gather sales and operational information from the internet and internal systems. For example, the server periodically calls a market research API to obtain the latest customer feedback data. It also extracts information about new products from the ERP system.

[0221] database

[0222] The collected data is stored in a database by the server. The data is organized by category and stored in the appropriate fields to enable quick searching and retrieval. For example, the server categorizes text-based feedback into positive, negative, and neutral categories and stores them in the corresponding fields.

[0223] User Interface

[0224] The user accesses the system through a terminal and requests specific information (e.g., promotional materials for a new product). The terminal then transmits the request to the server. For example, the user might type "Please provide detailed information about the new product" and press the submit button.

[0225] AI Module

[0226] The server receives a request and generates a query to retrieve relevant information from the database. The retrieved data is passed to an AI module for analysis. The AI ​​module processes the data using machine learning and natural language processing techniques to generate optimal information tailored to the user's needs. For example, the server might pass information about a new product to the AI ​​module, and the AI ​​might use that information to create promotional materials.

[0227] Emotional Engine

[0228] The emotion engine works in conjunction with the AI ​​module. The server sends user input and voice data to the emotion engine to recognize the user's emotions. For example, the emotion engine analyzes emotions (e.g., joy, sadness, anger) from the text entered by the user or the voice data sent.

[0229] Notification module

[0230] The generated response (e.g., promotional materials) is sent from the server to the terminal. The terminal sends a notification to the user, providing instructions on how to access the generated materials. For example, the terminal might notify the user, "New product promotional materials are ready. Please download them from here." The content of the response and the wording of the notification may be modified based on the results of the emotion engine's analysis.

[0231] Specific example

[0232] 1. Data Collection: The server collects the latest customer feedback from the internet and stores it in a database.

[0233] 2. Database organization: The server stores feedback data categorized into positive, negative, and neutral.

[0234] 3. Processing user requests: A user requests promotional materials for a new product from their device, and the device sends that request to the server.

[0235] 4. Data analysis and response generation: The server passes the request to the AI ​​module, which generates sales promotion materials.

[0236] 5. Emotion Recognition: The server sends the user's input to the emotion engine, which analyzes the user's emotional state. For example, if the user is feeling stressed, a response will be generated using appropriate language.

[0237] 6. Notification: The server sends a message tailored to the user's emotions to the device along with the generated promotional materials, notifying the user.

[0238] This system automates the entire process from collecting sales and business-related information to providing it to users, and further enables customization based on the user's emotional state, resulting in more effective information delivery.

[0239] The following describes the processing flow.

[0240] Step 1:

[0241] The server uses a data collection module to collect sales and operational data. The server sends requests to a market research API to retrieve customer feedback data. It also extracts new product information from the ERP system.

[0242] Step 2:

[0243] The server preprocesses the collected data and stores it in a database as structured data. For example, text-based feedback data is categorized into positive, negative, and neutral, and stored in the respective fields.

[0244] Step 3:

[0245] The user uses the device to request specific information. For example, if a user wants to obtain promotional materials for a new product, they would type "Please provide details about the new product" on the device's interface and press the send button.

[0246] Step 4:

[0247] The terminal sends a user request to the server. It uses an HTTP request to communicate the details of the request to the server.

[0248] Step 5:

[0249] The server receives the request and generates a query to retrieve relevant information from the database. The server generates an SQL query to search for new product information and sends it to the database.

[0250] Step 6:

[0251] The server passes the acquired data to the AI ​​module. The AI ​​module uses machine learning and natural language processing techniques to analyze the data and generate the most suitable response to the user's request. For example, it might create promotional materials focusing on the characteristics and benefits of a new product.

[0252] Step 7:

[0253] The server sends user input and voice data to the emotion engine. The emotion engine analyzes the user's emotions. For example, the emotion engine recognizes the user's emotional state (joy, sadness, anger, etc.) from the text and voice data entered by the user.

[0254] Step 8:

[0255] The server adaptively modifies the responses generated by the AI ​​module based on the analysis results of the emotion engine. For example, if the user is feeling stressed, the responses will be provided in a more considerate tone.

[0256] Step 9:

[0257] The server sends the final response to the device. It also sends the generated promotional materials and sentiment-sensitive messages to the device as an HTTP response.

[0258] Step 10:

[0259] The device sends a notification to the user. For example, it might display a message such as, "Promotional materials for our new product are ready. You can download them here." The notification message is also adaptively modified based on the analysis of the emotion engine.

[0260] Step 11:

[0261] Users download and view the provided materials using their devices.

[0262] (Example 2)

[0263] 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".

[0264] In today's business environment, collecting, analyzing, and providing information related to sales and operations is crucial. Traditional systems often require manual information collection and analysis, which is time-consuming and labor-intensive. Furthermore, they sometimes provide information uniformly without considering the user's emotional state, leading to low user satisfaction. To address these challenges, there is a need for a more efficient system that can provide information in a way that is sensitive to user emotions.

[0265] The identification processing performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for collecting information related to sales and operations using a data collection module, means for storing the collected data in a database and organizing it by category, means for users to input information requests using a terminal and receiving those requests, means for obtaining appropriate data from the database and generating answers through analysis using an AI module, means for delivering the generated answers to the user through a notification module, and means for analyzing user input and voice data with an emotion engine and adjusting the answers based on the user's emotional state. As a result, the process from information collection to provision to the user is automated, and customized information provision according to the user's emotional state becomes possible.

[0266] A "data collection module" is a device or software used to collect sales and business-related information from the internet or internal systems.

[0267] A "database" is an electronic information storage area used to store collected information and organize it by category.

[0268] A "user" is an entity that accesses a system and requests specific information.

[0269] A "terminal" is an electronic device that allows a user to access a system and provides a means of sending requests.

[0270] An "AI module" is a software component that uses machine learning and natural language processing technologies to analyze acquired data and generate appropriate responses.

[0271] An "emotion engine" is a device or software that analyzes user input and voice data to identify the user's emotional state.

[0272] A "notification module" is a device or software that delivers generated responses to users and provides notifications.

[0273] A "Market Research API" is an application programming interface for providing information about the market and customers.

[0274] An "ERP system" is an enterprise resource planning system, an information management system for integrating and managing various business processes within an organization.

[0275] A "generative AI model" is an algorithm or network model that has been trained using machine learning to perform data analysis and information generation.

[0276] A "prompt sentence" is a text of instructions or questions input into a generative AI model, intended to guide the output of specific information.

[0277] To implement the present invention, the system is configured using the following hardware and software. Specifically, the system consists of three components: a server, a terminal, and a user, and their roles and processing flow are described below.

[0278] Data Acquisition Module

[0279] The server uses a data collection module to gather information related to sales and operations. Specifically, the server periodically calls a market research API to obtain the latest customer feedback data. It also extracts information about new products from the ERP system. For example, when the server collects the latest customer feedback from the internet and extracts new product information from the ERP system, it calls the "getCustomerFeedback" API every hour and executes an SQL query on the ERP system.

[0280] database

[0281] The collected data is stored in a database by the server. The data is sorted by category and stored in appropriate fields to enable quick search and retrieval. As a specific example, the server performs text analysis on customer feedback obtained, classifies it into positive, negative, and neutral categories using the "SentimentAnalysis" function, and stores it in the database according to each category.

[0282] User Interface

[0283] The user accesses the system through a terminal and requests specific information (e.g., promotional materials for new products). The terminal conveys the request to the server. For example, the user enters "Please provide detailed information about the new product" in the search box and presses the send button. The terminal sends the request to the "POST / api / request" endpoint.

[0284] AI Module

[0285] The server receives the request and retrieves relevant information from the database. The obtained data is passed to the AI module for analysis. The AI module processes the data using machine learning and natural language processing techniques to generate optimal information according to the user's request. For example, the server executes an SQL query like "SELECT FROM Products WHERE Type='New'" on the database to obtain information and passes it to the "generatePromotionMaterial" function of the AI module to generate promotional materials.

[0286] Sentiment Engine

[0287] The sentiment engine works in cooperation with the AI module. The server sends the user's input content and voice data to the sentiment engine to recognize the user's sentiment. For example, from the text entered by the user or the voice data transmitted, the sentiment engine generates a "StressLevel" field and sets the value to "High". Based on this result, the expression of the response is adjusted.

[0288] Notification module

[0289] The generated response (e.g., promotional materials) is sent from the server to the terminal. The terminal sends a notification to the user, providing instructions on how to access the generated materials. For example, the terminal might send a notification to the user saying, "New product promotional materials are ready. Please download them from here." Based on the analysis results of the emotion engine, the content of the response and the wording of the notification may be modified.

[0290] Example of a prompt

[0291] "Please provide detailed information about the new product."

[0292] "Please generate a report based on the latest customer feedback."

[0293] "Retrieve the results from the market research API and save them to the database."

[0294] This system automates the entire process from collecting sales and business-related information to providing it to users, and further enables customization based on the user's emotional state, resulting in more effective information delivery.

[0295] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0296] Step 1: Data Collection

[0297] The server uses a data collection module to gather information related to sales and operations. Specifically, it periodically calls a market research API to retrieve customer feedback data and new product information from the ERP system. Inputs are API requests and SQL queries, and outputs are the retrieved customer feedback and new product information data. For example, the server periodically calls the "getCustomerFeedback" API and executes a query like "SELECT FROM NewProducts WHERE ReleaseDate > CURDATE()" in the ERP system.

[0298] Step 2: Save Database

[0299] The server stores the collected data in a database. The data is organized and stored into positive, negative, and neutral categories. The input is the data obtained in step 1, and the output is the database entries organized by category. For example, the server analyzes customer feedback using the "SentimentAnalysis" function and executes an SQL query such as "INSERT INTO Feedback (Category, Content) VALUES ('Positive', 'Great product!')".

[0300] Step 3: Accepting User Requests

[0301] A user requests specific information through their device. The user enters "Please provide details about the new product" into the search box and presses the submit button. The input is the user's request text, and the output is the request data sent to the server. The device sends this request to the "POST / api / request" endpoint.

[0302] Step 4: Data Analysis

[0303] The server receives a request and retrieves relevant information from the database. The retrieved data is passed to the AI module for analysis. The input is the request received in step 3 and the relevant data retrieved from the database, and the output is the generated material as the analysis result. For example, the server generates a query such as "SELECT FROM Products WHERE Type='New'", retrieves information from the database, and passes it to the "generatePromotionMaterial" function.

[0304] Step 5: Emotion Recognition

[0305] The server sends the user's input content to the emotion engine to recognize the user's emotion. The emotion is analyzed from the input text or voice data of the user. The input is the user's text or voice data, and the output is the emotion analysis result by the emotion engine. For example, the server uses the "analyzeEmotion" function and obtains "High Stress Level" as the analysis result.

[0306] Step 6: Answer Generation

[0307] The server receives the promotion material generated by the AI module and prepares to provide it to the user. The input is the generated material in step 4 and the emotion analysis result in step 5, and the output is the final material to be provided to the user. For example, based on the emotion analysis, the server generates a message such as "The document has been generated. Please check it immediately!"

[0308] Step 7: Notification Delivery

[0309] The server sends the generated answer and message to the terminal. The terminal notifies the user and provides a link to access the material. The input is the notification data from the server, and the output is the notification to the user. For example, the terminal sends a notification such as "The promotion material for the new product is ready. Please download it from here."

[0310] This series of steps allows the system to efficiently collect, analyze, and provide customized information to users.

[0311] (Application Example 2)

[0312] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0313] Traditional sales and business support systems had the functionality to collect and analyze data based on user requests, generate responses, and send notifications. However, they struggled to understand user emotions or provide customized services in real time. This resulted in challenges such as insufficient improvements in customer satisfaction and operational efficiency. Furthermore, in actual field use, the lack of real-time notification capabilities and integration with smart devices made immediate responses difficult.

[0314] In Application Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for collecting information related to sales and operations, means for storing the collected data in a database, means for receiving requests from users, means for obtaining and analyzing appropriate data from the database and generating a response, means for notifying the user of the generated response, and means for providing a customized service in real time based on the analyzed information. This enables the provision of a customized service in real time that responds to the user's emotional state, improving customer satisfaction and operational efficiency. Furthermore, collaboration with smart devices enables rapid response on-site.

[0315] "Information related to sales and operations" refers to data, feedback, market data, product information, and other information necessary for a company to conduct its business.

[0316] A "database" is a general term for a storage device and its management system that systematically stores various collected data and manages, searches, and retrieves it efficiently.

[0317] A "user request" refers to a request that a user of the system inputs or transmits in order to obtain specific information or services.

[0318] "Means for acquiring and analyzing appropriate data and generating responses" refers to methods and functions for acquiring relevant data from a database based on a user's request, analyzing it, and generating the response the user is seeking.

[0319] "Means for notifying the user of the generated response" refers to methods and functions for informing the user of the response generated by the analysis, such as through display or audio.

[0320] "Means for providing customized services in real time" refers to methods and functions for instantly providing optimized services based on the user's emotional state and request content.

[0321] "Internet and internal systems" refers to a general term encompassing networks accessible from the outside and information systems operated within a company.

[0322] "Using artificial intelligence to analyze data and generate responses" refers to the process of automatically analyzing data using machine learning and natural language processing technologies and generating responses that address user requests.

[0323] "Emotion recognition means" refers to methods and functions for automatically analyzing emotional states (e.g., joy, sadness, anger) from user input or voice data.

[0324] "Real-time notification means" refers to methods and functions for immediately notifying users or staff of analysis results and service details.

[0325] "Smart devices" is a general term for electronic devices that have internet connectivity and application execution capabilities, such as smartphones, smart glasses, tablets, and head-mounted displays.

[0326] To implement this invention, it is desirable that the system includes the following components. Specifically, it consists of three entities: a server, a terminal, and a user, and their respective roles and processing flows are described below.

[0327] Overall system configuration

[0328] The system consists of the following components:

[0329] 1. Data Acquisition Module

[0330] 2. Database

[0331] 3. User Interface

[0332] 4. AI Module

[0333] 5. Emotional Engine

[0334] 6. Notification Module

[0335] Data Acquisition Module

[0336] The server uses a data collection module to gather sales and business-related information from the internet and internal systems. For example, the server periodically calls a market research API to obtain the latest customer feedback data. It also extracts information about new products from the ERP system. The Python library 'requests' is used for API connections.

[0337] database

[0338] The collected data is stored in a database by the server. The data is organized by category and stored in appropriate fields to enable quick searching and retrieval. For example, customer feedback data is stored in positive, negative, and neutral categories, and a search index is assigned to each category. Examples of cloud databases used include AWS® RDS and Google Cloud SQL.

[0339] User Interface

[0340] Users access the system through their devices and request specific information (e.g., promotional materials for a new product). The device then transmits the request to the server. For example, a user might type "Please provide detailed information about the new product" and press the submit button. Hardware used includes smartphones and smart glasses, while software includes a web application interface.

[0341] AI Module

[0342] The server receives the request and generates a query to retrieve relevant information from the database. The retrieved data is passed to the AI ​​module for analysis. The AI ​​module processes the data using machine learning and natural language processing (NLP) techniques to generate optimal information tailored to the user's request. The software used includes Google Cloud AI and AWS SageMaker.

[0343] Emotional Engine

[0344] The emotion engine works in conjunction with the AI ​​module. The server sends user input and voice data to the emotion engine to recognize the user's emotions. For example, the emotion engine analyzes emotions (e.g., joy, sadness, anger) from the text entered by the user or the transmitted voice data. Software used includes IBM Watson® Tone Analyzer.

[0345] Notification module

[0346] The generated response (e.g., promotional materials) is sent from the server to the device. The device then sends a notification to the user, providing instructions on how to access the generated materials. For example, the device might notify the user, "New product promotional materials are ready. Please download them from here." Furthermore, the content of the response and the wording of the notification may be modified based on the analysis results of the sentiment engine. Firebase Cloud Messaging and Apple Push Notification Service are used as software for real-time notifications.

[0347] Specific example

[0348] For example, a server retrieves customer feedback from a market research API, such as "The texture of the new product was very good, but the price feels a little high," and stores it in a database. Then, when a user requests "Please provide detailed information about the new product" from their device, the server retrieves relevant information from the database and analyzes it with an AI module. At the same time, the user's emotional state is also analyzed by an emotion engine, and an appropriate response is generated. The generated response is notified to the device in real time using Firebase Cloud Messaging.

