System

Abstract

A system is provided.SOLUTION: A system comprising: means for receiving authorization information from a user and matching the authorization information against a database; means for providing a chat interface to a user device if the authorization is successful; means for receiving a query from the user and parsing the query to retrieve relevant information from a knowledgebase; means for generating an initial answer using a generative AI model; means for sending the generated initial answer to the user device; means for receiving a detailed survey request from the user and escalating to an appropriate contact person; and means for receiving feedback and updating the knowledgebase.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] As companies grow in size and collaboration between multiple companies and departments increases, employees are required to make inquiries in a variety of formats and flows. As a result, delays in responding to inquiries and employees not knowing how to respond are becoming commonplace. In particular, inquiries regarding internal rules, technology, application flows, and personnel systems are concentrated, and it has become common for responses to take a long time. In addition, because employees make inquiries from multiple devices and according to the rules of different companies and departments, they often find themselves unsure of where to turn. If this situation continues, employee productivity will decline and work efficiency will deteriorate. [Means for solving the problem]

[0005] In order to solve the above-mentioned problems, the present invention provides the following means. The system includes an authentication means for receiving a user's authentication information and comparing the authentication information with a database. The system also includes a means for providing a chat interface on the user's terminal that is available 24 hours a day, 365 days a year if authentication is successful. The system also includes a means for receiving inquiries from users, analyzing the inquiries, and searching for relevant information from a knowledge base. The system also includes a means for automatically generating an initial response using a generative AI model and sending it to the user's terminal. The system also includes a means for receiving requests for further investigation from users and escalating the request to an appropriate person. Finally, the above problems are solved by providing a system that includes a means for receiving feedback from users and updating the knowledge base.

[0006] "User authentication" is the process of verifying that a user has valid authority when accessing a system.

[0007] A "chat interface" is a screen or window that allows users to make text-based inquiries and communicate.

[0008] A "knowledge base" is a collection of information and data stored within a system, intended to provide reference information in response to inquiries.

[0009] A "generative AI model" is an artificial intelligence algorithm that automatically generates answers based on the content of a query.

[0010] "Escalation" is the process of passing an inquiry to a more specialized person or higher level of support when the initial response is insufficient.

[0011] A "user terminal" refers to a device such as a computer or smartphone operated by a user.

[0012] "Authentication information" refers to data, such as a user ID and password, used to identify a user and verify their authority.

[0013] "Feedback" refers to opinions and evaluations provided by users regarding the system's answers and responses.

[0014] A "database" is a collection of data that is structured to allow information to be efficiently stored, managed, and searched.

[0015] A "request for detailed investigation" is a request made by a user who has determined that the initial response is insufficient, seeking a more detailed response. [Brief explanation of the drawings]

[0016] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10]1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0017] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0018] First, the terms used in the following description will be explained.

[0019] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0020] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0021] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0022] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0023] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0024] [First embodiment]

[0025] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0026] 1, a 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.

[0027] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0028] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0029] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the 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.

[0030] 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 of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0031] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0032] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process 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.

[0034] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0035] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0036] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0037] The present invention provides a system that improves business efficiency by eliminating delays and confusion in responding to inquiries experienced by corporate employees. The system provides a chat interface with user authentication, automatically generates initial responses using a generative AI model, and escalates to personnel for further investigation as needed.

[0038] Overall system configuration

[0039] The system includes the following elements:

[0040] 1. User authentication method:

[0041] When a user accesses a system, they enter authentication information such as a user ID and password, and the server authenticates them by checking this information against a database.

[0042] 2. Chat interface provided by:

[0043] If the authentication is successful, the server will provide a chat interface on the user's device that is available 24 hours a day, 365 days a year, through which the user can make inquiries.

[0044] 3. Query analysis methods:

[0045] It is a means of receiving an inquiry from a user, analyzing the content of the inquiry, and searching for relevant information from a knowledge base.

[0046] 4. Generative AI model means:

[0047] Using a generative AI model, an initial response is automatically generated based on the analyzed inquiry content.

[0048] 5. Means of providing answers:

[0049] The generated initial answer is sent to the user terminal and displayed to the user.

[0050] 6. Further investigation escalation procedures:

[0051] If the user is not satisfied with the initial response, it is a means to receive further investigation requests and escalate them to the appropriate personnel.

[0052] 7. How we receive feedback and update our knowledge base:

[0053] It is a means of receiving feedback from users and updating the knowledge base.

[0054] A natural language description of the program's operation

[0055] User Authentication

[0056] 1. The terminal displays a login screen to the user, and the user enters their user ID and password.

[0057] 2. The server receives the entered authentication information and authenticates it by checking it against a database.

[0058] 3. If the authentication is successful, the server creates a user session and sends a success message to the terminal.

[0059] 4. If authentication fails, the server sends a failure message to the terminal and notifies the user.

[0060] Launching the chat interface and sending inquiries

[0061] 1. The server displays a chat interface on the user's device after successful authentication.

[0062] 2. The authenticated user enters and sends their inquiry into the chat interface.

[0063] 3. The server analyzes the received query and searches for relevant information from the knowledge base.

[0064] 4. The generative AI model generates an initial answer, which the server sends to the user device.

[0065] Initial response and detailed investigation request

[0066] 1. The device displays the initial answer from the AI ​​to the user.

[0067] 2. If the user is not satisfied with the initial response, they submit a further investigation request through the chat interface.

[0068] 3. The server receives the request for further investigation and escalates it to the appropriate person.

[0069] Feedback and Knowledge Base Updates

[0070] 1. After answering, the user submits feedback through the chat interface.

[0071] 2. The server receives the feedback and updates the knowledge base to reflect it.

[0072] Specific examples

[0073] Example 1: If you want to know the procedure for resetting your password

[0074] 1. The user types "How do I reset my password?" into the chat interface.

[0075] 2. The server receives the query, parses it, and searches its knowledge base for password reset instructions.

[0076] 3. The generative AI model generates a password reset procedure, which the server sends to the user's device.

[0077] 4. The user follows the instructions they receive to reset their password.

[0078] Example 2: Inquiry about the application flow

[0079] 1. The user types in the chat, "Please tell me the flow for applying for a business trip."

[0080] 2. The server searches the knowledge base for the travel request flow and generates a detailed flow using the generative AI model.

[0081] 3. The server sends the generated flow to the user's terminal, where the user can view it.

[0082] 4. The user follows the displayed flow to apply for a business trip.

[0083] This system allows users to instantly access the information they need, significantly reducing the amount of time they spend struggling.

[0084] The processing flow will be explained below.

[0085] Step 1:

[0086] The user accesses the system to display the login screen on their own terminal. The user enters the required authentication information (user ID and password) on the login screen.

[0087] Step 2:

[0088] The terminal transmits the input authentication information to the server. The authentication information is encrypted before transmission.

[0089] Step 3:

[0090] The server checks the received authentication information against the database, checking whether the authentication is successful by checking against the user information stored in the database.

[0091] Step 4:

[0092] If the authentication is successful, the server creates a user session and sends an authentication success message to the terminal. If the authentication fails, the server sends a failure message to the terminal and asks the terminal to enter authentication information again.

[0093] Step 5:

[0094] After receiving the authentication success message, the terminal displays a chat interface to the user, who then inputs an inquiry into the chat interface and sends it to the system.

[0095] Step 6:

[0096] The server receives the inquiry sent by the user, analyzes the inquiry, and searches the knowledge base for relevant information based on the inquiry.

[0097] Step 7:

[0098] The generative AI model generates an initial response based on the query, which is then sent to the device via the server.

[0099] Step 8:

[0100] The terminal displays the generated initial answer to the user, who then confirms the answer.

[0101] Step 9:

[0102] If the user is not satisfied with the initial response, the user submits a request for further investigation through the chat interface.

[0103] Step 10:

[0104] The server receives the request for further investigation and escalates it to the appropriate person, who begins the investigation and provides updates on its progress.

[0105] Step 11:

[0106] The terminal displays the status of the escalation to the user in real time, allowing the user to see how their inquiry is being handled.

[0107] Step 12:

[0108] After answering, users can submit feedback through the chat interface, which helps improve the quality and responsiveness of the answers.

[0109] Step 13:

[0110] The server receives feedback from users and updates the knowledge base to reflect that feedback. The contents of the knowledge base are checked regularly, and any new information or corrections that are needed are updated immediately.

[0111] Example 1

[0112] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0113] There is a need to eliminate delays and confusion in responding to inquiries faced by corporate employees and improve work efficiency. Providing information quickly and accurately is particularly important and directly impacts employee productivity. Conventional methods require a lot of time and effort to respond to inquiries, often overwhelming staff. The present invention is intended to solve these problems.

[0114] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0115] In this invention, the server includes means for receiving authentication information from a user and comparing the authentication information with a database, means for providing a chat interface to the user terminal if authentication is successful, means for receiving an inquiry from a user, analyzing the inquiry content, and searching for relevant information from a knowledge base, means for generating an initial answer using a generative AI model, means for transmitting the generated initial answer to the user terminal, means for receiving a request for detailed investigation from the user and escalating the request to an appropriate person in charge, and means for receiving feedback and updating the knowledge base, thereby enabling prompt and accurate response to inquiries.

[0116] "Authentication information" refers to information such as a user ID and password that a user provides when logging in to a system.

[0117] A "database" is an information system for storing authentication information, inquiry details, feedback, etc.

[0118] A "chat interface" is a communication method that allows users to make inquiries in real time through the system.

[0119] "Inquiry content" refers to the specific content of a question or request that a user makes to the system.

[0120] A "knowledge base" is a collection of data that stores information that the system provides in response to user inquiries.

[0121] A "generative AI model" is an algorithm or software that uses artificial intelligence to automatically generate answers based on analysis results.

[0122] An "initial response" is the first response automatically generated by a generative AI model in response to a user's inquiry.

[0123] A "request for further investigation" is a request where the user is not satisfied with the initial response and requests further detailed information or investigation.

[0124] "Escalation" is the process of passing a request for further investigation to the appropriate person.

[0125] "Feedback" refers to ratings and comments that users make on answers provided.

[0126] This system improves business efficiency by eliminating delays and confusion in responding to inquiries from company employees. The system provides a chat interface with user authentication, automatically generates initial responses using a generative AI model, and escalates to personnel for further investigation as needed.

[0127] Hardware and software used

[0128] The system includes the following elements:

[0129] 1. Server: Responsible for verifying user authentication information, analyzing queries, running generative AI models, and managing the knowledge base. The server uses hardware equipped with high-performance processors and large amounts of memory.

[0130] 2. Database: Includes software for storing data such as user information, authentication information, knowledge base, feedback, etc. This database uses an appropriate database management system such as SQL or NoSQL.

[0131] 3. Terminal: A device through which a user accesses the system, such as a PC, tablet, or smartphone. The terminal communicates with the server via a browser or dedicated app.

[0132] 4. Generative AI Model: An artificial intelligence model used to analyze queries and generate initial responses. This model utilizes advanced algorithms, including natural language processing techniques.

[0133] Detailed System Description

[0134] User Authentication

[0135] The device displays a login screen to the user, prompting them to enter their user ID and password. Once the user enters the information, the device sends it to the server. The server compares the received authentication information with its database to see if there is a matching record. If a match is found, a successful authentication message is sent to the device, and the user is able to use the chat interface.

[0136] Providing a chat interface

[0137] If the user is successfully authenticated, the server sends instructions to the terminal to display a chat interface, through which the user can make inquiries and send their details 24 hours a day, 365 days a year.

[0138] Inquiry analysis and initial response generation

[0139] The server analyzes the inquiry received from the user and searches for relevant information in the knowledge base.The generative AI model then generates an initial answer based on the analyzed content, and the server sends it to the user's device.

[0140] Escalation of further investigation

[0141] If the user is not satisfied with the initial response, they can submit a request for further investigation through the chat interface. The server receives this request and escalates it to the appropriate agent. The agent then investigates and returns the results to the server, which then provides a detailed response to the user.

[0142] Feedback and Knowledge Base Updates

[0143] Users can provide feedback on the answers they provide, and the device sends the feedback information to the server, which stores it in a database and updates the knowledge base, allowing the system to continually improve.

[0144] Specific examples

[0145] Example 1: If you want to know the procedure for resetting your password

[0146] 1. The user types "How do I reset my password?" into the chat interface.

[0147] 2. The terminal sends this query to the server.

[0148] 3. The server analyzes the received inquiry and searches the knowledge base for password reset instructions.

[0149] 4. The generative AI model generates an initial answer for the password reset procedure, and the server sends it to the user's device.

[0150] 5. The user follows the instructions they receive to reset their password.

[0151] Example 2: Inquiry about the application flow

[0152] 1. The user types "Please tell me the process for requesting a business trip" into the chat interface.

[0153] 2. The terminal sends this query to the server.

[0154] 3. The server searches the knowledge base for the travel request flow and generates a detailed flow using the generative AI model.

[0155] 4. The server sends the generated flow to the user's device so that the user can view it.

[0156] 5. The user follows the displayed flow to apply for a business trip.

[0157] In this way, the system can respond to inquiries quickly and accurately, significantly improving user convenience and business efficiency.

[0158] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0159] Step 1:

[0160] The terminal displays a login screen to the user and prompts them to enter their user ID and password. The entered authentication information (user ID and password) is sent to the server. The server compares the received authentication information with its database to see if there is a matching record. If authentication is successful, the server creates a user session and sends an authentication success message to the terminal. The input authentication information is the input data, and the database comparison and authentication results are the output data.

[0161] Step 2:

[0162] The server sends an instruction to the terminal that has been successfully authenticated to display a chat interface. Based on this instruction, the terminal displays the chat interface. The chat interface includes a text field for the user to enter their inquiry. The instruction based on successful authentication is input data, and the display of the chat interface is output data.

[0163] Step 3:

[0164] The user enters the inquiry into the chat interface and presses the send button. The terminal sends the entered inquiry to the server. The input of the inquiry is input data, and the transmission of the inquiry is output data.

[0165] Step 4:

[0166] The server analyzes the received inquiry and searches for related information from a knowledge base. This analysis uses natural language processing technology to break down the inquiry into topics and keywords. Based on the analysis results, the server searches for appropriate information from the knowledge base. The inquiry is the input data, and the search results for related information are the output data.

[0167] Step 5:

[0168] The generative AI model generates an initial answer based on the analyzed query content. This AI model uses prompts to generate the optimal answer corresponding to the query content. The analysis results are the input data, and the generated initial answer is the output data.

[0169] Step 6:

[0170] The server receives the initial answer generated by the generative AI model and sends it to the terminal. The terminal displays the received initial answer to the user. The generation of the initial answer is input data, and the displayed answer is output data.

[0171] Step 7:

[0172] If the user is not satisfied with the initial response, he / she inputs and sends a detailed investigation request to the chat interface. The terminal sends the detailed investigation request to the server. The input of the detailed investigation request is input data, and the transmission of the detailed investigation request is output data.

