system

Abstract

We provide a system that allows users to easily integrate generative AI with multiple existing business solutions. [Solution] A system including means for a user to input information and create an account, means for storing the input user information in a database, means for generating an authentication token and sending it back to the user, means for the user to log in and select a plugin that links with multiple external systems, means for inputting authentication information for the selected plugin and verifying the authentication information, means for periodically collecting data from external systems and sending it to an AI model, means for displaying the analysis results from the AI ​​model to the user, means for recording the user's usage status, calculating usage fees at the end of the month and generating an invoice, and means for sending the invoice to the user and confirming payment.

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 as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In order to link a conventional generative AI system with existing business solutions, highly specialized technical knowledge is required individually, which is time-consuming and laborious. Further, it has been a problem that it is difficult to manage different authentication information for each link and to automate data collection and analysis. The present invention aims to solve these problems and provide a system that enables a user to easily link a generative AI with a plurality of existing business solutions.

Means for Solving the Problems

[0005] The present invention includes means for a user to input information and create an account, means for storing the input user information in a database, and means for generating and returning an authentication token to the user. This system has means for a user to log in and select a plugin that interacts with multiple external systems, and means for inputting authentication information for the selected plugin and verifying the authentication information.

[0006] Furthermore, this system includes means for periodically collecting data from external systems and transmitting it to an AI model, and means for displaying the analysis results from the AI ​​model to the user. It also includes means for recording user usage, calculating usage fees at the end of the month, generating invoices, and sending invoices to users and confirming payment. Through these means, the present invention enables simple and effective integration of generative AI with existing business solutions.

[0007] "User" refers to a person or group that uses the system.

[0008] An "account" refers to a set of information used to identify and use a user.

[0009] A "database" is a collection of stored information that a system can access and use.

[0010] An "authentication token" is a unique identifier generated to authenticate a user.

[0011] "Collaboration" refers to different systems or plugins sharing information and working together.

[0012] A "plugin" refers to an external module used to extend specific functionality.

[0013] "Authentication information" refers to the information that a system needs to authenticate a user or another system.

[0014] "Server" refers to a computer that manages the entire system and processes requests from users.

[0015] "Terminal" refers to a device used by a user to access the system.

[0016] "AI model" refers to artificial intelligence trained to analyze data and execute specific tasks.

[0017] "Analysis result" refers to the conclusions or predictions generated by the AI model processing data.

[0018] "Usage situation" refers to a record of how a user has used the system.

[0019] "Usage fee" refers to the fee paid by the user for using the system.

[0020] "Invoice" refers to an official document for billing payment for the provided service.

[0021] "Payment method" refers to the method (e.g., credit card, automatic debit) selected by the user to pay the usage fee.

Brief Description of Drawings

[0022] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Mode for Carrying Out the Invention

[0023] 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.

[0024] First, the language used in the following description will be explained.

[0025] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), and APU (Accelerated Processing Unit).

[0026] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0027] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0028] 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).

[0029] 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."

[0030] [First Embodiment]

[0031] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0032] 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.

[0033] 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).

[0034] 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.

[0035] 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.

[0036] 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.

[0037] 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.

[0038] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0039] 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.

[0040] 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.

[0041] 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.

[0042] 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".

[0043] This invention provides a system that allows users to easily integrate generative artificial intelligence (AI) with multiple existing business solutions. The basic components of this system include a user interface, database, authentication tokens, plugin management, data collection modules, AI models, and usage fee calculation functions.

[0044] User registration and authentication

[0045] The user accesses the system and creates an account. This involves entering an email address and password and clicking the "Register" button. Next, the device sends the entered information to the server, which then saves it to the database, completing the user registration. The server generates an authentication token and sends it back to the user, making future logins easier.

[0046] Plugin selection and configuration

[0047] When a user logs in, the server displays a dashboard allowing the user to select the plugin they want to integrate. The user selects a plugin (e.g., CRM system, marketing tool) and enters the necessary API key and authentication information. This information is sent to the server via the terminal. The server verifies the authentication information, and if the integration is successful, saves the settings to the database.

[0048] Data integration and analysis

[0049] The server sets a schedule to periodically collect data from the plugin once the integration settings are complete. For example, the server might call the external system's API every hour to collect data. The collected data is temporarily stored, and then the server sends it to the AI ​​model. The AI ​​model processes the data and sends the analysis results back to the server. The server stores these results in a database and displays them on the user's dashboard.

[0050] For example, when a user integrates with a marketing tool, the server sends data collected from the marketing tool to an AI model for predictive analysis. As a result, the effectiveness of the marketing campaign and recommendations for the next action are displayed on a dashboard.

[0051] Calculation and billing of usage fees

[0052] The server records the user's plugin usage and compiles usage charges at the end of each month. This includes the number of API requests and data processing cycles. Based on the compiled results, the server calculates the usage charges, automatically generates an invoice, and sends it to the user. After the user reviews the invoice and pays the fee using their configured payment method, the server confirms receipt of payment and switches to the next month's usage.

[0053] This invention enables users to efficiently manage multiple business solutions and easily utilize the advanced analytical capabilities of generative AI.

[0054] The following describes the processing flow.

[0055] User registration and authentication

[0056] Step 1:

[0057] The user accesses the account creation page, enters their email address and password, and clicks the "Register" button.

[0058] Step 2:

[0059] The device sends a request to the server containing the information entered by the user.

[0060] Step 3:

[0061] The server receives the request and saves the entered email address and password to the database.

[0062] Step 4:

[0063] The server generates an authentication token for the user and stores it in the database.

[0064] Step 5:

[0065] The server generates an authentication token and sends it back to the user.

[0066] Plugin selection and configuration

[0067] Step 1:

[0068] The user enters their email address and password on the login screen and clicks the "Login" button.

[0069] Step 2:

[0070] The device sends login information to the server.

[0071] Step 3:

[0072] The server verifies the login information it receives, and if it is correct, it displays the dashboard screen to the user.

[0073] Step 4:

[0074] The user selects the plugin they want to integrate from the dashboard screen and clicks the "Settings" button.

[0075] Step 5:

[0076] The user enters the necessary API key and authentication information for the selected plugin and clicks the "Save" button.

[0077] Step 6:

[0078] The terminal sends the entered authentication information to the server.

[0079] Step 7:

[0080] The server uses the received authentication information to call the plugin's API and verify the authentication information.

[0081] Step 8:

[0082] If the server successfully authenticates, it saves the plugin integration settings to the database and notifies the user that the integration is complete.

[0083] Data integration and analysis

[0084] Step 1:

[0085] The server sets a schedule for collecting data from the plugin.

[0086] Step 2:

[0087] The server collects data by calling the plugin's API based on a configured schedule.

[0088] Step 3:

[0089] The server temporarily stores the collected data in storage.

[0090] Step 4:

[0091] The server sends the stored data to the AI ​​model and requests analysis processing.

[0092] Step 5:

[0093] The AI ​​model analyzes the received data and sends the processing results back to the server.

[0094] Step 6:

[0095] The server receives the processing results and saves them to the database.

[0096] Step 7:

[0097] The server displays the analysis results on the user's dashboard, allowing the user to review the results.

[0098] Calculation and billing of usage fees

[0099] Step 1:

[0100] The server periodically records user activity (number of API requests, number of data processing attempts, etc.).

[0101] Step 2:

[0102] The server compiles all usage data at the end of the month and calculates the usage fee.

[0103] Step 3:

[0104] The server automatically generates an invoice based on usage fees.

[0105] Step 4:

[0106] The server sends the generated invoice to the user's registered email address.

[0107] Step 5:

[0108] The user reviews the invoice and pays the fee using their designated payment method.

[0109] Step 6:

[0110] The server confirms receipt of payment and switches to the next month's usage.

[0111] These steps enable users to integrate generative AI with multiple business solutions and utilize them efficiently.

[0112] (Example 1)

[0113] 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."

[0114] When integrating multiple existing business solutions with generative artificial intelligence (AI), users often need to manually configure and manage individual settings, resulting in decreased efficiency and cumbersome operation. Furthermore, the process of centrally managing data from multiple systems and performing advanced analysis using AI is complex and burdensome for users.

[0115] 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.

[0116] In this invention, the server includes means for a user to input information and create an account, means for storing the input user information in a database, and means for generating and returning an authentication token to the user. This allows users to efficiently create accounts and easily access the system.

[0117] A "user" is a person or group that accesses a system and performs various operations on it.

[0118] "Means for inputting information" refers to interfaces or forms that allow users to provide necessary information such as email addresses and passwords.

[0119] "Means for creating an account" refers to a function that executes the process of generating a new user account based on the entered information.

[0120] "Means of saving to a database" refers to a data management system or software for permanently storing collected user information and other data.

[0121] A "token for authentication" is a unique identifier issued to verify a user's identity and facilitate future logins.

[0122] "Means for generating tokens" refers to the process or function for creating authentication tokens and providing the generated tokens to users.

[0123] "Means of storing tokens in a database" refers to a data management system that securely stores generated authentication tokens so that they can be verified and compared later.

[0124] "Means of returning to the user" refers to a method or protocol for sending the generated authentication token or other information to the user.

[0125] "External systems" refer to other software or services located outside the system, including those that can be interfaced through APIs.

[0126] A "plugin" is a software module that enables integration with external systems and adds specific functionality to a system.

[0127] "Authentication information" refers to information required when integrating with external systems, such as API keys, usernames, and passwords.

[0128] "Means of collecting data regularly" refers to the process of automatically collecting data from external systems according to a set schedule.

[0129] A "generative AI model" is an artificial intelligence algorithm that analyzes collected data and generates results.

[0130] "Means for displaying analysis results" refers to interfaces or dashboards that display the analysis results generated by the AI ​​model in a user-friendly format.

[0131] "Means for recording usage" refers to a system that tracks user behavior and system usage history and stores it as a log.

[0132] "Methods for calculating usage fees" refers to the process of calculating appropriate usage fees for users based on recorded usage data.

[0133] "Means for generating invoices" refers to a system that creates an invoice based on calculated usage fees and sends it to the user.

[0134] "Means of confirming payment" refers to a method or process for verifying whether a payment from a user has been completed and reflecting that result in the system.

[0135] This invention provides a system that allows users to easily integrate generative artificial intelligence (AI) with multiple existing business solutions. The following hardware and software are required to implement this system.

[0136] Hardware and software:

[0137] Server: Handles database management, authentication token generation, data collection scheduling, integration with AI models, results display, usage tracking, usage fee calculation, and invoice generation.

[0138] Terminal: This is the user interface (UI) where users enter information, create accounts, log in, select and configure plugins, and check the results.

[0139] Database: Stores user information, authentication tokens, integration plugin settings, collected data, analysis results, usage status, etc.

[0140] Generative AI model: An artificial intelligence algorithm used to analyze data and generate analysis results.

[0141] Processing flow:

[0142] User registration and authentication:

[0143] The user accesses the system, enters their email address and password, and creates an account. The information entered through the user interface is sent from the terminal to the server. The server stores the user information in a database, generates an authentication token, and sends it back to the user. This allows the user to log in easily later.

[0144] Plugin selection and configuration:

[0145] When a user logs into the system, the server displays a dashboard where the user can select the plugins they want to integrate. Examples include CRM systems and marketing tools. The user enters the API key and authentication information for the selected plugin, and this information is sent from the terminal to the server. The server verifies the authentication information and, if correct, saves it to the database.

[0146] Data integration and analysis:

[0147] Once the plugin integration is complete, the server sets a data collection schedule. For example, the server might call an external system's API to collect data at 1 AM every day. The collected data is temporarily stored and then sent by the server to a generative AI model. The generative AI model analyzes the data and sends the results back to the server. The server saves the analysis results to a database and displays them on the user's dashboard.

[0148] Calculation and billing of usage fees:

[0149] The server records user usage and compiles usage charges at the end of each month. This includes the number of API requests and data processing attempts. Based on the compiled results, the server calculates the usage charges, automatically generates an invoice, and sends it to the user. After the user reviews the invoice and pays the fee using the configured payment method, the server confirms receipt of payment and switches to the next month's usage.

[0150] Examples of specific cases and prompt statements:

[0151] As a concrete example, consider a scenario where a user wants to integrate with a marketing tool and have AI analyze sales data. The server configures the integration using the API key of the marketing tool entered by the user. The server collects data daily and sends it to a generated AI model for predictive analysis. The results are displayed on the dashboard as "Next month's sales forecast: 1 million yen."

[0152] Example of a prompt:

[0153] "We collect data from marketing tools daily, analyze it with an AI model, and predict next month's sales."

[0154] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0155] Step 1:

[0156] The user accesses the system and creates an account by entering their email address and password. The email address and password entered through the user interface are sent from the terminal to the server. The server stores the received information in its database and completes the registration. At this time, the server generates an authentication token and sends it back to the user. This makes subsequent logins easier.

[0157] Input: Email address, password

[0158] Output: Authentication token

[0159] Step 2:

[0160] When a user logs in, the server displays the dashboard using the user's authentication information. The dashboard displays a list of available plugins. The user selects the plugin they want to integrate and enters the necessary API key and authentication information.

[0161] Input: API key, credentials

[0162] Output: Plugin configuration information

[0163] Step 3:

[0164] The terminal sends plugin configuration information to the server, which verifies it. If the verification is successful, the server saves the configuration information to the database and completes the integration. Based on this integration configuration, the data collection described later is performed.

[0165] Input: Plugin configuration information

[0166] Output: Verification results, saved.

[0167] Step 4:

[0168] To periodically collect data from plugins whose integration settings are complete, the server sets a data collection schedule. For example, the server might call the external system's API at 1 AM every day to collect data. The collected data is temporarily stored.

[0169] Input: Data collection schedule

[0170] Output: Collected data

[0171] Step 5:

[0172] The server sends the collected data to the generative AI model. The generative AI model analyzes the received data and sends the analysis results back to the server.

[0173] Input: Collected data

[0174] Output: Analysis results

[0175] Step 6:

[0176] The server saves the analysis results to a database and displays them on the user's dashboard. The analysis results are provided in a visually easy-to-understand format, making them easily accessible to the user.

[0177] Input: Analysis results

[0178] Output: Dashboard update

[0179] Step 7:

[0180] The server records user plugin usage and compiles usage charges at the end of each month. This includes the number of API requests and data processing cycles. Based on the compiled results, the server calculates the usage charges, automatically generates an invoice, and sends it to the user.

[0181] Input: Usage data

[0182] Output: Invoice

[0183] Step 8:

[0184] After the user reviews the invoice, they pay the fee using their configured payment method. The server confirms receipt of the payment and switches to the next month's usage. This allows the user to continue using the system.

[0185] Input: Payment Information

[0186] Output: Payment confirmation, usage status update

[0187] (Application Example 1)

[0188] 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."

[0189] Modern logistics centers involve a complex interplay of numerous systems and processes, requiring efficient integration of their data. However, existing systems often rely on manual data collection, analysis, and optimization processes, resulting in significant time and effort, and consequently, reduced operational efficiency. Furthermore, the difficulty in real-time monitoring and issuing appropriate instructions leads to insufficient optimization of inventory and delivery management. The development of technologies to address these issues and improve the operational efficiency of logistics centers is highly desirable.

[0190] 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.

[0191] In this invention, the server includes means for a user to input information and create an account; means for storing the input user information in a database; means for generating and returning an authentication token to the user; means for the user to log in and select a plugin that links with multiple external systems; means for inputting authentication information for the selected plugin and verifying the authentication information; means for periodically collecting data from external systems and sending it to an AI model; means for displaying the analysis results from the AI ​​model to the user; means for recording the user's usage status, calculating usage fees at the end of the month, and generating an invoice; means for sending the invoice to the user and confirming payment; means including a generative AI model that links with the management system of a logistics center and optimizes inventory and delivery based on collected data; and means for performing predictive analysis from inventory and delivery data and notifying the analysis results to the smartphone in real time. This makes it possible to link multiple business processes in real time and manage them efficiently.

[0192] "User" refers to an individual or group that uses the system.

[0193] "Information" refers to all data that users enter when creating an account or using the system.

[0194] An "account" refers to the registration information required for a user to access the system.

[0195] A "database" refers to a storage area within a system used to store and manage information.

[0196] A "token" refers to a series of strings or codes generated for user authentication.

[0197] A "server" is a computer that forms the core of a system and is responsible for processing and storing data.

[0198] A "plugin" refers to an extension that adds new functionality to a system.

[0199] "Authentication information" refers to information such as API keys, IDs, and passwords that users need to connect with the plugin.

[0200] An "external system" refers to another system or service that exists outside of the system and interacts with it.

[0201] An "AI model" refers to an artificial intelligence model that analyzes collected data and performs predictions and optimizations.

[0202] "Analysis results" refer to the results derived by the AI ​​model after analyzing the data.

[0203] A "smartphone" refers to a portable information device that is capable of connecting to the internet and using a variety of applications.

[0204] A "logistics center" refers to a facility used to manage the storage and distribution of goods.

[0205] "Inventory" refers to the total amount of goods stored in a distribution center.

[0206] "Delivery" refers to the movement of goods from a logistics center to customers or other facilities.

[0207] "Predictive analytics" refers to an analytical method that predicts future trends and situations based on collected data.

[0208] This invention comprises a system including a user interface, database, authentication token, plugin management, data collection module, AI model, and usage fee calculation function. Users access this system and perform various operations using a smartphone.

[0209] The server first provides a means for the user to enter information and create an account. The information entered by the user is stored in a database, an authentication token is generated, and sent back to the user. This makes it easier for the user to log in on subsequent occasions.

[0210] When a user logs in, the server provides a means for selecting plugins that integrate with multiple external systems. The user selects a plugin, such as an inventory management system or a delivery management system, and enters the necessary authentication information. This authentication information is verified by the server, and the results are stored in the database.

[0211] The server periodically collects data from external systems and provides a means to send the collected data to an AI model. For example, data is collected regularly every hour, and the AI ​​model analyzes it. The analysis results are stored in a database by the server and notified to the user's smartphone in real time.

[0212] When integrated with a logistics center's management system, the AI ​​model performs predictive analytics based on collected inventory and delivery data. This enables the optimization of inventory and delivery within the logistics center. For example, it can provide information such as which products should have increased inventory next and which delivery routes are the most efficient.

[0213] The server also handles the usage fee calculation. The server records the user's plugin usage and calculates the usage fee at the end of the month. Based on the calculation, it generates an invoice and sends it to the user. Once the user reviews the invoice and pays the fee using the configured payment method, the server confirms receipt of the payment and switches to the next month's usage.

[0214] Hardware and software to be used:

[0215] Hardware: Smartphones, servers

[0216] Software: Flask (Web framework), SQLite (database), JWT (authentication token generation)

[0217] (Specific example)

[0218] Logistics center managers can use the "Logistics Center AI Assist" application to collect data from the inventory management system, analyze it with generative AI, and determine the optimal inventory placement method. For example, they can use prompts like the following:

[0219] Examples of prompts to input into a generative AI model:

[0220] Using data obtained from the inventory management system, forecast future demand and propose the optimal inventory allocation method. The data obtained is as follows:

[0221] {data}

[0222] This prompt prompts the AI ​​to analyze inventory data and suggest the optimal inventory placement method. This significantly improves the operational efficiency of the logistics center and reduces costs.

[0223] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0224] Step 1:

[0225] User registration and authentication

[0226] The server provides an input form for the user to enter information and create an account. The user enters the required information (e.g., email address, password) and clicks the "Register" button. Once the device sends the entered information to the server, the server stores it in its database, generates an authentication token, and sends it back to the user. This provides the user with data that will facilitate future logins.

[0227] Input: User information (email address, password)

[0228] Data processing: Saving user information to a database, generating authentication tokens.

[0229] Output: Authentication token

[0230] Step 2:

[0231] Plugin selection and configuration

[0232] When a user logs in, the server displays a dashboard screen allowing the user to select the plugin they want to integrate (e.g., inventory management system, shipping management system). The user enters the authentication information for the selected plugin (e.g., API key), and the device sends this information to the server. The server verifies the authentication information and saves the result to the database.

[0233] Input: Plugin credentials (API key)

[0234] Data processing: Verification of authentication information, saving to database.

[0235] Output: Plugin configuration result

[0236] Step 3:

[0237] Data collection and transmission

[0238] The server sets a schedule to periodically collect data from external systems. For example, the server calls APIs of external inventory management and delivery management systems every hour to collect data. The collected data is temporarily stored by the server and then sent to the AI ​​model.

[0239] Input: Data from an external system

[0240] Data processing: Data collection, temporary storage, and transmission to AI models.

[0241] Output: Data to send to the AI ​​model

[0242] Step 4:

[0243] Data analysis and result display

[0244] The AI ​​model analyzes the data sent from the server. The analysis results are sent back to the server, which then stores them in a database. The user's smartphone is notified of the analysis results in real time.

[0245] Input: Collected data

[0246] Data processing: Data analysis, generation of analysis results.

[0247] Output: Analysis results

[0248] Step 5:

[0249] Predictive analytics and optimization proposals

[0250] The AI ​​model works in conjunction with the logistics center's management system to perform predictive analytics based on inventory and delivery data. For example, it predicts which products should have increased inventory next and what the optimal delivery routes are. The analysis results are notified to smartphones, allowing users to check the details in real time via their phones.

[0251] Input: Collected data

[0252] Data processing: Predictive analytics, generation of optimization suggestions.

[0253] Output: Predictive analysis results, optimization suggestions

[0254] Step 6:

[0255] Calculation and billing of usage fees

[0256] The server records the user's plugin usage and calculates the usage fee at the end of the month. Based on the calculation, it automatically generates an invoice and sends it to the user. Once the user reviews the invoice and pays the fee using the configured payment method, the server confirms the payment and switches to the next month's usage.

[0257] Input: Plugin usage data

[0258] Data processing: Recording usage, calculating usage fees, generating invoices.