[0349] An example of a prompt message would be, "Perform sentiment analysis to identify the emotional state of this feedback."

[0350] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0351] Step 1:

[0352] The server uses a data collection module to gather sales and operational information from the internet and internal systems. Specifically, the server periodically calls a market research API to obtain the latest customer feedback data. It also extracts information about new products from the ERP system. The input is data from the market research API and the ERP system, and the output is the collected data.

[0353] Step 2:

[0354] The server stores the collected data in a database. The data is organized by category and stored in appropriate fields to enable quick searching and retrieval. Specifically, customer feedback data is categorized into positive, negative, and neutral, and a search index is assigned to each category. The input is the data collected in step 1, and the output is the organized database.

[0355] Step 3:

[0356] The user requests specific information through the terminal. The terminal then transmits that request to the server. For example, the user might type "Please provide detailed information about the new product" and press the submit button. The input is the user's request, and the output is the transmission of the request to the server.

[0357] Step 4:

[0358] The server receives a request from the user and generates a query to retrieve relevant information from the database. The retrieved data is passed to the AI ​​module for analysis. Specifically, it generates an SQL query containing the relevant information and retrieves data from the database. The input is the user's request and the data from the database, and the output is the data passed to the AI ​​module.

[0359] Step 5:

[0360] The server uses an AI module to analyze data and generate optimal information tailored to the user's request. The AI ​​module employs machine learning and natural language processing (NLP) techniques. During this analysis, an emotion engine also works in conjunction, analyzing emotions from the user's input and voice data. The input is the data passed to the AI ​​module, and the output is the optimal information resulting from the analysis.

[0361] Step 6:

[0362] The server passes the generated information to the notification module, which then sends a notification to the user's device. Specifically, it uses Firebase Cloud Messaging or Apple Push Notification Service to send the notification content to the device in real time. The input is the analysis results from the AI ​​module and the emotion engine, and the output is the notification sent to the user's device.

[0363] Step 7:

[0364] The terminal displays notifications to the user and provides instructions on how to access generated materials and information. For example, it might display a notification saying, "Promotional materials for our new product are ready. Please download them here." The input is the notification content sent from the server, and the output is the notification and access link for the user.

[0365] 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.

[0366] 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.

[0367] 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.

[0368] [Second Embodiment]

[0369] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0370] 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.

[0371] 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).

[0372] 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.

[0373] 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.

[0374] 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).

[0375] 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.

[0376] 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.

[0377] 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.

[0378] 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.

[0379] 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.

[0380] 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".

[0381] In order to implement the present invention, it is desirable that the system be configured as follows. Specifically, the system consists of three components: a server, a terminal, and a user, and their roles and processing flow are described below.

[0382] Overall system configuration

[0383] The system consists of the following components:

[0384] 1. Data Acquisition Module

[0385] 2. Database

[0386] 3. User Interface

[0387] 4. AI Module

[0388] 5. Notification Module

[0389] Data Acquisition Module

[0390] The server uses a data collection module to gather sales and operational information from the internet and internal systems. For example, the server periodically calls a market research API to collect customer feedback information. It also extracts information about new products from the ERP system.

[0391] database

[0392] The collected data is stored in a database by the server. The data is organized by category and stored in the appropriate fields to enable quick searching and retrieval. For example, the server categorizes text-based feedback data (positive, negative, neutral) and stores it in the corresponding field.

[0393] User Interface

[0394] The user accesses the system through a terminal and requests specific information (e.g., promotional materials for a new product). The terminal then transmits the request to the server. For example, the user might type "Please provide detailed information about the new product" and press the submit button.

[0395] AI Module

[0396] The server receives a request and generates a query to retrieve relevant information from the database. The retrieved data is passed to an AI module for analysis. The AI ​​module processes the data using machine learning and natural language processing techniques to generate optimal information tailored to the user's needs. For example, the server might pass information about a new product to the AI ​​module, and the AI ​​might use that information to create promotional materials.

[0397] Notification module

[0398] The generated response (e.g., promotional materials) is sent from the server to the terminal. The terminal then sends a notification to the user, providing instructions on how to access the generated materials. For example, the terminal might notify the user, "New product promotional materials are ready. Please download them from here."

[0399] Specific example

[0400] 1. Data Collection: The server collects the latest customer feedback from the internet and stores it in a database.

[0401] 2. Data organization: The server stores feedback data categorized into positive, negative, and neutral.

[0402] 3. Processing user requests: A user requests promotional materials for a new product from their device, and the device sends that request to the server.

[0403] 4. Data analysis and response generation: The server passes the request to the AI ​​module, which generates promotional materials.

[0404] 5. Notification: The server sends the generated promotional materials to the terminal and notifies the user.

[0405] This system automates the entire process from collecting sales and business-related information to providing it to users, thereby improving operational efficiency.

[0406] The following describes the processing flow.

[0407] Step 1:

[0408] The server uses a data collection module to gather sales and operational data. Specifically, the server sends requests to a market research API to obtain the latest customer feedback data. It also extracts information about new products from the ERP system.

[0409] Step 2:

[0410] The server preprocesses the collected data and stores it in a database as structured data. For example, the server categorizes text-based feedback into positive, negative, and neutral categories and stores them in their respective fields.

[0411] Step 3:

[0412] The user uses the device to request specific information. For example, if a user wants to obtain promotional materials for a new product, they would type "Please provide details about the new product" on the device's interface and press the send button.

[0413] Step 4:

[0414] The terminal sends the user's request to the server. An HTTP request is used to communicate the user's request to the server.

[0415] Step 5:

[0416] The server receives the request and generates a query to retrieve relevant information from the database. For example, it generates an SQL query to search for new product information and sends it to the database.

[0417] Step 6:

[0418] The server passes the acquired data to the AI ​​module. The AI ​​module uses machine learning and natural language processing techniques to analyze the data and generate the most suitable response to the user's request. For example, it can create promotional materials based on the attributes and features of a new product.

[0419] Step 7:

[0420] The server receives the response generated by the AI ​​module and sends it to the terminal as a means of notifying the user. For example, it might send the generated promotional material in PDF format to the terminal as an HTTP response.

[0421] Step 8:

[0422] The device sends a notification to the user. For example, it might display a message to the user saying, "Promotional materials for our new product are ready. You can download them here."

[0423] Users download and view materials using their devices.

[0424] (Example 1)

[0425] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0426] The processes of collecting, organizing, analyzing, and providing sales and business-related information are often performed manually, which can result in decreased operational efficiency and the occurrence of data omissions and errors. Furthermore, traditional systems struggle to process large amounts of data quickly, making it difficult to provide users with optimal information. Solving these problems is therefore essential.

[0427] 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.

[0428] In this invention, the server includes means for collecting sales and business-related information using a data collection module, means for organizing the collected data by category and storing it in a database, means for receiving information requests from users via a user interface, means for retrieving appropriate data from the database, analyzing it with an AI module, and generating a response, and means for notifying the user of the generated response. This automates the process from information collection to provision, improves operational efficiency, and enables the rapid provision of optimal information to users.

[0429] A "data collection module" is software or hardware used to collect sales and business-related information from the internet or internal systems.

[0430] A "database" is an information management system that organizes and stores collected data by category, enabling rapid searching and retrieval of data.

[0431] A "user interface" is the interface that a user uses to access a system and request information, and includes operation screens and input forms.

[0432] An "AI module" is a component that includes artificial intelligence technology used to analyze data and generate appropriate responses based on user requests.

[0433] "Notification means" refers to a means of informing the user of the generated response, and includes a function to send a notification to the user's device.

[0434] "Machine learning" refers to algorithms and techniques for learning patterns and knowledge from data, and for performing analysis and decision-making.

[0435] "Natural language processing" is a technology that enables machines to understand and process human language, and is used for text analysis and generation.

[0436] Modes for carrying out the invention

[0437] The system for implementing the present invention consists of three components: a server, a terminal, and a user. Their roles and processing flow are as follows. The components of this system include a data collection module, a database, a user interface, an AI module, and a notification module. Each component is described in detail below.

[0438] Data Acquisition Module

[0439] The server uses a data collection module to gather sales and operational information. This module has the capability to retrieve data from the internet and internal systems. For example, the server periodically calls a market research API to collect customer feedback information and accesses the ERP system to extract new product information.

[0440] database

[0441] The server stores the collected data in a database. The database is an information management system for organizing and storing the collected data by category, enabling quick searching and retrieval of data. Specifically, the server stores text-based feedback data, categorized into positive, negative, and neutral, and generates a search index.

[0442] User Interface

[0443] Users access the system through their terminals and use the user interface to request specific information. Users can make information requests using the terminal's operation screen and input forms. For example, if a user types "Please provide detailed information about the new product" and presses the submit button, the request is sent to the server.

[0444] AI Module

[0445] The server receives requests from users, retrieves relevant information from the database, and passes it to the AI ​​module. The AI ​​module analyzes the data using machine learning and natural language processing techniques to generate the optimal response. For example, the server retrieves information about a new product, and the AI ​​module analyzes that information to create promotional materials.

[0446] Notification module

[0447] The server sends the response generated by the AI ​​module to the terminal and notifies the user. The terminal sends a notification to the user and provides instructions on how to access the generated materials. For example, the terminal might notify the user, "Promotional materials for the new product are ready. Please download them from here."

[0448] Specific example

[0449] For example, here's a specific example of what to do when requesting promotional materials for a new product:

[0450] 1. Data Collection: The server uses a market research API to collect recent customer feedback and retrieves new product information from the ERP system.

[0451] 2. Data organization: The feedback data collected by the server is sorted into positive, negative, and neutral categories and stored in a database.

[0452] 3. Processing the user request: The user enters "Please provide promotional materials for the new product" into the device's UI and submits it.

[0453] 4. Data Analysis and Response Generation: The server analyzes the request, generates a data acquisition query, retrieves the necessary data from the database, passes it to the AI ​​module, and generates optimal promotional materials.

[0454] 5. Notification: The server sends the generated promotional materials to the terminal, and the terminal notifies the user that the materials are ready.

[0455] Example of a prompt

[0456] "Please create promotional materials for this new product."

[0457] "Analyze the latest customer feedback and generate a report."

[0458] "Please extract and provide information on new products from the ERP system."

[0459] The system according to the present invention automates the above processes, streamlining the entire process from information collection to provision. This improves operational efficiency and enables the rapid provision of optimal information to users.

[0460] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0461] Step 1: Data Collection

[0462] The server uses a data collection module to gather sales and operational information from the internet and internal systems. Inputs include market research APIs and data from ERP systems. The server periodically calls APIs to retrieve customer feedback information. It also accesses the ERP system to extract new product information. The output is the collected raw data.

[0463] Step 2: Saving and organizing in the database

[0464] The server organizes and stores the collected data in a database. The input is the raw data collected in step 1. The server categorizes the data into positive, negative, and neutral categories and stores them in the appropriate fields. Data organization and indexing enable rapid searching and retrieval. The output is database entries organized by category.

[0465] Step 3: Requesting information via the user interface

[0466] The user accesses the system using their device and requests specific information. The input is the request content entered by the user in the device's UI form (e.g., "Please provide details about the new product"). When the user clicks the submit button, the request is sent to the server via the device. The output is the information request sent to the server.

[0467] Step 4: Data analysis and response generation

[0468] The server receives information requests from users, retrieves relevant information from the database, and passes it to the AI ​​module. The input consists of the information requests received in step 3 and the data stored in step 2. The server generates appropriate queries and retrieves the necessary data. The AI ​​module analyzes the data using machine learning and natural language processing techniques to generate the optimal response. The output consists of reports and promotional materials generated by the AI ​​module.

[0469] Step 5: Notification and Information Provision

[0470] The server sends the generated responses to the terminal, and the terminal notifies the user that the materials are ready. The input is the response data generated in step 4. The server sends the generated materials to the terminal and notifies the user, "New product promotional materials are ready. Please download them from here." The user receives the notification and clicks the link on the terminal to download the materials. The output is the materials provided to the user and the notification.

[0471] (Specific examples of actions)

[0472] For example, the specific steps involved in requesting promotional materials for a new product are as follows:

[0473] 1. Data Collection: The server uses a market research API to collect customer feedback data and retrieves new product information from the ERP system.

[0474] 2. Data organization: The data collected by the server is classified into positive, negative, and neutral categories and stored in the database.

[0475] 3. Processing user requests: The user enters "Please provide promotional materials for the new product" into the device's UI and submits the request.

[0476] 4. Data Analysis and Response Generation: The server analyzes the request, generates a data retrieval query to obtain the necessary data from the database, and then the AI ​​module analyzes it to generate promotional materials.

[0477] 5. Notification and Information Provision: The server sends the generated promotional materials to the terminal, and the terminal notifies the user that the materials are ready. The user clicks the link to download the materials.

[0478] (Application Example 1)

[0479] 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."

[0480] In traditional brick-and-mortar stores, customers must directly ask store staff for detailed product information and promotional materials. This manual search and provision of information makes rapid service difficult. Furthermore, real-time responses to questions are challenging, contributing to decreased customer satisfaction. Additionally, inability to provide adequate product information can lead to missed sales opportunities. To address this, a system is needed that allows both staff and customers to access and verify information in real time.

[0481] 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.

[0482] In this invention, the server includes means for collecting sales and business-related data, means for storing the collected data in a database, means for receiving requests from users, means for retrieving and analyzing appropriate data from the database and generating responses, means for notifying the user of the generated responses, and means for users to check the information in real time via a smart device. This enables customers to obtain detailed product information and promotional materials in real time, and allows for the rapid and efficient provision of services.

[0483] "Means of collecting data" refers to a system for obtaining information related to sales and operations from multiple sources, such as the internet and internal systems.

[0484] "Means of storing data in a database" refers to a system that has the function of appropriately classifying and organizing collected information and storing it in a database.

[0485] "Means of receiving requests from users" refers to a mechanism that provides an interface for users to communicate their requests for information to the system.

[0486] "A means of retrieving and analyzing appropriate data from a database to generate an answer" refers to a system that searches for information stored in a database, analyzes it using artificial intelligence or other means, and generates the information the user is looking for.

[0487] "Means of notifying users of generated responses" refers to a system equipped with a notification function to convey generated information or documents to users.

[0488] "Means that allow users to check information in real time via smart devices" refers to a system that enables users to quickly acquire and check information via digital devices such as smart glasses and smartphones.

[0489] To implement the present invention, the system is preferably configured as follows. Specifically, it is a system that collects data related to sales and operations and generates and provides appropriate information according to user requests. The main components and their roles are described below.

[0490] Overall system configuration

[0491] The system consists of the following components:

[0492] 1. Data Acquisition Module

[0493] 2. Database

[0494] 3. User Interface

[0495] 4. AI Module

[0496] 5. Notification Module

[0497] 6. Smart devices

[0498] Data Acquisition Module

[0499] First, the server uses a data collection module to gather sales and operational information from the internet and internal systems. The server also periodically retrieves data from market research APIs and ERP systems to collect customer feedback and new product information.

[0500] database

[0501] The collected data is stored in a database by the server. The data is organized by category and stored in appropriate fields to enable quick searching and retrieval. For example, feedback data is stored in positive, negative, and neutral categories.

[0502] User Interface

[0503] Users access the system through a device (e.g., smart glasses or a smartphone) and request specific information. Requests can be made via text input or voice input. For example, a user might type "Please provide detailed information about the new product" and send it.

[0504] AI Module

[0505] Next, the server receives the request and generates a query to retrieve relevant information from the database. The AI ​​module analyzes the data using machine learning and natural language processing techniques to generate optimal information tailored to the user's request. For example, it might create promotional materials based on information about a new product.

[0506] Notification module

[0507] The generated response is sent from the server to the terminal. The terminal sends a notification to the user, providing instructions on how to access the generated materials. For example, the terminal might notify, "Promotional materials for the new product are ready. Please download them from here."

[0508] Smart devices

[0509] Smart devices such as smart glasses and smartphones provide users with the ability to access information in real time. This allows customers to quickly obtain detailed product information and promotional materials.

[0510] Specific example

[0511] For example, a customer enters the following prompt through smart glasses in a store:

[0512] "Please provide detailed information about the new product."

[0513] "Please display customer feedback for specific products."

[0514] "Please generate promotional materials."

[0515] This allows the system to provide timely and appropriate information, not only improving customer satisfaction but also maximizing sales opportunities.

[0516] Hardware and software to be used

[0517] Smart glasses: Google Glass, etc.