[0173] Step 8:

[0174] The server receives the detailed investigation request and escalates it to the appropriate person. The person in charge conducts the detailed investigation and returns the results to the server. The server receives the detailed response from the person in charge and sends it to the terminal. The receipt of the detailed investigation request is input data, and the provision of the detailed response is output data.

[0175] Step 9:

[0176] The terminal displays the detailed answer to the user. The user can provide feedback on the provided answer. The input of the feedback is input data, and the output of the feedback is output data.

[0177] Step 10:

[0178] The server analyzes the received feedback and updates the knowledge base. The received feedback is the input data, and the updates to the knowledge base are the output data. This allows the system to continuously improve.

[0179] (Application example 1)

[0180] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0181] When employees work at a logistics center, they have a variety of inquiries and things to confirm. However, if employees cannot get prompt and appropriate answers, work delays and efficiency declines. This situation has a negative impact on overall business performance, so a system that can provide prompt and appropriate answers is needed. Another challenge is creating an environment where employees can receive support 24 hours a day, 365 days a year.

[0182] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0183] In this invention, the server includes authentication means for receiving authentication information from a user and comparing the authentication information with a database, means for providing a chat interface to an information device if authentication is successful, means for receiving an inquiry from a user, analyzing the inquiry, and searching for relevant information from a knowledge base, means for generating an initial answer using a generative AI model, means for transmitting the generated initial answer to the information device, means for receiving a request for further investigation from a user and escalating the request to an appropriate person, means for receiving feedback and updating the knowledge base, processing means for responding to inquiries from employees at a logistics center and providing business support, and means for generating prompt sentences for the generative AI model and providing answers. This allows employees to quickly obtain appropriate answers, improving business efficiency and quality.

[0184] A "user authentication means" is a means by which a user enters authentication information such as a user ID and password when accessing a system, and the server compares this information with a database to perform authentication.

[0185] The "chat interface providing means" is a means for providing a chat interface that allows real-time interaction to the user terminal when authentication is successful.

[0186] The "inquiry analysis means" is a means for receiving an inquiry from a user, analyzing the content of the inquiry, and searching for relevant information from the knowledge base.

[0187] A "generative AI model means" is a means for using a generative AI model to automatically generate an initial response based on the analyzed inquiry content.

[0188] The "answer providing means" is a means for transmitting the generated initial answer to the user terminal and displaying it to the user.

[0189] The "detailed investigation escalation means" is a means for receiving a detailed investigation request and escalating it to an appropriate person in case the user is not satisfied with the initial response.

[0190] The "feedback receiving and knowledge base updating means" is a means for receiving feedback from users and updating the knowledge base based on that feedback.

[0191] A "logistics center" is a facility where goods are stored, sorted, shipped, etc., and where a wide variety of operations are carried out all at once.

[0192] "Information device" refers to a hardware device that allows a user to access a system, specifically a smartphone, tablet, or computer.

[0193] A "prompt sentence" is an instruction sentence used to generate a specific answer for a generative AI model, and is input information that enables the generative AI model to generate an appropriate answer based on the instruction sentence.

[0194] The present invention is a system for supporting efficient work of employees in a logistics center. The system includes the following components:

[0195] Hardware and Software Configuration

[0196] Server: This performs the central processing for user authentication, providing a chat interface, analyzing inquiries, generating answers using generative AI models, escalating detailed investigations, receiving feedback, and updating the knowledge base. The server is built using a web framework such as Flask.

[0197] Database: Use a database system such as SQLite to manage user authentication information and knowledge base information.

[0198] Information devices: Users use devices such as smartphones, tablets, and computers to access information.

[0199] System processing flow

[0200] 1. User authentication method:

[0201] The terminal displays a login screen to the user, and the user enters their user ID and password.

[0202] The server receives the entered authentication information and authenticates it by checking it against an SQLite database.

[0203] If the authentication is successful, the server creates a user session and sends a success message to the terminal.

[0204] 2. Chat interface provided by:

[0205] A chat interface is displayed on the user terminal on which authentication has been successful.

[0206] The terminal receives a user's inquiry through a chat interface.

[0207] 3. Query analysis methods:

[0208] The server analyzes the received query and searches for relevant information from a knowledge base.

[0209] 4. Generative AI model means:

[0210] A generative AI model is used to generate an initial answer based on the analyzed query content, specifically using OpenAI's API.

[0211] Example: The prompt sentence "Question about operations at a distribution center: I would like to know the current inventory status. How can I check it?" is input into the generative AI model.

[0212] 5. Means of providing answers:

[0213] The generated initial answer is sent to the terminal and displayed to the user.

[0214] Example: Show the user the initial response "You can check your current inventory status by logging into your Warehouse Management System (WMS) and clicking the 'Check Inventory' tab."

[0215] 6. Further investigation escalation procedures:

[0216] If the user is not satisfied with the initial response, they submit a request for further investigation through the chat interface.

[0217] The server receives the request for further investigation and escalates it to the appropriate person.

[0218] 7. How we receive feedback and update our knowledge base:

[0219] After the user answers, they submit their feedback through the chat interface.

[0220] The server receives the feedback and updates the knowledge base.

[0221] Specific use cases

[0222] Example 1: Inventory management inquiry

[0223] User: "I'd like to know the current inventory status. How can I check it?"

[0224] Prompt generated by the generative AI model: "Question about operations at a distribution center: I would like to know the current inventory status. How can I check it?"

[0225] Example answer: "You can check your current inventory by logging into your Warehouse Management System (WMS) and clicking on the 'Check Inventory' tab."

[0226] In this way, the present invention is a system that enables employees at a logistics center to quickly obtain appropriate answers, thereby improving work efficiency and quality.

[0227] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0228] Step 1:

[0229] A user opens a login screen using an information device and inputs a user ID and password. The input information includes the user ID and password.

[0230] Input: User ID and password

[0231] Operation: The device sends the input information to the server.

[0232] Step 2:

[0233] The server performs authentication by checking the received authentication information against the SQLite database. The server queries the database for the user ID and password.

[0234] Input: User ID and password

[0235] Data processing: The server converts the user ID and password into an SQL query and queries the database.

[0236] Output: Authentication success or failure

[0237] Operation: Generates an authentication result based on the database matching result, and if successful, creates a user session and sends a success message to the terminal.

[0238] Step 3:

[0239] If the authentication is successful, the server provides a chat interface to the information device.

[0240] Input: Successful authentication result

[0241] Operation: The server sends data to the user's device to display a real-time chat interface.

[0242] Step 4:

[0243] The user uses the chat interface to input and send the inquiry.

[0244] Input: Inquiry details

[0245] Operation: The device sends the query to the server.

[0246] Step 5:

[0247] The server analyzes the received query and searches for relevant information from a knowledge base using natural language processing algorithms.

[0248] Input: Inquiry details

[0249] Data processing: Analyze the inquiry content and extract related keywords.

[0250] Output: Search results (information in the knowledge base)

[0251] Operation: Based on the analysis results, the server searches the knowledge base for relevant information.

[0252] Step 6:

[0253] A generative AI model is used to generate an initial answer based on the analyzed query content. Specifically, a prompt sentence is input into the generative AI model to obtain an answer.

[0254] Input: Enquiry and Knowledge Base search results

[0255] Data processing: Convert the query content into a prompt sentence and input it into the generative AI model.

[0256] Output: Initial answer

[0257] How it works: The server receives an initial answer from the generative AI model.

[0258] Step 7:

[0259] The server transmits the generated initial answer to the user terminal and displays it to the user.

[0260] Input: Initial answer

[0261] Action: The server sends an initial response to the device, which displays it in the user's chat interface.

[0262] Step 8:

[0263] If the user is not satisfied with the initial response, they submit a request for further investigation through the chat interface.

[0264] Input: Further investigation request

[0265] Action: The device sends a detailed investigation request to the server.

[0266] Step 9:

[0267] The server receives the request for further investigation and escalates it to the appropriate person.

[0268] Input: Further investigation request

[0269] Behavior: The server generates and sends data to escalate the request to the appropriate person.

[0270] Step 10:

[0271] After the user answers, they submit their feedback through the chat interface.

[0272] Input: Feedback

[0273] Action: The device sends feedback to the server.

[0274] Step 11:

[0275] The server receives the feedback and updates the knowledge base.

[0276] Input: Feedback

[0277] Data processing: Analyze the feedback and generate data to be reflected in the knowledge base.

[0278] Action: The server updates the knowledge base to reflect the new information.

[0279] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0280] This invention provides a system that eliminates delays and confusion experienced by corporate employees in responding to inquiries and improves operational efficiency. This system provides a chat interface with user authentication, automatically generates initial responses using a generative AI model, and escalates to a human for further investigation as needed. It also incorporates an emotion engine that recognizes emotions from user input and the context of the conversation, enabling flexible responses based on the user's emotions.

[0281] Overall system configuration

[0282] The system includes the following elements:

[0283] 1. User authentication method:

[0284] When a user accesses a system, they enter authentication information such as a user ID and password, and the server authenticates them by checking this information against a database.

[0285] 2. Chat interface provided by:

[0286] If the authentication is successful, the server will provide a chat interface on the user's device that is available 24 hours a day, 365 days a year, through which the user can make inquiries.

[0287] 3. Query analysis methods:

[0288] It is a means of receiving an inquiry from a user, analyzing the content of the inquiry, and searching for relevant information from a knowledge base.

[0289] 4. Generative AI model means:

[0290] Using a generative AI model, an initial response is automatically generated based on the analyzed inquiry content.

[0291] 5. Means of providing answers:

[0292] The generated initial answer is sent to the user terminal and displayed to the user.

[0293] 6. Further investigation escalation procedures:

[0294] If the user is not satisfied with the initial response, it is a means to receive further investigation requests and escalate them to the appropriate personnel.

[0295] 7. How we receive feedback and update our knowledge base:

[0296] It is a means of receiving feedback from users and updating the knowledge base.

[0297] 8. Emotion Engine Means:

[0298] The emotion engine recognizes emotions from user input and the context of the conversation, allowing the generative AI model to adjust the tone and content of responses, and also incorporates emotional data into the knowledge base.

[0299] A natural language description of the program's operation

[0300] User Authentication

[0301] 1. The terminal displays a login screen to the user, and the user enters their user ID and password.

[0302] 2. The server receives the entered authentication information and authenticates it by checking it against a database.

[0303] 3. If the authentication is successful, the server creates a user session and sends a success message to the terminal.

[0304] 4. If authentication fails, the server sends a failure message to the terminal and notifies the user.

[0305] Launching the chat interface and sending inquiries

[0306] 1. The server displays a chat interface on the user's device after successful authentication.

[0307] 2. The authenticated user enters and sends their inquiry into the chat interface.

[0308] 3. The server analyzes the received query and searches the knowledge base for relevant information.

[0309] 4. The generative AI model generates an initial answer, which the server sends to the user device.

[0310] Initial response and detailed investigation request

[0311] 1. The device displays the initial answer from the AI ​​to the user.

[0312] 2. If the user is not satisfied with the initial response, they submit a further investigation request through the chat interface.

[0313] 3. The server receives the request for further investigation and escalates it to the appropriate person.

[0314] Emotion Engine Operation

[0315] 1. The server analyzes the user's input and recognizes the user's emotional state using an emotion engine.

[0316] 2. The generative AI model adjusts the tone and content of responses based on the perceived emotional state.

[0317] 3. The recognized emotion data is recorded for future user improvement.

[0318] Feedback and Knowledge Base Updates

[0319] 1. After answering, the user submits feedback through the chat interface.

[0320] 2. The server receives the feedback and updates the knowledge base to reflect it.

[0321] Specific examples

[0322] Example 1: If you want to know the procedure for resetting your password

[0323] 1. The user types "How do I reset my password?" into the chat interface.

[0324] 2. The server receives the query, parses it, and searches its knowledge base for password reset instructions.

[0325] 3. The generative AI model generates a password reset procedure, which the server sends to the user's device.

[0326] 4. The device displays the generated instructions to the user. The emotion engine measures the user's reaction and adjusts the response accordingly.

[0327] 5. The user follows the instructions they receive to reset their password.

[0328] Example 2: Inquiry about the application flow

[0329] 1. The user types in the chat, "Please tell me the flow for applying for a business trip."

[0330] 2. The server searches the knowledge base for the travel request flow and generates a detailed flow using the generative AI model.

[0331] 3. The server sends the generated flow to the user's terminal, where the user can view it.

[0332] 4. The terminal shows the displayed flow to the user. The emotion engine analyzes the user's emotional state and optimizes the answer.

[0333] 5. The user follows the displayed flow to apply for a business trip.

[0334] This system allows users to instantly access the information they need, significantly reducing the time they spend struggling. In addition, the introduction of an emotion engine enables more flexible and human-like responses, improving the user experience.

[0335] The processing flow will be explained below.

[0336] This invention provides a system that eliminates delays and confusion experienced by corporate employees in responding to inquiries and improves operational efficiency. This system provides a chat interface with user authentication, automatically generates initial responses using a generative AI model, and escalates to a human for further investigation as needed. It also incorporates an emotion engine that recognizes emotions from user input and the context of the conversation, enabling flexible responses based on the user's emotions.

[0337] Overall system configuration

[0338] The system includes the following elements:

[0339] 1. User authentication method:

[0340] When a user accesses a system, they enter authentication information such as a user ID and password, and the server authenticates them by checking this information against a database.

[0341] 2. Chat interface provided by:

[0342] If the authentication is successful, the server will provide a chat interface on the user's device that is available 24 hours a day, 365 days a year, through which the user can make inquiries.

[0343] 3. Query analysis methods:

[0344] It is a means of receiving an inquiry from a user, analyzing the content of the inquiry, and searching for relevant information from a knowledge base.

[0345] 4. Generative AI model means:

[0346] Using a generative AI model, an initial response is automatically generated based on the analyzed inquiry content.

[0347] 5. Means of providing answers:

[0348] The generated initial answer is sent to the user terminal and displayed to the user.

[0349] 6. Further investigation escalation procedures:

[0350] If the user is not satisfied with the initial response, it is a means to receive further investigation requests and escalate them to the appropriate personnel.

[0351] 7. How we receive feedback and update our knowledge base:

[0352] It is a means of receiving feedback from users and updating the knowledge base.

[0353] 8. Emotion Engine Means:

[0354] The emotion engine recognizes emotions from user input and the context of the conversation, allowing the generative AI model to adjust the tone and content of responses, and also incorporates emotional data into the knowledge base.

[0355] A natural language description of the program's operation

[0356] Step 1:

[0357] The user accesses the system to display the login screen on their own terminal. The user enters the required authentication information (user ID and password) on the login screen.