[0259] Output: Invoice, payment confirmation

[0260] This will allow logistics center management operations to be performed more efficiently and in real time.

[0261] 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.

[0262] This invention provides a system that improves user experience by combining a generative artificial intelligence (AI) with an emotion engine to integrate multiple existing business solutions. This system includes functions such as user registration and authentication, plugin selection and configuration, data integration and analysis, emotion engine integration, and usage fee calculation and billing.

[0263] User registration and authentication

[0264] The user accesses the system, enters their email address and password on the account creation page, and clicks the "Register" button. The device sends the entered information to the server, which stores it in its database. The server also generates and sends an authentication token back to the user. This authentication token allows for easier logins in the future.

[0265] Plugin selection and configuration

[0266] The user logs into the dashboard and selects the plugin they want to integrate (for example, a CRM system or marketing tool). The user enters the necessary API key and authentication information for the selected plugin and clicks the "Save" button. The device sends the entered information to the server, which verifies the authentication information. If authentication is successful, the plugin integration settings are saved to the database and the user is notified.

[0267] Data integration and analysis

[0268] The server sets a schedule for collecting data from the plugin. For example, it might call the plugin's API every hour to collect data. The collected data is temporarily stored and sent to the AI ​​model. The AI ​​model analyzes the data and sends the processing results back to the server. The server saves these results to a database and displays them to the user.

[0269] Emotional engine integration

[0270] An emotion engine is incorporated to recognize emotions from user input. For example, when a user enters a comment on the dashboard, the device sends the input information to the server, which uses the emotion engine to analyze the user's emotional state. The recognized emotional state is stored in a database along with other data. Based on the analysis results of the emotion engine, the server adjusts the data sent to connected external systems.

[0271] Presentation of analysis results tailored to emotions

[0272] Based on the analyzed emotions, the server changes how it presents the analysis results from the AI ​​model. For example, if the user is stressed, it displays concise and direct feedback, while if they are relaxed, it provides detailed analytics.

[0273] Calculation and billing of usage fees

[0274] The server records user usage and compiles the data at the end of each month. This includes the number of API requests and data processing cycles. Furthermore, sentiment data obtained using the sentiment engine is also incorporated into the usage fee calculation. The server automatically generates an invoice and sends it to the user. Once the user reviews the invoice and pays using the configured payment method, the server confirms receipt of payment and switches to the next month's usage.

[0275] In this way, the present invention enables the efficient integration of generative AI with multiple business solutions while taking into account the user's emotional state. A specific example is a scenario where a user interacts with a customer support tool, and the emotion engine analyzes the user's emotional state to provide appropriate responses and advice. This system is expected to improve operational efficiency and customer satisfaction.

[0276] The following describes the processing flow.

[0277] User registration and authentication

[0278] Step 1:

[0279] The user accesses the account creation page, enters their email address and password, and clicks the "Register" button.

[0280] Step 2:

[0281] The terminal sends the input information to the server.

[0282] Step 3:

[0283] The server saves the received information in the database.

[0284] Step 4:

[0285] The server generates an authentication token and saves it in the database.

[0286] Step 5:

[0287] The server returns the generated authentication token to the user.

[0288] Selection and configuration of plugins

[0289] Step 1:

[0290] The user enters the email address and password on the login screen and clicks the "Login" button.

[0291] Step 2:

[0292] The terminal sends the login information to the server.

[0293] Step 3:

[0294] The server verifies the login information and, if correct, displays the dashboard screen to the user.

[0295] Step 4:

[0296] The user selects the plugin to be linked, enters the API key and authentication information, and clicks the "Save" button.

[0297] Step 5:

[0298] The terminal sends the input information to the server.

[0299] Step 6:

[0300] The server uses the authentication information to call the API on the plugin side to verify the authentication information.

[0301] Step 7:

[0302] If the server successfully authenticates, it saves the plugin's linkage settings to the database and notifies the user.

[0303] Data Linkage and Analysis

[0304] Step 1:

[0305] The server sets a schedule for collecting data from the plugin.

[0306] Step 2:

[0307] The server calls the API of the plugin based on the set schedule to collect data.

[0308] Step 3:

[0309] The server temporarily stores the collected data in storage.

[0310] Step 4:

[0311] The server sends the stored data to the AI model and requests analysis processing.

[0312] Step 5:

[0313] The AI model analyzes the data and returns the processing results to the server.

[0314] Step 6:

[0315] The server receives the analysis results and saves them to the database.

[0316] Step 7:

[0317] The server displays the analysis results on the user's dashboard.

[0318] Emotional engine integration

[0319] Step 1:

[0320] Users enter comments and feedback on the dashboard.

[0321] Step 2:

[0322] The terminal sends the input information to the server.

[0323] Step 3:

[0324] The server sends the received data to the emotion engine, which then analyzes the user's emotional state.

[0325] Step 4:

[0326] The emotion engine sends the analysis results back to the server.

[0327] Step 5:

[0328] The server stores emotional states in a database and adjusts the content of data sent to external systems with which it collaborates.

[0329] Presentation of analysis results tailored to emotions

[0330] Step 1:

[0331] The server modifies the analysis results based on the user's emotional state.

[0332] Step 2:

[0333] For example, if a user is feeling stressed, the server displays concise feedback; if they are relaxed, it provides detailed analytics.

[0334] Calculation and billing of usage fees

[0335] Step 1:

[0336] The server records user activity (number of API requests, number of data processing attempts, etc.).

[0337] Step 2:

[0338] The server compiles all usage data at the end of the month and calculates the usage fee.

[0339] Step 3:

[0340] The server automatically generates invoices, including emotional data from an emotion engine.

[0341] Step 4:

[0342] The server sends the generated invoice to the user's registered email address.

[0343] Step 5:

[0344] The user reviews the invoice and pays the fee using their chosen payment method (e.g., credit card, bank transfer).

[0345] Step 6:

[0346] The server confirms receipt of payment and switches to the next month's usage.

[0347] In this way, the present invention, which integrates an emotion engine, can provide a system that enables the collaboration of generative AI and various business solutions while recognizing the user's emotional state. A specific example is a scenario in which the emotion engine analyzes the user's emotional state and supports appropriate responses when collaborating with a customer support tool. This system allows users to efficiently utilize data and provide higher service quality.

[0348] (Example 2)

[0349] 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".

[0350] Traditional business solution systems struggle to provide services that take into account the emotional state of users based on their input information, making it difficult to improve the quality of the user experience. Furthermore, they are unable to efficiently integrate with multiple external systems, placing a significant burden on users for manual configuration and adjustments. Additionally, tracking usage and calculating fees are cumbersome and lack automation.

[0351] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0352] In this invention, the server includes means for a user to input information and create an account; means for storing the input user information in a database; means for generating and returning an authentication token to the user; means for the user to log in and select a plugin that integrates with multiple external systems; means for inputting authentication information for the selected plugin and verifying the authentication information; means for periodically collecting data from external systems and sending it to a generating AI model; means for displaying the analysis results from the generating AI model to the user; means for integrating an emotion engine that analyzes the user's input information and generates emotion data; means for optimizing and displaying the results from the generating AI model based on the analyzed emotion data; means for recording the user's usage status, calculating usage fees at the end of the month, and generating an invoice; and means for sending the invoice to the user and confirming payment. This makes it possible to improve the quality of the user experience, efficiently integrate with multiple external systems, and realize the understanding of usage status and automatic calculation of usage fees.

[0353] A "user" refers to an individual or legal entity that uses the system, enters information to create an account, selects plugins, and receives services.

[0354] An "authentication token" refers to a unique code generated by a server to authenticate a user and sent back to the user.

[0355] A "database" refers to a storage system that stores a collection of data that is collected, stored, and managed by a system.

[0356] A "plugin" refers to a software module that provides additional functionality to a system and enables it to interact with various external systems.

[0357] "Authentication information" refers to information such as API keys, usernames, and passwords necessary to establish integration with the plugin.

[0358] A "generative AI model" refers to an artificial intelligence algorithm that analyzes collected data and returns the results to a server to provide advanced services.

[0359] An "emotion engine" refers to a machine learning model or algorithm that analyzes user input information to identify the user's emotional state and adjusts the system's operation based on that.

[0360] "Usage status" refers to data related to system usage (e.g., number of API requests, number of data processing attempts) and records based on that data.

[0361] An "invoice" refers to a document sent to a user that shows the costs incurred for using the system.

[0362] "External systems" refer to other software or platforms that provide services through integration with the system.

[0363] These definitions clarify the meaning of each term used in the patent claims, making them easier to understand.

[0364] This invention provides a system that improves user experience by combining an emotion engine with generative artificial intelligence (AI) and the integration of multiple existing business solutions. This system is implemented using the following hardware and software configuration.

[0365] Hardware and software configuration

[0366] 1. Server: Manages key backend functions such as data storage, processing, and API calls. Examples include using cloud servers such as AWS® or Google® Cloud Platform.

[0367] 2. Terminal: A device used to obtain user input information and send it to the server. This includes PCs, smartphones, tablets, etc.

[0368] 3. Database: Stores user information, plugin settings, analysis results, sentiment data, etc. Examples include MySQL® and PostgreSQL.

[0369] 4. Generative AI Model: An artificial intelligence algorithm that analyzes collected data and returns the results to the server. OpenAI® GPT-3® is used as an example.

[0370] 5. Emotion Engine: A machine learning model that analyzes user input and generates emotion data. IBM Watson® Tone Analyzer is used as an example.

[0371] User-server interaction

[0372] The user first accesses the system and enters their email address and password on the account creation page. Next, they click the "Register" button, and the device sends the entered information to the server. The server verifies this information, stores it in its database, generates an authentication token, and sends it back to the user. This token is used for subsequent logins.

[0373] After logging in, the user accesses the dashboard and selects the plugin they want to integrate (e.g., a CRM system or marketing tool). The user enters the plugin's API key and authentication information and clicks the "Save" button. The device sends the information to the server, which verifies the authentication information and saves it to the database.

[0374] Data analysis and emotion engine

[0375] The server periodically collects data from the plugin's API and sends it to the generative AI model. The generative AI model analyzes this data and sends the results back to the server. The returned analysis results are stored in a database and displayed to the user through a dashboard.

[0376] Furthermore, when a user enters a comment on the dashboard, the device sends that information to the server. The server uses an emotion engine to analyze the emotional state of the entered comment and stores the results in a database. Based on the analyzed emotional state, the server optimizes the results from the generative AI model and presents them to the user.

[0377] Calculation and billing

[0378] The server continuously records user activity and calculates usage fees at the end of the month based on API request counts, data processing counts, and sentiment data. The server automatically generates and sends an invoice to the user. Once the user reviews the invoice and completes payment, the server prepares for the next month's usage.

[0379] Specific example

[0380] For example, consider integration with a customer support tool. When a user posts an article to the customer support dashboard, the emotion engine analyzes the content of the post and identifies the emotional state. Based on this information, a generative AI model provides appropriate responses and advice. This process allows customers to receive more satisfying support and improves operational efficiency.

[0381] Example of a prompt:

[0382] Prompt text to input to the generative AI model:

[0383] User submissions in customer support:

[0384] Posted on: April 12, 2023

[0385] Post content: 'The system has been experiencing a lot of problems lately, and it's causing me a lot of trouble. Please tell me how to fix it immediately.'

[0386] Perform the following analysis on the generative AI model:

[0387] Identify the emotions the poster is feeling

[0388] Recommended countermeasures

[0389] In this way, the present invention makes it possible to efficiently link generative AI with multiple business solutions while taking into account the user's emotional state.

[0390] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0391] Specific processing steps of the program for this system

[0392] Step 1: User Registration

[0393] The user accesses the system and enters their email address and password on the account creation page.

[0394] The user clicks the "Register" button.

[0395] The device sends the entered email address and password to the server.

[0396] Enter: Email address, password

[0397] The server verifies whether the email address is in the correct format and whether the password is strong enough.

[0398] Data processing: Format check, password strength verification

[0399] Output: Verification results of input information

[0400] If verification is successful, the server saves the user information to a database (e.g., MySQL or PostgreSQL).

[0401] Data processing: Information storage

[0402] The server generates an authentication token and sends it to the user.

[0403] Output: Authentication token

[0404] Step 2: Select and configure plugins

[0405] The user logs into the dashboard.

[0406] The user selects the plugins they want to integrate with from multiple external systems (e.g., CRM systems and marketing tools).

[0407] The user enters the plugin's API key and authentication information and clicks the "Save" button.

[0408] The terminal sends the input information to the server.

[0409] Input: API key, authentication information

[0410] The server verifies the validity of the API key and authentication credentials, and saves the authentication credentials to the database.

[0411] Data processing: Verification and validity check of authentication information.

[0412] Output: Authentication result

[0413] If authentication is successful, the user will be notified that the plugin has been successfully configured.

[0414] Output: Configuration success notification

[0415] Step 3: Data Integration and Analysis

[0416] The server sets a data collection schedule for each plugin (e.g., calling the API every hour).

[0417] Input: Data collection schedule

[0418] Data processing: Schedule setting

[0419] When the scheduled time arrives, the server sends a request to the plugin's API to collect the data.

[0420] Input: API Request Information

[0421] Data processing: Data collection

[0422] Output: Collected data

[0423] The server temporarily stores the collected data and sends it to the generated AI model (e.g., OpenAI GPT-3).

[0424] Input: Collected data

[0425] Data processing: Temporary storage of data

[0426] Output: AI model input data

[0427] The generative AI model analyzes the data and sends the results back to the server.

[0428] Input: AI model input data

[0429] Data processing: Data analysis

[0430] Output: Analysis results

[0431] The server saves the analysis results to a database and displays them on the user's dashboard.

[0432] Input: Analysis results

[0433] Data processing: Saving results

[0434] Output: Dashboard display data

[0435] Step 4: Integrating the Emotional Engine

[0436] The user enters a comment on the dashboard and clicks the "Submit" button.

[0437] The terminal sends the input comment to the server.

[0438] Input: Comment data

[0439] Data processing: Sending comments

[0440] The server invokes a sentiment engine (e.g., IBM Watson Tone Analyzer) to analyze the comments.

[0441] Input: Comment data

[0442] Data processing: Sentiment analysis

[0443] Output: Sentiment data

[0444] The emotion engine sends the analysis results back to the server.

[0445] Input: Sentiment data

[0446] Data processing: Generation of analysis results

[0447] Output: Analysis results

[0448] The server stores emotional data in a database and adjusts the content of the data sent to external systems with which it collaborates.

[0449] Input: Sentiment data

[0450] Data processing: Data storage, adjustment of transmission content.

[0451] Output: Adjusted transmission data

[0452] Step 5: Presentation of analysis results tailored to emotions

[0453] The server checks the analysis results obtained from the emotion engine.

[0454] Input: Analyzed sentiment data

[0455] Data processing: Confirmation of emotional state

[0456] The server optimizes and displays the analysis results from the generated AI model based on the emotional state.

[0457] Input: Analysis results of the generated AI model, emotion data

[0458] Data processing: Result optimization

[0459] Output: Optimized display data

[0460] The server displays the optimized analysis results on the user's dashboard.

[0461] Input: Optimized display data

[0462] Data processing: Dashboard display

[0463] Output: Display result

[0464] Step 6: Calculation and billing of usage fees

[0465] The server continuously records user activity (e.g., number of API requests, number of data processing attempts).

[0466] Input: Usage data

[0467] Data processing: Recording of usage

[0468] At the end of the month, the server calculates the usage fee based on this usage data.

[0469] Input: Recorded usage data

[0470] Data processing: Usage fee calculation

[0471] Output: Billing data

[0472] The server automatically generates an invoice and sends it to the user.

[0473] Input: Billing data

[0474] Data processing: Invoice generation

[0475] Output: Invoice

[0476] The user reviews the invoice and completes the payment.

[0477] Input: Invoice

[0478] Data processing: Payment confirmation

[0479] Output: Payment completion notification

[0480] (Application Example 2)

[0481] 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".

[0482] Traditional AI models and external systems struggle to provide services that take into account the user's emotional state, resulting in problems such as decreased customer satisfaction and inconsistent service quality. Furthermore, especially on e-commerce sites, user stress and dissatisfaction often make problem-solving difficult, ultimately hindering customer retention. There is a need for a system that solves these problems and improves the user experience.

[0483] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0484] In this invention, the server includes means for integrating an emotion engine for recognizing emotions from user input information, means for changing the method of presenting analysis results from an AI model based on the recognized emotions, and means for recording user usage, calculating usage fees at the end of the month, and generating invoices. This makes it possible to appropriately adjust the content of service provision based on the user's emotional state and improve customer satisfaction.

[0485] A "user" is a person or group that uses the system.

[0486] "Means for inputting information" refers to the interface used by users to input the necessary data into the system when creating an account.

[0487] A "database" is a storage function within a system used to store entered user information and other data.

[0488] An "authentication token" is a unique string of characters generated to simplify user authentication.

[0489] A "plugin" is an additional function or module used to connect a system with an external system.

[0490] "Means for verifying authentication information" refers to a function that checks whether the entered authentication information is accurate.

[0491] An "AI model" is a machine learning algorithm used to analyze input data and output results.

[0492] An "emotion engine" is a system that recognizes and analyzes emotions from user input information.

[0493] "Means for changing the method of presenting analysis results" refers to a function that adjusts the display method of analysis results based on recognized emotions.

[0494] "Means for calculating usage fees" refers to a function that calculates charges based on the user's usage and generates an invoice.

[0495] "Means of generating invoices" refers to a function that creates electronic or paper-based invoices based on the calculated usage fees.

[0496] "Means of confirming payment" refers to a function that allows users to confirm that they have made a payment based on the invoice.

[0497] This invention provides a system that enhances customer support on e-commerce sites by integrating generative artificial intelligence (AI) and an emotion engine. This system has the function of analyzing the user's emotional state and optimizing responses based on that analysis. It also includes functions for managing user usage and calculating and billing usage fees.

[0498] Overall system configuration

[0499] Hardware and software configuration

[0500] Server: Includes database, AI models, emotion engine, and authentication system.

[0501] Database: Stores user information, analysis results, authentication tokens, and usage data.

[0502] Generative AI model: Analyzes data collected from users and provides results.

[0503] Emotion Engine: Analyzes user input information to identify emotional states.

[0504] Authentication system: User authentication is performed using OAuth 2.0 and a REST API.

[0505] Terminal: The interface in which the user inputs information. It is primarily provided as a smartphone application.

[0506] Smartphone application: Developed using React Native, and handles communication with the server.

[0507] API: An interface for data communication between a server and a terminal.

[0508] Processing details

[0509] 1. User Registration and Authentication

[0510] Users create an account by entering their email address and password through a smartphone application.

[0511] The server saves the entered information to the database, generates an authentication token, and sends it back to the user.

[0512] 2. Selecting and configuring plugins

[0513] After logging in, users access the dashboard, select the sentiment analysis plugin, and activate it.

[0514] Enter the required API key and authentication information, and the server will perform the authentication.

[0515] 3. Data Integration and Analysis

[0516] The server periodically collects user input data and sends it to the sentiment engine.

[0517] The emotion engine analyzes the data, identifies the user's emotional state, and sends the results back to the server.

[0518] 4. Integration of the Emotional Engine

[0519] Based on the user's emotional state, the server adjusts how the analysis results are displayed.

[0520] We provide concise and direct feedback to users who are feeling stressed, and detailed information to users who are relaxed.

[0521] 5. Calculation and billing of usage fees

[0522] The server records user usage and calculates usage fees at the end of the month.

[0523] Generate an invoice based on the calculated usage fee and send it to the user. Also, confirm payment.

[0524] Specific examples and prompt statements

[0525] The following are specific examples of how users interact with the application and the prompt messages they might encounter.

[0526] Example 1: Prompt message during user registration

[0527] Please enter your email address and password.

[0528] Example 2: Prompt message when performing sentiment analysis

[0529] Please send the message the user entered to customer support.

[0530] Example 3: Prompt text for emotionally-based response advice

[0531] If the user's emotion is "anger," please provide brief feedback.

[0532] If the user's emotion is "joy," please provide detailed support information.

[0533] The system described above enables flexible responses tailored to the user's emotional state, leading to improved customer satisfaction. Furthermore, accurate recording and management of user usage ensures proper billing.

[0534] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0535] Step 1: User Registration and Authentication

[0536] The user enters their email address and password through a smartphone application.

[0537] The entered information is sent from the terminal to the server.

[0538] The server stores the received information in a database, generates an authentication token, and sends it back to the user.

[0539] (Input) User information (email address, password)

[0540] (Processing) Information storage (database), authentication token generation

[0541] (Output) Authentication token

[0542] Step 2: Select and configure plugins

[0543] The user logs into the dashboard, selects the sentiment analysis plugin, and activates it.

[0544] Enter the required API key and authentication information, and send it to the server.

[0545] The server verifies the entered authentication information and saves the verification results to the database.

[0546] (Input) API key, authentication information

[0547] (Processing) Verification of authentication information, saving to database.

[0548] (Output) Verification results

[0549] Step 3: Data Integration and Analysis

[0550] The server periodically collects user input data and sends it to the sentiment engine.

[0551] The emotion engine analyzes the data, identifies the user's emotional state, and sends the results back to the server.

[0552] (Input) User input data

[0553] (Processing) Data collection (API connection), emotional state analysis (emotion engine)

[0554] (Output) Emotion analysis results

[0555] Step 4: Integrating the Emotional Engine

[0556] The server adjusts how the analysis results are presented based on the analyzed emotional state.

[0557] For example, users experiencing stress are given concise and direct feedback, while users who are relaxed are provided with detailed analytics.

[0558] (Input) Sentiment analysis results

[0559] (Processing) Adjustment of presentation method (customization of analysis results)

[0560] (Output) Adjusted analysis results

[0561] Step 5: Calculation and billing of usage fees

[0562] The server records user usage and calculates usage fees at the end of the month.