[0518] Smartphones: Typical Android and iOS devices

[0519] Server: Cloud server (Amazon Web Services, Google Cloud Platform, etc.)

[0520] Natural language processing libraries: spaCy, NLTK

[0521] Database: MySQL, PostgreSQL, etc.

[0522] This invention will streamline in-store services and facilitate smooth two-way communication between customers and staff.

[0523] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0524] Step 1:

[0525] Data collection

[0526] The server collects sales and operational data from the internet and internal systems. Specifically, it uses market research APIs to obtain the latest customer feedback and retrieves new product information from the ERP system. Once this data is collected, the server stores it in a database.

[0527] Input: Market research API, ERP system

[0528] Output: Feedback data, new product information (stored in database)

[0529] Step 2:

[0530] Data categorization

[0531] The server analyzes the collected customer feedback data using natural language processing (NLP) techniques and classifies it into positive, negative, and neutral categories. The server takes in the text of the acquired feedback data, estimates the category through the NLP model, and stores the results in a database.

[0532] Input: Feedback data (text)

[0533] Output: Category-specific feedback data (saved in database)

[0534] Step 3:

[0535] Receiving user requests

[0536] A user uses a smart device (such as smart glasses or a smartphone) to request specific information from the system. For example, they might enter a prompt like, "Please provide detailed information about the new product." The device receives this request and sends it to the server.

[0537] Input: User request (prompt text)

[0538] Output: Request data (sent to the server)

[0539] Step 4:

[0540] Acquisition and analysis of related information

[0541] The server analyzes the received request and generates a query to retrieve relevant information from the database. The retrieved data is passed to an AI module, where it is analyzed using machine learning and natural language processing techniques. Specifically, it generates appropriate promotional materials based on the content of the request.

[0542] Input: Request data, database

[0543] Output: Analysis results (generated promotional materials)

[0544] Step 5:

[0545] Information notification

[0546] The generated promotional materials and other responses are sent from the server to the terminal. The terminal sends a notification to the user, providing instructions on how to access the generated materials. For example, it might notify, "New product promotional materials are ready. Please download them from here."

[0547] Input: Analysis results (promotional materials)

[0548] Output: Notification message (user terminal)

[0549] Step 6:

[0550] Real-time information verification

[0551] Users can use smart devices to check notified information in real time. Information is displayed on smart glasses or smartphone screens, which users can then refer to. For example, smart glasses might display detailed information about a new product.

[0552] Input: Notification message

[0553] Output: Displayed information (smart device)

[0554] These steps enable the system to provide customers with quick and appropriate information, thereby improving the efficiency of services in physical stores.

[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] To implement the present invention, it is desirable that the system be configured as follows. Specifically, the system consists of three components: a server, a terminal, and a user, and their roles and processing flow are described below.

[0557] Overall system configuration

[0558] The system consists of the following components:

[0559] 1. Data Acquisition Module

[0560] 2. Database

[0561] 3. User Interface

[0562] 4. AI Module

[0563] 5. Emotional Engine

[0564] 6. Notification Module

[0565] Data Acquisition Module

[0566] The server uses a data collection module to gather sales and operational information from the internet and internal systems. For example, the server periodically calls a market research API to obtain the latest customer feedback data. It also extracts information about new products from the ERP system.

[0567] database

[0568] The collected data is stored in a database by the server. The data is organized by category and stored in the appropriate fields to enable quick searching and retrieval. For example, the server categorizes text-based feedback into positive, negative, and neutral categories and stores them in the corresponding fields.

[0569] User Interface

[0570] The user accesses the system through a terminal and requests specific information (e.g., promotional materials for a new product). The terminal then transmits the request to the server. For example, the user might type "Please provide detailed information about the new product" and press the submit button.

[0571] AI Module

[0572] The server receives a request and generates a query to retrieve relevant information from the database. The retrieved data is passed to an AI module for analysis. The AI ​​module processes the data using machine learning and natural language processing techniques to generate optimal information tailored to the user's needs. For example, the server might pass information about a new product to the AI ​​module, and the AI ​​might use that information to create promotional materials.

[0573] Emotional Engine

[0574] The emotion engine works in conjunction with the AI ​​module. The server sends user input and voice data to the emotion engine to recognize the user's emotions. For example, the emotion engine analyzes emotions (e.g., joy, sadness, anger) from the text entered by the user or the voice data sent.

[0575] Notification module

[0576] The generated response (e.g., promotional materials) is sent from the server to the terminal. The terminal sends a notification to the user, providing instructions on how to access the generated materials. For example, the terminal might notify the user, "New product promotional materials are ready. Please download them from here." The content of the response and the wording of the notification may be modified based on the results of the emotion engine's analysis.

[0577] Specific example

[0578] 1. Data Collection: The server collects the latest customer feedback from the internet and stores it in a database.

[0579] 2. Database organization: The server stores feedback data categorized into positive, negative, and neutral.

[0580] 3. Processing user requests: A user requests promotional materials for a new product from their device, and the device sends that request to the server.

[0581] 4. Data analysis and response generation: The server passes the request to the AI ​​module, which generates sales promotion materials.

[0582] 5. Emotion Recognition: The server sends the user's input to the emotion engine, which analyzes the user's emotional state. For example, if the user is feeling stressed, a response will be generated using appropriate language.

[0583] 6. Notification: The server sends a message tailored to the user's emotions to the device along with the generated promotional materials, notifying the user.

[0584] This system automates the entire process from collecting sales and business-related information to providing it to users, and further enables customization based on the user's emotional state, resulting in more effective information delivery.

[0585] The following describes the processing flow.

[0586] Step 1:

[0587] The server uses a data collection module to collect sales and operational data. The server sends requests to a market research API to retrieve customer feedback data. It also extracts new product information from the ERP system.

[0588] Step 2:

[0589] The server preprocesses the collected data and stores it in a database as structured data. For example, text-based feedback data is categorized into positive, negative, and neutral, and stored in the respective fields.

[0590] Step 3:

[0591] The user uses the device to request specific information. For example, if a user wants to obtain promotional materials for a new product, they would type "Please provide details about the new product" on the device's interface and press the send button.

[0592] Step 4:

[0593] The terminal sends a user request to the server. It uses an HTTP request to communicate the details of the request to the server.

[0594] Step 5:

[0595] The server receives the request and generates a query to retrieve relevant information from the database. The server generates an SQL query to search for new product information and sends it to the database.

[0596] Step 6:

[0597] The server passes the acquired data to the AI ​​module. The AI ​​module uses machine learning and natural language processing techniques to analyze the data and generate the most suitable response to the user's request. For example, it might create promotional materials focusing on the characteristics and benefits of a new product.

[0598] Step 7:

[0599] The server sends user input and voice data to the emotion engine. The emotion engine analyzes the user's emotions. For example, the emotion engine recognizes the user's emotional state (joy, sadness, anger, etc.) from the text and voice data entered by the user.

[0600] Step 8:

[0601] The server adaptively modifies the responses generated by the AI ​​module based on the analysis results of the emotion engine. For example, if the user is feeling stressed, the responses will be provided in a more considerate tone.

[0602] Step 9:

[0603] The server sends the final response to the device. It also sends the generated promotional materials and sentiment-sensitive messages to the device as an HTTP response.

[0604] Step 10:

[0605] The device sends a notification to the user. For example, it might display a message such as, "Promotional materials for our new product are ready. You can download them here." The notification message is also adaptively modified based on the analysis of the emotion engine.

[0606] Step 11:

[0607] Users download and view the provided materials using their devices.

[0608] (Example 2)

[0609] 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".

[0610] In today's business environment, collecting, analyzing, and providing information related to sales and operations is crucial. Traditional systems often require manual information collection and analysis, which is time-consuming and labor-intensive. Furthermore, they sometimes provide information uniformly without considering the user's emotional state, leading to low user satisfaction. To address these challenges, there is a need for a more efficient system that can provide information in a way that is sensitive to user emotions.

[0611] The identification processing performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for collecting information related to sales and operations using a data collection module, means for storing the collected data in a database and organizing it by category, means for users to input information requests using a terminal and receiving those requests, means for obtaining appropriate data from the database and generating answers through analysis using an AI module, means for delivering the generated answers to the user through a notification module, and means for analyzing user input and voice data with an emotion engine and adjusting the answers based on the user's emotional state. As a result, the process from information collection to provision to the user is automated, and customized information provision according to the user's emotional state becomes possible.

[0612] A "data collection module" is a device or software used to collect sales and business-related information from the internet or internal systems.

[0613] A "database" is an electronic information storage area used to store collected information and organize it by category.

[0614] A "user" is an entity that accesses a system and requests specific information.

[0615] A "terminal" is an electronic device that allows a user to access a system and provides a means of sending requests.

[0616] An "AI module" is a software component that uses machine learning and natural language processing technologies to analyze acquired data and generate appropriate responses.

[0617] An "emotion engine" is a device or software that analyzes user input and voice data to identify the user's emotional state.

[0618] A "notification module" is a device or software that delivers generated responses to users and provides notifications.

[0619] A "Market Research API" is an application programming interface for providing information about the market and customers.

[0620] An "ERP system" is an enterprise resource planning system, an information management system for integrating and managing various business processes within an organization.

[0621] A "generative AI model" is an algorithm or network model that has been trained using machine learning to perform data analysis and information generation.

[0622] A "prompt sentence" is a text of instructions or questions input into a generative AI model, intended to guide the output of specific information.

[0623] To implement the present invention, the system is configured using the following hardware and software. Specifically, the system consists of three components: a server, a terminal, and a user, and their roles and processing flow are described below.

[0624] Data Acquisition Module

[0625] The server uses a data collection module to gather information related to sales and operations. Specifically, the server periodically calls a market research API to obtain the latest customer feedback data. It also extracts information about new products from the ERP system. For example, when the server collects the latest customer feedback from the internet and extracts new product information from the ERP system, it calls the "getCustomerFeedback" API every hour and executes an SQL query on the ERP system.

[0626] database

[0627] The collected data is stored in a database by the server. The data is organized by category and stored in the appropriate fields to enable quick searching and retrieval. For example, the server analyzes customer feedback obtained as text, classifies it into positive, negative, and neutral categories using the "SentimentAnalysis" function, and stores it in the database according to each category.

[0628] User Interface

[0629] The user accesses the system through their device and requests specific information (e.g., promotional materials for a new product). The device then transmits this request to the server. For example, a user might type "Please provide detailed information about the new product" into the search box and press the submit button. The device sends the request to the "POST / api / request" endpoint.

[0630] AI Module

[0631] The server receives the request and retrieves relevant information from the database. The retrieved data is passed to the AI ​​module for analysis. The AI ​​module processes the data using machine learning and natural language processing techniques to generate optimal information tailored to the user's request. For example, the server might execute an SQL query like "SELECT FROM Products WHERE Type='New'" on the database to retrieve information, and then pass it to the AI ​​module's "generatePromotionMaterial" function to generate promotional materials.

[0632] Emotional Engine

[0633] The emotion engine works in conjunction with the AI ​​module. The server sends user input and voice data to the emotion engine to recognize the user's emotions. For example, from the text entered by the user or the voice data sent, the emotion engine generates a "StressLevel" field and sets its value to "High". Based on this result, the wording of the response is adjusted.

[0634] Notification module

[0635] The generated response (e.g., promotional materials) is sent from the server to the terminal. The terminal sends a notification to the user, providing instructions on how to access the generated materials. For example, the terminal might send a notification to the user saying, "New product promotional materials are ready. Please download them from here." Based on the analysis results of the emotion engine, the content of the response and the wording of the notification may be modified.

[0636] Example of a prompt

[0637] "Please provide detailed information about the new product."

[0638] "Please generate a report based on the latest customer feedback."

[0639] "Retrieve the results from the market research API and save them to the database."

[0640] This system automates the entire process from collecting sales and business-related information to providing it to users, and further enables customization based on the user's emotional state, resulting in more effective information delivery.

[0641] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0642] Step 1: Data Collection

[0643] The server uses a data collection module to gather information related to sales and operations. Specifically, it periodically calls a market research API to retrieve customer feedback data and new product information from the ERP system. Inputs are API requests and SQL queries, and outputs are the retrieved customer feedback and new product information data. For example, the server periodically calls the "getCustomerFeedback" API and executes a query like "SELECT FROM NewProducts WHERE ReleaseDate > CURDATE()" in the ERP system.

[0644] Step 2: Save Database

[0645] The server stores the collected data in a database. The data is organized and stored into positive, negative, and neutral categories. The input is the data obtained in step 1, and the output is the database entries organized by category. For example, the server analyzes customer feedback using the "SentimentAnalysis" function and executes an SQL query such as "INSERT INTO Feedback (Category, Content) VALUES ('Positive', 'Great product!')".

[0646] Step 3: Accepting User Requests

[0647] A user requests specific information through their device. The user enters "Please provide details about the new product" into the search box and presses the submit button. The input is the user's request text, and the output is the request data sent to the server. The device sends this request to the "POST / api / request" endpoint.

[0648] Step 4: Data Analysis

[0649] The server receives the request and retrieves relevant information from the database. The retrieved data is passed to the AI ​​module for analysis. The input is the request received in step 3 and the relevant data retrieved from the database, and the output is the generated material as a result of the analysis. For example, the server generates a query such as "SELECT FROM Products WHERE Type='New'", retrieves information from the database, and passes it to the "generatePromotionMaterial" function.

[0650] Step 5: Emotion Recognition

[0651] The server sends user input to the emotion engine to recognize the user's emotions. The emotion is analyzed from the user's input text and voice data. The input is the user's text and voice data, and the output is the emotion analysis result from the emotion engine. For example, the server might use the "analyzeEmotion" function and obtain "High Stress Level" as the analysis result.

[0652] Step 6: Generate Answer

[0653] The server receives the promotional materials generated by the AI ​​module and prepares them to be provided to the user. The input is the generated materials from step 4 and the sentiment analysis results from step 5, and the output is the final material to be provided to the user. For example, based on the sentiment analysis, the server generates a message saying, "Your document has been generated. Please check it now!"

[0654] Step 7: Notification Delivery

[0655] The server sends the generated response and message to the terminal. The terminal notifies the user and provides a link to access the materials. The input is the notification data from the server, and the output is the notification to the user. For example, the terminal sends a notification saying, "Promotional materials for the new product are ready. Please download them from here."

[0656] This series of steps allows the system to efficiently collect, analyze, and provide customized information to users.

[0657] (Application Example 2)

[0658] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0659] Traditional sales and business support systems had the functionality to collect and analyze data based on user requests, generate responses, and send notifications. However, they struggled to understand user emotions or provide customized services in real time. This resulted in challenges such as insufficient improvements in customer satisfaction and operational efficiency. Furthermore, in actual field use, the lack of real-time notification capabilities and integration with smart devices made immediate responses difficult.

[0660] In Application Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for collecting information related to sales and operations, means for storing the collected data in a database, means for receiving requests from users, means for obtaining and analyzing appropriate data from the database and generating a response, means for notifying the user of the generated response, and means for providing a customized service in real time based on the analyzed information. This enables the provision of a customized service in real time that responds to the user's emotional state, improving customer satisfaction and operational efficiency. Furthermore, collaboration with smart devices enables rapid response on-site.

[0661] "Information related to sales and operations" refers to data, feedback, market data, product information, and other information necessary for a company to conduct its business.

[0662] A "database" is a general term for a storage device and its management system that systematically stores various collected data and manages, searches, and retrieves it efficiently.

[0663] A "user request" refers to a request that a user of the system inputs or transmits in order to obtain specific information or services.

[0664] "Means for acquiring and analyzing appropriate data and generating responses" refers to methods and functions for acquiring relevant data from a database based on a user's request, analyzing it, and generating the response the user is seeking.

[0665] "Means for notifying the user of the generated response" refers to methods and functions for informing the user of the response generated by the analysis, such as through display or audio.

[0666] "Means for providing customized services in real time" refers to methods and functions for instantly providing optimized services based on the user's emotional state and request content.

[0667] "Internet and internal systems" refers to a general term encompassing networks accessible from the outside and information systems operated within a company.

[0668] "Using artificial intelligence to analyze data and generate responses" refers to the process of automatically analyzing data using machine learning and natural language processing technologies and generating responses that address user requests.

[0669] "Emotion recognition means" refers to methods and functions for automatically analyzing emotional states (e.g., joy, sadness, anger) from user input or voice data.