[0358] Step 2:

[0359] The terminal transmits the input authentication information to the server. The authentication information is encrypted before transmission.

[0360] Step 3:

[0361] The server checks the received authentication information against the database, checking whether the authentication is successful by checking against the user information stored in the database.

[0362] Step 4:

[0363] If the authentication is successful, the server creates a user session and sends an authentication success message to the terminal. If the authentication fails, the server sends a failure message to the terminal and asks the terminal to enter authentication information again.

[0364] Step 5:

[0365] After receiving the authentication success message, the terminal displays a chat interface to the user, who then inputs an inquiry into the chat interface and sends it to the system.

[0366] Step 6:

[0367] The server receives the inquiry sent by the user, analyzes the inquiry, and searches the knowledge base for relevant information based on the inquiry.

[0368] Step 7:

[0369] The generative AI model generates an initial response based on the query, which is then sent to the device via the server.

[0370] Step 8:

[0371] The terminal displays the generated initial answer to the user, and the server uses an emotion engine to analyze the user's emotional state.

[0372] Step 9:

[0373] The emotion engine recognizes emotions from the user's input and the context of the conversation, and the generative AI model adjusts the tone and content of the response based on the recognized emotional state. The adjusted response is then sent back to the device.

[0374] Step 10:

[0375] If the user is not satisfied with the initial response, the user submits a request for further investigation through the chat interface.

[0376] Step 11:

[0377] The server receives the request for further investigation and escalates it to the appropriate person, who begins the investigation and provides updates on its progress.

[0378] Step 12:

[0379] The terminal displays the status of the escalation to the user in real time, allowing the user to see how their inquiry is being handled.

[0380] Step 13:

[0381] After answering, users can submit feedback through the chat interface, which helps improve the quality and responsiveness of the answers.

[0382] Step 14:

[0383] The server receives feedback from users and updates the knowledge base to reflect that feedback. The contents of the knowledge base are checked regularly, and any new information or corrections that are needed are updated immediately.

[0384] Specific examples

[0385] Example 1: If you want to know the procedure for resetting your password

[0386] 1. The user types "How do I reset my password?" into the chat interface.

[0387] 2. The server receives the query, parses it, and searches its knowledge base for password reset instructions.

[0388] 3. The generative AI model generates a password reset procedure, which the server sends to the user's device.

[0389] 4. The device displays the generated instructions to the user. The emotion engine measures the user's reaction and adjusts the response accordingly.

[0390] 5. The user follows the instructions they receive to reset their password.

[0391] Example 2: Inquiry about the application flow

[0392] 1. The user types in the chat, "Please tell me the flow for applying for a business trip."

[0393] 2. The server searches the knowledge base for the travel request flow and generates a detailed flow using the generative AI model.

[0394] 3. The server sends the generated flow to the user's terminal, where the user can view it.

[0395] 4. The terminal shows the displayed flow to the user. The emotion engine analyzes the user's emotional state and optimizes the answer.

[0396] 5. The user follows the displayed flow to apply for a business trip.

[0397] This system allows users to instantly access the information they need, significantly reducing the time they spend struggling. In addition, the introduction of an emotion engine enables more flexible and human-like responses, improving the user experience.

[0398] Example 2

[0399] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0400] In modern companies, employees must respond to numerous inquiries every day, and delays and confusion in these responses can reduce work efficiency. While there is a demand for systems that allow employees to instantly access the information they need, existing systems lack the flexibility to respond to user feedback. Furthermore, there is the issue of how difficult it is to effectively incorporate user feedback and continuously improve the system.

[0401] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0402] In this invention, the server includes an authentication means for receiving authentication information from a user and comparing the authentication information with a storage device, a means for providing a dialogue interface to a user terminal if authentication is successful, a means for receiving an inquiry from a user, analyzing the inquiry, and searching for relevant information from a knowledge base, a means for generating an initial answer using a generation AI, a means for transmitting the generated initial answer to the user terminal, a means including an emotion engine for analyzing user input and recognizing the user's emotional state, a means for forwarding a request for further investigation to an appropriate person in an escalation process, and a means for receiving feedback and updating the knowledge base. This not only allows employees to access the information they need immediately, but also enables flexible responses to user emotions, and allows for continuous improvement of the system by effectively incorporating feedback.

[0403] An "authentication means" is a system component that has the function of receiving authentication information from a user and authenticating the user by comparing it with a storage device.

[0404] An "interactive interface" is an interface that provides a screen or window for a user to input a query.

[0405] A "knowledge base" is a database system that stores answers and information to user inquiries.

[0406] "Generative AI" is an artificial intelligence model that automatically generates an initial response based on the content of the inquiry.

[0407] An "emotion engine" is a system component that analyzes user input and recognizes the user's emotional state.

[0408] An "escalation mechanism" is a system component that receives a request for further investigation from a user and transfers it to the appropriate person.

[0409] A "feedback mechanism" is a system component that has the function of receiving feedback from users and updating the knowledge base.

[0410] "Storage" is hardware or software used to store authentication information, knowledge base data, etc.

[0411] This system aims to improve work efficiency by eliminating delays and confusion experienced by corporate employees in responding to inquiries. The system provides a dialogue interface that includes user authentication, automatically generates initial responses using generative AI, and escalates to personnel for further investigation as needed. It also incorporates an emotion engine that recognizes emotions from user input and the context of the dialogue, enabling flexible responses based on the user's emotions.

[0412] Overall system configuration

[0413] The system includes the following elements:

[0414] 1. User authentication method:

[0415] When a user accesses a system, they enter authentication information such as a user ID and password, and the server authenticates them by comparing this information with the stored data.

[0416] 2. Means for providing a dialogue interface:

[0417] If the authentication is successful, the server provides the user terminal with a conversation interface available 24 hours a day, 365 days a year, through which the user can make inquiries.

[0418] 3. Query analysis methods:

[0419] It is a means of receiving an inquiry from a user, analyzing the content of the inquiry, and searching for relevant information from a knowledge base.

[0420] 4. Generative AI model means:

[0421] Generative AI is used to automatically generate an initial response based on the analyzed inquiry content.

[0422] 5. Means of providing answers:

[0423] The generated initial answer is sent to the user terminal and displayed to the user.

[0424] 6. Further investigation escalation procedures:

[0425] If the user is not satisfied with the initial response, it is a means to receive further investigation requests and escalate them to the appropriate personnel.

[0426] 7. How we receive feedback and update our knowledge base:

[0427] It is a means of receiving feedback from users and updating the knowledge base.

[0428] 8. Emotion Engine Means:

[0429] The emotion engine recognizes emotions from user input and the context of the conversation, allowing the generative AI to adjust the tone and content of the response. It also reflects the emotional data in the knowledge base.

[0430] Specific examples

[0431] Example 1: If you want to know the procedure for resetting your password

[0432] 1. The user enters "How do I reset my password?" into the conversational interface.

[0433] 2. The server receives the query, parses it, and searches its knowledge base for password reset instructions.

[0434] 3. The generation AI generates a password reset procedure, and the server sends it to the user's device.

[0435] 4. The device displays the generated instructions to the user. The emotion engine measures the user's reaction and adjusts the response accordingly.

[0436] 5. The user follows the instructions they receive to reset their password.

[0437] Example 2: Inquiry about the application flow

[0438] 1. The user enters "Please tell me the flow for applying for a business trip" into the dialogue interface.

[0439] 2. The server searches the knowledge base for the travel request flow and generates a detailed flow using generation AI.

[0440] 3. The server sends the generated flow to the user's terminal, where the user can view it.

[0441] 4. The terminal shows the displayed flow to the user. The emotion engine analyzes the user's emotional state and optimizes the answer.

[0442] 5. The user follows the displayed flow to apply for a business trip.

[0443] This system allows users to instantly access the information they need, significantly reducing the time they spend struggling. In addition, the introduction of an emotion engine enables more flexible and human-like responses, improving the user experience.

[0444] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0445] Program processing flow

[0446] User authentication step

[0447] Step 1:

[0448] The user displays the login screen on the terminal and enters the user ID and password, thereby entering authentication information.

[0449] Step 2:

[0450] The terminal receives the entered authentication information, encrypts it, and sends it to the server. The input is the user ID and password, and the encrypted data is output and sent to the server.

[0451] Step 3:

[0452] The server checks the received authentication information against the storage device. Specifically, it uses an SQL query to search for the corresponding record in the database, compares the entered authentication information with the information in the database, and performs authentication. If the information matches, it outputs a successful authentication status.

[0453] Step 4:

[0454] If authentication is successful, the server creates a user session and sends a success message to the terminal. The input is the authentication success status, and the output is a success message including the session ID. If authentication fails, the server sends a failure message to the terminal and notifies the user. The input is the authentication failure status, and the output is a failure message.

[0455] Enquiry Processing Steps

[0456] Step 5:

[0457] If authentication is successful, the server displays an interactive interface on the user's device. This is achieved by dynamically generating an interactive window using HTML and JavaScript. The input is a session ID, and the output is an interactive interface.

[0458] Step 6:

[0459] The user inputs the inquiry into the dialogue interface and presses the send button, which sends the input inquiry.

[0460] Step 7:

[0461] The server analyzes the received query. It uses a natural language processing (NLP) engine to extract keywords and analyze intent. The query is input and the analysis results are output.

[0462] Step 8:

[0463] Based on the analysis results, the generative AI generates an initial answer. Specifically, the language model generates text based on the prompt sentence. The analysis results are input, and the generated initial answer is output.

[0464] Step 9:

[0465] The server sends the generated initial answer to the user terminal. The generated answer is input, and data to be sent to the user terminal is output. The user terminal displays the received initial answer to the user.

[0466] Further investigation and escalation steps

[0467] Step 10:

[0468] The terminal displays the received initial response to the user, allowing the user to confirm the initial response.

[0469] Step 11:

[0470] If the user is not satisfied with the initial answer, they submit a further investigation request through the dialogue interface, which takes the further investigation request as input and the submitted data as output.

[0471] Step 12:

[0472] The server receives the further investigation request and escalates it to the appropriate person. As part of the escalation process, the request is forwarded to the help desk system and the person in charge is notified. The further investigation request is input, and notification data for the person in charge is output.

[0473] Emotion recognition and response adjustment steps

[0474] Step 13:

[0475] The server analyzes the user's input and recognizes the user's emotional state using an emotion engine. User input is input and emotional data is output.

[0476] Step 14:

[0477] The generative AI adjusts the tone and content of the response based on the recognized emotional state, enabling it to respond appropriately to the user's emotions. It takes emotional data as input and outputs an adjusted response.

[0478] Feedback and Knowledge Base Update Steps

[0479] Step 15:

[0480] After answering, the user sends feedback through the dialogue interface. The feedback content is input and the transmitted data is output.

[0481] Step 16:

[0482] The server receives the feedback and updates the knowledge base to reflect it. It analyzes the feedback and improves the corresponding knowledge base entry. It takes the feedback as input and outputs the updated knowledge base entry.

[0483] (Application example 2)

[0484] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0485] Conventional inquiry response systems for content distribution services can be slow and confusing, resulting in low user satisfaction. Additionally, there are issues with the quality of initial responses being inconsistent and it being difficult to respond flexibly based on user emotions. Therefore, an efficient system that can solve these issues is needed.

[0486] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0487] In this invention, the server

[0488] authentication means for receiving authentication information from a user and comparing the authentication information with a database;

[0489] means for providing an interface to the terminal upon successful authentication;

[0490] means for receiving an inquiry from a user, analyzing the content of the inquiry, and searching for relevant information from a knowledge base;

[0491] a means for generating an initial answer using a generative AI model;

[0492] means for transmitting the generated initial response to a terminal;

[0493] A means of receiving further investigation requests from users and escalating them to the appropriate personnel;

[0494] a means for receiving feedback and updating the knowledge base;

[0495] an emotion analysis means for recognizing emotions from the content of a user's input and the context of the dialogue, and adjusting the content and tone of a response based on the emotions;

[0496] This will improve the efficiency and quality of inquiries and increase user satisfaction.

[0497] "User authentication means" refers to a means for checking authentication information provided by a user against a database and permitting or denying access to the system.

[0498] The "chat interface providing means" is a means for displaying an interface on the user terminal and allowing the user to input an inquiry if user authentication is successful.

[0499] The "inquiry analysis means" is a means for analyzing the content of an inquiry received from a user and searching for relevant information from a knowledge base.

[0500] A "generative AI model" is an artificial intelligence model that automatically generates an initial response based on the user's inquiry.

[0501] The "answer providing means" is a means for transmitting the initial answer generated by the generative AI model to the user terminal.

[0502] The "detailed investigation escalation means" is a means for receiving a detailed investigation request from a user and escalating it to an appropriate person in charge.

[0503] The "feedback receiving means" is a means for receiving feedback from a user.

[0504] A "knowledge base updater" is a means for updating the knowledge base based on received feedback.

[0505] The "emotion analysis means" is a means for recognizing emotions from the content of a user's input and the context of the dialogue, and adjusting the content and tone of the response based on the emotions.

[0506] The present invention provides a system for efficiently responding to user inquiries about content distribution services. Specifically, the system includes the following means and processing steps.

[0507] System Configuration

[0508] 1. User authentication method

[0509] When a user accesses the system, the server receives authentication information such as the user ID and password and authenticates them by checking them against the database. Only if the user is successfully authenticated can the server proceed to the next step.

[0510] 2. Means of providing a chat interface

[0511] If authentication is successful, the server displays a chat interface on the user's device, through which the user can make inquiries. This chat interface is designed to be available 24 hours a day, 365 days a year.

[0512] 3. Query Analysis Methods

[0513] When a user enters a query into the chat interface, the server analyzes the content and searches for relevant information in the knowledge base using natural language processing technology.

[0514] 4. Generative AI Model Means

[0515] The server uses a generative AI model to generate an initial answer based on the query content. This AI model uses a pre-trained algorithm to automatically generate the best answer to the user's question.

[0516] 5. Means of providing answers

[0517] The generated initial response is sent from the server to the user terminal and displayed to the user through the chat interface.

[0518] 6. Further investigation and escalation procedures

[0519] If the user is not satisfied with the initial response generated, they can submit a request for further investigation, which the server will receive and escalate to the appropriate personnel.

[0520] 7. How we receive feedback and update our knowledge base

[0521] If the user provides feedback after answering, the server receives this feedback, which is reflected in the knowledge base and used to improve the quality of future inquiries.

[0522] 8. Emotion analysis method

[0523] The server recognizes emotions from the user's input and the context of the conversation. This emotion analysis engine allows the generative AI model to adjust the content and tone of the response to enable flexible responses based on the user's emotions.