[0563] An invoice is generated based on the calculated usage fee and sent to the user. After payment is confirmed, the service is switched to the next month's usage.

[0564] (Input) Usage data

[0565] (Processing) Calculation of usage fees, invoice generation, payment confirmation.

[0566] (Output) Invoice, Payment Confirmation Notice

[0567] This system configuration allows for flexible responses tailored to the user's emotional state, thereby improving customer satisfaction. Furthermore, it enables effective management of user usage and proper billing.

[0568] 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.

[0569] 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.

[0570] 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.

[0571] [Second Embodiment]

[0572] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0573] 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.

[0574] 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).

[0575] 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.

[0576] 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.

[0577] 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).

[0578] 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.

[0579] 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.

[0580] 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.

[0581] 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.

[0582] 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.

[0583] 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".

[0584] This invention provides a system that allows users to easily integrate generative artificial intelligence (AI) with multiple existing business solutions. The basic components of this system include a user interface, database, authentication tokens, plugin management, data collection modules, AI models, and usage fee calculation functions.

[0585] User registration and authentication

[0586] The user accesses the system and creates an account. This involves entering an email address and password and clicking the "Register" button. Next, the device sends the entered information to the server, which then saves it to the database, completing the user registration. The server generates an authentication token and sends it back to the user, making future logins easier.

[0587] Plugin selection and configuration

[0588] When a user logs in, the server displays a dashboard allowing the user to select the plugin they want to integrate. The user selects a plugin (e.g., CRM system, marketing tool) and enters the necessary API key and authentication information. This information is sent to the server via the terminal. The server verifies the authentication information, and if the integration is successful, saves the settings to the database.

[0589] Data integration and analysis

[0590] The server sets a schedule to periodically collect data from the plugin once the integration settings are complete. For example, the server might call the external system's API every hour to collect data. The collected data is temporarily stored, and then the server sends it to the AI ​​model. The AI ​​model processes the data and sends the analysis results back to the server. The server stores these results in a database and displays them on the user's dashboard.

[0591] For example, when a user integrates with a marketing tool, the server sends data collected from the marketing tool to an AI model for predictive analysis. As a result, the effectiveness of the marketing campaign and recommendations for the next action are displayed on a dashboard.

[0592] Calculation and billing of usage fees

[0593] The server records the user's plugin usage and compiles usage charges at the end of each month. This includes the number of API requests and data processing cycles. Based on the compiled results, the server calculates the usage charges, automatically generates an invoice, and sends it to the user. After the user reviews the invoice and pays the fee using their configured payment method, the server confirms receipt of payment and switches to the next month's usage.

[0594] This invention enables users to efficiently manage multiple business solutions and easily utilize the advanced analytical capabilities of generative AI.

[0595] The following describes the processing flow.

[0596] User registration and authentication

[0597] Step 1:

[0598] The user accesses the account creation page, enters their email address and password, and clicks the "Register" button.

[0599] Step 2:

[0600] The device sends a request to the server containing the information entered by the user.

[0601] Step 3:

[0602] The server receives the request and saves the entered email address and password to the database.

[0603] Step 4:

[0604] The server generates an authentication token for the user and stores it in the database.

[0605] Step 5:

[0606] The server generates an authentication token and sends it back to the user.

[0607] Plugin selection and configuration

[0608] Step 1:

[0609] The user enters their email address and password on the login screen and clicks the "Login" button.

[0610] Step 2:

[0611] The device sends login information to the server.

[0612] Step 3:

[0613] The server verifies the login information it receives, and if it is correct, it displays the dashboard screen to the user.

[0614] Step 4:

[0615] The user selects the plugin they want to integrate from the dashboard screen and clicks the "Settings" button.

[0616] Step 5:

[0617] The user enters the necessary API key and authentication information for the selected plugin and clicks the "Save" button.

[0618] Step 6:

[0619] The terminal sends the entered authentication information to the server.

[0620] Step 7:

[0621] The server uses the received authentication information to call the plugin's API and verify the authentication information.

[0622] Step 8:

[0623] If the server successfully authenticates, it saves the plugin integration settings to the database and notifies the user that the integration is complete.

[0624] Data integration and analysis

[0625] Step 1:

[0626] The server sets a schedule for collecting data from the plugin.

[0627] Step 2:

[0628] The server collects data by calling the plugin's API based on a configured schedule.

[0629] Step 3:

[0630] The server temporarily stores the collected data in storage.

[0631] Step 4:

[0632] The server sends the stored data to the AI ​​model and requests analysis processing.

[0633] Step 5:

[0634] The AI ​​model analyzes the received data and sends the processing results back to the server.

[0635] Step 6:

[0636] The server receives the processing results and saves them to the database.

[0637] Step 7:

[0638] The server displays the analysis results on the user's dashboard, allowing the user to review the results.

[0639] Calculation and billing of usage fees

[0640] Step 1:

[0641] The server periodically records user activity (number of API requests, number of data processing attempts, etc.).

[0642] Step 2:

[0643] The server compiles all usage data at the end of the month and calculates the usage fee.

[0644] Step 3:

[0645] The server automatically generates an invoice based on usage fees.

[0646] Step 4:

[0647] The server sends the generated invoice to the user's registered email address.

[0648] Step 5:

[0649] The user reviews the invoice and pays the fee using their designated payment method.

[0650] Step 6:

[0651] The server confirms receipt of payment and switches to the next month's usage.

[0652] These steps enable users to integrate generative AI with multiple business solutions and utilize them efficiently.

[0653] (Example 1)

[0654] 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".

[0655] When integrating multiple existing business solutions with generative artificial intelligence (AI), users often need to manually configure and manage individual settings, resulting in decreased efficiency and cumbersome operation. Furthermore, the process of centrally managing data from multiple systems and performing advanced analysis using AI is complex and burdensome for users.

[0656] 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.

[0657] In this invention, the server includes means for a user to input information and create an account, means for storing the input user information in a database, and means for generating and returning an authentication token to the user. This allows users to efficiently create accounts and easily access the system.

[0658] A "user" is a person or group that accesses a system and performs various operations on it.

[0659] "Means for inputting information" refers to interfaces or forms that allow users to provide necessary information such as email addresses and passwords.

[0660] "Means for creating an account" refers to a function that executes the process of generating a new user account based on the entered information.

[0661] "Means of saving to a database" refers to a data management system or software for permanently storing collected user information and other data.

[0662] A "token for authentication" is a unique identifier issued to verify a user's identity and facilitate future logins.

[0663] "Means for generating tokens" refers to the process or function for creating authentication tokens and providing the generated tokens to users.

[0664] "Means of storing tokens in a database" refers to a data management system that securely stores generated authentication tokens so that they can be verified and compared later.

[0665] "Means of returning to the user" refers to a method or protocol for sending the generated authentication token or other information to the user.

[0666] "External systems" refer to other software or services located outside the system, including those that can be interfaced through APIs.

[0667] A "plugin" is a software module that enables integration with external systems and adds specific functionality to a system.

[0668] "Authentication information" refers to information required when integrating with external systems, such as API keys, usernames, and passwords.

[0669] "Means of collecting data regularly" refers to the process of automatically collecting data from external systems according to a set schedule.

[0670] A "generative AI model" is an artificial intelligence algorithm that analyzes collected data and generates results.

[0671] "Means for displaying analysis results" refers to interfaces or dashboards that display the analysis results generated by the AI ​​model in a user-friendly format.

[0672] "Means for recording usage" refers to a system that tracks user behavior and system usage history and stores it as a log.

[0673] "Methods for calculating usage fees" refers to the process of calculating appropriate usage fees for users based on recorded usage data.

[0674] "Means for generating invoices" refers to a system that creates an invoice based on calculated usage fees and sends it to the user.

[0675] "Means of confirming payment" refers to a method or process for verifying whether a payment from a user has been completed and reflecting that result in the system.

[0676] This invention provides a system that allows users to easily integrate generative artificial intelligence (AI) with multiple existing business solutions. The following hardware and software are required to implement this system.

[0677] Hardware and software:

[0678] Server: Handles database management, authentication token generation, data collection scheduling, integration with AI models, results display, usage tracking, usage fee calculation, and invoice generation.

[0679] Terminal: This is the user interface (UI) where users enter information, create accounts, log in, select and configure plugins, and check the results.

[0680] Database: Stores user information, authentication tokens, integration plugin settings, collected data, analysis results, usage status, etc.

[0681] Generative AI model: An artificial intelligence algorithm used to analyze data and generate analysis results.

[0682] Processing flow:

[0683] User registration and authentication:

[0684] The user accesses the system, enters their email address and password, and creates an account. The information entered through the user interface is sent from the terminal to the server. The server stores the user information in a database, generates an authentication token, and sends it back to the user. This allows the user to log in easily later.

[0685] Plugin selection and configuration:

[0686] When a user logs into the system, the server displays a dashboard where the user can select the plugins they want to integrate. Examples include CRM systems and marketing tools. The user enters the API key and authentication information for the selected plugin, and this information is sent from the terminal to the server. The server verifies the authentication information and, if correct, saves it to the database.

[0687] Data integration and analysis:

[0688] Once the plugin integration is complete, the server sets a data collection schedule. For example, the server might call an external system's API to collect data at 1 AM every day. The collected data is temporarily stored and then sent by the server to a generative AI model. The generative AI model analyzes the data and sends the results back to the server. The server saves the analysis results to a database and displays them on the user's dashboard.

[0689] Calculation and billing of usage fees:

[0690] The server records user usage and compiles usage charges at the end of each month. This includes the number of API requests and data processing attempts. Based on the compiled results, the server calculates the usage charges, automatically generates an invoice, and sends it to the user. After the user reviews the invoice and pays the fee using the configured payment method, the server confirms receipt of payment and switches to the next month's usage.

[0691] Examples of specific cases and prompt statements:

[0692] As a concrete example, consider a scenario where a user wants to integrate with a marketing tool and have AI analyze sales data. The server configures the integration using the API key of the marketing tool entered by the user. The server collects data daily and sends it to a generated AI model for predictive analysis. The results are displayed on the dashboard as "Next month's sales forecast: 1 million yen."

[0693] Example of a prompt:

[0694] "We collect data from marketing tools daily, analyze it with an AI model, and predict next month's sales."

[0695] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0696] Step 1:

[0697] The user accesses the system and creates an account by entering their email address and password. The email address and password entered through the user interface are sent from the terminal to the server. The server stores the received information in its database and completes the registration. At this time, the server generates an authentication token and sends it back to the user. This makes subsequent logins easier.

[0698] Input: Email address, password

[0699] Output: Authentication token

[0700] Step 2:

[0701] When a user logs in, the server displays the dashboard using the user's authentication information. The dashboard displays a list of available plugins. The user selects the plugin they want to integrate and enters the necessary API key and authentication information.

[0702] Input: API key, credentials

[0703] Output: Plugin configuration information

[0704] Step 3:

[0705] The terminal sends plugin configuration information to the server, which verifies it. If the verification is successful, the server saves the configuration information to the database and completes the integration. Based on this integration configuration, the data collection described later is performed.

[0706] Input: Plugin configuration information

[0707] Output: Verification results, saved.

[0708] Step 4:

[0709] To periodically collect data from plugins whose integration settings are complete, the server sets a data collection schedule. For example, the server might call the external system's API at 1 AM every day to collect data. The collected data is temporarily stored.

[0710] Input: Data collection schedule

[0711] Output: Collected data

[0712] Step 5:

[0713] The server sends the collected data to the generative AI model. The generative AI model analyzes the received data and sends the analysis results back to the server.

[0714] Input: Collected data

[0715] Output: Analysis results

[0716] Step 6:

[0717] The server saves the analysis results to a database and displays them on the user's dashboard. The analysis results are provided in a visually easy-to-understand format, making them easily accessible to the user.

[0718] Input: Analysis results

[0719] Output: Dashboard update

[0720] Step 7:

[0721] The server records user plugin usage and compiles usage charges at the end of each month. This includes the number of API requests and data processing cycles. Based on the compiled results, the server calculates the usage charges, automatically generates an invoice, and sends it to the user.

[0722] Input: Usage data

[0723] Output: Invoice

[0724] Step 8:

[0725] After the user reviews the invoice, they pay the fee using their configured payment method. The server confirms receipt of the payment and switches to the next month's usage. This allows the user to continue using the system.

[0726] Input: Payment Information

[0727] Output: Payment confirmation, usage status update

[0728] (Application Example 1)

[0729] 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."

[0730] Modern logistics centers involve a complex interplay of numerous systems and processes, requiring efficient integration of their data. However, existing systems often rely on manual data collection, analysis, and optimization processes, resulting in significant time and effort, and consequently, reduced operational efficiency. Furthermore, the difficulty in real-time monitoring and issuing appropriate instructions leads to insufficient optimization of inventory and delivery management. The development of technologies to address these issues and improve the operational efficiency of logistics centers is highly desirable.

[0731] 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.

[0732] In this invention, the server includes means for a user to input information and create an account; means for storing the input user information in a database; means for generating and returning an authentication token to the user; means for the user to log in and select a plugin that links with multiple external systems; means for inputting authentication information for the selected plugin and verifying the authentication information; means for periodically collecting data from external systems and sending it to an AI model; means for displaying the analysis results from the AI ​​model to the user; means for recording the user's usage status, calculating usage fees at the end of the month, and generating an invoice; means for sending the invoice to the user and confirming payment; means including a generative AI model that links with the management system of a logistics center and optimizes inventory and delivery based on collected data; and means for performing predictive analysis from inventory and delivery data and notifying the analysis results to the smartphone in real time. This makes it possible to link multiple business processes in real time and manage them efficiently.

[0733] "User" refers to an individual or group that uses the system.

[0734] "Information" refers to all data that users enter when creating an account or using the system.

[0735] An "account" refers to the registration information required for a user to access the system.

[0736] A "database" refers to a storage area within a system used to store and manage information.

[0737] A "token" refers to a series of strings or codes generated for user authentication.

[0738] A "server" is a computer that forms the core of a system and is responsible for processing and storing data.

[0739] A "plugin" refers to an extension that adds new functionality to a system.

[0740] "Authentication information" refers to information such as API keys, IDs, and passwords that users need to connect with the plugin.

[0741] An "external system" refers to another system or service that exists outside of the system and interacts with it.

[0742] An "AI model" refers to an artificial intelligence model that analyzes collected data and performs predictions and optimizations.

[0743] "Analysis results" refer to the results derived by the AI ​​model after analyzing the data.

[0744] A "smartphone" refers to a portable information device that is capable of connecting to the internet and using a variety of applications.

[0745] A "logistics center" refers to a facility used to manage the storage and distribution of goods.

[0746] "Inventory" refers to the total amount of goods stored in a distribution center.

[0747] "Delivery" refers to the movement of goods from a logistics center to customers or other facilities.

[0748] "Predictive analytics" refers to an analytical method that predicts future trends and situations based on collected data.

[0749] This invention comprises a system including a user interface, database, authentication token, plugin management, data collection module, AI model, and usage fee calculation function. Users access this system and perform various operations using a smartphone.

[0750] The server first provides a means for the user to enter information and create an account. The information entered by the user is stored in a database, an authentication token is generated, and sent back to the user. This makes it easier for the user to log in on subsequent occasions.

[0751] When a user logs in, the server provides a means for selecting plugins that integrate with multiple external systems. The user selects a plugin, such as an inventory management system or a delivery management system, and enters the necessary authentication information. This authentication information is verified by the server, and the results are stored in the database.

[0752] The server periodically collects data from external systems and provides a means to send the collected data to an AI model. For example, data is collected regularly every hour, and the AI ​​model analyzes it. The analysis results are stored in a database by the server and notified to the user's smartphone in real time.

[0753] When integrated with a logistics center's management system, the AI ​​model performs predictive analytics based on collected inventory and delivery data. This enables the optimization of inventory and delivery within the logistics center. For example, it can provide information such as which products should have increased inventory next and which delivery routes are the most efficient.

[0754] The server also handles the usage fee calculation. The server records the user's plugin usage and calculates the usage fee at the end of the month. Based on the calculation, it generates an invoice and sends it to the user. Once the user reviews the invoice and pays the fee using the configured payment method, the server confirms receipt of the payment and switches to the next month's usage.

[0755] Hardware and software to be used:

[0756] Hardware: Smartphones, servers

[0757] Software: Flask (Web framework), SQLite (database), JWT (authentication token generation)

[0758] (Specific example)

[0759] Logistics center managers can use the "Logistics Center AI Assist" application to collect data from the inventory management system, analyze it with generative AI, and determine the optimal inventory placement method. For example, they can use prompts like the following:

[0760] Examples of prompts to input into a generative AI model:

[0761] Using data obtained from the inventory management system, forecast future demand and propose the optimal inventory allocation method. The data obtained is as follows:

[0762] {data}

[0763] This prompt prompts the AI ​​to analyze inventory data and suggest the optimal inventory placement method. This significantly improves the operational efficiency of the logistics center and reduces costs.

[0764] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0765] Step 1:

[0766] User registration and authentication

[0767] The server provides an input form for the user to enter information and create an account. The user enters the required information (e.g., email address, password) and clicks the "Register" button. Once the device sends the entered information to the server, the server stores it in its database, generates an authentication token, and sends it back to the user. This provides the user with data that will facilitate future logins.

[0768] Input: User information (email address, password)

[0769] Data processing: Saving user information to a database, generating authentication tokens.

[0770] Output: Authentication token

[0771] Step 2:

[0772] Plugin selection and configuration

[0773] When a user logs in, the server displays a dashboard screen allowing the user to select the plugin they want to integrate (e.g., inventory management system, shipping management system). The user enters the authentication information for the selected plugin (e.g., API key), and the device sends this information to the server. The server verifies the authentication information and saves the result to the database.

[0774] Input: Plugin credentials (API key)

[0775] Data processing: Verification of authentication information, saving to database.

[0776] Output: Plugin configuration result

[0777] Step 3:

[0778] Data collection and transmission

[0779] The server sets a schedule to periodically collect data from external systems. For example, the server calls APIs of external inventory management and delivery management systems every hour to collect data. The collected data is temporarily stored by the server and then sent to the AI ​​model.

[0780] Input: Data from an external system

[0781] Data processing: Data collection, temporary storage, and transmission to AI models.

[0782] Output: Data to send to the AI ​​model

[0783] Step 4:

[0784] Data analysis and result display

[0785] The AI ​​model analyzes the data sent from the server. The analysis results are sent back to the server, which then stores them in a database. The user's smartphone is notified of the analysis results in real time.

[0786] Input: Collected data

[0787] Data processing: Data analysis, generation of analysis results.

[0788] Output: Analysis results

[0789] Step 5:

[0790] Predictive analytics and optimization proposals

[0791] The AI ​​model works in conjunction with the logistics center's management system to perform predictive analytics based on inventory and delivery data. For example, it predicts which products should have increased inventory next and what the optimal delivery routes are. The analysis results are notified to smartphones, allowing users to check the details in real time via their phones.

[0792] Input: Collected data

[0793] Data processing: Predictive analytics, generation of optimization suggestions.

[0794] Output: Predictive analysis results, optimization suggestions

[0795] Step 6:

[0796] Calculation and billing of usage fees

[0797] The server records the user's plugin usage and calculates the usage fee at the end of the month. Based on the calculation, it automatically generates an invoice and sends it to the user. Once the user reviews the invoice and pays the fee using the configured payment method, the server confirms the payment and switches to the next month's usage.

[0798] Input: Plugin usage data

[0799] Data processing: Recording usage, calculating usage fees, generating invoices.

[0800] Output: Invoice, payment confirmation

[0801] This will allow logistics center management operations to be performed more efficiently and in real time.

[0802] 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.

[0803] This invention provides a system that improves user experience by combining a generative artificial intelligence (AI) with an emotion engine to integrate multiple existing business solutions. This system includes functions such as user registration and authentication, plugin selection and configuration, data integration and analysis, emotion engine integration, and usage fee calculation and billing.

[0804] User registration and authentication

[0805] The user accesses the system, enters their email address and password on the account creation page, and clicks the "Register" button. The device sends the entered information to the server, which stores it in its database. The server also generates and sends an authentication token back to the user. This authentication token allows for easier logins in the future.

[0806] Plugin selection and configuration

[0807] The user logs into the dashboard and selects the plugin they want to integrate (for example, a CRM system or marketing tool). The user enters the necessary API key and authentication information for the selected plugin and clicks the "Save" button. The device sends the entered information to the server, which verifies the authentication information. If authentication is successful, the plugin integration settings are saved to the database and the user is notified.

[0808] Data integration and analysis

[0809] The server sets a schedule for collecting data from the plugin. For example, it might call the plugin's API every hour to collect data. The collected data is temporarily stored and sent to the AI ​​model. The AI ​​model analyzes the data and sends the processing results back to the server. The server saves these results to a database and displays them to the user.

[0810] Emotional engine integration

[0811] An emotion engine is incorporated to recognize emotions from user input. For example, when a user enters a comment on the dashboard, the device sends the input information to the server, which uses the emotion engine to analyze the user's emotional state. The recognized emotional state is stored in a database along with other data. Based on the analysis results of the emotion engine, the server adjusts the data sent to connected external systems.

[0812] Presentation of analysis results tailored to emotions

[0813] Based on the analyzed emotions, the server changes how it presents the analysis results from the AI ​​model. For example, if the user is stressed, it displays concise and direct feedback, while if they are relaxed, it provides detailed analytics.

[0814] Calculation and billing of usage fees

[0815] The server records user usage and compiles the data at the end of each month. This includes the number of API requests and data processing cycles. Furthermore, sentiment data obtained using the sentiment engine is also incorporated into the usage fee calculation. The server automatically generates an invoice and sends it to the user. Once the user reviews the invoice and pays using the configured payment method, the server confirms receipt of payment and switches to the next month's usage.