[0670] "Real-time notification means" refers to methods and functions for immediately notifying users or staff of analysis results and service details.

[0671] "Smart devices" is a general term for electronic devices that have internet connectivity and application execution capabilities, such as smartphones, smart glasses, tablets, and head-mounted displays.

[0672] To implement this invention, it is desirable that the system includes the following components. Specifically, it consists of three entities: a server, a terminal, and a user, and their respective roles and processing flows are described below.

[0673] Overall system configuration

[0674] The system consists of the following components:

[0675] 1. Data Acquisition Module

[0676] 2. Database

[0677] 3. User Interface

[0678] 4. AI Module

[0679] 5. Emotional Engine

[0680] 6. Notification Module

[0681] Data Acquisition Module

[0682] The server uses a data collection module to gather sales and business-related information from the internet and internal systems. For example, the server periodically calls a market research API to obtain the latest customer feedback data. It also extracts information about new products from the ERP system. The Python library 'requests' is used for API connections.

[0683] database

[0684] The collected data is stored in a database by the server. The data is organized by category and stored in appropriate fields to enable quick searching and retrieval. For example, customer feedback data is stored in positive, negative, and neutral categories, and a search index is assigned to each category. Examples of cloud databases used include AWS RDS and Google Cloud SQL.

[0685] User Interface

[0686] Users access the system through their devices and request specific information (e.g., promotional materials for a new product). The device then transmits the request to the server. For example, a user might type "Please provide detailed information about the new product" and press the submit button. Hardware used includes smartphones and smart glasses, while software includes a web application interface.

[0687] AI Module

[0688] The server receives the request and generates a query to retrieve relevant information from the database. The retrieved data is passed to the AI ​​module for analysis. The AI ​​module processes the data using machine learning and natural language processing (NLP) techniques to generate optimal information tailored to the user's request. The software used includes Google Cloud AI and AWS SageMaker.

[0689] Emotional Engine

[0690] The emotion engine works in conjunction with the AI ​​module. The server sends user input and voice data to the emotion engine to recognize the user's emotions. For example, the emotion engine analyzes emotions (e.g., joy, sadness, anger) from the text entered by the user or the transmitted voice data. One example of the software used is IBM Watson Tone Analyzer.

[0691] Notification module

[0692] The generated response (e.g., promotional materials) is sent from the server to the device. The device then sends a notification to the user, providing instructions on how to access the generated materials. For example, the device might notify the user, "New product promotional materials are ready. Please download them from here." Furthermore, the content of the response and the wording of the notification may be modified based on the analysis results of the sentiment engine. Firebase Cloud Messaging and Apple Push Notification Service are used as software for real-time notifications.

[0693] Specific example

[0694] For example, a server retrieves customer feedback from a market research API, such as "The texture of the new product was very good, but the price feels a little high," and stores it in a database. Then, when a user requests "Please provide detailed information about the new product" from their device, the server retrieves relevant information from the database and analyzes it with an AI module. At the same time, the user's emotional state is also analyzed by an emotion engine, and an appropriate response is generated. The generated response is notified to the device in real time using Firebase Cloud Messaging.

[0695] An example of a prompt message would be, "Perform sentiment analysis to identify the emotional state of this feedback."

[0696] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0697] Step 1:

[0698] The server uses a data collection module to gather sales and operational information from the internet and internal systems. Specifically, the server periodically calls a market research API to obtain the latest customer feedback data. It also extracts information about new products from the ERP system. The input is data from the market research API and the ERP system, and the output is the collected data.

[0699] Step 2:

[0700] The server stores the collected data in a database. The data is organized by category and stored in appropriate fields to enable quick searching and retrieval. Specifically, customer feedback data is categorized into positive, negative, and neutral, and a search index is assigned to each category. The input is the data collected in step 1, and the output is the organized database.

[0701] Step 3:

[0702] The user requests specific information through the terminal. The terminal then transmits that request to the server. For example, the user might type "Please provide detailed information about the new product" and press the submit button. The input is the user's request, and the output is the transmission of the request to the server.

[0703] Step 4:

[0704] The server receives a request from the user and generates a query to retrieve relevant information from the database. The retrieved data is passed to the AI ​​module for analysis. Specifically, it generates an SQL query containing the relevant information and retrieves data from the database. The input is the user's request and the data from the database, and the output is the data passed to the AI ​​module.

[0705] Step 5:

[0706] The server uses an AI module to analyze data and generate optimal information tailored to the user's request. The AI ​​module employs machine learning and natural language processing (NLP) techniques. During this analysis, an emotion engine also works in conjunction, analyzing emotions from the user's input and voice data. The input is the data passed to the AI ​​module, and the output is the optimal information resulting from the analysis.

[0707] Step 6:

[0708] The server passes the generated information to the notification module, which then sends a notification to the user's device. Specifically, it uses Firebase Cloud Messaging or Apple Push Notification Service to send the notification content to the device in real time. The input is the analysis results from the AI ​​module and the emotion engine, and the output is the notification sent to the user's device.

[0709] Step 7:

[0710] The terminal displays notifications to the user and provides instructions on how to access generated materials and information. For example, it might display a notification saying, "Promotional materials for our new product are ready. Please download them here." The input is the notification content sent from the server, and the output is the notification and access link for the user.

[0711] 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.

[0712] 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.

[0713] 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.

[0714] [Third Embodiment]

[0715] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0716] 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.

[0717] 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).

[0718] 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.

[0719] 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.

[0720] 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).

[0721] 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.

[0722] 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.

[0723] 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.

[0724] 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.

[0725] 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.

[0726] 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".

[0727] In order to implement the present invention, it is desirable that the system be configured as follows. Specifically, the system consists of three components: a server, a terminal, and a user, and their roles and processing flow are described below.

[0728] Overall system configuration

[0729] The system consists of the following components:

[0730] 1. Data Acquisition Module

[0731] 2. Database

[0732] 3. User Interface

[0733] 4. AI Module

[0734] 5. Notification Module

[0735] Data Acquisition Module

[0736] The server uses a data collection module to gather sales and operational information from the internet and internal systems. For example, the server periodically calls a market research API to collect customer feedback information. It also extracts information about new products from the ERP system.

[0737] database

[0738] The collected data is stored in a database by the server. The data is organized by category and stored in the appropriate fields to enable quick searching and retrieval. For example, the server categorizes text-based feedback data (positive, negative, neutral) and stores it in the corresponding field.

[0739] User Interface

[0740] The user accesses the system through a terminal and requests specific information (e.g., promotional materials for a new product). The terminal then transmits the request to the server. For example, the user might type "Please provide detailed information about the new product" and press the submit button.

[0741] AI Module

[0742] The server receives a request and generates a query to retrieve relevant information from the database. The retrieved data is passed to an AI module for analysis. The AI ​​module processes the data using machine learning and natural language processing techniques to generate optimal information tailored to the user's needs. For example, the server might pass information about a new product to the AI ​​module, and the AI ​​might use that information to create promotional materials.

[0743] Notification module

[0744] The generated response (e.g., promotional materials) is sent from the server to the terminal. The terminal then sends a notification to the user, providing instructions on how to access the generated materials. For example, the terminal might notify the user, "New product promotional materials are ready. Please download them from here."

[0745] Specific example

[0746] 1. Data Collection: The server collects the latest customer feedback from the internet and stores it in a database.

[0747] 2. Data organization: The server stores feedback data categorized into positive, negative, and neutral.

[0748] 3. Processing user requests: A user requests promotional materials for a new product from their device, and the device sends that request to the server.

[0749] 4. Data analysis and response generation: The server passes the request to the AI ​​module, which generates promotional materials.

[0750] 5. Notification: The server sends the generated promotional materials to the terminal and notifies the user.

[0751] This system automates the entire process from collecting sales and business-related information to providing it to users, thereby improving operational efficiency.

[0752] The following describes the processing flow.

[0753] Step 1:

[0754] The server uses a data collection module to gather sales and operational data. Specifically, the server sends requests to a market research API to obtain the latest customer feedback data. It also extracts information about new products from the ERP system.

[0755] Step 2:

[0756] The server preprocesses the collected data and stores it in a database as structured data. For example, the server categorizes text-based feedback into positive, negative, and neutral categories and stores them in their respective fields.

[0757] Step 3:

[0758] The user uses the device to request specific information. For example, if a user wants to obtain promotional materials for a new product, they would type "Please provide details about the new product" on the device's interface and press the send button.

[0759] Step 4:

[0760] The terminal sends the user's request to the server. An HTTP request is used to communicate the user's request to the server.

[0761] Step 5:

[0762] The server receives the request and generates a query to retrieve relevant information from the database. For example, it generates an SQL query to search for new product information and sends it to the database.

[0763] Step 6:

[0764] The server passes the acquired data to the AI ​​module. The AI ​​module uses machine learning and natural language processing techniques to analyze the data and generate the most suitable response to the user's request. For example, it can create promotional materials based on the attributes and features of a new product.

[0765] Step 7:

[0766] The server receives the response generated by the AI ​​module and sends it to the terminal as a means of notifying the user. For example, it might send the generated promotional material in PDF format to the terminal as an HTTP response.

[0767] Step 8:

[0768] The device sends a notification to the user. For example, it might display a message to the user saying, "Promotional materials for our new product are ready. You can download them here."

[0769] Users download and view materials using their devices.

[0770] (Example 1)

[0771] 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."

[0772] The processes of collecting, organizing, analyzing, and providing sales and business-related information are often performed manually, which can result in decreased operational efficiency and the occurrence of data omissions and errors. Furthermore, traditional systems struggle to process large amounts of data quickly, making it difficult to provide users with optimal information. Solving these problems is therefore essential.

[0773] 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.

[0774] In this invention, the server includes means for collecting sales and business-related information using a data collection module, means for organizing the collected data by category and storing it in a database, means for receiving information requests from users via a user interface, means for retrieving appropriate data from the database, analyzing it with an AI module, and generating a response, and means for notifying the user of the generated response. This automates the process from information collection to provision, improves operational efficiency, and enables the rapid provision of optimal information to users.

[0775] A "data collection module" is software or hardware used to collect sales and business-related information from the internet or internal systems.

[0776] A "database" is an information management system that organizes and stores collected data by category, enabling rapid searching and retrieval of data.

[0777] A "user interface" is the interface that a user uses to access a system and request information, and includes operation screens and input forms.

[0778] An "AI module" is a component that includes artificial intelligence technology used to analyze data and generate appropriate responses based on user requests.

[0779] "Notification means" refers to a means of informing the user of the generated response, and includes a function to send a notification to the user's device.

[0780] "Machine learning" refers to algorithms and techniques for learning patterns and knowledge from data, and for performing analysis and decision-making.

[0781] "Natural language processing" is a technology that enables machines to understand and process human language, and is used for text analysis and generation.

[0782] Modes for carrying out the invention

[0783] The system for implementing the present invention consists of three components: a server, a terminal, and a user. Their roles and processing flow are as follows. The components of this system include a data collection module, a database, a user interface, an AI module, and a notification module. Each component is described in detail below.

[0784] Data Acquisition Module

[0785] The server uses a data collection module to gather sales and operational information. This module has the capability to retrieve data from the internet and internal systems. For example, the server periodically calls a market research API to collect customer feedback information and accesses the ERP system to extract new product information.

[0786] database

[0787] The server stores the collected data in a database. The database is an information management system for organizing and storing the collected data by category, enabling quick searching and retrieval of data. Specifically, the server stores text-based feedback data, categorized into positive, negative, and neutral, and generates a search index.

[0788] User Interface

[0789] Users access the system through their terminals and use the user interface to request specific information. Users can make information requests using the terminal's operation screen and input forms. For example, if a user types "Please provide detailed information about the new product" and presses the submit button, the request is sent to the server.

[0790] AI Module

[0791] The server receives requests from users, retrieves relevant information from the database, and passes it to the AI ​​module. The AI ​​module analyzes the data using machine learning and natural language processing techniques to generate the optimal response. For example, the server retrieves information about a new product, and the AI ​​module analyzes that information to create promotional materials.

[0792] Notification module

[0793] The server sends the response generated by the AI ​​module to the terminal and notifies the user. The terminal sends a notification to the user and provides instructions on how to access the generated materials. For example, the terminal might notify the user, "Promotional materials for the new product are ready. Please download them from here."

[0794] Specific example

[0795] For example, here's a specific example of what to do when requesting promotional materials for a new product:

[0796] 1. Data Collection: The server uses a market research API to collect recent customer feedback and retrieves new product information from the ERP system.

[0797] 2. Data organization: The feedback data collected by the server is sorted into positive, negative, and neutral categories and stored in a database.

[0798] 3. Processing the user request: The user enters "Please provide promotional materials for the new product" into the device's UI and submits it.

[0799] 4. Data Analysis and Response Generation: The server analyzes the request, generates a data acquisition query, retrieves the necessary data from the database, passes it to the AI ​​module, and generates optimal promotional materials.

[0800] 5. Notification: The server sends the generated promotional materials to the terminal, and the terminal notifies the user that the materials are ready.

[0801] Example of a prompt

[0802] "Please create promotional materials for this new product."

[0803] "Analyze the latest customer feedback and generate a report."

[0804] "Please extract and provide information on new products from the ERP system."

[0805] The system according to the present invention automates the above processes, streamlining the entire process from information collection to provision. This improves operational efficiency and enables the rapid provision of optimal information to users.

[0806] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0807] Step 1: Data Collection

[0808] The server uses a data collection module to gather sales and operational information from the internet and internal systems. Inputs include market research APIs and data from ERP systems. The server periodically calls APIs to retrieve customer feedback information. It also accesses the ERP system to extract new product information. The output is the collected raw data.

[0809] Step 2: Saving and organizing in the database

[0810] The server organizes and stores the collected data in a database. The input is the raw data collected in step 1. The server categorizes the data into positive, negative, and neutral categories and stores them in the appropriate fields. Data organization and indexing enable rapid searching and retrieval. The output is database entries organized by category.

[0811] Step 3: Requesting information via the user interface

[0812] The user accesses the system using their device and requests specific information. The input is the request content entered by the user in the device's UI form (e.g., "Please provide details about the new product"). When the user clicks the submit button, the request is sent to the server via the device. The output is the information request sent to the server.

[0813] Step 4: Data analysis and response generation

[0814] The server receives information requests from users, retrieves relevant information from the database, and passes it to the AI ​​module. The input consists of the information requests received in step 3 and the data stored in step 2. The server generates appropriate queries and retrieves the necessary data. The AI ​​module analyzes the data using machine learning and natural language processing techniques to generate the optimal response. The output consists of reports and promotional materials generated by the AI ​​module.

[0815] Step 5: Notification and Information Provision

[0816] The server sends the generated responses to the terminal, and the terminal notifies the user that the materials are ready. The input is the response data generated in step 4. The server sends the generated materials to the terminal and notifies the user, "New product promotional materials are ready. Please download them from here." The user receives the notification and clicks the link on the terminal to download the materials. The output is the materials provided to the user and the notification.

[0817] (Specific examples of actions)

[0818] For example, the specific steps involved in requesting promotional materials for a new product are as follows:

[0819] 1. Data Collection: The server uses a market research API to collect customer feedback data and retrieves new product information from the ERP system.

[0820] 2. Data organization: The data collected by the server is classified into positive, negative, and neutral categories and stored in the database.

[0821] 3. Processing user requests: The user enters "Please provide promotional materials for the new product" into the device's UI and submits the request.

[0822] 4. Data Analysis and Response Generation: The server analyzes the request, generates a data retrieval query to obtain the necessary data from the database, and then the AI ​​module analyzes it to generate promotional materials.

[0823] 5. Notification and Information Provision: The server sends the generated promotional materials to the terminal, and the terminal notifies the user that the materials are ready. The user clicks the link to download the materials.

[0824] (Application Example 1)

[0825] 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."

[0826] In traditional brick-and-mortar stores, customers must directly ask store staff for detailed product information and promotional materials. This manual search and provision of information makes rapid service difficult. Furthermore, real-time responses to questions are challenging, contributing to decreased customer satisfaction. Additionally, inability to provide adequate product information can lead to missed sales opportunities. To address this, a system is needed that allows both staff and customers to access and verify information in real time.

[0827] 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.

[0828] In this invention, the server includes means for collecting sales and business-related data, means for storing the collected data in a database, means for receiving requests from users, means for retrieving and analyzing appropriate data from the database and generating responses, means for notifying the user of the generated responses, and means for users to check the information in real time via a smart device. This enables customers to obtain detailed product information and promotional materials in real time, and allows for the rapid and efficient provision of services.