[0524] Hardware and software used

[0525] Hardware: Smartphone

[0526] software:

[0527] UserAuth: User authentication module

[0528] ChatInterface: Chat interface module

[0529] InquiryParser: Inquiry parsing module (natural language processing)

[0530] AIModel: Answer generation module in generative AI model

[0531] SentimentEngine: Sentiment analysis and response adjustment module

[0532] EscalationManager: Further investigation escalation module

[0533] FeedbackManager: Feedback management module

[0534] Specific examples

[0535] Example 1: Inquiry about how to use the new movie recommendations feature

[0536] 1. The user types, "Please tell me the recommended features for new movies" through the chat interface of the smartphone app.

[0537] 2. The server receives and analyzes the query and retrieves relevant information from a knowledge base.

[0538] 3. The AI ​​model generates initial answers for new movie recommendations.

[0539] 4. The sentiment analysis engine analyzes the emotions expressed by the user's input and adjusts the tone and content of the response.

[0540] 5. An initial response is displayed, and if the user is not satisfied, the issue is escalated and handed over to a responsible person.

[0541] 6. Finally, the user leaves feedback, which is added to the knowledge base.

[0542] Example of an input prompt:

[0543] User: "What's the new movie recommendations feature?"

[0544] AI model: "The new movie recommendations feature is automatically generated by an algorithm based on your preferred genres and viewing history. Select the Recommendations tab from the app menu to see a list of new movies."

[0545] Emotion Engine: "If you have any other questions, please feel free to let me know."

[0546] In this way, users can receive prompt and appropriate support, improving the efficiency and quality of inquiries.

[0547] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0548] Step 1:

[0549] The server receives authentication information from the user. The user enters their user ID and password and sends them to the server from their terminal. The server receives this authentication information and authenticates the user by checking it against a database. If authentication is successful, a user session is created.

[0550] Step 2:

[0551] The server displays a chat interface on the terminal of a user who has been successfully authenticated. A chat window opens on the terminal, allowing the user to enter their inquiry. This interface is available 24 hours a day, 365 days a year.

[0552] Step 3:

[0553] The user enters their inquiry into the chat interface and sends it to the server, which then analyzes the received inquiry. Specifically, it uses natural language processing technology to analyze the text and retrieves relevant information from a knowledge base.

[0554] Step 4:

[0555] The server generates an initial answer using a generative AI model based on the analyzed query content. This AI model is a trained algorithm that automatically generates the best answer for the query content. The generated initial answer is stored internally on the server as a prompt text.

[0556] Step 5:

[0557] The server sends the generated initial response to the user's device. The user can view the initial response through the chat interface. In addition, the emotion analysis engine analyzes the user's input and adjusts the tone and content of the response based on the user's emotion. The adjusted response is also displayed on the user's device.

[0558] Step 6:

[0559] If the user is not satisfied with the initial response, they can send a request for further investigation through the chat interface. The server receives this request and escalates it to the appropriate agent. For this purpose, it uses the Escalation Manager module. After the agent takes over, the appropriate action is taken.

[0560] Step 7:

[0561] After answering, users provide feedback, which is sent to the server through the chat interface. The server analyzes the received feedback and updates the knowledge base. The feedback manager module is used to update the knowledge base and improve the quality of future inquiries.

[0562] In this way, users can receive prompt and appropriate support, improving the efficiency and quality of inquiries.

[0563] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0564] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0565] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0566] [Second embodiment]

[0567] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0568] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0569] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0570] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0571] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0572] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0573] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0574] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0575] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0576] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0577] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0578] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0579] The present invention provides a system that improves business efficiency by eliminating delays and confusion in responding to inquiries experienced by corporate employees. The system provides a chat interface with user authentication, automatically generates initial responses using a generative AI model, and escalates to personnel for further investigation as needed.

[0580] Overall system configuration

[0581] The system includes the following elements:

[0582] 1. User authentication method:

[0583] When a user accesses a system, they enter authentication information such as a user ID and password, and the server authenticates them by checking this information against a database.

[0584] 2. Chat interface provided by:

[0585] If the authentication is successful, the server will provide a chat interface on the user's device that is available 24 hours a day, 365 days a year, through which the user can make inquiries.

[0586] 3. Query analysis methods:

[0587] It is a means of receiving an inquiry from a user, analyzing the content of the inquiry, and searching for relevant information from a knowledge base.

[0588] 4. Generative AI model means:

[0589] Using a generative AI model, an initial response is automatically generated based on the analyzed inquiry content.

[0590] 5. Means of providing answers:

[0591] The generated initial answer is sent to the user terminal and displayed to the user.

[0592] 6. Further investigation escalation procedures:

[0593] If the user is not satisfied with the initial response, it is a means to receive further investigation requests and escalate them to the appropriate personnel.

[0594] 7. How we receive feedback and update our knowledge base:

[0595] It is a means of receiving feedback from users and updating the knowledge base.

[0596] A natural language description of the program's operation

[0597] User Authentication

[0598] 1. The terminal displays a login screen to the user, and the user enters their user ID and password.

[0599] 2. The server receives the entered authentication information and authenticates it by checking it against a database.

[0600] 3. If the authentication is successful, the server creates a user session and sends a success message to the terminal.

[0601] 4. If authentication fails, the server sends a failure message to the terminal and notifies the user.

[0602] Launching the chat interface and sending inquiries

[0603] 1. The server displays a chat interface on the user's device after successful authentication.

[0604] 2. The authenticated user enters and sends their inquiry into the chat interface.

[0605] 3. The server analyzes the received query and searches for relevant information from the knowledge base.

[0606] 4. The generative AI model generates an initial answer, which the server sends to the user device.

[0607] Initial response and detailed investigation request

[0608] 1. The device displays the initial answer from the AI ​​to the user.

[0609] 2. If the user is not satisfied with the initial response, they submit a further investigation request through the chat interface.

[0610] 3. The server receives the request for further investigation and escalates it to the appropriate person.

[0611] Feedback and Knowledge Base Updates

[0612] 1. After answering, the user submits feedback through the chat interface.

[0613] 2. The server receives the feedback and updates the knowledge base to reflect it.

[0614] Specific examples

[0615] Example 1: If you want to know the procedure for resetting your password

[0616] 1. The user types "How do I reset my password?" into the chat interface.

[0617] 2. The server receives the query, parses it, and searches its knowledge base for password reset instructions.

[0618] 3. The generative AI model generates a password reset procedure, which the server sends to the user's device.

[0619] 4. The user follows the instructions they receive to reset their password.

[0620] Example 2: Inquiry about the application flow

[0621] 1. The user types in the chat, "Please tell me the flow for applying for a business trip."

[0622] 2. The server searches the knowledge base for the travel request flow and generates a detailed flow using the generative AI model.

[0623] 3. The server sends the generated flow to the user's terminal, where the user can view it.

[0624] 4. The user follows the displayed flow to apply for a business trip.

[0625] This system allows users to instantly access the information they need, significantly reducing the amount of time they spend struggling.

[0626] The processing flow will be explained below.

[0627] Step 1:

[0628] The user accesses the system to display the login screen on their own terminal. The user enters the required authentication information (user ID and password) on the login screen.

[0629] Step 2:

[0630] The terminal transmits the input authentication information to the server. The authentication information is encrypted before transmission.

[0631] Step 3:

[0632] The server checks the received authentication information against the database, checking whether the authentication is successful by checking against the user information stored in the database.

[0633] Step 4:

[0634] If the authentication is successful, the server creates a user session and sends an authentication success message to the terminal. If the authentication fails, the server sends a failure message to the terminal and asks the terminal to enter authentication information again.

[0635] Step 5:

[0636] After receiving the authentication success message, the terminal displays a chat interface to the user, who then inputs an inquiry into the chat interface and sends it to the system.

[0637] Step 6:

[0638] The server receives the inquiry sent by the user, analyzes the inquiry, and searches the knowledge base for relevant information based on the inquiry.

[0639] Step 7:

[0640] The generative AI model generates an initial response based on the query, which is then sent to the device via the server.

[0641] Step 8:

[0642] The terminal displays the generated initial answer to the user, who then confirms the answer.

[0643] Step 9:

[0644] If the user is not satisfied with the initial response, the user submits a request for further investigation through the chat interface.

[0645] Step 10:

[0646] The server receives the request for further investigation and escalates it to the appropriate person, who begins the investigation and provides updates on its progress.

[0647] Step 11:

[0648] The terminal displays the status of the escalation to the user in real time, allowing the user to see how their inquiry is being handled.

[0649] Step 12:

[0650] After answering, users can submit feedback through the chat interface, which helps improve the quality and responsiveness of the answers.

[0651] Step 13:

[0652] The server receives feedback from users and updates the knowledge base to reflect that feedback. The contents of the knowledge base are checked regularly, and any new information or corrections that are needed are updated immediately.

[0653] Example 1

[0654] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0655] There is a need to eliminate delays and confusion in responding to inquiries faced by corporate employees and improve work efficiency. Providing information quickly and accurately is particularly important and directly impacts employee productivity. Conventional methods require a lot of time and effort to respond to inquiries, often overwhelming staff. The present invention is intended to solve these problems.

[0656] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0657] In this invention, the server includes means for receiving authentication information from a user and comparing the authentication information with a database, means for providing a chat interface to the user terminal if authentication is successful, means for receiving an inquiry from a user, analyzing the inquiry content, and searching for relevant information from a knowledge base, means for generating an initial answer using a generative AI model, means for transmitting the generated initial answer to the user terminal, means for receiving a request for detailed investigation from the user and escalating the request to an appropriate person in charge, and means for receiving feedback and updating the knowledge base, thereby enabling prompt and accurate response to inquiries.

[0658] "Authentication information" refers to information such as a user ID and password that a user provides when logging in to a system.

[0659] A "database" is an information system for storing authentication information, inquiry details, feedback, etc.

[0660] A "chat interface" is a communication method that allows users to make inquiries in real time through the system.

[0661] "Inquiry content" refers to the specific content of a question or request that a user makes to the system.

[0662] A "knowledge base" is a collection of data that stores information that the system provides in response to user inquiries.

[0663] A "generative AI model" is an algorithm or software that uses artificial intelligence to automatically generate answers based on analysis results.

[0664] An "initial response" is the first response automatically generated by a generative AI model in response to a user's inquiry.

[0665] A "request for further investigation" is a request where the user is not satisfied with the initial response and requests further detailed information or investigation.

[0666] "Escalation" is the process of passing a request for further investigation to the appropriate person.

[0667] "Feedback" refers to ratings and comments that users make on answers provided.

[0668] This system improves business efficiency by eliminating delays and confusion in responding to inquiries from company employees. The system provides a chat interface with user authentication, automatically generates initial responses using a generative AI model, and escalates to personnel for further investigation as needed.

[0669] Hardware and software used

[0670] The system includes the following elements:

[0671] 1. Server: Responsible for verifying user authentication information, analyzing queries, running generative AI models, and managing the knowledge base. The server uses hardware equipped with high-performance processors and large amounts of memory.

[0672] 2. Database: Includes software for storing data such as user information, authentication information, knowledge base, feedback, etc. This database uses an appropriate database management system such as SQL or NoSQL.

[0673] 3. Terminal: A device through which a user accesses the system, such as a PC, tablet, or smartphone. The terminal communicates with the server via a browser or dedicated app.

[0674] 4. Generative AI Model: An artificial intelligence model used to analyze queries and generate initial responses. This model utilizes advanced algorithms, including natural language processing techniques.

[0675] Detailed System Description

[0676] User Authentication

[0677] The device displays a login screen to the user, prompting them to enter their user ID and password. Once the user enters the information, the device sends it to the server. The server compares the received authentication information with its database to see if there is a matching record. If a match is found, a successful authentication message is sent to the device, and the user is able to use the chat interface.

[0678] Providing a chat interface

[0679] If the user is successfully authenticated, the server sends instructions to the terminal to display a chat interface, through which the user can make inquiries and send their details 24 hours a day, 365 days a year.

[0680] Inquiry analysis and initial response generation

[0681] The server analyzes the inquiry received from the user and searches for relevant information in the knowledge base.The generative AI model then generates an initial answer based on the analyzed content, and the server sends it to the user's device.

[0682] Escalation of further investigation

[0683] If the user is not satisfied with the initial response, they can submit a request for further investigation through the chat interface. The server receives this request and escalates it to the appropriate agent. The agent then investigates and returns the results to the server, which then provides a detailed response to the user.

[0684] Feedback and Knowledge Base Updates

[0685] Users can provide feedback on the answers they provide, and the device sends the feedback information to the server, which stores it in a database and updates the knowledge base, allowing the system to continually improve.

[0686] Specific examples

[0687] Example 1: If you want to know the procedure for resetting your password

[0688] 1. The user types "How do I reset my password?" into the chat interface.

[0689] 2. The terminal sends this query to the server.

[0690] 3. The server analyzes the received inquiry and searches the knowledge base for password reset instructions.

[0691] 4. The generative AI model generates an initial answer for the password reset procedure, and the server sends it to the user's device.

[0692] 5. The user follows the instructions they receive to reset their password.

[0693] Example 2: Inquiry about the application flow

[0694] 1. The user types "Please tell me the process for requesting a business trip" into the chat interface.

[0695] 2. The terminal sends this query to the server.

[0696] 3. The server searches the knowledge base for the travel request flow and generates a detailed flow using the generative AI model.

[0697] 4. The server sends the generated flow to the user's device so that the user can view it.

[0698] 5. The user follows the displayed flow to apply for a business trip.

[0699] In this way, the system can respond to inquiries quickly and accurately, significantly improving user convenience and business efficiency.

[0700] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0701] Step 1:

[0702] The terminal displays a login screen to the user and prompts them to enter their user ID and password. The entered authentication information (user ID and password) is sent to the server. The server compares the received authentication information with its database to see if there is a matching record. If authentication is successful, the server creates a user session and sends an authentication success message to the terminal. The input authentication information is the input data, and the database comparison and authentication results are the output data.

[0703] Step 2:

[0704] The server sends an instruction to the terminal that has been successfully authenticated to display a chat interface. Based on this instruction, the terminal displays the chat interface. The chat interface includes a text field for the user to enter their inquiry. The instruction based on successful authentication is input data, and the display of the chat interface is output data.

[0705] Step 3:

[0706] The user enters the inquiry into the chat interface and presses the send button. The terminal sends the entered inquiry to the server. The input of the inquiry is input data, and the transmission of the inquiry is output data.

[0707] Step 4:

[0708] The server analyzes the received inquiry and searches for related information from a knowledge base. This analysis uses natural language processing technology to break down the inquiry into topics and keywords. Based on the analysis results, the server searches for appropriate information from the knowledge base. The inquiry is the input data, and the search results for related information are the output data.

[0709] Step 5:

[0710] The generative AI model generates an initial answer based on the analyzed query content. This AI model uses prompts to generate the optimal answer corresponding to the query content. The analysis results are the input data, and the generated initial answer is the output data.