[0816] In this way, the present invention enables the efficient integration of generative AI with multiple business solutions while taking into account the user's emotional state. A specific example is a scenario where a user interacts with a customer support tool, and the emotion engine analyzes the user's emotional state to provide appropriate responses and advice. This system is expected to improve operational efficiency and customer satisfaction.

[0817] The following describes the processing flow.

[0818] User registration and authentication

[0819] Step 1:

[0820] The user accesses the account creation page, enters their email address and password, and clicks the "Register" button.

[0821] Step 2:

[0822] The terminal sends the input information to the server.

[0823] Step 3:

[0824] The server saves the information it receives to the database.

[0825] Step 4:

[0826] The server generates an authentication token and stores it in the database.

[0827] Step 5:

[0828] The server generates an authentication token and sends it back to the user.

[0829] Plugin selection and configuration

[0830] Step 1:

[0831] The user enters their email address and password on the login screen and clicks the "Login" button.

[0832] Step 2:

[0833] The device sends login information to the server.

[0834] Step 3:

[0835] The server verifies the login information, and if correct, displays the dashboard screen to the user.

[0836] Step 4:

[0837] The user selects the plugin they want to integrate, enters the API key and authentication information, and clicks the "Save" button.

[0838] Step 5:

[0839] The terminal sends the input information to the server.

[0840] Step 6:

[0841] The server uses the authentication information to call the plugin's API and verify the authentication information.

[0842] Step 7:

[0843] If the server successfully authenticates, it saves the plugin's integration settings to the database and notifies the user.

[0844] Data integration and analysis

[0845] Step 1:

[0846] The server sets a schedule for collecting data from the plugin.

[0847] Step 2:

[0848] The server calls the plugin's API based on a configured schedule to collect data.

[0849] Step 3:

[0850] The server temporarily stores the collected data in storage.

[0851] Step 4:

[0852] The server sends the stored data to the AI ​​model and requests analysis processing.

[0853] Step 5:

[0854] The AI ​​model analyzes the data and sends the processing results back to the server.

[0855] Step 6:

[0856] The server receives the analysis results and saves them to the database.

[0857] Step 7:

[0858] The server displays the analysis results on the user's dashboard.

[0859] Emotional engine integration

[0860] Step 1:

[0861] Users enter comments and feedback on the dashboard.

[0862] Step 2:

[0863] The terminal sends the input information to the server.

[0864] Step 3:

[0865] The server sends the received data to the emotion engine, which then analyzes the user's emotional state.

[0866] Step 4:

[0867] The emotion engine sends the analysis results back to the server.

[0868] Step 5:

[0869] The server stores emotional states in a database and adjusts the content of data sent to external systems with which it collaborates.

[0870] Presentation of analysis results tailored to emotions

[0871] Step 1:

[0872] The server modifies the analysis results based on the user's emotional state.

[0873] Step 2:

[0874] For example, if a user is feeling stressed, the server displays concise feedback; if they are relaxed, it provides detailed analytics.

[0875] Calculation and billing of usage fees

[0876] Step 1:

[0877] The server records user activity (number of API requests, number of data processing attempts, etc.).

[0878] Step 2:

[0879] The server compiles all usage data at the end of the month and calculates the usage fee.

[0880] Step 3:

[0881] The server automatically generates invoices, including emotional data from an emotion engine.

[0882] Step 4:

[0883] The server sends the generated invoice to the user's registered email address.

[0884] Step 5:

[0885] The user reviews the invoice and pays the fee using their chosen payment method (e.g., credit card, bank transfer).

[0886] Step 6:

[0887] The server confirms receipt of payment and switches to the next month's usage.

[0888] In this way, the present invention, which integrates an emotion engine, can provide a system that enables the collaboration of generative AI and various business solutions while recognizing the user's emotional state. A specific example is a scenario in which the emotion engine analyzes the user's emotional state and supports appropriate responses when collaborating with a customer support tool. This system allows users to efficiently utilize data and provide higher service quality.

[0889] (Example 2)

[0890] 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".

[0891] Traditional business solution systems struggle to provide services that take into account the emotional state of users based on their input information, making it difficult to improve the quality of the user experience. Furthermore, they are unable to efficiently integrate with multiple external systems, placing a significant burden on users for manual configuration and adjustments. Additionally, tracking usage and calculating fees are cumbersome and lack automation.

[0892] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0893] In this invention, the server includes means for a user to input information and create an account; means for storing the input user information in a database; means for generating and returning an authentication token to the user; means for the user to log in and select a plugin that integrates with multiple external systems; means for inputting authentication information for the selected plugin and verifying the authentication information; means for periodically collecting data from external systems and sending it to a generating AI model; means for displaying the analysis results from the generating AI model to the user; means for integrating an emotion engine that analyzes the user's input information and generates emotion data; means for optimizing and displaying the results from the generating AI model based on the analyzed emotion data; means for recording the user's usage status, calculating usage fees at the end of the month, and generating an invoice; and means for sending the invoice to the user and confirming payment. This makes it possible to improve the quality of the user experience, efficiently integrate with multiple external systems, and realize the understanding of usage status and automatic calculation of usage fees.

[0894] A "user" refers to an individual or legal entity that uses the system, enters information to create an account, selects plugins, and receives services.

[0895] An "authentication token" refers to a unique code generated by a server to authenticate a user and sent back to the user.

[0896] A "database" refers to a storage system that stores a collection of data that is collected, stored, and managed by a system.

[0897] A "plugin" refers to a software module that provides additional functionality to a system and enables it to interact with various external systems.

[0898] "Authentication information" refers to information such as API keys, usernames, and passwords necessary to establish integration with the plugin.

[0899] A "generative AI model" refers to an artificial intelligence algorithm that analyzes collected data and returns the results to a server to provide advanced services.

[0900] An "emotion engine" refers to a machine learning model or algorithm that analyzes user input information to identify the user's emotional state and adjusts the system's operation based on that.

[0901] "Usage status" refers to data related to system usage (e.g., number of API requests, number of data processing attempts) and records based on that data.

[0902] An "invoice" refers to a document sent to a user that shows the costs incurred for using the system.

[0903] "External systems" refer to other software or platforms that provide services through integration with the system.

[0904] These definitions clarify the meaning of each term used in the patent claims, making them easier to understand.

[0905] This invention provides a system that improves user experience by combining an emotion engine with generative artificial intelligence (AI) and the integration of multiple existing business solutions. This system is implemented using the following hardware and software configuration.

[0906] Hardware and software configuration

[0907] 1. Server: Manages key backend functions such as data storage, processing, and API calls. Examples include using cloud servers such as AWS or Google Cloud Platform.

[0908] 2. Terminal: A device used to obtain user input information and send it to the server. This includes PCs, smartphones, tablets, etc.

[0909] 3. Database: Stores user information, plugin settings, analysis results, sentiment data, etc. Examples include MySQL and PostgreSQL.

[0910] 4. Generative AI Model: An artificial intelligence algorithm that analyzes collected data and returns the results to the server. OpenAI GPT-3 is used as an example.

[0911] 5. Emotion Engine: A machine learning model that analyzes user input and generates emotion data. IBM Watson Tone Analyzer is used as an example.

[0912] User-server interaction

[0913] The user first accesses the system and enters their email address and password on the account creation page. Next, they click the "Register" button, and the device sends the entered information to the server. The server verifies this information, stores it in its database, generates an authentication token, and sends it back to the user. This token is used for subsequent logins.

[0914] After logging in, the user accesses the dashboard and selects the plugin they want to integrate (e.g., a CRM system or marketing tool). The user enters the plugin's API key and authentication information and clicks the "Save" button. The device sends the information to the server, which verifies the authentication information and saves it to the database.

[0915] Data analysis and emotion engine

[0916] The server periodically collects data from the plugin's API and sends it to the generative AI model. The generative AI model analyzes this data and sends the results back to the server. The returned analysis results are stored in a database and displayed to the user through a dashboard.

[0917] Furthermore, when a user enters a comment on the dashboard, the device sends that information to the server. The server uses an emotion engine to analyze the emotional state of the entered comment and stores the results in a database. Based on the analyzed emotional state, the server optimizes the results from the generative AI model and presents them to the user.

[0918] Calculation and billing

[0919] The server continuously records user activity and calculates usage fees at the end of the month based on API request counts, data processing counts, and sentiment data. The server automatically generates and sends an invoice to the user. Once the user reviews the invoice and completes payment, the server prepares for the next month's usage.

[0920] Specific example

[0921] For example, consider integration with a customer support tool. When a user posts an article to the customer support dashboard, the emotion engine analyzes the content of the post and identifies the emotional state. Based on this information, a generative AI model provides appropriate responses and advice. This process allows customers to receive more satisfying support and improves operational efficiency.

[0922] Example of a prompt:

[0923] Prompt text to input to the generative AI model:

[0924] User submissions in customer support:

[0925] Posted on: April 12, 2023

[0926] Post content: 'The system has been experiencing a lot of problems lately, and it's causing me a lot of trouble. Please tell me how to fix it immediately.'

[0927] Perform the following analysis on the generative AI model:

[0928] Identify the emotions the poster is feeling

[0929] Recommended countermeasures

[0930] In this way, the present invention makes it possible to efficiently link generative AI with multiple business solutions while taking into account the user's emotional state.

[0931] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0932] Specific processing steps of the program for this system

[0933] Step 1: User Registration

[0934] The user accesses the system and enters their email address and password on the account creation page.

[0935] The user clicks the "Register" button.

[0936] The device sends the entered email address and password to the server.

[0937] Enter: Email address, password

[0938] The server verifies whether the email address is in the correct format and whether the password is strong enough.

[0939] Data processing: Format check, password strength verification

[0940] Output: Verification results of input information

[0941] If verification is successful, the server saves the user information to a database (e.g., MySQL or PostgreSQL).

[0942] Data processing: Information storage

[0943] The server generates an authentication token and sends it to the user.

[0944] Output: Authentication token

[0945] Step 2: Select and configure plugins

[0946] The user logs into the dashboard.

[0947] The user selects the plugins they want to integrate with from multiple external systems (e.g., CRM systems and marketing tools).

[0948] The user enters the plugin's API key and authentication information and clicks the "Save" button.

[0949] The terminal sends the input information to the server.

[0950] Input: API key, authentication information

[0951] The server verifies the validity of the API key and authentication credentials, and saves the authentication credentials to the database.

[0952] Data processing: Verification and validity check of authentication information.

[0953] Output: Authentication result

[0954] If authentication is successful, the user will be notified that the plugin has been successfully configured.

[0955] Output: Configuration success notification

[0956] Step 3: Data Integration and Analysis

[0957] The server sets a data collection schedule for each plugin (e.g., calling the API every hour).

[0958] Input: Data collection schedule

[0959] Data processing: Schedule setting

[0960] When the scheduled time arrives, the server sends a request to the plugin's API to collect the data.

[0961] Input: API Request Information

[0962] Data processing: Data collection

[0963] Output: Collected data

[0964] The server temporarily stores the collected data and sends it to the generated AI model (e.g., OpenAI GPT-3).

[0965] Input: Collected data

[0966] Data processing: Temporary storage of data

[0967] Output: AI model input data

[0968] The generative AI model analyzes the data and sends the results back to the server.

[0969] Input: AI model input data

[0970] Data processing: Data analysis

[0971] Output: Analysis results

[0972] The server saves the analysis results to a database and displays them on the user's dashboard.

[0973] Input: Analysis results

[0974] Data processing: Saving results

[0975] Output: Dashboard display data

[0976] Step 4: Integrating the Emotional Engine

[0977] The user enters a comment on the dashboard and clicks the "Submit" button.

[0978] The terminal sends the input comment to the server.

[0979] Input: Comment data

[0980] Data processing: Sending comments

[0981] The server invokes a sentiment engine (e.g., IBM Watson Tone Analyzer) to analyze the comments.

[0982] Input: Comment data

[0983] Data processing: Sentiment analysis

[0984] Output: Sentiment data

[0985] The emotion engine sends the analysis results back to the server.

[0986] Input: Sentiment data

[0987] Data processing: Generation of analysis results

[0988] Output: Analysis results

[0989] The server stores emotional data in a database and adjusts the content of the data sent to external systems with which it collaborates.

[0990] Input: Sentiment data

[0991] Data processing: Data storage, adjustment of transmission content.

[0992] Output: Adjusted transmission data

[0993] Step 5: Presentation of analysis results tailored to emotions

[0994] The server checks the analysis results obtained from the emotion engine.

[0995] Input: Analyzed sentiment data

[0996] Data processing: Confirmation of emotional state

[0997] The server optimizes and displays the analysis results from the generated AI model based on the emotional state.

[0998] Input: Analysis results of the generated AI model, emotion data

[0999] Data processing: Result optimization

[1000] Output: Optimized display data

[1001] The server displays the optimized analysis results on the user's dashboard.

[1002] Input: Optimized display data

[1003] Data processing: Dashboard display

[1004] Output: Display result

[1005] Step 6: Calculation and billing of usage fees

[1006] The server continuously records user activity (e.g., number of API requests, number of data processing attempts).

[1007] Input: Usage data

[1008] Data processing: Recording of usage

[1009] At the end of the month, the server calculates the usage fee based on this usage data.

[1010] Input: Recorded usage data

[1011] Data processing: Usage fee calculation

[1012] Output: Billing data

[1013] The server automatically generates an invoice and sends it to the user.

[1014] Input: Billing data

[1015] Data processing: Invoice generation

[1016] Output: Invoice

[1017] The user reviews the invoice and completes the payment.

[1018] Input: Invoice

[1019] Data processing: Payment confirmation

[1020] Output: Payment completion notification

[1021] (Application Example 2)

[1022] 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."

[1023] Traditional AI models and external systems struggle to provide services that take into account the user's emotional state, resulting in problems such as decreased customer satisfaction and inconsistent service quality. Furthermore, especially on e-commerce sites, user stress and dissatisfaction often make problem-solving difficult, ultimately hindering customer retention. There is a need for a system that solves these problems and improves the user experience.

[1024] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[1025] In this invention, the server includes means for integrating an emotion engine for recognizing emotions from user input information, means for changing the method of presenting analysis results from an AI model based on the recognized emotions, and means for recording user usage, calculating usage fees at the end of the month, and generating invoices. This makes it possible to appropriately adjust the content of service provision based on the user's emotional state and improve customer satisfaction.

[1026] A "user" is a person or group that uses the system.

[1027] "Means for inputting information" refers to the interface used by users to input the necessary data into the system when creating an account.

[1028] A "database" is a storage function within a system used to store entered user information and other data.

[1029] An "authentication token" is a unique string of characters generated to simplify user authentication.

[1030] A "plugin" is an additional function or module used to connect a system with an external system.

[1031] "Means for verifying authentication information" refers to a function that checks whether the entered authentication information is accurate.

[1032] An "AI model" is a machine learning algorithm used to analyze input data and output results.

[1033] An "emotion engine" is a system that recognizes and analyzes emotions from user input information.

[1034] "Means for changing the method of presenting analysis results" refers to a function that adjusts the display method of analysis results based on recognized emotions.

[1035] "Means for calculating usage fees" refers to a function that calculates charges based on the user's usage and generates an invoice.

[1036] "Means of generating invoices" refers to a function that creates electronic or paper-based invoices based on the calculated usage fees.

[1037] "Means of confirming payment" refers to a function that allows users to confirm that they have made a payment based on the invoice.

[1038] This invention provides a system that enhances customer support on e-commerce sites by integrating generative artificial intelligence (AI) and an emotion engine. This system has the function of analyzing the user's emotional state and optimizing responses based on that analysis. It also includes functions for managing user usage and calculating and billing usage fees.

[1039] Overall system configuration

[1040] Hardware and software configuration

[1041] Server: Includes database, AI models, emotion engine, and authentication system.

[1042] Database: Stores user information, analysis results, authentication tokens, and usage data.

[1043] Generative AI model: Analyzes data collected from users and provides results.

[1044] Emotion Engine: Analyzes user input information to identify emotional states.

[1045] Authentication system: User authentication is performed using OAuth 2.0 and a REST API.

[1046] Terminal: The interface in which the user inputs information. It is primarily provided as a smartphone application.

[1047] Smartphone application: Developed using React Native, and handles communication with the server.

[1048] API: An interface for data communication between a server and a terminal.

[1049] Processing details

[1050] 1. User Registration and Authentication

[1051] Users create an account by entering their email address and password through a smartphone application.

[1052] The server saves the entered information to the database, generates an authentication token, and sends it back to the user.

[1053] 2. Selecting and configuring plugins

[1054] After logging in, users access the dashboard, select the sentiment analysis plugin, and activate it.

[1055] Enter the required API key and authentication information, and the server will perform the authentication.

[1056] 3. Data Integration and Analysis

[1057] The server periodically collects user input data and sends it to the sentiment engine.

[1058] The emotion engine analyzes the data, identifies the user's emotional state, and sends the results back to the server.

[1059] 4. Integration of the Emotional Engine

[1060] Based on the user's emotional state, the server adjusts how the analysis results are displayed.

[1061] We provide concise and direct feedback to users who are feeling stressed, and detailed information to users who are relaxed.

[1062] 5. Calculation and billing of usage fees

[1063] The server records user usage and calculates usage fees at the end of the month.

[1064] Generate an invoice based on the calculated usage fee and send it to the user. Also, confirm payment.

[1065] Specific examples and prompt statements

[1066] The following are specific examples of how users interact with the application and the prompt messages they might encounter.

[1067] Example 1: Prompt message during user registration

[1068] Please enter your email address and password.

[1069] Example 2: Prompt message when performing sentiment analysis

[1070] Please send the message the user entered to customer support.

[1071] Example 3: Prompt text for emotionally-based response advice

[1072] If the user's emotion is "anger," please provide brief feedback.

[1073] If the user's emotion is "joy," please provide detailed support information.

[1074] The system described above enables flexible responses tailored to the user's emotional state, leading to improved customer satisfaction. Furthermore, accurate recording and management of user usage ensures proper billing.

[1075] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1076] Step 1: User Registration and Authentication

[1077] The user enters their email address and password through a smartphone application.

[1078] The entered information is sent from the terminal to the server.

[1079] The server stores the received information in a database, generates an authentication token, and sends it back to the user.

[1080] (Input) User information (email address, password)

[1081] (Processing) Information storage (database), authentication token generation

[1082] (Output) Authentication token

[1083] Step 2: Select and configure plugins

[1084] The user logs into the dashboard, selects the sentiment analysis plugin, and activates it.

[1085] Enter the required API key and authentication information, and send it to the server.

[1086] The server verifies the entered authentication information and saves the verification results to the database.

[1087] (Input) API key, authentication information

[1088] (Processing) Verification of authentication information, saving to database.

[1089] (Output) Verification results

[1090] Step 3: Data Integration and Analysis

[1091] The server periodically collects user input data and sends it to the sentiment engine.

[1092] The emotion engine analyzes the data, identifies the user's emotional state, and sends the results back to the server.

[1093] (Input) User input data

[1094] (Processing) Data collection (API connection), emotional state analysis (emotion engine)

[1095] (Output) Emotion analysis results

[1096] Step 4: Integrating the Emotional Engine

[1097] The server adjusts how the analysis results are presented based on the analyzed emotional state.

[1098] For example, users experiencing stress are given concise and direct feedback, while users who are relaxed are provided with detailed analytics.

[1099] (Input) Sentiment analysis results

[1100] (Processing) Adjustment of presentation method (customization of analysis results)

[1101] (Output) Adjusted analysis results

[1102] Step 5: Calculation and billing of usage fees

[1103] The server records user usage and calculates usage fees at the end of the month.

[1104] An invoice is generated based on the calculated usage fee and sent to the user. After payment is confirmed, the service is switched to the next month's usage.

[1105] (Input) Usage data

[1106] (Processing) Calculation of usage fees, invoice generation, payment confirmation.

[1107] (Output) Invoice, Payment Confirmation Notice

[1108] This system configuration allows for flexible responses tailored to the user's emotional state, thereby improving customer satisfaction. Furthermore, it enables effective management of user usage and proper billing.

[1109] 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.

[1110] 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.

[1111] 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.

[1112] [Third Embodiment]

[1113] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[1114] 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.

[1115] 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).

[1116] 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.

[1117] 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.

[1118] 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).

[1119] 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.

[1120] 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.

[1121] 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.

[1122] 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.

[1123] 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.

[1124] 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".

[1125] This invention provides a system that allows users to easily integrate generative artificial intelligence (AI) with multiple existing business solutions. The basic components of this system include a user interface, database, authentication tokens, plugin management, data collection modules, AI models, and usage fee calculation functions.

[1126] User registration and authentication

[1127] The user accesses the system and creates an account. This involves entering an email address and password and clicking the "Register" button. Next, the device sends the entered information to the server, which then saves it to the database, completing the user registration. The server generates an authentication token and sends it back to the user, making future logins easier.

[1128] Plugin selection and configuration

[1129] When a user logs in, the server displays a dashboard allowing the user to select the plugin they want to integrate. The user selects a plugin (e.g., CRM system, marketing tool) and enters the necessary API key and authentication information. This information is sent to the server via the terminal. The server verifies the authentication information, and if the integration is successful, saves the settings to the database.

[1130] Data integration and analysis

[1131] The server sets a schedule to periodically collect data from the plugin once the integration settings are complete. For example, the server might call the external system's API every hour to collect data. The collected data is temporarily stored, and then the server sends it to the AI ​​model. The AI ​​model processes the data and sends the analysis results back to the server. The server stores these results in a database and displays them on the user's dashboard.

[1132] For example, when a user integrates with a marketing tool, the server sends data collected from the marketing tool to an AI model for predictive analysis. As a result, the effectiveness of the marketing campaign and recommendations for the next action are displayed on a dashboard.