[0829] "Means of collecting data" refers to a system for obtaining information related to sales and operations from multiple sources, such as the internet and internal systems.

[0830] "Means of storing data in a database" refers to a system that has the function of appropriately classifying and organizing collected information and storing it in a database.

[0831] "Means of receiving requests from users" refers to a mechanism that provides an interface for users to communicate their requests for information to the system.

[0832] "A means of retrieving and analyzing appropriate data from a database to generate an answer" refers to a system that searches for information stored in a database, analyzes it using artificial intelligence or other means, and generates the information the user is looking for.

[0833] "Means of notifying users of generated responses" refers to a system equipped with a notification function to convey generated information or documents to users.

[0834] "Means that allow users to check information in real time via smart devices" refers to a system that enables users to quickly acquire and check information via digital devices such as smart glasses and smartphones.

[0835] To implement the present invention, the system is preferably configured as follows. Specifically, it is a system that collects data related to sales and operations and generates and provides appropriate information according to user requests. The main components and their roles are described below.

[0836] Overall system configuration

[0837] The system consists of the following components:

[0838] 1. Data Acquisition Module

[0839] 2. Database

[0840] 3. User Interface

[0841] 4. AI Module

[0842] 5. Notification Module

[0843] 6. Smart devices

[0844] Data Acquisition Module

[0845] First, the server uses a data collection module to gather sales and operational information from the internet and internal systems. The server also periodically retrieves data from market research APIs and ERP systems to collect customer feedback and new product information.

[0846] database

[0847] The collected data is stored in a database by the server. The data is organized by category and stored in appropriate fields to enable quick searching and retrieval. For example, feedback data is stored in positive, negative, and neutral categories.

[0848] User Interface

[0849] Users access the system through a device (e.g., smart glasses or a smartphone) and request specific information. Requests can be made via text input or voice input. For example, a user might type "Please provide detailed information about the new product" and send it.

[0850] AI Module

[0851] Next, the server receives the request and generates a query to retrieve relevant information from the database. The AI ​​module analyzes the data using machine learning and natural language processing techniques to generate optimal information tailored to the user's request. For example, it might create promotional materials based on information about a new product.

[0852] Notification module

[0853] The generated response is sent from the server to the terminal. The terminal sends a notification to the user, providing instructions on how to access the generated materials. For example, the terminal might notify, "Promotional materials for the new product are ready. Please download them from here."

[0854] Smart devices

[0855] Smart devices such as smart glasses and smartphones provide users with the ability to access information in real time. This allows customers to quickly obtain detailed product information and promotional materials.

[0856] Specific example

[0857] For example, a customer enters the following prompt through smart glasses in a store:

[0858] "Please provide detailed information about the new product."

[0859] "Please display customer feedback for specific products."

[0860] "Please generate promotional materials."

[0861] This allows the system to provide timely and appropriate information, not only improving customer satisfaction but also maximizing sales opportunities.

[0862] Hardware and software to be used

[0863] Smart glasses: Google Glass, etc.

[0864] Smartphones: Typical Android and iOS devices

[0865] Server: Cloud server (Amazon Web Services, Google Cloud Platform, etc.)

[0866] Natural language processing libraries: spaCy, NLTK

[0867] Database: MySQL, PostgreSQL, etc.

[0868] This invention will streamline in-store services and facilitate smooth two-way communication between customers and staff.

[0869] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0870] Step 1:

[0871] Data collection

[0872] The server collects sales and operational data from the internet and internal systems. Specifically, it uses market research APIs to obtain the latest customer feedback and retrieves new product information from the ERP system. Once this data is collected, the server stores it in a database.

[0873] Input: Market research API, ERP system

[0874] Output: Feedback data, new product information (stored in database)

[0875] Step 2:

[0876] Data categorization

[0877] The server analyzes the collected customer feedback data using natural language processing (NLP) techniques and classifies it into positive, negative, and neutral categories. The server takes in the text of the acquired feedback data, estimates the category through the NLP model, and stores the results in a database.

[0878] Input: Feedback data (text)

[0879] Output: Category-specific feedback data (saved in database)

[0880] Step 3:

[0881] Receiving user requests

[0882] A user uses a smart device (such as smart glasses or a smartphone) to request specific information from the system. For example, they might enter a prompt like, "Please provide detailed information about the new product." The device receives this request and sends it to the server.

[0883] Input: User request (prompt text)

[0884] Output: Request data (sent to the server)

[0885] Step 4:

[0886] Acquisition and analysis of related information

[0887] The server analyzes the received request and generates a query to retrieve relevant information from the database. The retrieved data is passed to an AI module, where it is analyzed using machine learning and natural language processing techniques. Specifically, it generates appropriate promotional materials based on the content of the request.

[0888] Input: Request data, database

[0889] Output: Analysis results (generated promotional materials)

[0890] Step 5:

[0891] Information notification

[0892] The generated promotional materials and other responses are sent from the server to the terminal. The terminal sends a notification to the user, providing instructions on how to access the generated materials. For example, it might notify, "New product promotional materials are ready. Please download them from here."

[0893] Input: Analysis results (promotional materials)

[0894] Output: Notification message (user terminal)

[0895] Step 6:

[0896] Real-time information verification

[0897] Users can use smart devices to check notified information in real time. Information is displayed on smart glasses or smartphone screens, which users can then refer to. For example, smart glasses might display detailed information about a new product.

[0898] Input: Notification message

[0899] Output: Displayed information (smart device)

[0900] These steps enable the system to provide customers with quick and appropriate information, thereby improving the efficiency of services in physical stores.

[0901] 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.

[0902] To implement the present invention, it is desirable that the system be configured as follows. Specifically, the system consists of three components: a server, a terminal, and a user, and their roles and processing flow are described below.

[0903] Overall system configuration

[0904] The system consists of the following components:

[0905] 1. Data Acquisition Module

[0906] 2. Database

[0907] 3. User Interface

[0908] 4. AI Module

[0909] 5. Emotional Engine

[0910] 6. Notification Module

[0911] Data Acquisition Module

[0912] The server uses a data collection module to gather sales and operational information from the internet and internal systems. For example, the server periodically calls a market research API to obtain the latest customer feedback data. It also extracts information about new products from the ERP system.

[0913] database

[0914] The collected data is stored in a database by the server. The data is organized by category and stored in the appropriate fields to enable quick searching and retrieval. For example, the server categorizes text-based feedback into positive, negative, and neutral categories and stores them in the corresponding fields.

[0915] User Interface

[0916] The user accesses the system through a terminal and requests specific information (e.g., promotional materials for a new product). The terminal then transmits the request to the server. For example, the user might type "Please provide detailed information about the new product" and press the submit button.

[0917] AI Module

[0918] The server receives a request and generates a query to retrieve relevant information from the database. The retrieved data is passed to an AI module for analysis. The AI ​​module processes the data using machine learning and natural language processing techniques to generate optimal information tailored to the user's needs. For example, the server might pass information about a new product to the AI ​​module, and the AI ​​might use that information to create promotional materials.

[0919] Emotional Engine

[0920] The emotion engine works in conjunction with the AI ​​module. The server sends user input and voice data to the emotion engine to recognize the user's emotions. For example, the emotion engine analyzes emotions (e.g., joy, sadness, anger) from the text entered by the user or the voice data sent.

[0921] Notification module

[0922] The generated response (e.g., promotional materials) is sent from the server to the terminal. The terminal sends a notification to the user, providing instructions on how to access the generated materials. For example, the terminal might notify the user, "New product promotional materials are ready. Please download them from here." The content of the response and the wording of the notification may be modified based on the results of the emotion engine's analysis.

[0923] Specific example

[0924] 1. Data Collection: The server collects the latest customer feedback from the internet and stores it in a database.

[0925] 2. Database organization: The server stores feedback data categorized into positive, negative, and neutral.

[0926] 3. Processing user requests: A user requests promotional materials for a new product from their device, and the device sends that request to the server.

[0927] 4. Data analysis and response generation: The server passes the request to the AI ​​module, which generates sales promotion materials.

[0928] 5. Emotion Recognition: The server sends the user's input to the emotion engine, which analyzes the user's emotional state. For example, if the user is feeling stressed, a response will be generated using appropriate language.

[0929] 6. Notification: The server sends a message tailored to the user's emotions to the device along with the generated promotional materials, notifying the user.

[0930] This system automates the entire process from collecting sales and business-related information to providing it to users, and further enables customization based on the user's emotional state, resulting in more effective information delivery.

[0931] The following describes the processing flow.

[0932] Step 1:

[0933] The server uses a data collection module to collect sales and operational data. The server sends requests to a market research API to retrieve customer feedback data. It also extracts new product information from the ERP system.

[0934] Step 2:

[0935] The server preprocesses the collected data and stores it in a database as structured data. For example, text-based feedback data is categorized into positive, negative, and neutral, and stored in the respective fields.

[0936] Step 3:

[0937] The user uses the device to request specific information. For example, if a user wants to obtain promotional materials for a new product, they would type "Please provide details about the new product" on the device's interface and press the send button.

[0938] Step 4:

[0939] The terminal sends a user request to the server. It uses an HTTP request to communicate the details of the request to the server.

[0940] Step 5:

[0941] The server receives the request and generates a query to retrieve relevant information from the database. The server generates an SQL query to search for new product information and sends it to the database.

[0942] Step 6:

[0943] The server passes the acquired data to the AI ​​module. The AI ​​module uses machine learning and natural language processing techniques to analyze the data and generate the most suitable response to the user's request. For example, it might create promotional materials focusing on the characteristics and benefits of a new product.

[0944] Step 7:

[0945] The server sends user input and voice data to the emotion engine. The emotion engine analyzes the user's emotions. For example, the emotion engine recognizes the user's emotional state (joy, sadness, anger, etc.) from the text and voice data entered by the user.

[0946] Step 8:

[0947] The server adaptively modifies the responses generated by the AI ​​module based on the analysis results of the emotion engine. For example, if the user is feeling stressed, the responses will be provided in a more considerate tone.

[0948] Step 9:

[0949] The server sends the final response to the device. It also sends the generated promotional materials and sentiment-sensitive messages to the device as an HTTP response.

[0950] Step 10:

[0951] The device sends a notification to the user. For example, it might display a message such as, "Promotional materials for our new product are ready. You can download them here." The notification message is also adaptively modified based on the analysis of the emotion engine.

[0952] Step 11:

[0953] Users download and view the provided materials using their devices.

[0954] (Example 2)

[0955] 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."

[0956] In today's business environment, collecting, analyzing, and providing information related to sales and operations is crucial. Traditional systems often require manual information collection and analysis, which is time-consuming and labor-intensive. Furthermore, they sometimes provide information uniformly without considering the user's emotional state, leading to low user satisfaction. To address these challenges, there is a need for a more efficient system that can provide information in a way that is sensitive to user emotions.

[0957] The identification processing performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for collecting information related to sales and operations using a data collection module, means for storing the collected data in a database and organizing it by category, means for users to input information requests using a terminal and receiving those requests, means for obtaining appropriate data from the database and generating answers through analysis using an AI module, means for delivering the generated answers to the user through a notification module, and means for analyzing user input and voice data with an emotion engine and adjusting the answers based on the user's emotional state. As a result, the process from information collection to provision to the user is automated, and customized information provision according to the user's emotional state becomes possible.

[0958] A "data collection module" is a device or software used to collect sales and business-related information from the internet or internal systems.

[0959] A "database" is an electronic information storage area used to store collected information and organize it by category.

[0960] A "user" is an entity that accesses a system and requests specific information.

[0961] A "terminal" is an electronic device that allows a user to access a system and provides a means of sending requests.

[0962] An "AI module" is a software component that uses machine learning and natural language processing technologies to analyze acquired data and generate appropriate responses.

[0963] An "emotion engine" is a device or software that analyzes user input and voice data to identify the user's emotional state.

[0964] A "notification module" is a device or software that delivers generated responses to users and provides notifications.

[0965] A "Market Research API" is an application programming interface for providing information about the market and customers.

[0966] An "ERP system" is an enterprise resource planning system, an information management system for integrating and managing various business processes within an organization.

[0967] A "generative AI model" is an algorithm or network model that has been trained using machine learning to perform data analysis and information generation.

[0968] A "prompt sentence" is a text of instructions or questions input into a generative AI model, intended to guide the output of specific information.

[0969] To implement the present invention, the system is configured using the following hardware and software. Specifically, the system consists of three components: a server, a terminal, and a user, and their roles and processing flow are described below.

[0970] Data Acquisition Module

[0971] The server uses a data collection module to gather information related to sales and operations. Specifically, the server periodically calls a market research API to obtain the latest customer feedback data. It also extracts information about new products from the ERP system. For example, when the server collects the latest customer feedback from the internet and extracts new product information from the ERP system, it calls the "getCustomerFeedback" API every hour and executes an SQL query on the ERP system.

[0972] database

[0973] The collected data is stored in a database by the server. The data is organized by category and stored in the appropriate fields to enable quick searching and retrieval. For example, the server analyzes customer feedback obtained as text, classifies it into positive, negative, and neutral categories using the "SentimentAnalysis" function, and stores it in the database according to each category.

[0974] User Interface

[0975] The user accesses the system through their device and requests specific information (e.g., promotional materials for a new product). The device then transmits this request to the server. For example, a user might type "Please provide detailed information about the new product" into the search box and press the submit button. The device sends the request to the "POST / api / request" endpoint.

[0976] AI Module

[0977] The server receives the request and retrieves relevant information from the database. The retrieved data is passed to the AI ​​module for analysis. The AI ​​module processes the data using machine learning and natural language processing techniques to generate optimal information tailored to the user's request. For example, the server might execute an SQL query like "SELECT FROM Products WHERE Type='New'" on the database to retrieve information, and then pass it to the AI ​​module's "generatePromotionMaterial" function to generate promotional materials.

[0978] Emotional Engine

[0979] The emotion engine works in conjunction with the AI ​​module. The server sends user input and voice data to the emotion engine to recognize the user's emotions. For example, from the text entered by the user or the voice data sent, the emotion engine generates a "StressLevel" field and sets its value to "High". Based on this result, the wording of the response is adjusted.

[0980] Notification module

[0981] The generated response (e.g., promotional materials) is sent from the server to the terminal. The terminal sends a notification to the user, providing instructions on how to access the generated materials. For example, the terminal might send a notification to the user saying, "New product promotional materials are ready. Please download them from here." Based on the analysis results of the emotion engine, the content of the response and the wording of the notification may be modified.

[0982] Example of a prompt

[0983] "Please provide detailed information about the new product."

[0984] "Please generate a report based on the latest customer feedback."

[0985] "Retrieve the results from the market research API and save them to the database."

[0986] This system automates the entire process from collecting sales and business-related information to providing it to users, and further enables customization based on the user's emotional state, resulting in more effective information delivery.

[0987] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0988] Step 1: Data Collection

[0989] The server uses a data collection module to gather information related to sales and operations. Specifically, it periodically calls a market research API to retrieve customer feedback data and new product information from the ERP system. Inputs are API requests and SQL queries, and outputs are the retrieved customer feedback and new product information data. For example, the server periodically calls the "getCustomerFeedback" API and executes a query like "SELECT FROM NewProducts WHERE ReleaseDate > CURDATE()" in the ERP system.

[0990] Step 2: Save Database

[0991] The server stores the collected data in a database. The data is organized and stored into positive, negative, and neutral categories. The input is the data obtained in step 1, and the output is the database entries organized by category. For example, the server analyzes customer feedback using the "SentimentAnalysis" function and executes an SQL query such as "INSERT INTO Feedback (Category, Content) VALUES ('Positive', 'Great product!')".

[0992] Step 3: Accepting User Requests

[0993] A user requests specific information through their device. The user enters "Please provide details about the new product" into the search box and presses the submit button. The input is the user's request text, and the output is the request data sent to the server. The device sends this request to the "POST / api / request" endpoint.

[0994] Step 4: Data Analysis

[0995] The server receives the request and retrieves relevant information from the database. The retrieved data is passed to the AI ​​module for analysis. The input is the request received in step 3 and the relevant data retrieved from the database, and the output is the generated material as a result of the analysis. For example, the server generates a query such as "SELECT FROM Products WHERE Type='New'", retrieves information from the database, and passes it to the "generatePromotionMaterial" function.