[0711] Step 6:

[0712] The server receives the initial answer generated by the generative AI model and sends it to the terminal. The terminal displays the received initial answer to the user. The generation of the initial answer is input data, and the displayed answer is output data.

[0713] Step 7:

[0714] If the user is not satisfied with the initial response, he / she inputs and sends a detailed investigation request to the chat interface. The terminal sends the detailed investigation request to the server. The input of the detailed investigation request is input data, and the transmission of the detailed investigation request is output data.

[0715] Step 8:

[0716] The server receives the detailed investigation request and escalates it to the appropriate person. The person in charge conducts the detailed investigation and returns the results to the server. The server receives the detailed response from the person in charge and sends it to the terminal. The receipt of the detailed investigation request is input data, and the provision of the detailed response is output data.

[0717] Step 9:

[0718] The terminal displays the detailed answer to the user. The user can provide feedback on the provided answer. The input of the feedback is input data, and the output of the feedback is output data.

[0719] Step 10:

[0720] The server analyzes the received feedback and updates the knowledge base. The received feedback is the input data, and the updates to the knowledge base are the output data. This allows the system to continuously improve.

[0721] (Application example 1)

[0722] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0723] When employees work at a logistics center, they have a variety of inquiries and things to confirm. However, if employees cannot get prompt and appropriate answers, work delays and efficiency declines. This situation has a negative impact on overall business performance, so a system that can provide prompt and appropriate answers is needed. Another challenge is creating an environment where employees can receive support 24 hours a day, 365 days a year.

[0724] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0725] In this invention, the server includes authentication means for receiving authentication information from a user and comparing the authentication information with a database, means for providing a chat interface to an information device if authentication is successful, means for receiving an inquiry from a user, analyzing the inquiry, and searching for relevant information from a knowledge base, means for generating an initial answer using a generative AI model, means for transmitting the generated initial answer to the information device, means for receiving a request for further investigation from a user and escalating the request to an appropriate person, means for receiving feedback and updating the knowledge base, processing means for responding to inquiries from employees at a logistics center and providing business support, and means for generating prompt sentences for the generative AI model and providing answers. This allows employees to quickly obtain appropriate answers, improving business efficiency and quality.

[0726] A "user authentication means" is a means by which a user enters authentication information such as a user ID and password when accessing a system, and the server compares this information with a database to perform authentication.

[0727] The "chat interface providing means" is a means for providing a chat interface that allows real-time interaction to the user terminal when authentication is successful.

[0728] The "inquiry analysis means" is a means for receiving an inquiry from a user, analyzing the content of the inquiry, and searching for relevant information from the knowledge base.

[0729] A "generative AI model means" is a means for using a generative AI model to automatically generate an initial response based on the analyzed inquiry content.

[0730] The "answer providing means" is a means for transmitting the generated initial answer to the user terminal and displaying it to the user.

[0731] The "detailed investigation escalation means" is a means for receiving a detailed investigation request and escalating it to an appropriate person in case the user is not satisfied with the initial response.

[0732] The "feedback receiving and knowledge base updating means" is a means for receiving feedback from users and updating the knowledge base based on that feedback.

[0733] A "logistics center" is a facility where goods are stored, sorted, shipped, etc., and where a wide variety of operations are carried out all at once.

[0734] "Information device" refers to a hardware device that allows a user to access a system, specifically a smartphone, tablet, or computer.

[0735] A "prompt sentence" is an instruction sentence used to generate a specific answer for a generative AI model, and is input information that enables the generative AI model to generate an appropriate answer based on the instruction sentence.

[0736] The present invention is a system for supporting efficient work of employees in a logistics center. The system includes the following components:

[0737] Hardware and Software Configuration

[0738] Server: This performs the central processing for user authentication, providing a chat interface, analyzing inquiries, generating answers using generative AI models, escalating detailed investigations, receiving feedback, and updating the knowledge base. The server is built using a web framework such as Flask.

[0739] Database: Use a database system such as SQLite to manage user authentication information and knowledge base information.

[0740] Information devices: Users use devices such as smartphones, tablets, and computers to access information.

[0741] System processing flow

[0742] 1. User authentication method:

[0743] The terminal displays a login screen to the user, and the user enters their user ID and password.

[0744] The server receives the entered authentication information and authenticates it by checking it against an SQLite database.

[0745] If the authentication is successful, the server creates a user session and sends a success message to the terminal.

[0746] 2. Chat interface provided by:

[0747] A chat interface is displayed on the user terminal on which authentication has been successful.

[0748] The terminal receives a user's inquiry through a chat interface.

[0749] 3. Query analysis methods:

[0750] The server analyzes the received query and searches for relevant information from a knowledge base.

[0751] 4. Generative AI model means:

[0752] A generative AI model is used to generate an initial answer based on the analyzed query content, specifically using OpenAI's API.

[0753] Example: The prompt sentence "Question about operations at a distribution center: I would like to know the current inventory status. How can I check it?" is input into the generative AI model.

[0754] 5. Means of providing answers:

[0755] The generated initial answer is sent to the terminal and displayed to the user.

[0756] Example: Show the user the initial response "You can check your current inventory status by logging into your Warehouse Management System (WMS) and clicking the 'Check Inventory' tab."

[0757] 6. Further investigation escalation procedures:

[0758] If the user is not satisfied with the initial response, they submit a request for further investigation through the chat interface.

[0759] The server receives the request for further investigation and escalates it to the appropriate person.

[0760] 7. How we receive feedback and update our knowledge base:

[0761] After the user answers, they submit their feedback through the chat interface.

[0762] The server receives the feedback and updates the knowledge base.

[0763] Specific use cases

[0764] Example 1: Inventory management inquiry

[0765] User: "I'd like to know the current inventory status. How can I check it?"

[0766] Prompt generated by the generative AI model: "Question about operations at a distribution center: I would like to know the current inventory status. How can I check it?"

[0767] Example answer: "You can check your current inventory by logging into your Warehouse Management System (WMS) and clicking on the 'Check Inventory' tab."

[0768] In this way, the present invention is a system that enables employees at a logistics center to quickly obtain appropriate answers, thereby improving work efficiency and quality.

[0769] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0770] Step 1:

[0771] A user opens a login screen using an information device and inputs a user ID and password. The input information includes the user ID and password.

[0772] Input: User ID and password

[0773] Operation: The device sends the input information to the server.

[0774] Step 2:

[0775] The server performs authentication by checking the received authentication information against the SQLite database. The server queries the database for the user ID and password.

[0776] Input: User ID and password

[0777] Data processing: The server converts the user ID and password into an SQL query and queries the database.

[0778] Output: Authentication success or failure

[0779] Operation: Generates an authentication result based on the database matching result, and if successful, creates a user session and sends a success message to the terminal.

[0780] Step 3:

[0781] If the authentication is successful, the server provides a chat interface to the information device.

[0782] Input: Successful authentication result

[0783] Operation: The server sends data to the user's device to display a real-time chat interface.

[0784] Step 4:

[0785] The user uses the chat interface to input and send the inquiry.

[0786] Input: Inquiry details

[0787] Operation: The device sends the query to the server.

[0788] Step 5:

[0789] The server analyzes the received query and searches for relevant information from a knowledge base using natural language processing algorithms.

[0790] Input: Inquiry details

[0791] Data processing: Analyze the inquiry content and extract related keywords.

[0792] Output: Search results (information in the knowledge base)

[0793] Operation: Based on the analysis results, the server searches the knowledge base for relevant information.

[0794] Step 6:

[0795] A generative AI model is used to generate an initial answer based on the analyzed query content. Specifically, a prompt sentence is input into the generative AI model to obtain an answer.

[0796] Input: Enquiry and Knowledge Base search results

[0797] Data processing: Convert the query content into a prompt sentence and input it into the generative AI model.

[0798] Output: Initial answer

[0799] How it works: The server receives an initial answer from the generative AI model.

[0800] Step 7:

[0801] The server transmits the generated initial answer to the user terminal and displays it to the user.

[0802] Input: Initial answer

[0803] Action: The server sends an initial response to the device, which displays it in the user's chat interface.

[0804] Step 8:

[0805] If the user is not satisfied with the initial response, they submit a request for further investigation through the chat interface.

[0806] Input: Further investigation request

[0807] Action: The device sends a detailed investigation request to the server.

[0808] Step 9:

[0809] The server receives the request for further investigation and escalates it to the appropriate person.

[0810] Input: Further investigation request

[0811] Behavior: The server generates and sends data to escalate the request to the appropriate person.

[0812] Step 10:

[0813] After the user answers, they submit their feedback through the chat interface.

[0814] Input: Feedback

[0815] Action: The device sends feedback to the server.

[0816] Step 11:

[0817] The server receives the feedback and updates the knowledge base.

[0818] Input: Feedback

[0819] Data processing: Analyze the feedback and generate data to be reflected in the knowledge base.

[0820] Action: The server updates the knowledge base to reflect the new information.

[0821] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0822] This invention provides a system that eliminates delays and confusion experienced by corporate employees in responding to inquiries and improves operational efficiency. This system provides a chat interface with user authentication, automatically generates initial responses using a generative AI model, and escalates to a human for further investigation as needed. It also incorporates an emotion engine that recognizes emotions from user input and the context of the conversation, enabling flexible responses based on the user's emotions.

[0823] Overall system configuration

[0824] The system includes the following elements:

[0825] 1. User authentication method:

[0826] When a user accesses a system, they enter authentication information such as a user ID and password, and the server authenticates them by checking this information against a database.

[0827] 2. Chat interface provided by:

[0828] If the authentication is successful, the server will provide a chat interface on the user's device that is available 24 hours a day, 365 days a year, through which the user can make inquiries.

[0829] 3. Query analysis methods:

[0830] It is a means of receiving an inquiry from a user, analyzing the content of the inquiry, and searching for relevant information from a knowledge base.

[0831] 4. Generative AI model means:

[0832] Using a generative AI model, an initial response is automatically generated based on the analyzed inquiry content.

[0833] 5. Means of providing answers:

[0834] The generated initial answer is sent to the user terminal and displayed to the user.

[0835] 6. Further investigation escalation procedures:

[0836] If the user is not satisfied with the initial response, it is a means to receive further investigation requests and escalate them to the appropriate personnel.

[0837] 7. How we receive feedback and update our knowledge base:

[0838] It is a means of receiving feedback from users and updating the knowledge base.

[0839] 8. Emotion Engine Means:

[0840] The emotion engine recognizes emotions from user input and the context of the conversation, allowing the generative AI model to adjust the tone and content of responses, and also incorporates emotional data into the knowledge base.

[0841] A natural language description of the program's operation

[0842] User Authentication

[0843] 1. The terminal displays a login screen to the user, and the user enters their user ID and password.

[0844] 2. The server receives the entered authentication information and authenticates it by checking it against a database.

[0845] 3. If the authentication is successful, the server creates a user session and sends a success message to the terminal.

[0846] 4. If authentication fails, the server sends a failure message to the terminal and notifies the user.

[0847] Launching the chat interface and sending inquiries

[0848] 1. The server displays a chat interface on the user's device after successful authentication.

[0849] 2. The authenticated user enters and sends their inquiry into the chat interface.

[0850] 3. The server analyzes the received query and searches the knowledge base for relevant information.

[0851] 4. The generative AI model generates an initial answer, which the server sends to the user device.

[0852] Initial response and detailed investigation request

[0853] 1. The device displays the initial answer from the AI ​​to the user.

[0854] 2. If the user is not satisfied with the initial response, they submit a further investigation request through the chat interface.

[0855] 3. The server receives the request for further investigation and escalates it to the appropriate person.

[0856] Emotion Engine Operation

[0857] 1. The server analyzes the user's input and recognizes the user's emotional state using an emotion engine.

[0858] 2. The generative AI model adjusts the tone and content of responses based on the perceived emotional state.

[0859] 3. The recognized emotion data is recorded for future user improvement.

[0860] Feedback and Knowledge Base Updates

[0861] 1. After answering, the user submits feedback through the chat interface.

[0862] 2. The server receives the feedback and updates the knowledge base to reflect it.

[0863] Specific examples

[0864] Example 1: If you want to know the procedure for resetting your password

[0865] 1. The user types "How do I reset my password?" into the chat interface.

[0866] 2. The server receives the query, parses it, and searches its knowledge base for password reset instructions.

[0867] 3. The generative AI model generates a password reset procedure, which the server sends to the user's device.

[0868] 4. The device displays the generated instructions to the user. The emotion engine measures the user's reaction and adjusts the response accordingly.

[0869] 5. The user follows the instructions they receive to reset their password.

[0870] Example 2: Inquiry about the application flow

[0871] 1. The user types in the chat, "Please tell me the flow for applying for a business trip."

[0872] 2. The server searches the knowledge base for the travel request flow and generates a detailed flow using the generative AI model.

[0873] 3. The server sends the generated flow to the user's terminal, where the user can view it.

[0874] 4. The terminal shows the displayed flow to the user. The emotion engine analyzes the user's emotional state and optimizes the answer.

[0875] 5. The user follows the displayed flow to apply for a business trip.

[0876] This system allows users to instantly access the information they need, significantly reducing the time they spend struggling. In addition, the introduction of an emotion engine enables more flexible and human-like responses, improving the user experience.

[0877] The processing flow will be explained below.

[0878] This invention provides a system that eliminates delays and confusion experienced by corporate employees in responding to inquiries and improves operational efficiency. This system provides a chat interface with user authentication, automatically generates initial responses using a generative AI model, and escalates to a human for further investigation as needed. It also incorporates an emotion engine that recognizes emotions from user input and the context of the conversation, enabling flexible responses based on the user's emotions.

[0879] Overall system configuration

[0880] The system includes the following elements:

[0881] 1. User authentication method:

[0882] When a user accesses a system, they enter authentication information such as a user ID and password, and the server authenticates them by checking this information against a database.

[0883] 2. Chat interface provided by:

[0884] If the authentication is successful, the server will provide a chat interface on the user's device that is available 24 hours a day, 365 days a year, through which the user can make inquiries.

[0885] 3. Query analysis methods:

[0886] It is a means of receiving an inquiry from a user, analyzing the content of the inquiry, and searching for relevant information from a knowledge base.

[0887] 4. Generative AI model means:

[0888] Using a generative AI model, an initial response is automatically generated based on the analyzed inquiry content.

[0889] 5. Means of providing answers:

[0890] The generated initial answer is sent to the user terminal and displayed to the user.

[0891] 6. Further investigation escalation procedures:

[0892] If the user is not satisfied with the initial response, it is a means to receive further investigation requests and escalate them to the appropriate personnel.

[0893] 7. How we receive feedback and update our knowledge base:

[0894] It is a means of receiving feedback from users and updating the knowledge base.

[0895] 8. Emotion Engine Means:

[0896] The emotion engine recognizes emotions from user input and the context of the conversation, allowing the generative AI model to adjust the tone and content of responses, and also incorporates emotional data into the knowledge base.