[1133] Calculation and billing of usage fees

[1134] The server records the user's plugin usage and compiles usage charges at the end of each month. This includes the number of API requests and data processing cycles. Based on the compiled results, the server calculates the usage charges, automatically generates an invoice, and sends it to the user. After the user reviews the invoice and pays the fee using their configured payment method, the server confirms receipt of payment and switches to the next month's usage.

[1135] This invention enables users to efficiently manage multiple business solutions and easily utilize the advanced analytical capabilities of generative AI.

[1136] The following describes the processing flow.

[1137] User registration and authentication

[1138] Step 1:

[1139] The user accesses the account creation page, enters their email address and password, and clicks the "Register" button.

[1140] Step 2:

[1141] The device sends a request to the server containing the information entered by the user.

[1142] Step 3:

[1143] The server receives the request and saves the entered email address and password to the database.

[1144] Step 4:

[1145] The server generates an authentication token for the user and stores it in the database.

[1146] Step 5:

[1147] The server generates an authentication token and sends it back to the user.

[1148] Plugin selection and configuration

[1149] Step 1:

[1150] The user enters their email address and password on the login screen and clicks the "Login" button.

[1151] Step 2:

[1152] The device sends login information to the server.

[1153] Step 3:

[1154] The server verifies the login information it receives, and if it is correct, it displays the dashboard screen to the user.

[1155] Step 4:

[1156] The user selects the plugin they want to integrate from the dashboard screen and clicks the "Settings" button.

[1157] Step 5:

[1158] The user enters the necessary API key and authentication information for the selected plugin and clicks the "Save" button.

[1159] Step 6:

[1160] The terminal sends the entered authentication information to the server.

[1161] Step 7:

[1162] The server uses the received authentication information to call the plugin's API and verify the authentication information.

[1163] Step 8:

[1164] If the server successfully authenticates, it saves the plugin integration settings to the database and notifies the user that the integration is complete.

[1165] Data integration and analysis

[1166] Step 1:

[1167] The server sets a schedule for collecting data from the plugin.

[1168] Step 2:

[1169] The server collects data by calling the plugin's API based on a configured schedule.

[1170] Step 3:

[1171] The server temporarily stores the collected data in storage.

[1172] Step 4:

[1173] The server sends the stored data to the AI ​​model and requests analysis processing.

[1174] Step 5:

[1175] The AI ​​model analyzes the received data and sends the processing results back to the server.

[1176] Step 6:

[1177] The server receives the processing results and saves them to the database.

[1178] Step 7:

[1179] The server displays the analysis results on the user's dashboard, allowing the user to review the results.

[1180] Calculation and billing of usage fees

[1181] Step 1:

[1182] The server periodically records user activity (number of API requests, number of data processing attempts, etc.).

[1183] Step 2:

[1184] The server compiles all usage data at the end of the month and calculates the usage fee.

[1185] Step 3:

[1186] The server automatically generates an invoice based on usage fees.

[1187] Step 4:

[1188] The server sends the generated invoice to the user's registered email address.

[1189] Step 5:

[1190] The user reviews the invoice and pays the fee using their designated payment method.

[1191] Step 6:

[1192] The server confirms receipt of payment and switches to the next month's usage.

[1193] These steps enable users to integrate generative AI with multiple business solutions and utilize them efficiently.

[1194] (Example 1)

[1195] 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."

[1196] When integrating multiple existing business solutions with generative artificial intelligence (AI), users often need to manually configure and manage individual settings, resulting in decreased efficiency and cumbersome operation. Furthermore, the process of centrally managing data from multiple systems and performing advanced analysis using AI is complex and burdensome for users.

[1197] 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.

[1198] In this invention, the server includes means for a user to input information and create an account, means for storing the input user information in a database, and means for generating and returning an authentication token to the user. This allows users to efficiently create accounts and easily access the system.

[1199] A "user" is a person or group that accesses a system and performs various operations on it.

[1200] "Means for inputting information" refers to interfaces or forms that allow users to provide necessary information such as email addresses and passwords.

[1201] "Means for creating an account" refers to a function that executes the process of generating a new user account based on the entered information.

[1202] "Means of saving to a database" refers to a data management system or software for permanently storing collected user information and other data.

[1203] A "token for authentication" is a unique identifier issued to verify a user's identity and facilitate future logins.

[1204] "Means for generating tokens" refers to the process or function for creating authentication tokens and providing the generated tokens to users.

[1205] "Means of storing tokens in a database" refers to a data management system that securely stores generated authentication tokens so that they can be verified and compared later.

[1206] "Means of returning to the user" refers to a method or protocol for sending the generated authentication token or other information to the user.

[1207] "External systems" refer to other software or services located outside the system, including those that can be interfaced through APIs.

[1208] A "plugin" is a software module that enables integration with external systems and adds specific functionality to a system.

[1209] "Authentication information" refers to information required when integrating with external systems, such as API keys, usernames, and passwords.

[1210] "Means of collecting data regularly" refers to the process of automatically collecting data from external systems according to a set schedule.

[1211] A "generative AI model" is an artificial intelligence algorithm that analyzes collected data and generates results.

[1212] "Means for displaying analysis results" refers to interfaces or dashboards that display the analysis results generated by the AI ​​model in a user-friendly format.

[1213] "Means for recording usage" refers to a system that tracks user behavior and system usage history and stores it as a log.

[1214] "Methods for calculating usage fees" refers to the process of calculating appropriate usage fees for users based on recorded usage data.

[1215] "Means for generating invoices" refers to a system that creates an invoice based on calculated usage fees and sends it to the user.

[1216] "Means of confirming payment" refers to a method or process for verifying whether a payment from a user has been completed and reflecting that result in the system.

[1217] This invention provides a system that allows users to easily integrate generative artificial intelligence (AI) with multiple existing business solutions. The following hardware and software are required to implement this system.

[1218] Hardware and software:

[1219] Server: Handles database management, authentication token generation, data collection scheduling, integration with AI models, results display, usage tracking, usage fee calculation, and invoice generation.

[1220] Terminal: This is the user interface (UI) where users enter information, create accounts, log in, select and configure plugins, and check the results.

[1221] Database: Stores user information, authentication tokens, integration plugin settings, collected data, analysis results, usage status, etc.

[1222] Generative AI model: An artificial intelligence algorithm used to analyze data and generate analysis results.

[1223] Processing flow:

[1224] User registration and authentication:

[1225] The user accesses the system, enters their email address and password, and creates an account. The information entered through the user interface is sent from the terminal to the server. The server stores the user information in a database, generates an authentication token, and sends it back to the user. This allows the user to log in easily later.

[1226] Plugin selection and configuration:

[1227] When a user logs into the system, the server displays a dashboard where the user can select the plugins they want to integrate. Examples include CRM systems and marketing tools. The user enters the API key and authentication information for the selected plugin, and this information is sent from the terminal to the server. The server verifies the authentication information and, if correct, saves it to the database.

[1228] Data integration and analysis:

[1229] Once the plugin integration is complete, the server sets a data collection schedule. For example, the server might call an external system's API to collect data at 1 AM every day. The collected data is temporarily stored and then sent by the server to a generative AI model. The generative AI model analyzes the data and sends the results back to the server. The server saves the analysis results to a database and displays them on the user's dashboard.

[1230] Calculation and billing of usage fees:

[1231] The server records user usage and compiles usage charges at the end of each month. This includes the number of API requests and data processing attempts. Based on the compiled results, the server calculates the usage charges, automatically generates an invoice, and sends it to the user. After the user reviews the invoice and pays the fee using the configured payment method, the server confirms receipt of payment and switches to the next month's usage.

[1232] Examples of specific cases and prompt statements:

[1233] As a concrete example, consider a scenario where a user wants to integrate with a marketing tool and have AI analyze sales data. The server configures the integration using the API key of the marketing tool entered by the user. The server collects data daily and sends it to a generated AI model for predictive analysis. The results are displayed on the dashboard as "Next month's sales forecast: 1 million yen."

[1234] Example of a prompt:

[1235] "We collect data from marketing tools daily, analyze it with an AI model, and predict next month's sales."

[1236] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1237] Step 1:

[1238] The user accesses the system and creates an account by entering their email address and password. The email address and password entered through the user interface are sent from the terminal to the server. The server stores the received information in its database and completes the registration. At this time, the server generates an authentication token and sends it back to the user. This makes subsequent logins easier.

[1239] Input: Email address, password

[1240] Output: Authentication token

[1241] Step 2:

[1242] When a user logs in, the server displays the dashboard using the user's authentication information. The dashboard displays a list of available plugins. The user selects the plugin they want to integrate and enters the necessary API key and authentication information.

[1243] Input: API key, credentials

[1244] Output: Plugin configuration information

[1245] Step 3:

[1246] The terminal sends plugin configuration information to the server, which verifies it. If the verification is successful, the server saves the configuration information to the database and completes the integration. Based on this integration configuration, the data collection described later is performed.

[1247] Input: Plugin configuration information

[1248] Output: Verification results, saved.

[1249] Step 4:

[1250] To periodically collect data from plugins whose integration settings are complete, the server sets a data collection schedule. For example, the server might call the external system's API at 1 AM every day to collect data. The collected data is temporarily stored.

[1251] Input: Data collection schedule

[1252] Output: Collected data

[1253] Step 5:

[1254] The server sends the collected data to the generative AI model. The generative AI model analyzes the received data and sends the analysis results back to the server.

[1255] Input: Collected data

[1256] Output: Analysis results

[1257] Step 6:

[1258] The server saves the analysis results to a database and displays them on the user's dashboard. The analysis results are provided in a visually easy-to-understand format, making them easily accessible to the user.

[1259] Input: Analysis results

[1260] Output: Dashboard update

[1261] Step 7:

[1262] The server records user plugin usage and compiles usage charges at the end of each month. This includes the number of API requests and data processing cycles. Based on the compiled results, the server calculates the usage charges, automatically generates an invoice, and sends it to the user.

[1263] Input: Usage data

[1264] Output: Invoice

[1265] Step 8:

[1266] After the user reviews the invoice, they pay the fee using their configured payment method. The server confirms receipt of the payment and switches to the next month's usage. This allows the user to continue using the system.

[1267] Input: Payment Information

[1268] Output: Payment confirmation, usage status update

[1269] (Application Example 1)

[1270] 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."

[1271] Modern logistics centers involve a complex interplay of numerous systems and processes, requiring efficient integration of their data. However, existing systems often rely on manual data collection, analysis, and optimization processes, resulting in significant time and effort, and consequently, reduced operational efficiency. Furthermore, the difficulty in real-time monitoring and issuing appropriate instructions leads to insufficient optimization of inventory and delivery management. The development of technologies to address these issues and improve the operational efficiency of logistics centers is highly desirable.

[1272] 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.

[1273] In this invention, the server includes means for a user to input information and create an account; means for storing the input user information in a database; means for generating and returning an authentication token to the user; means for the user to log in and select a plugin that links with multiple external systems; means for inputting authentication information for the selected plugin and verifying the authentication information; means for periodically collecting data from external systems and sending it to an AI model; means for displaying the analysis results from the AI ​​model to the user; means for recording the user's usage status, calculating usage fees at the end of the month, and generating an invoice; means for sending the invoice to the user and confirming payment; means including a generative AI model that links with the management system of a logistics center and optimizes inventory and delivery based on collected data; and means for performing predictive analysis from inventory and delivery data and notifying the analysis results to the smartphone in real time. This makes it possible to link multiple business processes in real time and manage them efficiently.

[1274] "User" refers to an individual or group that uses the system.

[1275] "Information" refers to all data that users enter when creating an account or using the system.

[1276] An "account" refers to the registration information required for a user to access the system.

[1277] A "database" refers to a storage area within a system used to store and manage information.

[1278] A "token" refers to a series of strings or codes generated for user authentication.

[1279] A "server" is a computer that forms the core of a system and is responsible for processing and storing data.

[1280] A "plugin" refers to an extension that adds new functionality to a system.

[1281] "Authentication information" refers to information such as API keys, IDs, and passwords that users need to connect with the plugin.

[1282] An "external system" refers to another system or service that exists outside of the system and interacts with it.

[1283] An "AI model" refers to an artificial intelligence model that analyzes collected data and performs predictions and optimizations.

[1284] "Analysis results" refer to the results derived by the AI ​​model after analyzing the data.

[1285] A "smartphone" refers to a portable information device that is capable of connecting to the internet and using a variety of applications.

[1286] A "logistics center" refers to a facility used to manage the storage and distribution of goods.

[1287] "Inventory" refers to the total amount of goods stored in a distribution center.

[1288] "Delivery" refers to the movement of goods from a logistics center to customers or other facilities.

[1289] "Predictive analytics" refers to an analytical method that predicts future trends and situations based on collected data.

[1290] This invention comprises a system including a user interface, database, authentication token, plugin management, data collection module, AI model, and usage fee calculation function. Users access this system and perform various operations using a smartphone.

[1291] The server first provides a means for the user to enter information and create an account. The information entered by the user is stored in a database, an authentication token is generated, and sent back to the user. This makes it easier for the user to log in on subsequent occasions.

[1292] When a user logs in, the server provides a means for selecting plugins that integrate with multiple external systems. The user selects a plugin, such as an inventory management system or a delivery management system, and enters the necessary authentication information. This authentication information is verified by the server, and the results are stored in the database.

[1293] The server periodically collects data from external systems and provides a means to send the collected data to an AI model. For example, data is collected regularly every hour, and the AI ​​model analyzes it. The analysis results are stored in a database by the server and notified to the user's smartphone in real time.

[1294] When integrated with a logistics center's management system, the AI ​​model performs predictive analytics based on collected inventory and delivery data. This enables the optimization of inventory and delivery within the logistics center. For example, it can provide information such as which products should have increased inventory next and which delivery routes are the most efficient.

[1295] The server also handles the usage fee calculation. The server records the user's plugin usage and calculates the usage fee at the end of the month. Based on the calculation, it generates an invoice and sends it to the user. Once the user reviews the invoice and pays the fee using the configured payment method, the server confirms receipt of the payment and switches to the next month's usage.

[1296] Hardware and software to be used:

[1297] Hardware: Smartphones, servers

[1298] Software: Flask (Web framework), SQLite (database), JWT (authentication token generation)

[1299] (Specific example)

[1300] Logistics center managers can use the "Logistics Center AI Assist" application to collect data from the inventory management system, analyze it with generative AI, and determine the optimal inventory placement method. For example, they can use prompts like the following:

[1301] Examples of prompts to input into a generative AI model:

[1302] Using data obtained from the inventory management system, forecast future demand and propose the optimal inventory allocation method. The data obtained is as follows:

[1303] {data}

[1304] This prompt prompts the AI ​​to analyze inventory data and suggest the optimal inventory placement method. This significantly improves the operational efficiency of the logistics center and reduces costs.

[1305] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1306] Step 1:

[1307] User registration and authentication

[1308] The server provides an input form for the user to enter information and create an account. The user enters the required information (e.g., email address, password) and clicks the "Register" button. Once the device sends the entered information to the server, the server stores it in its database, generates an authentication token, and sends it back to the user. This provides the user with data that will facilitate future logins.

[1309] Input: User information (email address, password)

[1310] Data processing: Saving user information to a database, generating authentication tokens.

[1311] Output: Authentication token

[1312] Step 2:

[1313] Plugin selection and configuration

[1314] When a user logs in, the server displays a dashboard screen allowing the user to select the plugin they want to integrate (e.g., inventory management system, shipping management system). The user enters the authentication information for the selected plugin (e.g., API key), and the device sends this information to the server. The server verifies the authentication information and saves the result to the database.

[1315] Input: Plugin credentials (API key)

[1316] Data processing: Verification of authentication information, saving to database.

[1317] Output: Plugin configuration result

[1318] Step 3:

[1319] Data collection and transmission

[1320] The server sets a schedule to periodically collect data from external systems. For example, the server calls APIs of external inventory management and delivery management systems every hour to collect data. The collected data is temporarily stored by the server and then sent to the AI ​​model.

[1321] Input: Data from an external system

[1322] Data processing: Data collection, temporary storage, and transmission to AI models.

[1323] Output: Data to send to the AI ​​model

[1324] Step 4:

[1325] Data analysis and result display

[1326] The AI ​​model analyzes the data sent from the server. The analysis results are sent back to the server, which then stores them in a database. The user's smartphone is notified of the analysis results in real time.

[1327] Input: Collected data

[1328] Data processing: Data analysis, generation of analysis results.

[1329] Output: Analysis results

[1330] Step 5:

[1331] Predictive analytics and optimization proposals

[1332] The AI ​​model works in conjunction with the logistics center's management system to perform predictive analytics based on inventory and delivery data. For example, it predicts which products should have increased inventory next and what the optimal delivery routes are. The analysis results are notified to smartphones, allowing users to check the details in real time via their phones.

[1333] Input: Collected data

[1334] Data processing: Predictive analytics, generation of optimization suggestions.

[1335] Output: Predictive analysis results, optimization suggestions

[1336] Step 6:

[1337] Calculation and billing of usage fees

[1338] The server records the user's plugin usage and calculates the usage fee at the end of the month. Based on the calculation, it automatically generates an invoice and sends it to the user. Once the user reviews the invoice and pays the fee using the configured payment method, the server confirms the payment and switches to the next month's usage.

[1339] Input: Plugin usage data

[1340] Data processing: Recording usage, calculating usage fees, generating invoices.

[1341] Output: Invoice, payment confirmation

[1342] This will allow logistics center management operations to be performed more efficiently and in real time.

[1343] 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.

[1344] This invention provides a system that improves user experience by combining a generative artificial intelligence (AI) with an emotion engine to integrate multiple existing business solutions. This system includes functions such as user registration and authentication, plugin selection and configuration, data integration and analysis, emotion engine integration, and usage fee calculation and billing.

[1345] User registration and authentication

[1346] The user accesses the system, enters their email address and password on the account creation page, and clicks the "Register" button. The device sends the entered information to the server, which stores it in its database. The server also generates and sends an authentication token back to the user. This authentication token allows for easier logins in the future.

[1347] Plugin selection and configuration

[1348] The user logs into the dashboard and selects the plugin they want to integrate (for example, a CRM system or marketing tool). The user enters the necessary API key and authentication information for the selected plugin and clicks the "Save" button. The device sends the entered information to the server, which verifies the authentication information. If authentication is successful, the plugin integration settings are saved to the database and the user is notified.

[1349] Data integration and analysis

[1350] The server sets a schedule for collecting data from the plugin. For example, it might call the plugin's API every hour to collect data. The collected data is temporarily stored and sent to the AI ​​model. The AI ​​model analyzes the data and sends the processing results back to the server. The server saves these results to a database and displays them to the user.

[1351] Emotional engine integration

[1352] An emotion engine is incorporated to recognize emotions from user input. For example, when a user enters a comment on the dashboard, the device sends the input information to the server, which uses the emotion engine to analyze the user's emotional state. The recognized emotional state is stored in a database along with other data. Based on the analysis results of the emotion engine, the server adjusts the data sent to connected external systems.

[1353] Presentation of analysis results tailored to emotions

[1354] Based on the analyzed emotions, the server changes how it presents the analysis results from the AI ​​model. For example, if the user is stressed, it displays concise and direct feedback, while if they are relaxed, it provides detailed analytics.

[1355] Calculation and billing of usage fees

[1356] The server records user usage and compiles the data at the end of each month. This includes the number of API requests and data processing cycles. Furthermore, sentiment data obtained using the sentiment engine is also incorporated into the usage fee calculation. The server automatically generates an invoice and sends it to the user. Once the user reviews the invoice and pays using the configured payment method, the server confirms receipt of payment and switches to the next month's usage.

[1357] In this way, the present invention enables the efficient integration of generative AI with multiple business solutions while taking into account the user's emotional state. A specific example is a scenario where a user interacts with a customer support tool, and the emotion engine analyzes the user's emotional state to provide appropriate responses and advice. This system is expected to improve operational efficiency and customer satisfaction.

[1358] The following describes the processing flow.

[1359] User registration and authentication

[1360] Step 1:

[1361] The user accesses the account creation page, enters their email address and password, and clicks the "Register" button.

[1362] Step 2:

[1363] The terminal sends the input information to the server.

[1364] Step 3:

[1365] The server saves the information it receives to the database.

[1366] Step 4:

[1367] The server generates an authentication token and stores it in the database.

[1368] Step 5:

[1369] The server generates an authentication token and sends it back to the user.

[1370] Plugin selection and configuration

[1371] Step 1:

[1372] The user enters their email address and password on the login screen and clicks the "Login" button.

[1373] Step 2:

[1374] The device sends login information to the server.

[1375] Step 3:

[1376] The server verifies the login information, and if correct, displays the dashboard screen to the user.

[1377] Step 4:

[1378] The user selects the plugin they want to integrate, enters the API key and authentication information, and clicks the "Save" button.

[1379] Step 5:

[1380] The terminal sends the input information to the server.

[1381] Step 6:

[1382] The server uses the authentication information to call the plugin's API and verify the authentication information.

[1383] Step 7:

[1384] If the server successfully authenticates, it saves the plugin's integration settings to the database and notifies the user.

[1385] Data integration and analysis

[1386] Step 1:

[1387] The server sets a schedule for collecting data from the plugin.

[1388] Step 2:

[1389] The server calls the plugin's API based on a configured schedule to collect data.

[1390] Step 3:

[1391] The server temporarily stores the collected data in storage.

[1392] Step 4:

[1393] The server sends the stored data to the AI ​​model and requests analysis processing.

[1394] Step 5:

[1395] The AI ​​model analyzes the data and sends the processing results back to the server.

[1396] Step 6:

[1397] The server receives the analysis results and saves them to the database.

[1398] Step 7:

[1399] The server displays the analysis results on the user's dashboard.

[1400] Emotional engine integration

[1401] Step 1:

[1402] Users enter comments and feedback on the dashboard.

[1403] Step 2:

[1404] The terminal sends the input information to the server.