[0996] Step 5: Emotion Recognition

[0997] The server sends user input to the emotion engine to recognize the user's emotions. The emotion is analyzed from the user's input text and voice data. The input is the user's text and voice data, and the output is the emotion analysis result from the emotion engine. For example, the server might use the "analyzeEmotion" function and obtain "High Stress Level" as the analysis result.

[0998] Step 6: Generate Answer

[0999] The server receives the promotional materials generated by the AI ​​module and prepares them to be provided to the user. The input is the generated materials from step 4 and the sentiment analysis results from step 5, and the output is the final material to be provided to the user. For example, based on the sentiment analysis, the server generates a message saying, "Your document has been generated. Please check it now!"

[1000] Step 7: Notification Delivery

[1001] The server sends the generated response and message to the terminal. The terminal notifies the user and provides a link to access the materials. The input is the notification data from the server, and the output is the notification to the user. For example, the terminal sends a notification saying, "Promotional materials for the new product are ready. Please download them from here."

[1002] This series of steps allows the system to efficiently collect, analyze, and provide customized information to users.

[1003] (Application Example 2)

[1004] 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."

[1005] Traditional sales and business support systems had the functionality to collect and analyze data based on user requests, generate responses, and send notifications. However, they struggled to understand user emotions or provide customized services in real time. This resulted in challenges such as insufficient improvements in customer satisfaction and operational efficiency. Furthermore, in actual field use, the lack of real-time notification capabilities and integration with smart devices made immediate responses difficult.

[1006] In Application Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for collecting information related to sales and operations, means for storing the collected data in a database, means for receiving requests from users, means for obtaining and analyzing appropriate data from the database and generating a response, means for notifying the user of the generated response, and means for providing a customized service in real time based on the analyzed information. This enables the provision of a customized service in real time that responds to the user's emotional state, improving customer satisfaction and operational efficiency. Furthermore, collaboration with smart devices enables rapid response on-site.

[1007] "Information related to sales and operations" refers to data, feedback, market data, product information, and other information necessary for a company to conduct its business.

[1008] A "database" is a general term for a storage device and its management system that systematically stores various collected data and manages, searches, and retrieves it efficiently.

[1009] A "user request" refers to a request that a user of the system inputs or transmits in order to obtain specific information or services.

[1010] "Means for acquiring and analyzing appropriate data and generating responses" refers to methods and functions for acquiring relevant data from a database based on a user's request, analyzing it, and generating the response the user is seeking.

[1011] "Means for notifying the user of the generated response" refers to methods and functions for informing the user of the response generated by the analysis, such as through display or audio.

[1012] "Means for providing customized services in real time" refers to methods and functions for instantly providing optimized services based on the user's emotional state and request content.

[1013] "Internet and internal systems" refers to a general term encompassing networks accessible from the outside and information systems operated within a company.

[1014] "Using artificial intelligence to analyze data and generate responses" refers to the process of automatically analyzing data using machine learning and natural language processing technologies and generating responses that address user requests.

[1015] "Emotion recognition means" refers to methods and functions for automatically analyzing emotional states (e.g., joy, sadness, anger) from user input or voice data.

[1016] "Real-time notification means" refers to methods and functions for immediately notifying users or staff of analysis results and service details.

[1017] "Smart devices" is a general term for electronic devices that have internet connectivity and application execution capabilities, such as smartphones, smart glasses, tablets, and head-mounted displays.

[1018] To implement this invention, it is desirable that the system includes the following components. Specifically, it consists of three entities: a server, a terminal, and a user, and their respective roles and processing flows are described below.

[1019] Overall system configuration

[1020] The system consists of the following components:

[1021] 1. Data Acquisition Module

[1022] 2. Database

[1023] 3. User Interface

[1024] 4. AI Module

[1025] 5. Emotional Engine

[1026] 6. Notification Module

[1027] Data Acquisition Module

[1028] The server uses a data collection module to gather sales and business-related information from the internet and internal systems. For example, the server periodically calls a market research API to obtain the latest customer feedback data. It also extracts information about new products from the ERP system. The Python library 'requests' is used for API connections.

[1029] database

[1030] The collected data is stored in a database by the server. The data is organized by category and stored in appropriate fields to enable quick searching and retrieval. For example, customer feedback data is stored in positive, negative, and neutral categories, and a search index is assigned to each category. Examples of cloud databases used include AWS RDS and Google Cloud SQL.

[1031] User Interface

[1032] Users access the system through their devices and request specific information (e.g., promotional materials for a new product). The device then transmits the request to the server. For example, a user might type "Please provide detailed information about the new product" and press the submit button. Hardware used includes smartphones and smart glasses, while software includes a web application interface.

[1033] AI Module

[1034] The server receives the request and generates a query to retrieve relevant information from the database. The retrieved data is passed to the AI ​​module for analysis. The AI ​​module processes the data using machine learning and natural language processing (NLP) techniques to generate optimal information tailored to the user's request. The software used includes Google Cloud AI and AWS SageMaker.

[1035] Emotional Engine

[1036] The emotion engine works in conjunction with the AI ​​module. The server sends user input and voice data to the emotion engine to recognize the user's emotions. For example, the emotion engine analyzes emotions (e.g., joy, sadness, anger) from the text entered by the user or the transmitted voice data. One example of the software used is IBM Watson Tone Analyzer.

[1037] Notification module

[1038] The generated response (e.g., promotional materials) is sent from the server to the device. The device then sends a notification to the user, providing instructions on how to access the generated materials. For example, the device might notify the user, "New product promotional materials are ready. Please download them from here." Furthermore, the content of the response and the wording of the notification may be modified based on the analysis results of the sentiment engine. Firebase Cloud Messaging and Apple Push Notification Service are used as software for real-time notifications.

[1039] Specific example

[1040] For example, a server retrieves customer feedback from a market research API, such as "The texture of the new product was very good, but the price feels a little high," and stores it in a database. Then, when a user requests "Please provide detailed information about the new product" from their device, the server retrieves relevant information from the database and analyzes it with an AI module. At the same time, the user's emotional state is also analyzed by an emotion engine, and an appropriate response is generated. The generated response is notified to the device in real time using Firebase Cloud Messaging.

[1041] An example of a prompt message would be, "Perform sentiment analysis to identify the emotional state of this feedback."

[1042] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1043] Step 1:

[1044] The server uses a data collection module to gather sales and operational information from the internet and internal systems. Specifically, the server periodically calls a market research API to obtain the latest customer feedback data. It also extracts information about new products from the ERP system. The input is data from the market research API and the ERP system, and the output is the collected data.

[1045] Step 2:

[1046] The server stores the collected data in a database. The data is organized by category and stored in appropriate fields to enable quick searching and retrieval. Specifically, customer feedback data is categorized into positive, negative, and neutral, and a search index is assigned to each category. The input is the data collected in step 1, and the output is the organized database.

[1047] Step 3:

[1048] The user requests specific information through the terminal. The terminal then transmits that request to the server. For example, the user might type "Please provide detailed information about the new product" and press the submit button. The input is the user's request, and the output is the transmission of the request to the server.

[1049] Step 4:

[1050] The server receives a request from the user and generates a query to retrieve relevant information from the database. The retrieved data is passed to the AI ​​module for analysis. Specifically, it generates an SQL query containing the relevant information and retrieves data from the database. The input is the user's request and the data from the database, and the output is the data passed to the AI ​​module.

[1051] Step 5:

[1052] The server uses an AI module to analyze data and generate optimal information tailored to the user's request. The AI ​​module employs machine learning and natural language processing (NLP) techniques. During this analysis, an emotion engine also works in conjunction, analyzing emotions from the user's input and voice data. The input is the data passed to the AI ​​module, and the output is the optimal information resulting from the analysis.

[1053] Step 6:

[1054] The server passes the generated information to the notification module, which then sends a notification to the user's device. Specifically, it uses Firebase Cloud Messaging or Apple Push Notification Service to send the notification content to the device in real time. The input is the analysis results from the AI ​​module and the emotion engine, and the output is the notification sent to the user's device.

[1055] Step 7:

[1056] The terminal displays notifications to the user and provides instructions on how to access generated materials and information. For example, it might display a notification saying, "Promotional materials for our new product are ready. Please download them here." The input is the notification content sent from the server, and the output is the notification and access link for the user.

[1057] 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.

[1058] 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.

[1059] 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.

[1060] [Fourth Embodiment]

[1061] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[1062] 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.

[1063] 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).

[1064] 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.

[1065] 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.

[1066] 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).

[1067] 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.

[1068] 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.

[1069] 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.

[1070] 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.

[1071] 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.

[1072] 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.

[1073] 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".

[1074] In order to implement the present invention, it is desirable that the system be configured as follows. Specifically, the system consists of three components: a server, a terminal, and a user, and their roles and processing flow are described below.

[1075] Overall system configuration

[1076] The system consists of the following components:

[1077] 1. Data Acquisition Module

[1078] 2. Database

[1079] 3. User Interface

[1080] 4. AI Module

[1081] 5. Notification Module

[1082] Data Acquisition Module

[1083] The server uses a data collection module to gather sales and operational information from the internet and internal systems. For example, the server periodically calls a market research API to collect customer feedback information. It also extracts information about new products from the ERP system.

[1084] database

[1085] The collected data is stored in a database by the server. The data is organized by category and stored in the appropriate fields to enable quick searching and retrieval. For example, the server categorizes text-based feedback data (positive, negative, neutral) and stores it in the corresponding field.

[1086] User Interface

[1087] The user accesses the system through a terminal and requests specific information (e.g., promotional materials for a new product). The terminal then transmits the request to the server. For example, the user might type "Please provide detailed information about the new product" and press the submit button.

[1088] AI Module

[1089] The server receives a request and generates a query to retrieve relevant information from the database. The retrieved data is passed to an AI module for analysis. The AI ​​module processes the data using machine learning and natural language processing techniques to generate optimal information tailored to the user's needs. For example, the server might pass information about a new product to the AI ​​module, and the AI ​​might use that information to create promotional materials.

[1090] Notification module

[1091] The generated response (e.g., promotional materials) is sent from the server to the terminal. The terminal then sends a notification to the user, providing instructions on how to access the generated materials. For example, the terminal might notify the user, "New product promotional materials are ready. Please download them from here."

[1092] Specific example

[1093] 1. Data Collection: The server collects the latest customer feedback from the internet and stores it in a database.

[1094] 2. Data organization: The server stores feedback data categorized into positive, negative, and neutral.

[1095] 3. Processing user requests: A user requests promotional materials for a new product from their device, and the device sends that request to the server.

[1096] 4. Data analysis and response generation: The server passes the request to the AI ​​module, which generates promotional materials.

[1097] 5. Notification: The server sends the generated promotional materials to the terminal and notifies the user.

[1098] This system automates the entire process from collecting sales and business-related information to providing it to users, thereby improving operational efficiency.

[1099] The following describes the processing flow.

[1100] Step 1:

[1101] The server uses a data collection module to gather sales and operational data. Specifically, the server sends requests to a market research API to obtain the latest customer feedback data. It also extracts information about new products from the ERP system.

[1102] Step 2:

[1103] The server preprocesses the collected data and stores it in a database as structured data. For example, the server categorizes text-based feedback into positive, negative, and neutral categories and stores them in their respective fields.

[1104] Step 3:

[1105] The user uses the device to request specific information. For example, if a user wants to obtain promotional materials for a new product, they would type "Please provide details about the new product" on the device's interface and press the send button.

[1106] Step 4:

[1107] The terminal sends the user's request to the server. An HTTP request is used to communicate the user's request to the server.

[1108] Step 5:

[1109] The server receives the request and generates a query to retrieve relevant information from the database. For example, it generates an SQL query to search for new product information and sends it to the database.

[1110] Step 6:

[1111] The server passes the acquired data to the AI ​​module. The AI ​​module uses machine learning and natural language processing techniques to analyze the data and generate the most suitable response to the user's request. For example, it can create promotional materials based on the attributes and features of a new product.

[1112] Step 7:

[1113] The server receives the response generated by the AI ​​module and sends it to the terminal as a means of notifying the user. For example, it might send the generated promotional material in PDF format to the terminal as an HTTP response.

[1114] Step 8:

[1115] The device sends a notification to the user. For example, it might display a message to the user saying, "Promotional materials for our new product are ready. You can download them here."

[1116] Users download and view materials using their devices.

[1117] (Example 1)

[1118] 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".

[1119] The processes of collecting, organizing, analyzing, and providing sales and business-related information are often performed manually, which can result in decreased operational efficiency and the occurrence of data omissions and errors. Furthermore, traditional systems struggle to process large amounts of data quickly, making it difficult to provide users with optimal information. Solving these problems is therefore essential.

[1120] 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.

[1121] In this invention, the server includes means for collecting sales and business-related information using a data collection module, means for organizing the collected data by category and storing it in a database, means for receiving information requests from users via a user interface, means for retrieving appropriate data from the database, analyzing it with an AI module, and generating a response, and means for notifying the user of the generated response. This automates the process from information collection to provision, improves operational efficiency, and enables the rapid provision of optimal information to users.

[1122] A "data collection module" is software or hardware used to collect sales and business-related information from the internet or internal systems.

[1123] A "database" is an information management system that organizes and stores collected data by category, enabling rapid searching and retrieval of data.

[1124] A "user interface" is the interface that a user uses to access a system and request information, and includes operation screens and input forms.

[1125] An "AI module" is a component that includes artificial intelligence technology used to analyze data and generate appropriate responses based on user requests.

[1126] "Notification means" refers to a means of informing the user of the generated response, and includes a function to send a notification to the user's device.

[1127] "Machine learning" refers to algorithms and techniques for learning patterns and knowledge from data, and for performing analysis and decision-making.

[1128] "Natural language processing" is a technology that enables machines to understand and process human language, and is used for text analysis and generation.

[1129] Modes for carrying out the invention

[1130] The system for implementing the present invention consists of three components: a server, a terminal, and a user. Their roles and processing flow are as follows. The components of this system include a data collection module, a database, a user interface, an AI module, and a notification module. Each component is described in detail below.

[1131] Data Acquisition Module

[1132] The server uses a data collection module to gather sales and operational information. This module has the capability to retrieve data from the internet and internal systems. For example, the server periodically calls a market research API to collect customer feedback information and accesses the ERP system to extract new product information.

[1133] database

[1134] The server stores the collected data in a database. The database is an information management system for organizing and storing the collected data by category, enabling quick searching and retrieval of data. Specifically, the server stores text-based feedback data, categorized into positive, negative, and neutral, and generates a search index.

[1135] User Interface

[1136] Users access the system through their terminals and use the user interface to request specific information. Users can make information requests using the terminal's operation screen and input forms. For example, if a user types "Please provide detailed information about the new product" and presses the submit button, the request is sent to the server.

[1137] AI Module

[1138] The server receives requests from users, retrieves relevant information from the database, and passes it to the AI ​​module. The AI ​​module analyzes the data using machine learning and natural language processing techniques to generate the optimal response. For example, the server retrieves information about a new product, and the AI ​​module analyzes that information to create promotional materials.

[1139] Notification module

[1140] The server sends the response generated by the AI ​​module to the terminal and notifies the user. The terminal sends a notification to the user and provides instructions on how to access the generated materials. For example, the terminal might notify the user, "Promotional materials for the new product are ready. Please download them from here."

[1141] Specific example

[1142] For example, here's a specific example of what to do when requesting promotional materials for a new product:

[1143] 1. Data Collection: The server uses a market research API to collect recent customer feedback and retrieves new product information from the ERP system.

[1144] 2. Data organization: The feedback data collected by the server is sorted into positive, negative, and neutral categories and stored in a database.

[1145] 3. Processing the user request: The user enters "Please provide promotional materials for the new product" into the device's UI and submits it.

[1146] 4. Data Analysis and Response Generation: The server analyzes the request, generates a data acquisition query, retrieves the necessary data from the database, passes it to the AI ​​module, and generates optimal promotional materials.

[1147] 5. Notification: The server sends the generated promotional materials to the terminal, and the terminal notifies the user that the materials are ready.

[1148] Example of a prompt

[1149] "Please create promotional materials for this new product."

[1150] "Analyze the latest customer feedback and generate a report."

[1151] "Please extract and provide information on new products from the ERP system."

[1152] The system according to the present invention automates the above processes, streamlining the entire process from information collection to provision. This improves operational efficiency and enables the rapid provision of optimal information to users.