[0897] A natural language description of the program's operation

[0898] Step 1:

[0899] The user accesses the system to display the login screen on their own terminal. The user enters the required authentication information (user ID and password) on the login screen.

[0900] Step 2:

[0901] The terminal transmits the input authentication information to the server. The authentication information is encrypted before transmission.

[0902] Step 3:

[0903] The server checks the received authentication information against the database, checking whether the authentication is successful by checking against the user information stored in the database.

[0904] Step 4:

[0905] If the authentication is successful, the server creates a user session and sends an authentication success message to the terminal. If the authentication fails, the server sends a failure message to the terminal and asks the terminal to enter authentication information again.

[0906] Step 5:

[0907] After receiving the authentication success message, the terminal displays a chat interface to the user, who then inputs an inquiry into the chat interface and sends it to the system.

[0908] Step 6:

[0909] The server receives the inquiry sent by the user, analyzes the inquiry, and searches the knowledge base for relevant information based on the inquiry.

[0910] Step 7:

[0911] The generative AI model generates an initial response based on the query, which is then sent to the device via the server.

[0912] Step 8:

[0913] The terminal displays the generated initial answer to the user, and the server uses an emotion engine to analyze the user's emotional state.

[0914] Step 9:

[0915] The emotion engine recognizes emotions from the user's input and the context of the conversation, and the generative AI model adjusts the tone and content of the response based on the recognized emotional state. The adjusted response is then sent back to the device.

[0916] Step 10:

[0917] If the user is not satisfied with the initial response, the user submits a request for further investigation through the chat interface.

[0918] Step 11:

[0919] The server receives the request for further investigation and escalates it to the appropriate person, who begins the investigation and provides updates on its progress.

[0920] Step 12:

[0921] The terminal displays the status of the escalation to the user in real time, allowing the user to see how their inquiry is being handled.

[0922] Step 13:

[0923] After answering, users can submit feedback through the chat interface, which helps improve the quality and responsiveness of the answers.

[0924] Step 14:

[0925] The server receives feedback from users and updates the knowledge base to reflect that feedback. The contents of the knowledge base are checked regularly, and any new information or corrections that are needed are updated immediately.

[0926] Specific examples

[0927] Example 1: If you want to know the procedure for resetting your password

[0928] 1. The user types "How do I reset my password?" into the chat interface.

[0929] 2. The server receives the query, parses it, and searches its knowledge base for password reset instructions.

[0930] 3. The generative AI model generates a password reset procedure, which the server sends to the user's device.

[0931] 4. The device displays the generated instructions to the user. The emotion engine measures the user's reaction and adjusts the response accordingly.

[0932] 5. The user follows the instructions they receive to reset their password.

[0933] Example 2: Inquiry about the application flow

[0934] 1. The user types in the chat, "Please tell me the flow for applying for a business trip."

[0935] 2. The server searches the knowledge base for the travel request flow and generates a detailed flow using the generative AI model.

[0936] 3. The server sends the generated flow to the user's terminal, where the user can view it.

[0937] 4. The terminal shows the displayed flow to the user. The emotion engine analyzes the user's emotional state and optimizes the answer.

[0938] 5. The user follows the displayed flow to apply for a business trip.

[0939] This system allows users to instantly access the information they need, significantly reducing the time they spend struggling. In addition, the introduction of an emotion engine enables more flexible and human-like responses, improving the user experience.

[0940] Example 2

[0941] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0942] In modern companies, employees must respond to numerous inquiries every day, and delays and confusion in these responses can reduce work efficiency. While there is a demand for systems that allow employees to instantly access the information they need, existing systems lack the flexibility to respond to user feedback. Furthermore, there is the issue of how difficult it is to effectively incorporate user feedback and continuously improve the system.

[0943] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0944] In this invention, the server includes an authentication means for receiving authentication information from a user and comparing the authentication information with a storage device, a means for providing a dialogue interface to a user terminal if authentication is successful, a means for receiving an inquiry from a user, analyzing the inquiry, and searching for relevant information from a knowledge base, a means for generating an initial answer using a generation AI, a means for transmitting the generated initial answer to the user terminal, a means including an emotion engine for analyzing user input and recognizing the user's emotional state, a means for forwarding a request for further investigation to an appropriate person in an escalation process, and a means for receiving feedback and updating the knowledge base. This not only allows employees to access the information they need immediately, but also enables flexible responses to user emotions, and allows for continuous improvement of the system by effectively incorporating feedback.

[0945] An "authentication means" is a system component that has the function of receiving authentication information from a user and authenticating the user by comparing it with a storage device.

[0946] An "interactive interface" is an interface that provides a screen or window for a user to input a query.

[0947] A "knowledge base" is a database system that stores answers and information to user inquiries.

[0948] "Generative AI" is an artificial intelligence model that automatically generates an initial response based on the content of the inquiry.

[0949] An "emotion engine" is a system component that analyzes user input and recognizes the user's emotional state.

[0950] An "escalation mechanism" is a system component that receives a request for further investigation from a user and transfers it to the appropriate person.

[0951] A "feedback mechanism" is a system component that has the function of receiving feedback from users and updating the knowledge base.

[0952] "Storage" is hardware or software used to store authentication information, knowledge base data, etc.

[0953] This system aims to improve work efficiency by eliminating delays and confusion experienced by corporate employees in responding to inquiries. The system provides a dialogue interface that includes user authentication, automatically generates initial responses using generative AI, and escalates to personnel for further investigation as needed. It also incorporates an emotion engine that recognizes emotions from user input and the context of the dialogue, enabling flexible responses based on the user's emotions.

[0954] Overall system configuration

[0955] The system includes the following elements:

[0956] 1. User authentication method:

[0957] When a user accesses a system, they enter authentication information such as a user ID and password, and the server authenticates them by comparing this information with the stored data.

[0958] 2. Means for providing a dialogue interface:

[0959] If the authentication is successful, the server provides the user terminal with a conversation interface available 24 hours a day, 365 days a year, through which the user can make inquiries.

[0960] 3. Query analysis methods:

[0961] It is a means of receiving an inquiry from a user, analyzing the content of the inquiry, and searching for relevant information from a knowledge base.

[0962] 4. Generative AI model means:

[0963] Generative AI is used to automatically generate an initial response based on the analyzed inquiry content.

[0964] 5. Means of providing answers:

[0965] The generated initial answer is sent to the user terminal and displayed to the user.

[0966] 6. Further investigation escalation procedures:

[0967] If the user is not satisfied with the initial response, it is a means to receive further investigation requests and escalate them to the appropriate personnel.

[0968] 7. How we receive feedback and update our knowledge base:

[0969] It is a means of receiving feedback from users and updating the knowledge base.

[0970] 8. Emotion Engine Means:

[0971] The emotion engine recognizes emotions from user input and the context of the conversation, allowing the generative AI to adjust the tone and content of the response. It also reflects the emotional data in the knowledge base.

[0972] Specific examples

[0973] Example 1: If you want to know the procedure for resetting your password

[0974] 1. The user enters "How do I reset my password?" into the conversational interface.

[0975] 2. The server receives the query, parses it, and searches its knowledge base for password reset instructions.

[0976] 3. The generation AI generates a password reset procedure, and the server sends it to the user's device.

[0977] 4. The device displays the generated instructions to the user. The emotion engine measures the user's reaction and adjusts the response accordingly.

[0978] 5. The user follows the instructions they receive to reset their password.

[0979] Example 2: Inquiry about the application flow

[0980] 1. The user enters "Please tell me the flow for applying for a business trip" into the dialogue interface.

[0981] 2. The server searches the knowledge base for the travel request flow and generates a detailed flow using generation AI.

[0982] 3. The server sends the generated flow to the user's terminal, where the user can view it.

[0983] 4. The terminal shows the displayed flow to the user. The emotion engine analyzes the user's emotional state and optimizes the answer.

[0984] 5. The user follows the displayed flow to apply for a business trip.

[0985] This system allows users to instantly access the information they need, significantly reducing the time they spend struggling. In addition, the introduction of an emotion engine enables more flexible and human-like responses, improving the user experience.

[0986] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0987] Program processing flow

[0988] User authentication step

[0989] Step 1:

[0990] The user displays the login screen on the terminal and enters the user ID and password, thereby entering authentication information.

[0991] Step 2:

[0992] The terminal receives the entered authentication information, encrypts it, and sends it to the server. The input is the user ID and password, and the encrypted data is output and sent to the server.

[0993] Step 3:

[0994] The server checks the received authentication information against the storage device. Specifically, it uses an SQL query to search for the corresponding record in the database, compares the entered authentication information with the information in the database, and performs authentication. If the information matches, it outputs a successful authentication status.

[0995] Step 4:

[0996] If authentication is successful, the server creates a user session and sends a success message to the terminal. The input is the authentication success status, and the output is a success message including the session ID. If authentication fails, the server sends a failure message to the terminal and notifies the user. The input is the authentication failure status, and the output is a failure message.

[0997] Enquiry Processing Steps

[0998] Step 5:

[0999] If authentication is successful, the server displays an interactive interface on the user's device. This is achieved by dynamically generating an interactive window using HTML and JavaScript. The input is a session ID, and the output is an interactive interface.

[1000] Step 6:

[1001] The user inputs the inquiry into the dialogue interface and presses the send button, which sends the input inquiry.

[1002] Step 7:

[1003] The server analyzes the received query. It uses a natural language processing (NLP) engine to extract keywords and analyze intent. The query is input and the analysis results are output.

[1004] Step 8:

[1005] Based on the analysis results, the generative AI generates an initial answer. Specifically, the language model generates text based on the prompt sentence. The analysis results are input, and the generated initial answer is output.

[1006] Step 9:

[1007] The server sends the generated initial answer to the user terminal. The generated answer is input, and data to be sent to the user terminal is output. The user terminal displays the received initial answer to the user.

[1008] Further investigation and escalation steps

[1009] Step 10:

[1010] The terminal displays the received initial response to the user, allowing the user to confirm the initial response.

[1011] Step 11:

[1012] If the user is not satisfied with the initial answer, they submit a further investigation request through the dialogue interface, which takes the further investigation request as input and the submitted data as output.

[1013] Step 12:

[1014] The server receives the further investigation request and escalates it to the appropriate person. As part of the escalation process, the request is forwarded to the help desk system and the person in charge is notified. The further investigation request is input, and notification data for the person in charge is output.

[1015] Emotion recognition and response adjustment steps

[1016] Step 13:

[1017] The server analyzes the user's input and recognizes the user's emotional state using an emotion engine. User input is input and emotional data is output.

[1018] Step 14:

[1019] The generative AI adjusts the tone and content of the response based on the recognized emotional state, enabling it to respond appropriately to the user's emotions. It takes emotional data as input and outputs an adjusted response.

[1020] Feedback and Knowledge Base Update Steps

[1021] Step 15:

[1022] After answering, the user sends feedback through the dialogue interface. The feedback content is input and the transmitted data is output.

[1023] Step 16:

[1024] The server receives the feedback and updates the knowledge base to reflect it. It analyzes the feedback and improves the corresponding knowledge base entry. It takes the feedback as input and outputs the updated knowledge base entry.

[1025] (Application example 2)

[1026] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1027] Conventional inquiry response systems for content distribution services can be slow and confusing, resulting in low user satisfaction. Additionally, there are issues with the quality of initial responses being inconsistent and it being difficult to respond flexibly based on user emotions. Therefore, an efficient system that can solve these issues is needed.

[1028] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1029] In this invention, the server

[1030] authentication means for receiving authentication information from a user and comparing the authentication information with a database;

[1031] means for providing an interface to the terminal upon successful authentication;

[1032] means for receiving an inquiry from a user, analyzing the content of the inquiry, and searching for relevant information from a knowledge base;

[1033] a means for generating an initial answer using a generative AI model;

[1034] means for transmitting the generated initial response to a terminal;

[1035] A means of receiving further investigation requests from users and escalating them to the appropriate personnel;

[1036] a means for receiving feedback and updating the knowledge base;

[1037] an emotion analysis means for recognizing emotions from the content of a user's input and the context of the dialogue, and adjusting the content and tone of a response based on the emotions;

[1038] This will improve the efficiency and quality of inquiries and increase user satisfaction.

[1039] "User authentication means" refers to a means for checking authentication information provided by a user against a database and permitting or denying access to the system.

[1040] The "chat interface providing means" is a means for displaying an interface on the user terminal and allowing the user to input an inquiry if user authentication is successful.

[1041] The "inquiry analysis means" is a means for analyzing the content of an inquiry received from a user and searching for relevant information from a knowledge base.

[1042] A "generative AI model" is an artificial intelligence model that automatically generates an initial response based on the user's inquiry.

[1043] The "answer providing means" is a means for transmitting the initial answer generated by the generative AI model to the user terminal.

[1044] The "detailed investigation escalation means" is a means for receiving a detailed investigation request from a user and escalating it to an appropriate person in charge.

[1045] The "feedback receiving means" is a means for receiving feedback from a user.

[1046] A "knowledge base updater" is a means for updating the knowledge base based on received feedback.

[1047] The "emotion analysis means" is a means for recognizing emotions from the content of a user's input and the context of the dialogue, and adjusting the content and tone of the response based on the emotions.

[1048] The present invention provides a system for efficiently responding to user inquiries about content distribution services. Specifically, the system includes the following means and processing steps.

[1049] System Configuration

[1050] 1. User authentication method

[1051] When a user accesses the system, the server receives authentication information such as the user ID and password and authenticates them by checking them against the database. Only if the user is successfully authenticated can the server proceed to the next step.

[1052] 2. Means of providing a chat interface

[1053] If authentication is successful, the server displays a chat interface on the user's device, through which the user can make inquiries. This chat interface is designed to be available 24 hours a day, 365 days a year.

[1054] 3. Query Analysis Methods

[1055] When a user enters a query into the chat interface, the server analyzes the content and searches for relevant information in the knowledge base using natural language processing technology.

[1056] 4. Generative AI Model Means

[1057] The server uses a generative AI model to generate an initial answer based on the query content. This AI model uses a pre-trained algorithm to automatically generate the best answer to the user's question.

[1058] 5. Means of providing answers

[1059] The generated initial response is sent from the server to the user terminal and displayed to the user through the chat interface.

[1060] 6. Further investigation and escalation procedures

[1061] If the user is not satisfied with the initial response generated, they can submit a request for further investigation, which the server will receive and escalate to the appropriate personnel.

[1062] 7. How we receive feedback and update our knowledge base

[1063] If the user provides feedback after answering, the server receives this feedback, which is reflected in the knowledge base and used to improve the quality of future inquiries.

[1064] 8. Emotion analysis method

[1065] The server recognizes emotions from the user's input and the context of the conversation. This emotion analysis engine allows the generative AI model to adjust the content and tone of the response to enable flexible responses based on the user's emotions.