[1405] Step 3:

[1406] The server sends the received data to the emotion engine, which then analyzes the user's emotional state.

[1407] Step 4:

[1408] The emotion engine sends the analysis results back to the server.

[1409] Step 5:

[1410] The server stores emotional states in a database and adjusts the content of data sent to external systems with which it collaborates.

[1411] Presentation of analysis results tailored to emotions

[1412] Step 1:

[1413] The server modifies the analysis results based on the user's emotional state.

[1414] Step 2:

[1415] For example, if a user is feeling stressed, the server displays concise feedback; if they are relaxed, it provides detailed analytics.

[1416] Calculation and billing of usage fees

[1417] Step 1:

[1418] The server records user activity (number of API requests, number of data processing attempts, etc.).

[1419] Step 2:

[1420] The server compiles all usage data at the end of the month and calculates the usage fee.

[1421] Step 3:

[1422] The server automatically generates invoices, including emotional data from an emotion engine.

[1423] Step 4:

[1424] The server sends the generated invoice to the user's registered email address.

[1425] Step 5:

[1426] The user reviews the invoice and pays the fee using their chosen payment method (e.g., credit card, bank transfer).

[1427] Step 6:

[1428] The server confirms receipt of payment and switches to the next month's usage.

[1429] In this way, the present invention, which integrates an emotion engine, can provide a system that enables the collaboration of generative AI and various business solutions while recognizing the user's emotional state. A specific example is a scenario in which the emotion engine analyzes the user's emotional state and supports appropriate responses when collaborating with a customer support tool. This system allows users to efficiently utilize data and provide higher service quality.

[1430] (Example 2)

[1431] 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."

[1432] Traditional business solution systems struggle to provide services that take into account the emotional state of users based on their input information, making it difficult to improve the quality of the user experience. Furthermore, they are unable to efficiently integrate with multiple external systems, placing a significant burden on users for manual configuration and adjustments. Additionally, tracking usage and calculating fees are cumbersome and lack automation.

[1433] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[1434] In this invention, the server includes means for a user to input information and create an account; means for storing the input user information in a database; means for generating and returning an authentication token to the user; means for the user to log in and select a plugin that integrates with multiple external systems; means for inputting authentication information for the selected plugin and verifying the authentication information; means for periodically collecting data from external systems and sending it to a generating AI model; means for displaying the analysis results from the generating AI model to the user; means for integrating an emotion engine that analyzes the user's input information and generates emotion data; means for optimizing and displaying the results from the generating AI model based on the analyzed emotion data; means for recording the user's usage status, calculating usage fees at the end of the month, and generating an invoice; and means for sending the invoice to the user and confirming payment. This makes it possible to improve the quality of the user experience, efficiently integrate with multiple external systems, and realize the understanding of usage status and automatic calculation of usage fees.

[1435] A "user" refers to an individual or legal entity that uses the system, enters information to create an account, selects plugins, and receives services.

[1436] An "authentication token" refers to a unique code generated by a server to authenticate a user and sent back to the user.

[1437] A "database" refers to a storage system that stores a collection of data that is collected, stored, and managed by a system.

[1438] A "plugin" refers to a software module that provides additional functionality to a system and enables it to interact with various external systems.

[1439] "Authentication information" refers to information such as API keys, usernames, and passwords necessary to establish integration with the plugin.

[1440] A "generative AI model" refers to an artificial intelligence algorithm that analyzes collected data and returns the results to a server to provide advanced services.

[1441] An "emotion engine" refers to a machine learning model or algorithm that analyzes user input information to identify the user's emotional state and adjusts the system's operation based on that.

[1442] "Usage status" refers to data related to system usage (e.g., number of API requests, number of data processing attempts) and records based on that data.

[1443] An "invoice" refers to a document sent to a user that shows the costs incurred for using the system.

[1444] "External systems" refer to other software or platforms that provide services through integration with the system.

[1445] These definitions clarify the meaning of each term used in the patent claims, making them easier to understand.

[1446] This invention provides a system that improves user experience by combining an emotion engine with generative artificial intelligence (AI) and the integration of multiple existing business solutions. This system is implemented using the following hardware and software configuration.

[1447] Hardware and software configuration

[1448] 1. Server: Manages key backend functions such as data storage, processing, and API calls. Examples include using cloud servers such as AWS or Google Cloud Platform.

[1449] 2. Terminal: A device used to obtain user input information and send it to the server. This includes PCs, smartphones, tablets, etc.

[1450] 3. Database: Stores user information, plugin settings, analysis results, sentiment data, etc. Examples include MySQL and PostgreSQL.

[1451] 4. Generative AI Model: An artificial intelligence algorithm that analyzes collected data and returns the results to the server. OpenAI GPT-3 is used as an example.

[1452] 5. Emotion Engine: A machine learning model that analyzes user input and generates emotion data. IBM Watson Tone Analyzer is used as an example.

[1453] User-server interaction

[1454] The user first accesses the system and enters their email address and password on the account creation page. Next, they click the "Register" button, and the device sends the entered information to the server. The server verifies this information, stores it in its database, generates an authentication token, and sends it back to the user. This token is used for subsequent logins.

[1455] After logging in, the user accesses the dashboard and selects the plugin they want to integrate (e.g., a CRM system or marketing tool). The user enters the plugin's API key and authentication information and clicks the "Save" button. The device sends the information to the server, which verifies the authentication information and saves it to the database.

[1456] Data analysis and emotion engine

[1457] The server periodically collects data from the plugin's API and sends it to the generative AI model. The generative AI model analyzes this data and sends the results back to the server. The returned analysis results are stored in a database and displayed to the user through a dashboard.

[1458] Furthermore, when a user enters a comment on the dashboard, the device sends that information to the server. The server uses an emotion engine to analyze the emotional state of the entered comment and stores the results in a database. Based on the analyzed emotional state, the server optimizes the results from the generative AI model and presents them to the user.

[1459] Calculation and billing

[1460] The server continuously records user activity and calculates usage fees at the end of the month based on API request counts, data processing counts, and sentiment data. The server automatically generates and sends an invoice to the user. Once the user reviews the invoice and completes payment, the server prepares for the next month's usage.

[1461] Specific example

[1462] For example, consider integration with a customer support tool. When a user posts an article to the customer support dashboard, the emotion engine analyzes the content of the post and identifies the emotional state. Based on this information, a generative AI model provides appropriate responses and advice. This process allows customers to receive more satisfying support and improves operational efficiency.

[1463] Example of a prompt:

[1464] Prompt text to input to the generative AI model:

[1465] User submissions in customer support:

[1466] Posted on: April 12, 2023

[1467] Post content: 'The system has been experiencing a lot of problems lately, and it's causing me a lot of trouble. Please tell me how to fix it immediately.'

[1468] Perform the following analysis on the generative AI model:

[1469] Identify the emotions the poster is feeling

[1470] Recommended countermeasures

[1471] In this way, the present invention makes it possible to efficiently link generative AI with multiple business solutions while taking into account the user's emotional state.

[1472] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1473] Specific processing steps of the program for this system

[1474] Step 1: User Registration

[1475] The user accesses the system and enters their email address and password on the account creation page.

[1476] The user clicks the "Register" button.

[1477] The device sends the entered email address and password to the server.

[1478] Enter: Email address, password

[1479] The server verifies whether the email address is in the correct format and whether the password is strong enough.

[1480] Data processing: Format check, password strength verification

[1481] Output: Verification results of input information

[1482] If verification is successful, the server saves the user information to a database (e.g., MySQL or PostgreSQL).

[1483] Data processing: Information storage

[1484] The server generates an authentication token and sends it to the user.

[1485] Output: Authentication token

[1486] Step 2: Select and configure plugins

[1487] The user logs into the dashboard.

[1488] The user selects the plugins they want to integrate with from multiple external systems (e.g., CRM systems and marketing tools).

[1489] The user enters the plugin's API key and authentication information and clicks the "Save" button.

[1490] The terminal sends the input information to the server.

[1491] Input: API key, authentication information

[1492] The server verifies the validity of the API key and authentication credentials, and saves the authentication credentials to the database.

[1493] Data processing: Verification and validity check of authentication information.

[1494] Output: Authentication result

[1495] If authentication is successful, the user will be notified that the plugin has been successfully configured.

[1496] Output: Configuration success notification

[1497] Step 3: Data Integration and Analysis

[1498] The server sets a data collection schedule for each plugin (e.g., calling the API every hour).

[1499] Input: Data collection schedule

[1500] Data processing: Schedule setting

[1501] When the scheduled time arrives, the server sends a request to the plugin's API to collect the data.

[1502] Input: API Request Information

[1503] Data processing: Data collection

[1504] Output: Collected data

[1505] The server temporarily stores the collected data and sends it to the generated AI model (e.g., OpenAI GPT-3).

[1506] Input: Collected data

[1507] Data processing: Temporary storage of data

[1508] Output: AI model input data

[1509] The generative AI model analyzes the data and sends the results back to the server.

[1510] Input: AI model input data

[1511] Data processing: Data analysis

[1512] Output: Analysis results

[1513] The server saves the analysis results to a database and displays them on the user's dashboard.

[1514] Input: Analysis results

[1515] Data processing: Saving results

[1516] Output: Dashboard display data

[1517] Step 4: Integrating the Emotional Engine

[1518] The user enters a comment on the dashboard and clicks the "Submit" button.

[1519] The terminal sends the input comment to the server.

[1520] Input: Comment data

[1521] Data processing: Sending comments

[1522] The server invokes a sentiment engine (e.g., IBM Watson Tone Analyzer) to analyze the comments.

[1523] Input: Comment data

[1524] Data processing: Sentiment analysis

[1525] Output: Sentiment data

[1526] The emotion engine sends the analysis results back to the server.

[1527] Input: Sentiment data

[1528] Data processing: Generation of analysis results

[1529] Output: Analysis results

[1530] The server stores emotional data in a database and adjusts the content of the data sent to external systems with which it collaborates.

[1531] Input: Sentiment data

[1532] Data processing: Data storage, adjustment of transmission content.

[1533] Output: Adjusted transmission data

[1534] Step 5: Presentation of analysis results tailored to emotions

[1535] The server checks the analysis results obtained from the emotion engine.

[1536] Input: Analyzed sentiment data

[1537] Data processing: Confirmation of emotional state

[1538] The server optimizes and displays the analysis results from the generated AI model based on the emotional state.

[1539] Input: Analysis results of the generated AI model, emotion data

[1540] Data processing: Result optimization

[1541] Output: Optimized display data

[1542] The server displays the optimized analysis results on the user's dashboard.

[1543] Input: Optimized display data

[1544] Data processing: Dashboard display

[1545] Output: Display result

[1546] Step 6: Calculation and billing of usage fees

[1547] The server continuously records user activity (e.g., number of API requests, number of data processing attempts).

[1548] Input: Usage data

[1549] Data processing: Recording of usage

[1550] At the end of the month, the server calculates the usage fee based on this usage data.

[1551] Input: Recorded usage data

[1552] Data processing: Usage fee calculation

[1553] Output: Billing data

[1554] The server automatically generates an invoice and sends it to the user.

[1555] Input: Billing data

[1556] Data processing: Invoice generation

[1557] Output: Invoice

[1558] The user reviews the invoice and completes the payment.

[1559] Input: Invoice

[1560] Data processing: Payment confirmation

[1561] Output: Payment completion notification

[1562] (Application Example 2)

[1563] 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."

[1564] Traditional AI models and external systems struggle to provide services that take into account the user's emotional state, resulting in problems such as decreased customer satisfaction and inconsistent service quality. Furthermore, especially on e-commerce sites, user stress and dissatisfaction often make problem-solving difficult, ultimately hindering customer retention. There is a need for a system that solves these problems and improves the user experience.

[1565] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[1566] In this invention, the server includes means for integrating an emotion engine for recognizing emotions from user input information, means for changing the method of presenting analysis results from an AI model based on the recognized emotions, and means for recording user usage, calculating usage fees at the end of the month, and generating invoices. This makes it possible to appropriately adjust the content of service provision based on the user's emotional state and improve customer satisfaction.

[1567] A "user" is a person or group that uses the system.

[1568] "Means for inputting information" refers to the interface used by users to input the necessary data into the system when creating an account.

[1569] A "database" is a storage function within a system used to store entered user information and other data.

[1570] An "authentication token" is a unique string of characters generated to simplify user authentication.

[1571] A "plugin" is an additional function or module used to connect a system with an external system.

[1572] "Means for verifying authentication information" refers to a function that checks whether the entered authentication information is accurate.

[1573] An "AI model" is a machine learning algorithm used to analyze input data and output results.

[1574] An "emotion engine" is a system that recognizes and analyzes emotions from user input information.

[1575] "Means for changing the method of presenting analysis results" refers to a function that adjusts the display method of analysis results based on recognized emotions.

[1576] "Means for calculating usage fees" refers to a function that calculates charges based on the user's usage and generates an invoice.

[1577] "Means of generating invoices" refers to a function that creates electronic or paper-based invoices based on the calculated usage fees.

[1578] "Means of confirming payment" refers to a function that allows users to confirm that they have made a payment based on the invoice.

[1579] This invention provides a system that enhances customer support on e-commerce sites by integrating generative artificial intelligence (AI) and an emotion engine. This system has the function of analyzing the user's emotional state and optimizing responses based on that analysis. It also includes functions for managing user usage and calculating and billing usage fees.

[1580] Overall system configuration

[1581] Hardware and software configuration

[1582] Server: Includes database, AI models, emotion engine, and authentication system.

[1583] Database: Stores user information, analysis results, authentication tokens, and usage data.

[1584] Generative AI model: Analyzes data collected from users and provides results.

[1585] Emotion Engine: Analyzes user input information to identify emotional states.

[1586] Authentication system: User authentication is performed using OAuth 2.0 and a REST API.

[1587] Terminal: The interface in which the user inputs information. It is primarily provided as a smartphone application.

[1588] Smartphone application: Developed using React Native, and handles communication with the server.

[1589] API: An interface for data communication between a server and a terminal.

[1590] Processing details

[1591] 1. User Registration and Authentication

[1592] Users create an account by entering their email address and password through a smartphone application.

[1593] The server saves the entered information to the database, generates an authentication token, and sends it back to the user.

[1594] 2. Selecting and configuring plugins

[1595] After logging in, users access the dashboard, select the sentiment analysis plugin, and activate it.

[1596] Enter the required API key and authentication information, and the server will perform the authentication.

[1597] 3. Data Integration and Analysis

[1598] The server periodically collects user input data and sends it to the sentiment engine.

[1599] The emotion engine analyzes the data, identifies the user's emotional state, and sends the results back to the server.

[1600] 4. Integration of the Emotional Engine

[1601] Based on the user's emotional state, the server adjusts how the analysis results are displayed.

[1602] We provide concise and direct feedback to users who are feeling stressed, and detailed information to users who are relaxed.

[1603] 5. Calculation and billing of usage fees

[1604] The server records user usage and calculates usage fees at the end of the month.

[1605] Generate an invoice based on the calculated usage fee and send it to the user. Also, confirm payment.

[1606] Specific examples and prompt statements

[1607] The following are specific examples of how users interact with the application and the prompt messages they might encounter.

[1608] Example 1: Prompt message during user registration

[1609] Please enter your email address and password.

[1610] Example 2: Prompt message when performing sentiment analysis

[1611] Please send the message the user entered to customer support.

[1612] Example 3: Prompt text for emotionally-based response advice

[1613] If the user's emotion is "anger," please provide brief feedback.

[1614] If the user's emotion is "joy," please provide detailed support information.

[1615] The system described above enables flexible responses tailored to the user's emotional state, leading to improved customer satisfaction. Furthermore, accurate recording and management of user usage ensures proper billing.

[1616] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1617] Step 1: User Registration and Authentication

[1618] The user enters their email address and password through a smartphone application.

[1619] The entered information is sent from the terminal to the server.

[1620] The server stores the received information in a database, generates an authentication token, and sends it back to the user.

[1621] (Input) User information (email address, password)

[1622] (Processing) Information storage (database), authentication token generation

[1623] (Output) Authentication token

[1624] Step 2: Select and configure plugins

[1625] The user logs into the dashboard, selects the sentiment analysis plugin, and activates it.

[1626] Enter the required API key and authentication information, and send it to the server.

[1627] The server verifies the entered authentication information and saves the verification results to the database.

[1628] (Input) API key, authentication information

[1629] (Processing) Verification of authentication information, saving to database.

[1630] (Output) Verification results

[1631] Step 3: Data Integration and Analysis

[1632] The server periodically collects user input data and sends it to the sentiment engine.

[1633] The emotion engine analyzes the data, identifies the user's emotional state, and sends the results back to the server.

[1634] (Input) User input data

[1635] (Processing) Data collection (API connection), emotional state analysis (emotion engine)

[1636] (Output) Emotion analysis results

[1637] Step 4: Integrating the Emotional Engine

[1638] The server adjusts how the analysis results are presented based on the analyzed emotional state.

[1639] For example, users experiencing stress are given concise and direct feedback, while users who are relaxed are provided with detailed analytics.

[1640] (Input) Sentiment analysis results

[1641] (Processing) Adjustment of presentation method (customization of analysis results)

[1642] (Output) Adjusted analysis results

[1643] Step 5: Calculation and billing of usage fees

[1644] The server records user usage and calculates usage fees at the end of the month.

[1645] An invoice is generated based on the calculated usage fee and sent to the user. After payment is confirmed, the service is switched to the next month's usage.

[1646] (Input) Usage data

[1647] (Processing) Calculation of usage fees, invoice generation, payment confirmation.

[1648] (Output) Invoice, Payment Confirmation Notice

[1649] This system configuration allows for flexible responses tailored to the user's emotional state, thereby improving customer satisfaction. Furthermore, it enables effective management of user usage and proper billing.

[1650] 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.

[1651] 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.

[1652] 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.

[1653] [Fourth Embodiment]

[1654] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[1655] 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.

[1656] 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).

[1657] 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.

[1658] 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.

[1659] 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).

[1660] 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.

[1661] 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.

[1662] 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.

[1663] 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.

[1664] 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.

[1665] 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.

[1666] 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".

[1667] This invention provides a system that allows users to easily integrate generative artificial intelligence (AI) with multiple existing business solutions. The basic components of this system include a user interface, database, authentication tokens, plugin management, data collection modules, AI models, and usage fee calculation functions.

[1668] User registration and authentication

[1669] The user accesses the system and creates an account. This involves entering an email address and password and clicking the "Register" button. Next, the device sends the entered information to the server, which then saves it to the database, completing the user registration. The server generates an authentication token and sends it back to the user, making future logins easier.

[1670] Plugin selection and configuration

[1671] When a user logs in, the server displays a dashboard allowing the user to select the plugin they want to integrate. The user selects a plugin (e.g., CRM system, marketing tool) and enters the necessary API key and authentication information. This information is sent to the server via the terminal. The server verifies the authentication information, and if the integration is successful, saves the settings to the database.

[1672] Data integration and analysis

[1673] The server sets a schedule to periodically collect data from the plugin once the integration settings are complete. For example, the server might call the external system's API every hour to collect data. The collected data is temporarily stored, and then the server sends it to the AI ​​model. The AI ​​model processes the data and sends the analysis results back to the server. The server stores these results in a database and displays them on the user's dashboard.

[1674] For example, when a user integrates with a marketing tool, the server sends data collected from the marketing tool to an AI model for predictive analysis. As a result, the effectiveness of the marketing campaign and recommendations for the next action are displayed on a dashboard.

[1675] Calculation and billing of usage fees

[1676] The server records the user's plugin usage and compiles usage charges at the end of each month. This includes the number of API requests and data processing cycles. Based on the compiled results, the server calculates the usage charges, automatically generates an invoice, and sends it to the user. After the user reviews the invoice and pays the fee using their configured payment method, the server confirms receipt of payment and switches to the next month's usage.

[1677] This invention enables users to efficiently manage multiple business solutions and easily utilize the advanced analytical capabilities of generative AI.

[1678] The following describes the processing flow.

[1679] User registration and authentication

[1680] Step 1:

[1681] The user accesses the account creation page, enters their email address and password, and clicks the "Register" button.

[1682] Step 2:

[1683] The device sends a request to the server containing the information entered by the user.

[1684] Step 3:

[1685] The server receives the request and saves the entered email address and password to the database.

[1686] Step 4:

[1687] The server generates an authentication token for the user and stores it in the database.

[1688] Step 5:

[1689] The server generates an authentication token and sends it back to the user.

[1690] Plugin selection and configuration

[1691] Step 1:

[1692] The user enters their email address and password on the login screen and clicks the "Login" button.

[1693] Step 2:

[1694] The device sends login information to the server.

[1695] Step 3:

[1696] The server verifies the login information it receives, and if it is correct, it displays the dashboard screen to the user.

[1697] Step 4:

[1698] The user selects the plugin they want to integrate from the dashboard screen and clicks the "Settings" button.

[1699] Step 5:

[1700] The user enters the necessary API key and authentication information for the selected plugin and clicks the "Save" button.

[1701] Step 6:

[1702] The terminal sends the entered authentication information to the server.

[1703] Step 7:

[1704] The server uses the received authentication information to call the plugin's API and verify the authentication information.

[1705] Step 8:

[1706] If the server successfully authenticates, it saves the plugin integration settings to the database and notifies the user that the integration is complete.

[1707] Data integration and analysis

[1708] Step 1:

[1709] The server sets a schedule for collecting data from the plugin.

[1710] Step 2:

[1711] The server collects data by calling the plugin's API based on a configured schedule.

[1712] Step 3:

[1713] The server temporarily stores the collected data in storage.

[1714] Step 4:

[1715] The server sends the stored data to the AI ​​model and requests analysis processing.

[1716] Step 5:

[1717] The AI ​​model analyzes the received data and sends the processing results back to the server.

[1718] Step 6:

[1719] The server receives the processing results and saves them to the database.