[1153] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1154] Step 1: Data Collection

[1155] The server uses a data collection module to gather sales and operational information from the internet and internal systems. Inputs include market research APIs and data from ERP systems. The server periodically calls APIs to retrieve customer feedback information. It also accesses the ERP system to extract new product information. The output is the collected raw data.

[1156] Step 2: Saving and organizing in the database

[1157] The server organizes and stores the collected data in a database. The input is the raw data collected in step 1. The server categorizes the data into positive, negative, and neutral categories and stores them in the appropriate fields. Data organization and indexing enable rapid searching and retrieval. The output is database entries organized by category.

[1158] Step 3: Requesting information via the user interface

[1159] The user accesses the system using their device and requests specific information. The input is the request content entered by the user in the device's UI form (e.g., "Please provide details about the new product"). When the user clicks the submit button, the request is sent to the server via the device. The output is the information request sent to the server.

[1160] Step 4: Data analysis and response generation

[1161] The server receives information requests from users, retrieves relevant information from the database, and passes it to the AI ​​module. The input consists of the information requests received in step 3 and the data stored in step 2. The server generates appropriate queries and retrieves the necessary data. The AI ​​module analyzes the data using machine learning and natural language processing techniques to generate the optimal response. The output consists of reports and promotional materials generated by the AI ​​module.

[1162] Step 5: Notification and Information Provision

[1163] The server sends the generated responses to the terminal, and the terminal notifies the user that the materials are ready. The input is the response data generated in step 4. The server sends the generated materials to the terminal and notifies the user, "New product promotional materials are ready. Please download them from here." The user receives the notification and clicks the link on the terminal to download the materials. The output is the materials provided to the user and the notification.

[1164] (Specific examples of actions)

[1165] For example, the specific steps involved in requesting promotional materials for a new product are as follows:

[1166] 1. Data Collection: The server uses a market research API to collect customer feedback data and retrieves new product information from the ERP system.

[1167] 2. Data organization: The data collected by the server is classified into positive, negative, and neutral categories and stored in the database.

[1168] 3. Processing user requests: The user enters "Please provide promotional materials for the new product" into the device's UI and submits the request.

[1169] 4. Data Analysis and Response Generation: The server analyzes the request, generates a data retrieval query to obtain the necessary data from the database, and then the AI ​​module analyzes it to generate promotional materials.

[1170] 5. Notification and Information Provision: The server sends the generated promotional materials to the terminal, and the terminal notifies the user that the materials are ready. The user clicks the link to download the materials.

[1171] (Application Example 1)

[1172] 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".

[1173] In traditional brick-and-mortar stores, customers must directly ask store staff for detailed product information and promotional materials. This manual search and provision of information makes rapid service difficult. Furthermore, real-time responses to questions are challenging, contributing to decreased customer satisfaction. Additionally, inability to provide adequate product information can lead to missed sales opportunities. To address this, a system is needed that allows both staff and customers to access and verify information in real time.

[1174] 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.

[1175] In this invention, the server includes means for collecting sales and business-related data, means for storing the collected data in a database, means for receiving requests from users, means for retrieving and analyzing appropriate data from the database and generating responses, means for notifying the user of the generated responses, and means for users to check the information in real time via a smart device. This enables customers to obtain detailed product information and promotional materials in real time, and allows for the rapid and efficient provision of services.

[1176] "Means of collecting data" refers to a system for obtaining information related to sales and operations from multiple sources, such as the internet and internal systems.

[1177] "Means of storing data in a database" refers to a system that has the function of appropriately classifying and organizing collected information and storing it in a database.

[1178] "Means of receiving requests from users" refers to a mechanism that provides an interface for users to communicate their requests for information to the system.

[1179] "A means of retrieving and analyzing appropriate data from a database to generate an answer" refers to a system that searches for information stored in a database, analyzes it using artificial intelligence or other means, and generates the information the user is looking for.

[1180] "Means of notifying users of generated responses" refers to a system equipped with a notification function to convey generated information or documents to users.

[1181] "Means that allow users to check information in real time via smart devices" refers to a system that enables users to quickly acquire and check information via digital devices such as smart glasses and smartphones.

[1182] To implement the present invention, the system is preferably configured as follows. Specifically, it is a system that collects data related to sales and operations and generates and provides appropriate information according to user requests. The main components and their roles are described below.

[1183] Overall system configuration

[1184] The system consists of the following components:

[1185] 1. Data Acquisition Module

[1186] 2. Database

[1187] 3. User Interface

[1188] 4. AI Module

[1189] 5. Notification Module

[1190] 6. Smart devices

[1191] Data Acquisition Module

[1192] First, the server uses a data collection module to gather sales and operational information from the internet and internal systems. The server also periodically retrieves data from market research APIs and ERP systems to collect customer feedback and new product information.

[1193] database

[1194] The collected data is stored in a database by the server. The data is organized by category and stored in appropriate fields to enable quick searching and retrieval. For example, feedback data is stored in positive, negative, and neutral categories.

[1195] User Interface

[1196] Users access the system through a device (e.g., smart glasses or a smartphone) and request specific information. Requests can be made via text input or voice input. For example, a user might type "Please provide detailed information about the new product" and send it.

[1197] AI Module

[1198] Next, the server receives the request and generates a query to retrieve relevant information from the database. The AI ​​module analyzes the data using machine learning and natural language processing techniques to generate optimal information tailored to the user's request. For example, it might create promotional materials based on information about a new product.

[1199] Notification module

[1200] The generated response is sent from the server to the terminal. The terminal sends a notification to the user, providing instructions on how to access the generated materials. For example, the terminal might notify, "Promotional materials for the new product are ready. Please download them from here."

[1201] Smart devices

[1202] Smart devices such as smart glasses and smartphones provide users with the ability to access information in real time. This allows customers to quickly obtain detailed product information and promotional materials.

[1203] Specific example

[1204] For example, a customer enters the following prompt through smart glasses in a store:

[1205] "Please provide detailed information about the new product."

[1206] "Please display customer feedback for specific products."

[1207] "Please generate promotional materials."

[1208] This allows the system to provide timely and appropriate information, not only improving customer satisfaction but also maximizing sales opportunities.

[1209] Hardware and software to be used

[1210] Smart glasses: Google Glass, etc.

[1211] Smartphones: Typical Android and iOS devices

[1212] Server: Cloud server (Amazon Web Services, Google Cloud Platform, etc.)

[1213] Natural language processing libraries: spaCy, NLTK

[1214] Database: MySQL, PostgreSQL, etc.

[1215] This invention will streamline in-store services and facilitate smooth two-way communication between customers and staff.

[1216] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1217] Step 1:

[1218] Data collection

[1219] The server collects sales and operational data from the internet and internal systems. Specifically, it uses market research APIs to obtain the latest customer feedback and retrieves new product information from the ERP system. Once this data is collected, the server stores it in a database.

[1220] Input: Market research API, ERP system

[1221] Output: Feedback data, new product information (stored in database)

[1222] Step 2:

[1223] Data categorization

[1224] The server analyzes the collected customer feedback data using natural language processing (NLP) techniques and classifies it into positive, negative, and neutral categories. The server takes in the text of the acquired feedback data, estimates the category through the NLP model, and stores the results in a database.

[1225] Input: Feedback data (text)

[1226] Output: Category-specific feedback data (saved in database)

[1227] Step 3:

[1228] Receiving user requests

[1229] A user uses a smart device (such as smart glasses or a smartphone) to request specific information from the system. For example, they might enter a prompt like, "Please provide detailed information about the new product." The device receives this request and sends it to the server.

[1230] Input: User request (prompt text)

[1231] Output: Request data (sent to the server)

[1232] Step 4:

[1233] Acquisition and analysis of related information

[1234] The server analyzes the received request and generates a query to retrieve relevant information from the database. The retrieved data is passed to an AI module, where it is analyzed using machine learning and natural language processing techniques. Specifically, it generates appropriate promotional materials based on the content of the request.

[1235] Input: Request data, database

[1236] Output: Analysis results (generated promotional materials)

[1237] Step 5:

[1238] Information notification

[1239] The generated promotional materials and other responses are sent from the server to the terminal. The terminal sends a notification to the user, providing instructions on how to access the generated materials. For example, it might notify, "New product promotional materials are ready. Please download them from here."

[1240] Input: Analysis results (promotional materials)

[1241] Output: Notification message (user terminal)

[1242] Step 6:

[1243] Real-time information verification

[1244] Users can use smart devices to check notified information in real time. Information is displayed on smart glasses or smartphone screens, which users can then refer to. For example, smart glasses might display detailed information about a new product.

[1245] Input: Notification message

[1246] Output: Displayed information (smart device)

[1247] These steps enable the system to provide customers with quick and appropriate information, thereby improving the efficiency of services in physical stores.

[1248] 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.

[1249] To implement the present invention, it is desirable that the system be configured as follows. Specifically, the system consists of three components: a server, a terminal, and a user, and their roles and processing flow are described below.

[1250] Overall system configuration

[1251] The system consists of the following components:

[1252] 1. Data Acquisition Module

[1253] 2. Database

[1254] 3. User Interface

[1255] 4. AI Module

[1256] 5. Emotional Engine

[1257] 6. Notification Module

[1258] Data Acquisition Module

[1259] The server uses a data collection module to gather sales and operational information from the internet and internal systems. For example, the server periodically calls a market research API to obtain the latest customer feedback data. It also extracts information about new products from the ERP system.

[1260] database

[1261] The collected data is stored in a database by the server. The data is organized by category and stored in the appropriate fields to enable quick searching and retrieval. For example, the server categorizes text-based feedback into positive, negative, and neutral categories and stores them in the corresponding fields.

[1262] User Interface

[1263] The user accesses the system through a terminal and requests specific information (e.g., promotional materials for a new product). The terminal then transmits the request to the server. For example, the user might type "Please provide detailed information about the new product" and press the submit button.

[1264] AI Module

[1265] The server receives a request and generates a query to retrieve relevant information from the database. The retrieved data is passed to an AI module for analysis. The AI ​​module processes the data using machine learning and natural language processing techniques to generate optimal information tailored to the user's needs. For example, the server might pass information about a new product to the AI ​​module, and the AI ​​might use that information to create promotional materials.

[1266] Emotional Engine

[1267] The emotion engine works in conjunction with the AI ​​module. The server sends user input and voice data to the emotion engine to recognize the user's emotions. For example, the emotion engine analyzes emotions (e.g., joy, sadness, anger) from the text entered by the user or the voice data sent.

[1268] Notification module

[1269] The generated response (e.g., promotional materials) is sent from the server to the terminal. The terminal sends a notification to the user, providing instructions on how to access the generated materials. For example, the terminal might notify the user, "New product promotional materials are ready. Please download them from here." The content of the response and the wording of the notification may be modified based on the results of the emotion engine's analysis.

[1270] Specific example

[1271] 1. Data Collection: The server collects the latest customer feedback from the internet and stores it in a database.

[1272] 2. Database organization: The server stores feedback data categorized into positive, negative, and neutral.

[1273] 3. Processing user requests: A user requests promotional materials for a new product from their device, and the device sends that request to the server.

[1274] 4. Data analysis and response generation: The server passes the request to the AI ​​module, which generates sales promotion materials.

[1275] 5. Emotion Recognition: The server sends the user's input to the emotion engine, which analyzes the user's emotional state. For example, if the user is feeling stressed, a response will be generated using appropriate language.

[1276] 6. Notification: The server sends a message tailored to the user's emotions to the device along with the generated promotional materials, notifying the user.

[1277] This system automates the entire process from collecting sales and business-related information to providing it to users, and further enables customization based on the user's emotional state, resulting in more effective information delivery.

[1278] The following describes the processing flow.

[1279] Step 1:

[1280] The server uses a data collection module to collect sales and operational data. The server sends requests to a market research API to retrieve customer feedback data. It also extracts new product information from the ERP system.

[1281] Step 2:

[1282] The server preprocesses the collected data and stores it in a database as structured data. For example, text-based feedback data is categorized into positive, negative, and neutral, and stored in the respective fields.

[1283] Step 3:

[1284] The user uses the device to request specific information. For example, if a user wants to obtain promotional materials for a new product, they would type "Please provide details about the new product" on the device's interface and press the send button.

[1285] Step 4:

[1286] The terminal sends a user request to the server. It uses an HTTP request to communicate the details of the request to the server.

[1287] Step 5:

[1288] The server receives the request and generates a query to retrieve relevant information from the database. The server generates an SQL query to search for new product information and sends it to the database.

[1289] Step 6:

[1290] The server passes the acquired data to the AI ​​module. The AI ​​module uses machine learning and natural language processing techniques to analyze the data and generate the most suitable response to the user's request. For example, it might create promotional materials focusing on the characteristics and benefits of a new product.

[1291] Step 7:

[1292] The server sends user input and voice data to the emotion engine. The emotion engine analyzes the user's emotions. For example, the emotion engine recognizes the user's emotional state (joy, sadness, anger, etc.) from the text and voice data entered by the user.

[1293] Step 8:

[1294] The server adaptively modifies the responses generated by the AI ​​module based on the analysis results of the emotion engine. For example, if the user is feeling stressed, the responses will be provided in a more considerate tone.

[1295] Step 9:

[1296] The server sends the final response to the device. It also sends the generated promotional materials and sentiment-sensitive messages to the device as an HTTP response.

[1297] Step 10:

[1298] The device sends a notification to the user. For example, it might display a message such as, "Promotional materials for our new product are ready. You can download them here." The notification message is also adaptively modified based on the analysis of the emotion engine.

[1299] Step 11:

[1300] Users download and view the provided materials using their devices.

[1301] (Example 2)

[1302] 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".

[1303] In today's business environment, collecting, analyzing, and providing information related to sales and operations is crucial. Traditional systems often require manual information collection and analysis, which is time-consuming and labor-intensive. Furthermore, they sometimes provide information uniformly without considering the user's emotional state, leading to low user satisfaction. To address these challenges, there is a need for a more efficient system that can provide information in a way that is sensitive to user emotions.

[1304] The identification processing performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for collecting information related to sales and operations using a data collection module, means for storing the collected data in a database and organizing it by category, means for users to input information requests using a terminal and receiving those requests, means for obtaining appropriate data from the database and generating answers through analysis using an AI module, means for delivering the generated answers to the user through a notification module, and means for analyzing user input and voice data with an emotion engine and adjusting the answers based on the user's emotional state. As a result, the process from information collection to provision to the user is automated, and customized information provision according to the user's emotional state becomes possible.

[1305] A "data collection module" is a device or software used to collect sales and business-related information from the internet or internal systems.

[1306] A "database" is an electronic information storage area used to store collected information and organize it by category.

[1307] A "user" is an entity that accesses a system and requests specific information.

[1308] A "terminal" is an electronic device that allows a user to access a system and provides a means of sending requests.

[1309] An "AI module" is a software component that uses machine learning and natural language processing technologies to analyze acquired data and generate appropriate responses.

[1310] An "emotion engine" is a device or software that analyzes user input and voice data to identify the user's emotional state.

[1311] A "notification module" is a device or software that delivers generated responses to users and provides notifications.

[1312] A "Market Research API" is an application programming interface for providing information about the market and customers.

[1313] An "ERP system" is an enterprise resource planning system, an information management system for integrating and managing various business processes within an organization.

[1314] A "generative AI model" is an algorithm or network model that has been trained using machine learning to perform data analysis and information generation.

[1315] A "prompt sentence" is a text of instructions or questions input into a generative AI model, intended to guide the output of specific information.

[1316] To implement the present invention, the system is configured using the following hardware and software. Specifically, the system consists of three components: a server, a terminal, and a user, and their roles and processing flow are described below.

[1317] Data Acquisition Module

[1318] The server uses a data collection module to gather information related to sales and operations. Specifically, the server periodically calls a market research API to obtain the latest customer feedback data. It also extracts information about new products from the ERP system. For example, when the server collects the latest customer feedback from the internet and extracts new product information from the ERP system, it calls the "getCustomerFeedback" API every hour and executes an SQL query on the ERP system.

[1319] database

[1320] The collected data is stored in a database by the server. The data is organized by category and stored in the appropriate fields to enable quick searching and retrieval. For example, the server analyzes customer feedback obtained as text, classifies it into positive, negative, and neutral categories using the "SentimentAnalysis" function, and stores it in the database according to each category.