[1066] Hardware and software used

[1067] Hardware: Smartphone

[1068] software:

[1069] UserAuth: User authentication module

[1070] ChatInterface: Chat interface module

[1071] InquiryParser: Inquiry parsing module (natural language processing)

[1072] AIModel: Answer generation module in generative AI model

[1073] SentimentEngine: Sentiment analysis and response adjustment module

[1074] EscalationManager: Further investigation escalation module

[1075] FeedbackManager: Feedback management module

[1076] Specific examples

[1077] Example 1: Inquiry about how to use the new movie recommendations feature

[1078] 1. The user types, "Please tell me the recommended features for new movies" through the chat interface of the smartphone app.

[1079] 2. The server receives and analyzes the query and retrieves relevant information from a knowledge base.

[1080] 3. The AI ​​model generates initial answers for new movie recommendations.

[1081] 4. The sentiment analysis engine analyzes the emotions expressed by the user's input and adjusts the tone and content of the response.

[1082] 5. An initial response is displayed, and if the user is not satisfied, the issue is escalated and handed over to a responsible person.

[1083] 6. Finally, the user leaves feedback, which is added to the knowledge base.

[1084] Example of an input prompt:

[1085] User: "What's the new movie recommendations feature?"

[1086] AI model: "The new movie recommendations feature is automatically generated by an algorithm based on your preferred genres and viewing history. Select the Recommendations tab from the app menu to see a list of new movies."

[1087] Emotion Engine: "If you have any other questions, please feel free to let me know."

[1088] In this way, users can receive prompt and appropriate support, improving the efficiency and quality of inquiries.

[1089] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1090] Step 1:

[1091] The server receives authentication information from the user. The user enters their user ID and password and sends them to the server from their terminal. The server receives this authentication information and authenticates the user by checking it against a database. If authentication is successful, a user session is created.

[1092] Step 2:

[1093] The server displays a chat interface on the terminal of a user who has been successfully authenticated. A chat window opens on the terminal, allowing the user to enter their inquiry. This interface is available 24 hours a day, 365 days a year.

[1094] Step 3:

[1095] The user enters their inquiry into the chat interface and sends it to the server, which then analyzes the received inquiry. Specifically, it uses natural language processing technology to analyze the text and retrieves relevant information from a knowledge base.

[1096] Step 4:

[1097] The server generates an initial answer using a generative AI model based on the analyzed query content. This AI model is a trained algorithm that automatically generates the best answer for the query content. The generated initial answer is stored internally on the server as a prompt text.

[1098] Step 5:

[1099] The server sends the generated initial response to the user's device. The user can view the initial response through the chat interface. In addition, the emotion analysis engine analyzes the user's input and adjusts the tone and content of the response based on the user's emotion. The adjusted response is also displayed on the user's device.

[1100] Step 6:

[1101] If the user is not satisfied with the initial response, they can send a request for further investigation through the chat interface. The server receives this request and escalates it to the appropriate agent. For this purpose, it uses the Escalation Manager module. After the agent takes over, the appropriate action is taken.

[1102] Step 7:

[1103] After answering, users provide feedback, which is sent to the server through the chat interface. The server analyzes the received feedback and updates the knowledge base. The feedback manager module is used to update the knowledge base and improve the quality of future inquiries.

[1104] In this way, users can receive prompt and appropriate support, improving the efficiency and quality of inquiries.

[1105] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1106] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1107] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[1108] [Third embodiment]

[1109] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1110] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[1111] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1112] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[1113] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1114] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1115] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1116] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1117] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[1118] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1119] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1120] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[1121] The present invention provides a system that improves business efficiency by eliminating delays and confusion in responding to inquiries experienced by corporate employees. The system provides a chat interface with user authentication, automatically generates initial responses using a generative AI model, and escalates to personnel for further investigation as needed.

[1122] Overall system configuration

[1123] The system includes the following elements:

[1124] 1. User authentication method:

[1125] When a user accesses a system, they enter authentication information such as a user ID and password, and the server authenticates them by checking this information against a database.

[1126] 2. Chat interface provided by:

[1127] If the authentication is successful, the server will provide a chat interface on the user's device that is available 24 hours a day, 365 days a year, through which the user can make inquiries.

[1128] 3. Query analysis methods:

[1129] It is a means of receiving an inquiry from a user, analyzing the content of the inquiry, and searching for relevant information from a knowledge base.

[1130] 4. Generative AI model means:

[1131] Using a generative AI model, an initial response is automatically generated based on the analyzed inquiry content.

[1132] 5. Means of providing answers:

[1133] The generated initial answer is sent to the user terminal and displayed to the user.

[1134] 6. Further investigation escalation procedures:

[1135] If the user is not satisfied with the initial response, it is a means to receive further investigation requests and escalate them to the appropriate personnel.

[1136] 7. How we receive feedback and update our knowledge base:

[1137] It is a means of receiving feedback from users and updating the knowledge base.

[1138] A natural language description of the program's operation

[1139] User Authentication

[1140] 1. The terminal displays a login screen to the user, and the user enters their user ID and password.

[1141] 2. The server receives the entered authentication information and authenticates it by checking it against a database.

[1142] 3. If the authentication is successful, the server creates a user session and sends a success message to the terminal.

[1143] 4. If authentication fails, the server sends a failure message to the terminal and notifies the user.

[1144] Launching the chat interface and sending inquiries

[1145] 1. The server displays a chat interface on the user's device after successful authentication.

[1146] 2. The authenticated user enters and sends their inquiry into the chat interface.

[1147] 3. The server analyzes the received query and searches for relevant information from the knowledge base.

[1148] 4. The generative AI model generates an initial answer, which the server sends to the user device.

[1149] Initial response and detailed investigation request

[1150] 1. The device displays the initial answer from the AI ​​to the user.

[1151] 2. If the user is not satisfied with the initial response, they submit a further investigation request through the chat interface.

[1152] 3. The server receives the request for further investigation and escalates it to the appropriate person.

[1153] Feedback and Knowledge Base Updates

[1154] 1. After answering, the user submits feedback through the chat interface.

[1155] 2. The server receives the feedback and updates the knowledge base to reflect it.

[1156] Specific examples

[1157] Example 1: If you want to know the procedure for resetting your password

[1158] 1. The user types "How do I reset my password?" into the chat interface.

[1159] 2. The server receives the query, parses it, and searches its knowledge base for password reset instructions.

[1160] 3. The generative AI model generates a password reset procedure, which the server sends to the user's device.

[1161] 4. The user follows the instructions they receive to reset their password.

[1162] Example 2: Inquiry about the application flow

[1163] 1. The user types in the chat, "Please tell me the flow for applying for a business trip."

[1164] 2. The server searches the knowledge base for the travel request flow and generates a detailed flow using the generative AI model.

[1165] 3. The server sends the generated flow to the user's terminal, where the user can view it.

[1166] 4. The user follows the displayed flow to apply for a business trip.

[1167] This system allows users to instantly access the information they need, significantly reducing the amount of time they spend struggling.

[1168] The processing flow will be explained below.

[1169] Step 1:

[1170] The user accesses the system to display the login screen on their own terminal. The user enters the required authentication information (user ID and password) on the login screen.

[1171] Step 2:

[1172] The terminal transmits the input authentication information to the server. The authentication information is encrypted before transmission.

[1173] Step 3:

[1174] The server checks the received authentication information against the database, checking whether the authentication is successful by checking against the user information stored in the database.

[1175] Step 4:

[1176] If the authentication is successful, the server creates a user session and sends an authentication success message to the terminal. If the authentication fails, the server sends a failure message to the terminal and asks the terminal to enter authentication information again.

[1177] Step 5:

[1178] After receiving the authentication success message, the terminal displays a chat interface to the user, who then inputs an inquiry into the chat interface and sends it to the system.

[1179] Step 6:

[1180] The server receives the inquiry sent by the user, analyzes the inquiry, and searches the knowledge base for relevant information based on the inquiry.

[1181] Step 7:

[1182] The generative AI model generates an initial response based on the query, which is then sent to the device via the server.

[1183] Step 8:

[1184] The terminal displays the generated initial answer to the user, who then confirms the answer.

[1185] Step 9:

[1186] If the user is not satisfied with the initial response, the user submits a request for further investigation through the chat interface.

[1187] Step 10:

[1188] The server receives the request for further investigation and escalates it to the appropriate person, who begins the investigation and provides updates on its progress.

[1189] Step 11:

[1190] The terminal displays the status of the escalation to the user in real time, allowing the user to see how their inquiry is being handled.

[1191] Step 12:

[1192] After answering, users can submit feedback through the chat interface, which helps improve the quality and responsiveness of the answers.

[1193] Step 13:

[1194] The server receives feedback from users and updates the knowledge base to reflect that feedback. The contents of the knowledge base are checked regularly, and any new information or corrections that are needed are updated immediately.

[1195] Example 1

[1196] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1197] There is a need to eliminate delays and confusion in responding to inquiries faced by corporate employees and improve work efficiency. Providing information quickly and accurately is particularly important and directly impacts employee productivity. Conventional methods require a lot of time and effort to respond to inquiries, often overwhelming staff. The present invention is intended to solve these problems.

[1198] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1199] In this invention, the server includes means for receiving authentication information from a user and comparing the authentication information with a database, means for providing a chat interface to the user terminal if authentication is successful, means for receiving an inquiry from a user, analyzing the inquiry content, and searching for relevant information from a knowledge base, means for generating an initial answer using a generative AI model, means for transmitting the generated initial answer to the user terminal, means for receiving a request for detailed investigation from the user and escalating the request to an appropriate person in charge, and means for receiving feedback and updating the knowledge base, thereby enabling prompt and accurate response to inquiries.

[1200] "Authentication information" refers to information such as a user ID and password that a user provides when logging in to a system.

[1201] A "database" is an information system for storing authentication information, inquiry details, feedback, etc.

[1202] A "chat interface" is a communication method that allows users to make inquiries in real time through the system.

[1203] "Inquiry content" refers to the specific content of a question or request that a user makes to the system.

[1204] A "knowledge base" is a collection of data that stores information that the system provides in response to user inquiries.

[1205] A "generative AI model" is an algorithm or software that uses artificial intelligence to automatically generate answers based on analysis results.

[1206] An "initial response" is the first response automatically generated by a generative AI model in response to a user's inquiry.

[1207] A "request for further investigation" is a request where the user is not satisfied with the initial response and requests further detailed information or investigation.

[1208] "Escalation" is the process of passing a request for further investigation to the appropriate person.

[1209] "Feedback" refers to ratings and comments that users make on answers provided.

[1210] This system improves business efficiency by eliminating delays and confusion in responding to inquiries from company employees. The system provides a chat interface with user authentication, automatically generates initial responses using a generative AI model, and escalates to personnel for further investigation as needed.

[1211] Hardware and software used

[1212] The system includes the following elements:

[1213] 1. Server: Responsible for verifying user authentication information, analyzing queries, running generative AI models, and managing the knowledge base. The server uses hardware equipped with high-performance processors and large amounts of memory.

[1214] 2. Database: Includes software for storing data such as user information, authentication information, knowledge base, feedback, etc. This database uses an appropriate database management system such as SQL or NoSQL.

[1215] 3. Terminal: A device through which a user accesses the system, such as a PC, tablet, or smartphone. The terminal communicates with the server via a browser or dedicated app.

[1216] 4. Generative AI Model: An artificial intelligence model used to analyze queries and generate initial responses. This model utilizes advanced algorithms, including natural language processing techniques.

[1217] Detailed System Description

[1218] User Authentication

[1219] The device displays a login screen to the user, prompting them to enter their user ID and password. Once the user enters the information, the device sends it to the server. The server compares the received authentication information with its database to see if there is a matching record. If a match is found, a successful authentication message is sent to the device, and the user is able to use the chat interface.

[1220] Providing a chat interface

[1221] If the user is successfully authenticated, the server sends instructions to the terminal to display a chat interface, through which the user can make inquiries and send their details 24 hours a day, 365 days a year.

[1222] Inquiry analysis and initial response generation

[1223] The server analyzes the inquiry received from the user and searches for relevant information in the knowledge base.The generative AI model then generates an initial answer based on the analyzed content, and the server sends it to the user's device.

[1224] Escalation of further investigation

[1225] If the user is not satisfied with the initial response, they can submit a request for further investigation through the chat interface. The server receives this request and escalates it to the appropriate agent. The agent then investigates and returns the results to the server, which then provides a detailed response to the user.

[1226] Feedback and Knowledge Base Updates

[1227] Users can provide feedback on the answers they provide, and the device sends the feedback information to the server, which stores it in a database and updates the knowledge base, allowing the system to continually improve.

[1228] Specific examples

[1229] Example 1: If you want to know the procedure for resetting your password

[1230] 1. The user types "How do I reset my password?" into the chat interface.

[1231] 2. The terminal sends this query to the server.

[1232] 3. The server analyzes the received inquiry and searches the knowledge base for password reset instructions.

[1233] 4. The generative AI model generates an initial answer for the password reset procedure, and the server sends it to the user's device.

[1234] 5. The user follows the instructions they receive to reset their password.

[1235] Example 2: Inquiry about the application flow

[1236] 1. The user types "Please tell me the process for requesting a business trip" into the chat interface.

[1237] 2. The terminal sends this query to the server.

[1238] 3. The server searches the knowledge base for the travel request flow and generates a detailed flow using the generative AI model.

[1239] 4. The server sends the generated flow to the user's device so that the user can view it.

[1240] 5. The user follows the displayed flow to apply for a business trip.

[1241] In this way, the system can respond to inquiries quickly and accurately, significantly improving user convenience and business efficiency.

[1242] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1243] Step 1:

[1244] The terminal displays a login screen to the user and prompts them to enter their user ID and password. The entered authentication information (user ID and password) is sent to the server. The server compares the received authentication information with its database to see if there is a matching record. If authentication is successful, the server creates a user session and sends an authentication success message to the terminal. The input authentication information is the input data, and the database comparison and authentication results are the output data.

[1245] Step 2:

[1246] The server sends an instruction to the terminal that has been successfully authenticated to display a chat interface. Based on this instruction, the terminal displays the chat interface. The chat interface includes a text field for the user to enter their inquiry. The instruction based on successful authentication is input data, and the display of the chat interface is output data.

[1247] Step 3:

[1248] The user enters the inquiry into the chat interface and presses the send button. The terminal sends the entered inquiry to the server. The input of the inquiry is input data, and the transmission of the inquiry is output data.

[1249] Step 4:

[1250] The server analyzes the received inquiry and searches for related information from a knowledge base. This analysis uses natural language processing technology to break down the inquiry into topics and keywords. Based on the analysis results, the server searches for appropriate information from the knowledge base. The inquiry is the input data, and the search results for related information are the output data.