[1720] Step 7:

[1721] The server displays the analysis results on the user's dashboard, allowing the user to review the results.

[1722] Calculation and billing of usage fees

[1723] Step 1:

[1724] The server periodically records user activity (number of API requests, number of data processing attempts, etc.).

[1725] Step 2:

[1726] The server compiles all usage data at the end of the month and calculates the usage fee.

[1727] Step 3:

[1728] The server automatically generates an invoice based on usage fees.

[1729] Step 4:

[1730] The server sends the generated invoice to the user's registered email address.

[1731] Step 5:

[1732] The user reviews the invoice and pays the fee using their designated payment method.

[1733] Step 6:

[1734] The server confirms receipt of payment and switches to the next month's usage.

[1735] These steps enable users to integrate generative AI with multiple business solutions and utilize them efficiently.

[1736] (Example 1)

[1737] 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".

[1738] When integrating multiple existing business solutions with generative artificial intelligence (AI), users often need to manually configure and manage individual settings, resulting in decreased efficiency and cumbersome operation. Furthermore, the process of centrally managing data from multiple systems and performing advanced analysis using AI is complex and burdensome for users.

[1739] 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.

[1740] In this invention, the server includes means for a user to input information and create an account, means for storing the input user information in a database, and means for generating and returning an authentication token to the user. This allows users to efficiently create accounts and easily access the system.

[1741] A "user" is a person or group that accesses a system and performs various operations on it.

[1742] "Means for inputting information" refers to interfaces or forms that allow users to provide necessary information such as email addresses and passwords.

[1743] "Means for creating an account" refers to a function that executes the process of generating a new user account based on the entered information.

[1744] "Means of saving to a database" refers to a data management system or software for permanently storing collected user information and other data.

[1745] A "token for authentication" is a unique identifier issued to verify a user's identity and facilitate future logins.

[1746] "Means for generating tokens" refers to the process or function for creating authentication tokens and providing the generated tokens to users.

[1747] "Means of storing tokens in a database" refers to a data management system that securely stores generated authentication tokens so that they can be verified and compared later.

[1748] "Means of returning to the user" refers to a method or protocol for sending the generated authentication token or other information to the user.

[1749] "External systems" refer to other software or services located outside the system, including those that can be interfaced through APIs.

[1750] A "plugin" is a software module that enables integration with external systems and adds specific functionality to a system.

[1751] "Authentication information" refers to information required when integrating with external systems, such as API keys, usernames, and passwords.

[1752] "Means of collecting data regularly" refers to the process of automatically collecting data from external systems according to a set schedule.

[1753] A "generative AI model" is an artificial intelligence algorithm that analyzes collected data and generates results.

[1754] "Means for displaying analysis results" refers to interfaces or dashboards that display the analysis results generated by the AI ​​model in a user-friendly format.

[1755] "Means for recording usage" refers to a system that tracks user behavior and system usage history and stores it as a log.

[1756] "Methods for calculating usage fees" refers to the process of calculating appropriate usage fees for users based on recorded usage data.

[1757] "Means for generating invoices" refers to a system that creates an invoice based on calculated usage fees and sends it to the user.

[1758] "Means of confirming payment" refers to a method or process for verifying whether a payment from a user has been completed and reflecting that result in the system.

[1759] This invention provides a system that allows users to easily integrate generative artificial intelligence (AI) with multiple existing business solutions. The following hardware and software are required to implement this system.

[1760] Hardware and software:

[1761] Server: Handles database management, authentication token generation, data collection scheduling, integration with AI models, results display, usage tracking, usage fee calculation, and invoice generation.

[1762] Terminal: This is the user interface (UI) where users enter information, create accounts, log in, select and configure plugins, and check the results.

[1763] Database: Stores user information, authentication tokens, integration plugin settings, collected data, analysis results, usage status, etc.

[1764] Generative AI model: An artificial intelligence algorithm used to analyze data and generate analysis results.

[1765] Processing flow:

[1766] User registration and authentication:

[1767] The user accesses the system, enters their email address and password, and creates an account. The information entered through the user interface is sent from the terminal to the server. The server stores the user information in a database, generates an authentication token, and sends it back to the user. This allows the user to log in easily later.

[1768] Plugin selection and configuration:

[1769] When a user logs into the system, the server displays a dashboard where the user can select the plugins they want to integrate. Examples include CRM systems and marketing tools. The user enters the API key and authentication information for the selected plugin, and this information is sent from the terminal to the server. The server verifies the authentication information and, if correct, saves it to the database.

[1770] Data integration and analysis:

[1771] Once the plugin integration is complete, the server sets a data collection schedule. For example, the server might call an external system's API to collect data at 1 AM every day. The collected data is temporarily stored and then sent by the server to a generative AI model. The generative AI model analyzes the data and sends the results back to the server. The server saves the analysis results to a database and displays them on the user's dashboard.

[1772] Calculation and billing of usage fees:

[1773] The server records user usage and compiles usage charges at the end of each month. This includes the number of API requests and data processing attempts. Based on the compiled results, the server calculates the usage charges, automatically generates an invoice, and sends it to the user. After the user reviews the invoice and pays the fee using the configured payment method, the server confirms receipt of payment and switches to the next month's usage.

[1774] Examples of specific cases and prompt statements:

[1775] As a concrete example, consider a scenario where a user wants to integrate with a marketing tool and have AI analyze sales data. The server configures the integration using the API key of the marketing tool entered by the user. The server collects data daily and sends it to a generated AI model for predictive analysis. The results are displayed on the dashboard as "Next month's sales forecast: 1 million yen."

[1776] Example of a prompt:

[1777] "We collect data from marketing tools daily, analyze it with an AI model, and predict next month's sales."

[1778] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1779] Step 1:

[1780] The user accesses the system and creates an account by entering their email address and password. The email address and password entered through the user interface are sent from the terminal to the server. The server stores the received information in its database and completes the registration. At this time, the server generates an authentication token and sends it back to the user. This makes subsequent logins easier.

[1781] Input: Email address, password

[1782] Output: Authentication token

[1783] Step 2:

[1784] When a user logs in, the server displays the dashboard using the user's authentication information. The dashboard displays a list of available plugins. The user selects the plugin they want to integrate and enters the necessary API key and authentication information.

[1785] Input: API key, credentials

[1786] Output: Plugin configuration information

[1787] Step 3:

[1788] The terminal sends plugin configuration information to the server, which verifies it. If the verification is successful, the server saves the configuration information to the database and completes the integration. Based on this integration configuration, the data collection described later is performed.

[1789] Input: Plugin configuration information

[1790] Output: Verification results, saved.

[1791] Step 4:

[1792] To periodically collect data from plugins whose integration settings are complete, the server sets a data collection schedule. For example, the server might call the external system's API at 1 AM every day to collect data. The collected data is temporarily stored.

[1793] Input: Data collection schedule

[1794] Output: Collected data

[1795] Step 5:

[1796] The server sends the collected data to the generative AI model. The generative AI model analyzes the received data and sends the analysis results back to the server.

[1797] Input: Collected data

[1798] Output: Analysis results

[1799] Step 6:

[1800] The server saves the analysis results to a database and displays them on the user's dashboard. The analysis results are provided in a visually easy-to-understand format, making them easily accessible to the user.

[1801] Input: Analysis results

[1802] Output: Dashboard update

[1803] Step 7:

[1804] The server records user plugin usage and compiles usage charges at the end of each month. This includes the number of API requests and data processing cycles. Based on the compiled results, the server calculates the usage charges, automatically generates an invoice, and sends it to the user.

[1805] Input: Usage data

[1806] Output: Invoice

[1807] Step 8:

[1808] After the user reviews the invoice, they pay the fee using their configured payment method. The server confirms receipt of the payment and switches to the next month's usage. This allows the user to continue using the system.

[1809] Input: Payment Information

[1810] Output: Payment confirmation, usage status update

[1811] (Application Example 1)

[1812] 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".

[1813] Modern logistics centers involve a complex interplay of numerous systems and processes, requiring efficient integration of their data. However, existing systems often rely on manual data collection, analysis, and optimization processes, resulting in significant time and effort, and consequently, reduced operational efficiency. Furthermore, the difficulty in real-time monitoring and issuing appropriate instructions leads to insufficient optimization of inventory and delivery management. The development of technologies to address these issues and improve the operational efficiency of logistics centers is highly desirable.

[1814] 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.

[1815] In this invention, the server includes means for a user to input information and create an account; means for storing the input user information in a database; means for generating and returning an authentication token to the user; means for the user to log in and select a plugin that links with multiple external systems; means for inputting authentication information for the selected plugin and verifying the authentication information; means for periodically collecting data from external systems and sending it to an AI model; means for displaying the analysis results from the AI ​​model to the user; means for recording the user's usage status, calculating usage fees at the end of the month, and generating an invoice; means for sending the invoice to the user and confirming payment; means including a generative AI model that links with the management system of a logistics center and optimizes inventory and delivery based on collected data; and means for performing predictive analysis from inventory and delivery data and notifying the analysis results to the smartphone in real time. This makes it possible to link multiple business processes in real time and manage them efficiently.

[1816] "User" refers to an individual or group that uses the system.

[1817] "Information" refers to all data that users enter when creating an account or using the system.

[1818] An "account" refers to the registration information required for a user to access the system.

[1819] A "database" refers to a storage area within a system used to store and manage information.

[1820] A "token" refers to a series of strings or codes generated for user authentication.

[1821] A "server" is a computer that forms the core of a system and is responsible for processing and storing data.

[1822] A "plugin" refers to an extension that adds new functionality to a system.

[1823] "Authentication information" refers to information such as API keys, IDs, and passwords that users need to connect with the plugin.

[1824] An "external system" refers to another system or service that exists outside of the system and interacts with it.

[1825] An "AI model" refers to an artificial intelligence model that analyzes collected data and performs predictions and optimizations.

[1826] "Analysis results" refer to the results derived by the AI ​​model after analyzing the data.

[1827] A "smartphone" refers to a portable information device that is capable of connecting to the internet and using a variety of applications.

[1828] A "logistics center" refers to a facility used to manage the storage and distribution of goods.

[1829] "Inventory" refers to the total amount of goods stored in a distribution center.

[1830] "Delivery" refers to the movement of goods from a logistics center to customers or other facilities.

[1831] "Predictive analytics" refers to an analytical method that predicts future trends and situations based on collected data.

[1832] This invention comprises a system including a user interface, database, authentication token, plugin management, data collection module, AI model, and usage fee calculation function. Users access this system and perform various operations using a smartphone.

[1833] The server first provides a means for the user to enter information and create an account. The information entered by the user is stored in a database, an authentication token is generated, and sent back to the user. This makes it easier for the user to log in on subsequent occasions.

[1834] When a user logs in, the server provides a means for selecting plugins that integrate with multiple external systems. The user selects a plugin, such as an inventory management system or a delivery management system, and enters the necessary authentication information. This authentication information is verified by the server, and the results are stored in the database.

[1835] The server periodically collects data from external systems and provides a means to send the collected data to an AI model. For example, data is collected regularly every hour, and the AI ​​model analyzes it. The analysis results are stored in a database by the server and notified to the user's smartphone in real time.

[1836] When integrated with a logistics center's management system, the AI ​​model performs predictive analytics based on collected inventory and delivery data. This enables the optimization of inventory and delivery within the logistics center. For example, it can provide information such as which products should have increased inventory next and which delivery routes are the most efficient.

[1837] The server also handles the usage fee calculation. The server records the user's plugin usage and calculates the usage fee at the end of the month. Based on the calculation, it generates an invoice and sends it to the user. Once the user reviews the invoice and pays the fee using the configured payment method, the server confirms receipt of the payment and switches to the next month's usage.

[1838] Hardware and software to be used:

[1839] Hardware: Smartphones, servers

[1840] Software: Flask (Web framework), SQLite (database), JWT (authentication token generation)

[1841] (Specific example)

[1842] Logistics center managers can use the "Logistics Center AI Assist" application to collect data from the inventory management system, analyze it with generative AI, and determine the optimal inventory placement method. For example, they can use prompts like the following:

[1843] Examples of prompts to input into a generative AI model:

[1844] Using data obtained from the inventory management system, forecast future demand and propose the optimal inventory allocation method. The data obtained is as follows:

[1845] {data}

[1846] This prompt prompts the AI ​​to analyze inventory data and suggest the optimal inventory placement method. This significantly improves the operational efficiency of the logistics center and reduces costs.

[1847] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1848] Step 1:

[1849] User registration and authentication

[1850] The server provides an input form for the user to enter information and create an account. The user enters the required information (e.g., email address, password) and clicks the "Register" button. Once the device sends the entered information to the server, the server stores it in its database, generates an authentication token, and sends it back to the user. This provides the user with data that will facilitate future logins.

[1851] Input: User information (email address, password)

[1852] Data processing: Saving user information to a database, generating authentication tokens.

[1853] Output: Authentication token

[1854] Step 2:

[1855] Plugin selection and configuration

[1856] When a user logs in, the server displays a dashboard screen allowing the user to select the plugin they want to integrate (e.g., inventory management system, shipping management system). The user enters the authentication information for the selected plugin (e.g., API key), and the device sends this information to the server. The server verifies the authentication information and saves the result to the database.

[1857] Input: Plugin credentials (API key)

[1858] Data processing: Verification of authentication information, saving to database.

[1859] Output: Plugin configuration result

[1860] Step 3:

[1861] Data collection and transmission

[1862] The server sets a schedule to periodically collect data from external systems. For example, the server calls APIs of external inventory management and delivery management systems every hour to collect data. The collected data is temporarily stored by the server and then sent to the AI ​​model.

[1863] Input: Data from an external system

[1864] Data processing: Data collection, temporary storage, and transmission to AI models.

[1865] Output: Data to send to the AI ​​model

[1866] Step 4:

[1867] Data analysis and result display

[1868] The AI ​​model analyzes the data sent from the server. The analysis results are sent back to the server, which then stores them in a database. The user's smartphone is notified of the analysis results in real time.

[1869] Input: Collected data

[1870] Data processing: Data analysis, generation of analysis results.

[1871] Output: Analysis results

[1872] Step 5:

[1873] Predictive analytics and optimization proposals

[1874] The AI ​​model works in conjunction with the logistics center's management system to perform predictive analytics based on inventory and delivery data. For example, it predicts which products should have increased inventory next and what the optimal delivery routes are. The analysis results are notified to smartphones, allowing users to check the details in real time via their phones.

[1875] Input: Collected data

[1876] Data processing: Predictive analytics, generation of optimization suggestions.

[1877] Output: Predictive analysis results, optimization suggestions

[1878] Step 6:

[1879] Calculation and billing of usage fees

[1880] The server records the user's plugin usage and calculates the usage fee at the end of the month. Based on the calculation, it automatically generates an invoice and sends it to the user. Once the user reviews the invoice and pays the fee using the configured payment method, the server confirms the payment and switches to the next month's usage.

[1881] Input: Plugin usage data

[1882] Data processing: Recording usage, calculating usage fees, generating invoices.

[1883] Output: Invoice, payment confirmation

[1884] This will allow logistics center management operations to be performed more efficiently and in real time.

[1885] 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.

[1886] This invention provides a system that improves user experience by combining a generative artificial intelligence (AI) with an emotion engine to integrate multiple existing business solutions. This system includes functions such as user registration and authentication, plugin selection and configuration, data integration and analysis, emotion engine integration, and usage fee calculation and billing.

[1887] User registration and authentication

[1888] The user accesses the system, enters their email address and password on the account creation page, and clicks the "Register" button. The device sends the entered information to the server, which stores it in its database. The server also generates and sends an authentication token back to the user. This authentication token allows for easier logins in the future.

[1889] Plugin selection and configuration

[1890] The user logs into the dashboard and selects the plugin they want to integrate (for example, a CRM system or marketing tool). The user enters the necessary API key and authentication information for the selected plugin and clicks the "Save" button. The device sends the entered information to the server, which verifies the authentication information. If authentication is successful, the plugin integration settings are saved to the database and the user is notified.

[1891] Data integration and analysis

[1892] The server sets a schedule for collecting data from the plugin. For example, it might call the plugin's API every hour to collect data. The collected data is temporarily stored and sent to the AI ​​model. The AI ​​model analyzes the data and sends the processing results back to the server. The server saves these results to a database and displays them to the user.

[1893] Emotional engine integration

[1894] An emotion engine is incorporated to recognize emotions from user input. For example, when a user enters a comment on the dashboard, the device sends the input information to the server, which uses the emotion engine to analyze the user's emotional state. The recognized emotional state is stored in a database along with other data. Based on the analysis results of the emotion engine, the server adjusts the data sent to connected external systems.

[1895] Presentation of analysis results tailored to emotions

[1896] Based on the analyzed emotions, the server changes how it presents the analysis results from the AI ​​model. For example, if the user is stressed, it displays concise and direct feedback, while if they are relaxed, it provides detailed analytics.

[1897] Calculation and billing of usage fees

[1898] The server records user usage and compiles the data at the end of each month. This includes the number of API requests and data processing cycles. Furthermore, sentiment data obtained using the sentiment engine is also incorporated into the usage fee calculation. The server automatically generates an invoice and sends it to the user. Once the user reviews the invoice and pays using the configured payment method, the server confirms receipt of payment and switches to the next month's usage.

[1899] In this way, the present invention enables the efficient integration of generative AI with multiple business solutions while taking into account the user's emotional state. A specific example is a scenario where a user interacts with a customer support tool, and the emotion engine analyzes the user's emotional state to provide appropriate responses and advice. This system is expected to improve operational efficiency and customer satisfaction.

[1900] The following describes the processing flow.

[1901] User registration and authentication

[1902] Step 1:

[1903] The user accesses the account creation page, enters their email address and password, and clicks the "Register" button.

[1904] Step 2:

[1905] The terminal sends the input information to the server.

[1906] Step 3:

[1907] The server saves the information it receives to the database.

[1908] Step 4:

[1909] The server generates an authentication token and stores it in the database.

[1910] Step 5:

[1911] The server generates an authentication token and sends it back to the user.

[1912] Plugin selection and configuration

[1913] Step 1:

[1914] The user enters their email address and password on the login screen and clicks the "Login" button.

[1915] Step 2:

[1916] The device sends login information to the server.

[1917] Step 3:

[1918] The server verifies the login information, and if correct, displays the dashboard screen to the user.

[1919] Step 4:

[1920] The user selects the plugin they want to integrate, enters the API key and authentication information, and clicks the "Save" button.

[1921] Step 5:

[1922] The terminal sends the input information to the server.

[1923] Step 6:

[1924] The server uses the authentication information to call the plugin's API and verify the authentication information.

[1925] Step 7:

[1926] If the server successfully authenticates, it saves the plugin's integration settings to the database and notifies the user.

[1927] Data integration and analysis

[1928] Step 1:

[1929] The server sets a schedule for collecting data from the plugin.

[1930] Step 2:

[1931] The server calls the plugin's API based on a configured schedule to collect data.

[1932] Step 3:

[1933] The server temporarily stores the collected data in storage.

[1934] Step 4:

[1935] The server sends the stored data to the AI ​​model and requests analysis processing.

[1936] Step 5:

[1937] The AI ​​model analyzes the data and sends the processing results back to the server.

[1938] Step 6:

[1939] The server receives the analysis results and saves them to the database.

[1940] Step 7:

[1941] The server displays the analysis results on the user's dashboard.

[1942] Emotional engine integration

[1943] Step 1:

[1944] Users enter comments and feedback on the dashboard.

[1945] Step 2:

[1946] The terminal sends the input information to the server.

[1947] Step 3:

[1948] The server sends the received data to the emotion engine, which then analyzes the user's emotional state.

[1949] Step 4:

[1950] The emotion engine sends the analysis results back to the server.

[1951] Step 5:

[1952] The server stores emotional states in a database and adjusts the content of data sent to external systems with which it collaborates.

[1953] Presentation of analysis results tailored to emotions

[1954] Step 1:

[1955] The server modifies the analysis results based on the user's emotional state.

[1956] Step 2:

[1957] For example, if a user is feeling stressed, the server displays concise feedback; if they are relaxed, it provides detailed analytics.

[1958] Calculation and billing of usage fees

[1959] Step 1:

[1960] The server records user activity (number of API requests, number of data processing attempts, etc.).

[1961] Step 2:

[1962] The server compiles all usage data at the end of the month and calculates the usage fee.

[1963] Step 3:

[1964] The server automatically generates invoices, including emotional data from an emotion engine.

[1965] Step 4:

[1966] The server sends the generated invoice to the user's registered email address.

[1967] Step 5:

[1968] The user reviews the invoice and pays the fee using their chosen payment method (e.g., credit card, bank transfer).

[1969] Step 6:

[1970] The server confirms receipt of payment and switches to the next month's usage.

[1971] In this way, the present invention, which integrates an emotion engine, can provide a system that enables the collaboration of generative AI and various business solutions while recognizing the user's emotional state. A specific example is a scenario in which the emotion engine analyzes the user's emotional state and supports appropriate responses when collaborating with a customer support tool. This system allows users to efficiently utilize data and provide higher service quality.

[1972] (Example 2)

[1973] 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".

[1974] Traditional business solution systems struggle to provide services that take into account the emotional state of users based on their input information, making it difficult to improve the quality of the user experience. Furthermore, they are unable to efficiently integrate with multiple external systems, placing a significant burden on users for manual configuration and adjustments. Additionally, tracking usage and calculating fees are cumbersome and lack automation.

[1975] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[1976] In this invention, the server includes means for a user to input information and create an account; means for storing the input user information in a database; means for generating and returning an authentication token to the user; means for the user to log in and select a plugin that integrates with multiple external systems; means for inputting authentication information for the selected plugin and verifying the authentication information; means for periodically collecting data from external systems and sending it to a generating AI model; means for displaying the analysis results from the generating AI model to the user; means for integrating an emotion engine that analyzes the user's input information and generates emotion data; means for optimizing and displaying the results from the generating AI model based on the analyzed emotion data; means for recording the user's usage status, calculating usage fees at the end of the month, and generating an invoice; and means for sending the invoice to the user and confirming payment. This makes it possible to improve the quality of the user experience, efficiently integrate with multiple external systems, and realize the understanding of usage status and automatic calculation of usage fees.