[1321] User Interface

[1322] The user accesses the system through their device and requests specific information (e.g., promotional materials for a new product). The device then transmits this request to the server. For example, a user might type "Please provide detailed information about the new product" into the search box and press the submit button. The device sends the request to the "POST / api / request" endpoint.

[1323] AI Module

[1324] The server receives the request and retrieves relevant information from the database. The retrieved data is passed to the AI ​​module for analysis. The AI ​​module processes the data using machine learning and natural language processing techniques to generate optimal information tailored to the user's request. For example, the server might execute an SQL query like "SELECT FROM Products WHERE Type='New'" on the database to retrieve information, and then pass it to the AI ​​module's "generatePromotionMaterial" function to generate promotional materials.

[1325] Emotional Engine

[1326] The emotion engine works in conjunction with the AI ​​module. The server sends user input and voice data to the emotion engine to recognize the user's emotions. For example, from the text entered by the user or the voice data sent, the emotion engine generates a "StressLevel" field and sets its value to "High". Based on this result, the wording of the response is adjusted.

[1327] Notification module

[1328] The generated response (e.g., promotional materials) is sent from the server to the terminal. The terminal sends a notification to the user, providing instructions on how to access the generated materials. For example, the terminal might send a notification to the user saying, "New product promotional materials are ready. Please download them from here." Based on the analysis results of the emotion engine, the content of the response and the wording of the notification may be modified.

[1329] Example of a prompt

[1330] "Please provide detailed information about the new product."

[1331] "Please generate a report based on the latest customer feedback."

[1332] "Retrieve the results from the market research API and save them to the database."

[1333] This system automates the entire process from collecting sales and business-related information to providing it to users, and further enables customization based on the user's emotional state, resulting in more effective information delivery.

[1334] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1335] Step 1: Data Collection

[1336] The server uses a data collection module to gather information related to sales and operations. Specifically, it periodically calls a market research API to retrieve customer feedback data and new product information from the ERP system. Inputs are API requests and SQL queries, and outputs are the retrieved customer feedback and new product information data. For example, the server periodically calls the "getCustomerFeedback" API and executes a query like "SELECT FROM NewProducts WHERE ReleaseDate > CURDATE()" in the ERP system.

[1337] Step 2: Save Database

[1338] The server stores the collected data in a database. The data is organized and stored into positive, negative, and neutral categories. The input is the data obtained in step 1, and the output is the database entries organized by category. For example, the server analyzes customer feedback using the "SentimentAnalysis" function and executes an SQL query such as "INSERT INTO Feedback (Category, Content) VALUES ('Positive', 'Great product!')".

[1339] Step 3: Accepting User Requests

[1340] A user requests specific information through their device. The user enters "Please provide details about the new product" into the search box and presses the submit button. The input is the user's request text, and the output is the request data sent to the server. The device sends this request to the "POST / api / request" endpoint.

[1341] Step 4: Data Analysis

[1342] The server receives the request and retrieves relevant information from the database. The retrieved data is passed to the AI ​​module for analysis. The input is the request received in step 3 and the relevant data retrieved from the database, and the output is the generated material as a result of the analysis. For example, the server generates a query such as "SELECT FROM Products WHERE Type='New'", retrieves information from the database, and passes it to the "generatePromotionMaterial" function.

[1343] Step 5: Emotion Recognition

[1344] The server sends user input to the emotion engine to recognize the user's emotions. The emotion is analyzed from the user's input text and voice data. The input is the user's text and voice data, and the output is the emotion analysis result from the emotion engine. For example, the server might use the "analyzeEmotion" function and obtain "High Stress Level" as the analysis result.

[1345] Step 6: Generate Answer

[1346] The server receives the promotional materials generated by the AI ​​module and prepares them to be provided to the user. The input is the generated materials from step 4 and the sentiment analysis results from step 5, and the output is the final material to be provided to the user. For example, based on the sentiment analysis, the server generates a message saying, "Your document has been generated. Please check it now!"

[1347] Step 7: Notification Delivery

[1348] The server sends the generated response and message to the terminal. The terminal notifies the user and provides a link to access the materials. The input is the notification data from the server, and the output is the notification to the user. For example, the terminal sends a notification saying, "Promotional materials for the new product are ready. Please download them from here."

[1349] This series of steps allows the system to efficiently collect, analyze, and provide customized information to users.

[1350] (Application Example 2)

[1351] 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".

[1352] Traditional sales and business support systems had the functionality to collect and analyze data based on user requests, generate responses, and send notifications. However, they struggled to understand user emotions or provide customized services in real time. This resulted in challenges such as insufficient improvements in customer satisfaction and operational efficiency. Furthermore, in actual field use, the lack of real-time notification capabilities and integration with smart devices made immediate responses difficult.

[1353] In Application Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for collecting information related to sales and operations, means for storing the collected data in a database, means for receiving requests from users, means for obtaining and analyzing appropriate data from the database and generating a response, means for notifying the user of the generated response, and means for providing a customized service in real time based on the analyzed information. This enables the provision of a customized service in real time that responds to the user's emotional state, improving customer satisfaction and operational efficiency. Furthermore, collaboration with smart devices enables rapid response on-site.

[1354] "Information related to sales and operations" refers to data, feedback, market data, product information, and other information necessary for a company to conduct its business.

[1355] A "database" is a general term for a storage device and its management system that systematically stores various collected data and manages, searches, and retrieves it efficiently.

[1356] A "user request" refers to a request that a user of the system inputs or transmits in order to obtain specific information or services.

[1357] "Means for acquiring and analyzing appropriate data and generating responses" refers to methods and functions for acquiring relevant data from a database based on a user's request, analyzing it, and generating the response the user is seeking.

[1358] "Means for notifying the user of the generated response" refers to methods and functions for informing the user of the response generated by the analysis, such as through display or audio.

[1359] "Means for providing customized services in real time" refers to methods and functions for instantly providing optimized services based on the user's emotional state and request content.

[1360] "Internet and internal systems" refers to a general term encompassing networks accessible from the outside and information systems operated within a company.

[1361] "Using artificial intelligence to analyze data and generate responses" refers to the process of automatically analyzing data using machine learning and natural language processing technologies and generating responses that address user requests.

[1362] "Emotion recognition means" refers to methods and functions for automatically analyzing emotional states (e.g., joy, sadness, anger) from user input or voice data.

[1363] "Real-time notification means" refers to methods and functions for immediately notifying users or staff of analysis results and service details.

[1364] "Smart devices" is a general term for electronic devices that have internet connectivity and application execution capabilities, such as smartphones, smart glasses, tablets, and head-mounted displays.

[1365] To implement this invention, it is desirable that the system includes the following components. Specifically, it consists of three entities: a server, a terminal, and a user, and their respective roles and processing flows are described below.

[1366] Overall system configuration

[1367] The system consists of the following components:

[1368] 1. Data Acquisition Module

[1369] 2. Database

[1370] 3. User Interface

[1371] 4. AI Module

[1372] 5. Emotional Engine

[1373] 6. Notification Module

[1374] Data Acquisition Module

[1375] The server uses a data collection module to gather sales and business-related information from the internet and internal systems. For example, the server periodically calls a market research API to obtain the latest customer feedback data. It also extracts information about new products from the ERP system. The Python library 'requests' is used for API connections.

[1376] database

[1377] The collected data is stored in a database by the server. The data is organized by category and stored in appropriate fields to enable quick searching and retrieval. For example, customer feedback data is stored in positive, negative, and neutral categories, and a search index is assigned to each category. Examples of cloud databases used include AWS RDS and Google Cloud SQL.

[1378] User Interface

[1379] Users access the system through their devices and request specific information (e.g., promotional materials for a new product). The device then transmits the request to the server. For example, a user might type "Please provide detailed information about the new product" and press the submit button. Hardware used includes smartphones and smart glasses, while software includes a web application interface.

[1380] AI Module

[1381] The server receives the request and generates a query to retrieve relevant information from the database. The retrieved data is passed to the AI ​​module for analysis. The AI ​​module processes the data using machine learning and natural language processing (NLP) techniques to generate optimal information tailored to the user's request. The software used includes Google Cloud AI and AWS SageMaker.

[1382] Emotional Engine

[1383] The emotion engine works in conjunction with the AI ​​module. The server sends user input and voice data to the emotion engine to recognize the user's emotions. For example, the emotion engine analyzes emotions (e.g., joy, sadness, anger) from the text entered by the user or the transmitted voice data. One example of the software used is IBM Watson Tone Analyzer.

[1384] Notification module

[1385] The generated response (e.g., promotional materials) is sent from the server to the device. The device then sends a notification to the user, providing instructions on how to access the generated materials. For example, the device might notify the user, "New product promotional materials are ready. Please download them from here." Furthermore, the content of the response and the wording of the notification may be modified based on the analysis results of the sentiment engine. Firebase Cloud Messaging and Apple Push Notification Service are used as software for real-time notifications.

[1386] Specific example

[1387] For example, a server retrieves customer feedback from a market research API, such as "The texture of the new product was very good, but the price feels a little high," and stores it in a database. Then, when a user requests "Please provide detailed information about the new product" from their device, the server retrieves relevant information from the database and analyzes it with an AI module. At the same time, the user's emotional state is also analyzed by an emotion engine, and an appropriate response is generated. The generated response is notified to the device in real time using Firebase Cloud Messaging.

[1388] An example of a prompt message would be, "Perform sentiment analysis to identify the emotional state of this feedback."

[1389] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1390] Step 1:

[1391] The server uses a data collection module to gather sales and operational information from the internet and internal systems. Specifically, the server periodically calls a market research API to obtain the latest customer feedback data. It also extracts information about new products from the ERP system. The input is data from the market research API and the ERP system, and the output is the collected data.

[1392] Step 2:

[1393] The server stores the collected data in a database. The data is organized by category and stored in appropriate fields to enable quick searching and retrieval. Specifically, customer feedback data is categorized into positive, negative, and neutral, and a search index is assigned to each category. The input is the data collected in step 1, and the output is the organized database.

[1394] Step 3:

[1395] The user requests specific information through the terminal. The terminal then transmits that request to the server. For example, the user might type "Please provide detailed information about the new product" and press the submit button. The input is the user's request, and the output is the transmission of the request to the server.

[1396] Step 4:

[1397] The server receives a request from the user and generates a query to retrieve relevant information from the database. The retrieved data is passed to the AI ​​module for analysis. Specifically, it generates an SQL query containing the relevant information and retrieves data from the database. The input is the user's request and the data from the database, and the output is the data passed to the AI ​​module.

[1398] Step 5:

[1399] The server uses an AI module to analyze data and generate optimal information tailored to the user's request. The AI ​​module employs machine learning and natural language processing (NLP) techniques. During this analysis, an emotion engine also works in conjunction, analyzing emotions from the user's input and voice data. The input is the data passed to the AI ​​module, and the output is the optimal information resulting from the analysis.

[1400] Step 6:

[1401] The server passes the generated information to the notification module, which then sends a notification to the user's device. Specifically, it uses Firebase Cloud Messaging or Apple Push Notification Service to send the notification content to the device in real time. The input is the analysis results from the AI ​​module and the emotion engine, and the output is the notification sent to the user's device.

[1402] Step 7:

[1403] The terminal displays notifications to the user and provides instructions on how to access generated materials and information. For example, it might display a notification saying, "Promotional materials for our new product are ready. Please download them here." The input is the notification content sent from the server, and the output is the notification and access link for the user.

[1404] 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.

[1405] 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.

[1406] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[1407] 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.

[1408] 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.

[1409] 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.

[1410] 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.

[1411] 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.

[1412] 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."

[1413] 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.

[1414] 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.

[1415] 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.

[1416] 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.

[1417] 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.

[1418] 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.

[1419] 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.

[1420] 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.

[1421] 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.

[1422] 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.

[1423] 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.

[1424] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[1425] The following is further disclosed regarding the embodiments described above.

[1426] (Claim 1)

[1427] Means of collecting data related to sales and operations,

[1428] A means of storing the collected data in a database,

[1429] A means of receiving requests from users,

[1430] A means of retrieving appropriate data from a database, analyzing it, and generating an answer,

[1431] A means of notifying the user of the generated response,

[1432] A system that includes this.

[1433] (Claim 2)

[1434] The system according to claim 1, further comprising means for collecting the aforementioned data from the Internet and an internal system.

[1435] (Claim 3)

[1436] The system according to claim 1, characterized in that the analysis means uses artificial intelligence to analyze data and generate an answer.

[1437] "Example 1"

[1438] (Claim 1)

[1439] A means of collecting information related to sales and operations using a data collection module,

[1440] A means of organizing the collected data by category and storing it in a database,

[1441] A means for receiving information requests from users via a user interface,

[1442] A means of obtaining appropriate data from a database, analyzing it with an AI module, and generating an answer,

[1443] A means of notifying the user of the generated response,

[1444] A system that includes this.

[1445] (Claim 2)

[1446] The system according to claim 1, further comprising means for collecting the aforementioned data from the Internet and a business management system.

[1447] (Claim 3)

[1448] The system according to claim 1, characterized in that the analysis means uses machine learning and natural language processing techniques to analyze data and generate answers.

[1449] "Application Example 1"

[1450] (Claim 1)

[1451] Means of collecting data related to sales and operations,

[1452] A means of storing the collected data in a database,

[1453] A means of receiving requests from users,

[1454] A means of retrieving appropriate data from a database, analyzing it, and generating an answer,

[1455] A means of notifying the user of the generated response,

[1456] A means for users to check information in real time via smart devices,

[1457] A system that includes this.

[1458] (Claim 2)

[1459] The system according to claim 1, further comprising means for collecting the aforementioned data from the Internet and an internal system.

[1460] (Claim 3)

[1461] The system according to claim 1, characterized in that the analysis means uses artificial intelligence to analyze data and generate an answer.

[1462] (Claim 4)

[1463] The system according to claim 1, characterized in that the means for real-time confirmation operates via a smart device.

[1464] "Example 2 of combining an emotion engine"

[1465] (Claim 1)

[1466] A means of collecting information related to sales and operations using a data collection module,

[1467] A means of storing the collected data in a database and organizing it by category,

[1468] A means by which a user inputs an information request using a terminal and a means by which that request is received.

[1469] A means of obtaining appropriate data from a database and generating an answer through analysis using an AI module,

[1470] A means of delivering the generated response to the user through a notification module,

[1471] A means of analyzing user input and voice data using an emotion engine and adjusting responses based on the user's emotional state,

[1472] A system that includes this.

[1473] (Claim 2)

[1474] The system according to claim 1, further comprising means for collecting the aforementioned data from the Internet and internal systems, and for obtaining specific information through market research APIs and ERP systems.

[1475] (Claim 3)

[1476] The system according to claim 1, characterized in that the analysis means analyzes data using a generation AI model and natural language processing technology to generate optimal information.

[1477] "Application example 2 when combining with an emotional engine"

[1478] (Claim 1)

[1479] Means of collecting data related to sales and operations,

[1480] A means of storing the collected data in a database,

[1481] A means of receiving requests from users,

[1482] A means of retrieving appropriate data from a database, analyzing it, and generating an answer,

[1483] A means of notifying the user of the generated response,

[1484] A means to provide customized services in real time based on analyzed information,

[1485] A system that includes this.

[1486] (Claim 2)

[1487] The system according to claim 1, further comprising means for collecting the aforementioned data from the Internet and an internal system.

[1488] (Claim 3)

[1489] The system according to claim 1, characterized in that the analysis means uses artificial intelligence to analyze data and generate an answer.

[1490] (Claim 4)

[1491] The system according to claim 1, further comprising emotion recognition means for analyzing the user's input content or voice data to recognize the emotional state.

[1492] (Claim 5)

[1493] The system according to claim 4, characterized in that the customized service is optimized based on the customer's emotional state.

[1494] (Claim 6)

[1495] The system according to claim 1, further comprising real-time notification means for notifying staff of the customized service.

[1496] (Claim 7)

[1497] The system according to claim 1, characterized in that the customized service is provided on a smart device. [Explanation of symbols]

[1498] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. Means of collecting data related to sales and operations, A means of storing the collected data in a database, A means of receiving requests from users, A means of retrieving appropriate data from a database, analyzing it, and generating an answer, A means of notifying the user of the generated response, A system that includes this.

2. The system according to claim 1, further comprising means for collecting the aforementioned data from the Internet and an internal system.

3. The system according to claim 1, characterized in that the analysis means uses artificial intelligence to analyze data and generate an answer.

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