[1977] A "user" refers to an individual or legal entity that uses the system, enters information to create an account, selects plugins, and receives services.

[1978] An "authentication token" refers to a unique code generated by a server to authenticate a user and sent back to the user.

[1979] A "database" refers to a storage system that stores a collection of data that is collected, stored, and managed by a system.

[1980] A "plugin" refers to a software module that provides additional functionality to a system and enables it to interact with various external systems.

[1981] "Authentication information" refers to information such as API keys, usernames, and passwords necessary to establish integration with the plugin.

[1982] A "generative AI model" refers to an artificial intelligence algorithm that analyzes collected data and returns the results to a server to provide advanced services.

[1983] An "emotion engine" refers to a machine learning model or algorithm that analyzes user input information to identify the user's emotional state and adjusts the system's operation based on that.

[1984] "Usage status" refers to data related to system usage (e.g., number of API requests, number of data processing attempts) and records based on that data.

[1985] An "invoice" refers to a document sent to a user that shows the costs incurred for using the system.

[1986] "External systems" refer to other software or platforms that provide services through integration with the system.

[1987] These definitions clarify the meaning of each term used in the patent claims, making them easier to understand.

[1988] This invention provides a system that improves user experience by combining an emotion engine with generative artificial intelligence (AI) and the integration of multiple existing business solutions. This system is implemented using the following hardware and software configuration.

[1989] Hardware and software configuration

[1990] 1. Server: Manages key backend functions such as data storage, processing, and API calls. Examples include using cloud servers such as AWS or Google Cloud Platform.

[1991] 2. Terminal: A device used to obtain user input information and send it to the server. This includes PCs, smartphones, tablets, etc.

[1992] 3. Database: Stores user information, plugin settings, analysis results, sentiment data, etc. Examples include MySQL and PostgreSQL.

[1993] 4. Generative AI Model: An artificial intelligence algorithm that analyzes collected data and returns the results to the server. OpenAI GPT-3 is used as an example.

[1994] 5. Emotion Engine: A machine learning model that analyzes user input and generates emotion data. IBM Watson Tone Analyzer is used as an example.

[1995] User-server interaction

[1996] The user first accesses the system and enters their email address and password on the account creation page. Next, they click the "Register" button, and the device sends the entered information to the server. The server verifies this information, stores it in its database, generates an authentication token, and sends it back to the user. This token is used for subsequent logins.

[1997] After logging in, the user accesses the dashboard and selects the plugin they want to integrate (e.g., a CRM system or marketing tool). The user enters the plugin's API key and authentication information and clicks the "Save" button. The device sends the information to the server, which verifies the authentication information and saves it to the database.

[1998] Data analysis and emotion engine

[1999] The server periodically collects data from the plugin's API and sends it to the generative AI model. The generative AI model analyzes this data and sends the results back to the server. The returned analysis results are stored in a database and displayed to the user through a dashboard.

[2000] Furthermore, when a user enters a comment on the dashboard, the device sends that information to the server. The server uses an emotion engine to analyze the emotional state of the entered comment and stores the results in a database. Based on the analyzed emotional state, the server optimizes the results from the generative AI model and presents them to the user.

[2001] Calculation and billing

[2002] The server continuously records user activity and calculates usage fees at the end of the month based on API request counts, data processing counts, and sentiment data. The server automatically generates and sends an invoice to the user. Once the user reviews the invoice and completes payment, the server prepares for the next month's usage.

[2003] Specific example

[2004] For example, consider integration with a customer support tool. When a user posts an article to the customer support dashboard, the emotion engine analyzes the content of the post and identifies the emotional state. Based on this information, a generative AI model provides appropriate responses and advice. This process allows customers to receive more satisfying support and improves operational efficiency.

[2005] Example of a prompt:

[2006] Prompt text to input to the generative AI model:

[2007] User submissions in customer support:

[2008] Posted on: April 12, 2023

[2009] Post content: 'The system has been experiencing a lot of problems lately, and it's causing me a lot of trouble. Please tell me how to fix it immediately.'

[2010] Perform the following analysis on the generative AI model:

[2011] Identify the emotions the poster is feeling

[2012] Recommended countermeasures

[2013] In this way, the present invention makes it possible to efficiently link generative AI with multiple business solutions while taking into account the user's emotional state.

[2014] The flow of the specific processing in Example 2 will be explained using Figure 13.

[2015] Specific processing steps of the program for this system

[2016] Step 1: User Registration

[2017] The user accesses the system and enters their email address and password on the account creation page.

[2018] The user clicks the "Register" button.

[2019] The device sends the entered email address and password to the server.

[2020] Enter: Email address, password

[2021] The server verifies whether the email address is in the correct format and whether the password is strong enough.

[2022] Data processing: Format check, password strength verification

[2023] Output: Verification results of input information

[2024] If verification is successful, the server saves the user information to a database (e.g., MySQL or PostgreSQL).

[2025] Data processing: Information storage

[2026] The server generates an authentication token and sends it to the user.

[2027] Output: Authentication token

[2028] Step 2: Select and configure plugins

[2029] The user logs into the dashboard.

[2030] The user selects the plugins they want to integrate with from multiple external systems (e.g., CRM systems and marketing tools).

[2031] The user enters the plugin's API key and authentication information and clicks the "Save" button.

[2032] The terminal sends the input information to the server.

[2033] Input: API key, authentication information

[2034] The server verifies the validity of the API key and authentication credentials, and saves the authentication credentials to the database.

[2035] Data processing: Verification and validity check of authentication information.

[2036] Output: Authentication result

[2037] If authentication is successful, the user will be notified that the plugin has been successfully configured.

[2038] Output: Configuration success notification

[2039] Step 3: Data Integration and Analysis

[2040] The server sets a data collection schedule for each plugin (e.g., calling the API every hour).

[2041] Input: Data collection schedule

[2042] Data processing: Schedule setting

[2043] When the scheduled time arrives, the server sends a request to the plugin's API to collect the data.

[2044] Input: API Request Information

[2045] Data processing: Data collection

[2046] Output: Collected data

[2047] The server temporarily stores the collected data and sends it to the generated AI model (e.g., OpenAI GPT-3).

[2048] Input: Collected data

[2049] Data processing: Temporary storage of data

[2050] Output: AI model input data

[2051] The generative AI model analyzes the data and sends the results back to the server.

[2052] Input: AI model input data

[2053] Data processing: Data analysis

[2054] Output: Analysis results

[2055] The server saves the analysis results to a database and displays them on the user's dashboard.

[2056] Input: Analysis results

[2057] Data processing: Saving results

[2058] Output: Dashboard display data

[2059] Step 4: Integrating the Emotional Engine

[2060] The user enters a comment on the dashboard and clicks the "Submit" button.

[2061] The terminal sends the input comment to the server.

[2062] Input: Comment data

[2063] Data processing: Sending comments

[2064] The server invokes a sentiment engine (e.g., IBM Watson Tone Analyzer) to analyze the comments.

[2065] Input: Comment data

[2066] Data processing: Sentiment analysis

[2067] Output: Sentiment data

[2068] The emotion engine sends the analysis results back to the server.

[2069] Input: Sentiment data

[2070] Data processing: Generation of analysis results

[2071] Output: Analysis results

[2072] The server stores emotional data in a database and adjusts the content of the data sent to external systems with which it collaborates.

[2073] Input: Sentiment data

[2074] Data processing: Data storage, adjustment of transmission content.

[2075] Output: Adjusted transmission data

[2076] Step 5: Presentation of analysis results tailored to emotions

[2077] The server checks the analysis results obtained from the emotion engine.

[2078] Input: Analyzed sentiment data

[2079] Data processing: Confirmation of emotional state

[2080] The server optimizes and displays the analysis results from the generated AI model based on the emotional state.

[2081] Input: Analysis results of the generated AI model, emotion data

[2082] Data processing: Result optimization

[2083] Output: Optimized display data

[2084] The server displays the optimized analysis results on the user's dashboard.

[2085] Input: Optimized display data

[2086] Data processing: Dashboard display

[2087] Output: Display result

[2088] Step 6: Calculation and billing of usage fees

[2089] The server continuously records user activity (e.g., number of API requests, number of data processing attempts).

[2090] Input: Usage data

[2091] Data processing: Recording of usage

[2092] At the end of the month, the server calculates the usage fee based on this usage data.

[2093] Input: Recorded usage data

[2094] Data processing: Usage fee calculation

[2095] Output: Billing data

[2096] The server automatically generates an invoice and sends it to the user.

[2097] Input: Billing data

[2098] Data processing: Invoice generation

[2099] Output: Invoice

[2100] The user reviews the invoice and completes the payment.

[2101] Input: Invoice

[2102] Data processing: Payment confirmation

[2103] Output: Payment completion notification

[2104] (Application Example 2)

[2105] 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".

[2106] Traditional AI models and external systems struggle to provide services that take into account the user's emotional state, resulting in problems such as decreased customer satisfaction and inconsistent service quality. Furthermore, especially on e-commerce sites, user stress and dissatisfaction often make problem-solving difficult, ultimately hindering customer retention. There is a need for a system that solves these problems and improves the user experience.

[2107] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[2108] In this invention, the server includes means for integrating an emotion engine for recognizing emotions from user input information, means for changing the method of presenting analysis results from an AI model based on the recognized emotions, and means for recording user usage, calculating usage fees at the end of the month, and generating invoices. This makes it possible to appropriately adjust the content of service provision based on the user's emotional state and improve customer satisfaction.

[2109] A "user" is a person or group that uses the system.

[2110] "Means for inputting information" refers to the interface used by users to input the necessary data into the system when creating an account.

[2111] A "database" is a storage function within a system used to store entered user information and other data.

[2112] An "authentication token" is a unique string of characters generated to simplify user authentication.

[2113] A "plugin" is an additional function or module used to connect a system with an external system.

[2114] "Means for verifying authentication information" refers to a function that checks whether the entered authentication information is accurate.

[2115] An "AI model" is a machine learning algorithm used to analyze input data and output results.

[2116] An "emotion engine" is a system that recognizes and analyzes emotions from user input information.

[2117] "Means for changing the method of presenting analysis results" refers to a function that adjusts the display method of analysis results based on recognized emotions.

[2118] "Means for calculating usage fees" refers to a function that calculates charges based on the user's usage and generates an invoice.

[2119] "Means of generating invoices" refers to a function that creates electronic or paper-based invoices based on the calculated usage fees.

[2120] "Means of confirming payment" refers to a function that allows users to confirm that they have made a payment based on the invoice.

[2121] This invention provides a system that enhances customer support on e-commerce sites by integrating generative artificial intelligence (AI) and an emotion engine. This system has the function of analyzing the user's emotional state and optimizing responses based on that analysis. It also includes functions for managing user usage and calculating and billing usage fees.

[2122] Overall system configuration

[2123] Hardware and software configuration

[2124] Server: Includes database, AI models, emotion engine, and authentication system.

[2125] Database: Stores user information, analysis results, authentication tokens, and usage data.

[2126] Generative AI model: Analyzes data collected from users and provides results.

[2127] Emotion Engine: Analyzes user input information to identify emotional states.

[2128] Authentication system: User authentication is performed using OAuth 2.0 and a REST API.

[2129] Terminal: The interface in which the user inputs information. It is primarily provided as a smartphone application.

[2130] Smartphone application: Developed using React Native, and handles communication with the server.

[2131] API: An interface for data communication between a server and a terminal.

[2132] Processing details

[2133] 1. User Registration and Authentication

[2134] Users create an account by entering their email address and password through a smartphone application.

[2135] The server saves the entered information to the database, generates an authentication token, and sends it back to the user.

[2136] 2. Selecting and configuring plugins

[2137] After logging in, users access the dashboard, select the sentiment analysis plugin, and activate it.

[2138] Enter the required API key and authentication information, and the server will perform the authentication.

[2139] 3. Data Integration and Analysis

[2140] The server periodically collects user input data and sends it to the sentiment engine.

[2141] The emotion engine analyzes the data, identifies the user's emotional state, and sends the results back to the server.

[2142] 4. Integration of the Emotional Engine

[2143] Based on the user's emotional state, the server adjusts how the analysis results are displayed.

[2144] We provide concise and direct feedback to users who are feeling stressed, and detailed information to users who are relaxed.

[2145] 5. Calculation and billing of usage fees

[2146] The server records user usage and calculates usage fees at the end of the month.

[2147] Generate an invoice based on the calculated usage fee and send it to the user. Also, confirm payment.

[2148] Specific examples and prompt statements

[2149] The following are specific examples of how users interact with the application and the prompt messages they might encounter.

[2150] Example 1: Prompt message during user registration

[2151] Please enter your email address and password.

[2152] Example 2: Prompt message when performing sentiment analysis

[2153] Please send the message the user entered to customer support.

[2154] Example 3: Prompt text for emotionally-based response advice

[2155] If the user's emotion is "anger," please provide brief feedback.

[2156] If the user's emotion is "joy," please provide detailed support information.

[2157] The system described above enables flexible responses tailored to the user's emotional state, leading to improved customer satisfaction. Furthermore, accurate recording and management of user usage ensures proper billing.

[2158] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[2159] Step 1: User Registration and Authentication

[2160] The user enters their email address and password through a smartphone application.

[2161] The entered information is sent from the terminal to the server.

[2162] The server stores the received information in a database, generates an authentication token, and sends it back to the user.

[2163] (Input) User information (email address, password)

[2164] (Processing) Information storage (database), authentication token generation

[2165] (Output) Authentication token

[2166] Step 2: Select and configure plugins

[2167] The user logs into the dashboard, selects the sentiment analysis plugin, and activates it.

[2168] Enter the required API key and authentication information, and send it to the server.

[2169] The server verifies the entered authentication information and saves the verification results to the database.

[2170] (Input) API key, authentication information

[2171] (Processing) Verification of authentication information, saving to database.

[2172] (Output) Verification results

[2173] Step 3: Data Integration and Analysis

[2174] The server periodically collects user input data and sends it to the sentiment engine.

[2175] The emotion engine analyzes the data, identifies the user's emotional state, and sends the results back to the server.

[2176] (Input) User input data

[2177] (Processing) Data collection (API connection), emotional state analysis (emotion engine)

[2178] (Output) Emotion analysis results

[2179] Step 4: Integrating the Emotional Engine

[2180] The server adjusts how the analysis results are presented based on the analyzed emotional state.

[2181] For example, users experiencing stress are given concise and direct feedback, while users who are relaxed are provided with detailed analytics.

[2182] (Input) Sentiment analysis results

[2183] (Processing) Adjustment of presentation method (customization of analysis results)

[2184] (Output) Adjusted analysis results

[2185] Step 5: Calculation and billing of usage fees

[2186] The server records user usage and calculates usage fees at the end of the month.

[2187] An invoice is generated based on the calculated usage fee and sent to the user. After payment is confirmed, the service is switched to the next month's usage.

[2188] (Input) Usage data

[2189] (Processing) Calculation of usage fees, invoice generation, payment confirmation.

[2190] (Output) Invoice, Payment Confirmation Notice

[2191] This system configuration allows for flexible responses tailored to the user's emotional state, thereby improving customer satisfaction. Furthermore, it enables effective management of user usage and proper billing.

[2192] 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.

[2193] 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.

[2194] 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.

[2195] 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.

[2196] 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.

[2197] 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.

[2198] 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.

[2199] 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.

[2200] 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."

[2201] 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.

[2202] 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.

[2203] 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.

[2204] 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.

[2205] 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.

[2206] 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.

[2207] 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.

[2208] 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.

[2209] 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.

[2210] 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.

[2211] 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.

[2212] 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.

[2213] The following is further disclosed regarding the embodiments described above.

[2214] (Claim 1)

[2215] A means for users to enter information and create an account,

[2216] A means of saving the entered user information to a database,

[2217] A means of generating an authentication token and returning it to the user,

[2218] A means for users to log in and select plugins that integrate with multiple external systems,

[2219] A means to enter authentication information for the selected plugin and verify the authentication information,

[2220] A means of periodically collecting data from external systems and sending it to an AI model,

[2221] A means of displaying the analysis results from the AI ​​model to the user,

[2222] A means of recording user usage, calculating usage fees at the end of the month, and generating invoices,

[2223] A system that includes means for sending invoices to users and confirming payments.

[2224] (Claim 2)

[2225] In the system described in claim 1,

[2226] A system in which the means for generating authentication tokens includes means for storing the tokens in a database.

[2227] (Claim 3)

[2228] In the system described in claim 1,

[2229] A system that includes a means of setting a schedule for data collection, as well as data storage and transmission to an AI model.

[2230] "Example 1"

[2231] (Claim 1)

[2232] A means for users to enter information and create an account,

[2233] A means of saving the entered user information to a database,

[2234] A means of generating an authentication token and returning it to the user,

[2235] A means for users to log in and select plugins that integrate with multiple external systems,

[2236] A means to enter authentication information for the selected plugin and verify the authentication information,

[2237] A means of periodically collecting data from external systems and sending it to the generated AI model,

[2238] A means of displaying the analysis results from the generated AI model to the user,

[2239] A means of recording user usage, calculating usage fees at the end of the month, and generating invoices,

[2240] A system that includes means for sending invoices to users and confirming payments.

[2241] (Claim 2)

[2242] The system according to claim 1, wherein the means for generating an authentication token includes means for storing the token in a database.

[2243] (Claim 3)

[2244] The system according to claim 1, wherein means for setting a schedule for data collection include storing the data and transmitting it to a generated AI model.

[2245] "Application Example 1"

[2246] (Claim 1)

[2247] A means for users to enter information and create an account,

[2248] A means of saving the entered user information to a database,

[2249] A means of generating an authentication token and returning it to the user,

[2250] A means for users to log in and select plugins that integrate with multiple external systems,

[2251] A means to enter authentication information for the selected plugin and verify the authentication information,

[2252] A means of periodically collecting data from external systems and sending it to an AI model,

[2253] A means of displaying the analysis results from the AI ​​model to the user,

[2254] A means of recording user usage, calculating usage fees at the end of the month, and generating invoices,

[2255] A means of sending invoices to users and confirming payment,

[2256] A means including a generative AI model for optimizing inventory and delivery based on collected data, in conjunction with the management system of a logistics center,

[2257] A system that performs predictive analytics based on inventory and delivery data, and includes a means to notify smartphones of the analysis results in real time.

[2258] (Claim 2)

[2259] The system according to claim 1, wherein the means for generating an authentication token includes means for storing the token in a database.

[2260] (Claim 3)

[2261] The system according to claim 1, wherein means for setting a schedule for data collection include storing the data and transmitting it to an AI model.

[2262] "Example 2 of combining an emotion engine"

[2263] (Claim 1)

[2264] A means for users to enter information and create an account,

[2265] A means of saving the entered user information to a database,

[2266] A means of generating an authentication token and returning it to the user,

[2267] A means for users to log in and select plugins that integrate with multiple external systems,

[2268] A means to enter authentication information for the selected plugin and verify the authentication information,

[2269] A means of periodically collecting data from external systems and sending it to the generated AI model,

[2270] A means of displaying the analysis results from the generated AI model to the user,

[2271] A means of integrating an emotion engine that analyzes user input information and generates emotion data,

[2272] A means for optimizing and displaying the results from a generative AI model based on analyzed sentiment data,

[2273] A means of recording user usage, calculating usage fees at the end of the month, and generating invoices,

[2274] A system that includes means for sending invoices to users and confirming payments.

[2275] (Claim 2)

[2276] The system according to claim 1, wherein the means for generating an authentication token includes storing the token in a database.

[2277] (Claim 3)

[2278] The system according to claim 1, wherein means for setting a schedule for data collection include storing the data and transmitting it to a generated AI model.

[2279] "Application example 2 when combining with an emotional engine"

[2280] (Claim 1)

[2281] A means for users to enter information and create an account,

[2282] A means of saving the entered user information to a database,

[2283] A means of generating an authentication token and returning it to the user,

[2284] A means for users to log in and select plugins that integrate with multiple external systems,

[2285] A means to enter authentication information for the selected plugin and verify the authentication information,

[2286] A means of periodically collecting data from external systems and sending it to an AI model,

[2287] A means of displaying the analysis results from the AI ​​model to the user,

[2288] A means of integrating an emotion engine to recognize emotions from user input information,

[2289] A means of changing the way analysis results from the AI ​​model are presented based on recognized emotions,

[2290] A means of recording user usage, calculating usage fees at the end of the month, and generating invoices,

[2291] A system that includes means for sending invoices to users and confirming payments.

[2292] (Claim 2)

[2293] The system according to claim 1, wherein the means for generating an authentication token includes means for storing the token in a database.

[2294] (Claim 3)

[2295] The system according to claim 1, wherein means for setting a schedule for data collection include storing the data and transmitting it to an AI model. [Explanation of Symbols]

[2296] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for users to enter information and create an account, A means of saving the entered user information to a database, A means of generating an authentication token and returning it to the user, A means for users to log in and select plugins that integrate with multiple external systems, A means to enter authentication information for the selected plugin and verify the authentication information, A means of periodically collecting data from external systems and sending it to an AI model, A means of displaying the analysis results from the AI ​​model to the user, A means of recording user usage, calculating usage fees at the end of the month, and generating invoices, A system that includes means for sending invoices to users and confirming payments.

2. In the system described in claim 1, A system in which the means for generating authentication tokens includes means for storing the tokens in a database.

3. In the system described in claim 1, A system that includes a means of setting a schedule for data collection, as well as data storage and transmission to an AI model.

Images