[1251] Step 5:

[1252] The generative AI model generates an initial answer based on the analyzed query content. This AI model uses prompts to generate the optimal answer corresponding to the query content. The analysis results are the input data, and the generated initial answer is the output data.

[1253] Step 6:

[1254] The server receives the initial answer generated by the generative AI model and sends it to the terminal. The terminal displays the received initial answer to the user. The generation of the initial answer is input data, and the displayed answer is output data.

[1255] Step 7:

[1256] If the user is not satisfied with the initial response, he / she inputs and sends a detailed investigation request to the chat interface. The terminal sends the detailed investigation request to the server. The input of the detailed investigation request is input data, and the transmission of the detailed investigation request is output data.

[1257] Step 8:

[1258] The server receives the detailed investigation request and escalates it to the appropriate person. The person in charge conducts the detailed investigation and returns the results to the server. The server receives the detailed response from the person in charge and sends it to the terminal. The receipt of the detailed investigation request is input data, and the provision of the detailed response is output data.

[1259] Step 9:

[1260] The terminal displays the detailed answer to the user. The user can provide feedback on the provided answer. The input of the feedback is input data, and the output of the feedback is output data.

[1261] Step 10:

[1262] The server analyzes the received feedback and updates the knowledge base. The received feedback is the input data, and the updates to the knowledge base are the output data. This allows the system to continuously improve.

[1263] (Application example 1)

[1264] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1265] When employees work at a logistics center, they have a variety of inquiries and things to confirm. However, if employees cannot get prompt and appropriate answers, work delays and efficiency declines. This situation has a negative impact on overall business performance, so a system that can provide prompt and appropriate answers is needed. Another challenge is creating an environment where employees can receive support 24 hours a day, 365 days a year.

[1266] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1267] In this invention, the server includes authentication means for receiving authentication information from a user and comparing the authentication information with a database, means for providing a chat interface to an information device if authentication is successful, means for receiving an inquiry from a user, analyzing the inquiry, and searching for relevant information from a knowledge base, means for generating an initial answer using a generative AI model, means for transmitting the generated initial answer to the information device, means for receiving a request for further investigation from a user and escalating the request to an appropriate person, means for receiving feedback and updating the knowledge base, processing means for responding to inquiries from employees at a logistics center and providing business support, and means for generating prompt sentences for the generative AI model and providing answers. This allows employees to quickly obtain appropriate answers, improving business efficiency and quality.

[1268] A "user authentication means" is a means by which a user enters authentication information such as a user ID and password when accessing a system, and the server compares this information with a database to perform authentication.

[1269] The "chat interface providing means" is a means for providing a chat interface that allows real-time interaction to the user terminal when authentication is successful.

[1270] The "inquiry analysis means" is a means for receiving an inquiry from a user, analyzing the content of the inquiry, and searching for relevant information from the knowledge base.

[1271] A "generative AI model means" is a means for using a generative AI model to automatically generate an initial response based on the analyzed inquiry content.

[1272] The "answer providing means" is a means for transmitting the generated initial answer to the user terminal and displaying it to the user.

[1273] The "detailed investigation escalation means" is a means for receiving a detailed investigation request and escalating it to an appropriate person in case the user is not satisfied with the initial response.

[1274] The "feedback receiving and knowledge base updating means" is a means for receiving feedback from users and updating the knowledge base based on that feedback.

[1275] A "logistics center" is a facility where goods are stored, sorted, shipped, etc., and where a wide variety of operations are carried out all at once.

[1276] "Information device" refers to a hardware device that allows a user to access a system, specifically a smartphone, tablet, or computer.

[1277] A "prompt sentence" is an instruction sentence used to generate a specific answer for a generative AI model, and is input information that enables the generative AI model to generate an appropriate answer based on the instruction sentence.

[1278] The present invention is a system for supporting efficient work of employees in a logistics center. The system includes the following components:

[1279] Hardware and Software Configuration

[1280] Server: This performs the central processing for user authentication, providing a chat interface, analyzing inquiries, generating answers using generative AI models, escalating detailed investigations, receiving feedback, and updating the knowledge base. The server is built using a web framework such as Flask.

[1281] Database: Use a database system such as SQLite to manage user authentication information and knowledge base information.

[1282] Information devices: Users use devices such as smartphones, tablets, and computers to access information.

[1283] System processing flow

[1284] 1. User authentication method:

[1285] The terminal displays a login screen to the user, and the user enters their user ID and password.

[1286] The server receives the entered authentication information and authenticates it by checking it against an SQLite database.

[1287] If the authentication is successful, the server creates a user session and sends a success message to the terminal.

[1288] 2. Chat interface provided by:

[1289] A chat interface is displayed on the user terminal on which authentication has been successful.

[1290] The terminal receives a user's inquiry through a chat interface.

[1291] 3. Query analysis methods:

[1292] The server analyzes the received query and searches for relevant information from a knowledge base.

[1293] 4. Generative AI model means:

[1294] A generative AI model is used to generate an initial answer based on the analyzed query content, specifically using OpenAI's API.

[1295] Example: The prompt sentence "Question about operations at a distribution center: I would like to know the current inventory status. How can I check it?" is input into the generative AI model.

[1296] 5. Means of providing answers:

[1297] The generated initial answer is sent to the terminal and displayed to the user.

[1298] Example: Show the user the initial response "You can check your current inventory status by logging into your Warehouse Management System (WMS) and clicking the 'Check Inventory' tab."

[1299] 6. Further investigation escalation procedures:

[1300] If the user is not satisfied with the initial response, they submit a request for further investigation through the chat interface.

[1301] The server receives the request for further investigation and escalates it to the appropriate person.

[1302] 7. How we receive feedback and update our knowledge base:

[1303] After the user answers, they submit their feedback through the chat interface.

[1304] The server receives the feedback and updates the knowledge base.

[1305] Specific use cases

[1306] Example 1: Inventory management inquiry

[1307] User: "I'd like to know the current inventory status. How can I check it?"

[1308] Prompt generated by the generative AI model: "Question about operations at a distribution center: I would like to know the current inventory status. How can I check it?"

[1309] Example answer: "You can check your current inventory by logging into your Warehouse Management System (WMS) and clicking on the 'Check Inventory' tab."

[1310] In this way, the present invention is a system that enables employees at a logistics center to quickly obtain appropriate answers, thereby improving work efficiency and quality.

[1311] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1312] Step 1:

[1313] A user opens a login screen using an information device and inputs a user ID and password. The input information includes the user ID and password.

[1314] Input: User ID and password

[1315] Operation: The device sends the input information to the server.

[1316] Step 2:

[1317] The server performs authentication by checking the received authentication information against the SQLite database. The server queries the database for the user ID and password.

[1318] Input: User ID and password

[1319] Data processing: The server converts the user ID and password into an SQL query and queries the database.

[1320] Output: Authentication success or failure

[1321] Operation: Generates an authentication result based on the database matching result, and if successful, creates a user session and sends a success message to the terminal.

[1322] Step 3:

[1323] If the authentication is successful, the server provides a chat interface to the information device.

[1324] Input: Successful authentication result

[1325] Operation: The server sends data to the user's device to display a real-time chat interface.

[1326] Step 4:

[1327] The user uses the chat interface to input and send the inquiry.

[1328] Input: Inquiry details

[1329] Operation: The device sends the query to the server.

[1330] Step 5:

[1331] The server analyzes the received query and searches for relevant information from a knowledge base using natural language processing algorithms.

[1332] Input: Inquiry details

[1333] Data processing: Analyze the inquiry content and extract related keywords.

[1334] Output: Search results (information in the knowledge base)

[1335] Operation: Based on the analysis results, the server searches the knowledge base for relevant information.

[1336] Step 6:

[1337] A generative AI model is used to generate an initial answer based on the analyzed query content. Specifically, a prompt sentence is input into the generative AI model to obtain an answer.

[1338] Input: Enquiry and Knowledge Base search results

[1339] Data processing: Convert the query content into a prompt sentence and input it into the generative AI model.

[1340] Output: Initial answer

[1341] How it works: The server receives an initial answer from the generative AI model.

[1342] Step 7:

[1343] The server transmits the generated initial answer to the user terminal and displays it to the user.

[1344] Input: Initial answer

[1345] Action: The server sends an initial response to the device, which displays it in the user's chat interface.

[1346] Step 8:

[1347] If the user is not satisfied with the initial response, they submit a request for further investigation through the chat interface.

[1348] Input: Further investigation request

[1349] Action: The device sends a detailed investigation request to the server.

[1350] Step 9:

[1351] The server receives the request for further investigation and escalates it to the appropriate person.

[1352] Input: Further investigation request

[1353] Behavior: The server generates and sends data to escalate the request to the appropriate person.

[1354] Step 10:

[1355] After the user answers, they submit their feedback through the chat interface.

[1356] Input: Feedback

[1357] Action: The device sends feedback to the server.

[1358] Step 11:

[1359] The server receives the feedback and updates the knowledge base.

[1360] Input: Feedback

[1361] Data processing: Analyze the feedback and generate data to be reflected in the knowledge base.

[1362] Action: The server updates the knowledge base to reflect the new information.

[1363] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1364] This invention provides a system that eliminates delays and confusion experienced by corporate employees in responding to inquiries and improves operational efficiency. This system provides a chat interface with user authentication, automatically generates initial responses using a generative AI model, and escalates to a human for further investigation as needed. It also incorporates an emotion engine that recognizes emotions from user input and the context of the conversation, enabling flexible responses based on the user's emotions.

[1365] Overall system configuration

[1366] The system includes the following elements:

[1367] 1. User authentication method:

[1368] When a user accesses a system, they enter authentication information such as a user ID and password, and the server authenticates them by checking this information against a database.

[1369] 2. Chat interface provided by:

[1370] If the authentication is successful, the server will provide a chat interface on the user's device that is available 24 hours a day, 365 days a year, through which the user can make inquiries.

[1371] 3. Query analysis methods:

[1372] It is a means of receiving an inquiry from a user, analyzing the content of the inquiry, and searching for relevant information from a knowledge base.

[1373] 4. Generative AI model means:

[1374] Using a generative AI model, an initial response is automatically generated based on the analyzed inquiry content.

[1375] 5. Means of providing answers:

[1376] The generated initial answer is sent to the user terminal and displayed to the user.

[1377] 6. Further investigation escalation procedures:

[1378] If the user is not satisfied with the initial response, it is a means to receive further investigation requests and escalate them to the appropriate personnel.

[1379] 7. How we receive feedback and update our knowledge base:

[1380] It is a means of receiving feedback from users and updating the knowledge base.

[1381] 8. Emotion Engine Means:

[1382] The emotion engine recognizes emotions from user input and the context of the conversation, allowing the generative AI model to adjust the tone and content of responses, and also incorporates emotional data into the knowledge base.

[1383] A natural language description of the program's operation

[1384] User Authentication

[1385] 1. The terminal displays a login screen to the user, and the user enters their user ID and password.

[1386] 2. The server receives the entered authentication information and authenticates it by checking it against a database.

[1387] 3. If the authentication is successful, the server creates a user session and sends a success message to the terminal.

[1388] 4. If authentication fails, the server sends a failure message to the terminal and notifies the user.

[1389] Launching the chat interface and sending inquiries

[1390] 1. The server displays a chat interface on the user's device after successful authentication.

[1391] 2. The authenticated user enters and sends their inquiry into the chat interface.

[1392] 3. The server analyzes the received query and searches the knowledge base for relevant information.

[1393] 4. The generative AI model generates an initial answer, which the server sends to the user device.

[1394] Initial response and detailed investigation request

[1395] 1. The device displays the initial answer from the AI ​​to the user.

[1396] 2. If the user is not satisfied with the initial response, they submit a further investigation request through the chat interface.

[1397] 3. The server receives the request for further investigation and escalates it to the appropriate person.

[1398] Emotion Engine Operation

[1399] 1. The server analyzes the user's input and recognizes the user's emotional state using an emotion engine.

[1400] 2. The generative AI model adjusts the tone and content of responses based on the perceived emotional state.

[1401] 3. The recognized emotion data is recorded for future user improvement.

[1402] Feedback and Knowledge Base Updates

[1403] 1. After answering, the user submits feedback through the chat interface.

[1404] 2. The server receives the feedback and updates the knowledge base to reflect it.

[1405] Specific examples

[1406] Example 1: If you want to know the procedure for resetting your password

[1407] 1. The user types "How do I reset my password?" into the chat interface.

[1408] 2. The server receives the query, parses it, and searches its knowledge base for password reset instructions.

[1409] 3. The generative AI model generates a password reset procedure, which the server sends to the user's device.

[1410] 4. The device displays the generated instructions to the user. The emotion engine measures the user's reaction and adjusts the response accordingly.

[1411] 5. The user follows the instructions they receive to reset their password.

[1412] Example 2: Inquiry about the application flow

[1413] 1. The user types in the chat, "Please tell me the flow for applying for a business trip."

[1414] 2. The server searches the knowledge base for the travel request flow and generates a detailed flow using the generative AI model.

[1415] 3. The server sends the generated flow to the user's terminal, where the user can view it.

[1416] 4. The terminal shows the displayed flow to the user. The emotion engine analyzes the user's emotional state and optimizes the answer.

[1417] 5. The user follows the displayed flow to apply for a business trip.

[1418] This system allows users to instantly access the information they need, significantly reducing the time they spend struggling. In addition, the introduction of an emotion engine enables more flexible and human-like responses, improving the user experience.

[1419] The processing flow will be explained below.

[1420] This invention provides a system that eliminates delays and confusion experienced by corporate employees in responding to inquiries and improves operational efficiency. This system provides a chat interface with user authentication, automatically generates initial responses using a generative AI model, and escalates to a human for further investigation as needed. It also incorporates an emotion engine that recognizes emotions from user input and the context of the conversation, enabling flexible responses based on the user's emotions.

[1421] Overall system configuration

[1422] The system includes the following elements:

[1423] 1. User authentication method:

[1424] When a user accesses a system, they enter authentication information such as a user ID and password, and the server authenticates them by checking this information against a database.

[1425] 2. Chat interface provided by:

[1426] If the authentication is successful, the server will provide a chat interface on the user's device that is available 24 hours a day, 365 days a year, through which the user can make inquiries.

[1427] 3. Query analysis methods:

[1428] It is a means of receiving an inquiry from a user, analyzing the content of the inquiry, and searching for relevant information from a knowledge base.

[1429] 4. Generative AI model means:

[1430] Using a generative AI model, an initial response is automatically generated based on the analyzed inquiry content.

[1431] 5. Means of providing answers:

[1432] The generated initial answer is sent to the user terminal and displayed to the user.

[1433] 6. Further investigation escalation procedures:

[1434] If the user is not satisfied with the initial response, it is a means to receive further investigation requests and escalate them to the appropriate personnel.

[1435] 7. How we receive feedback and update our knowledge base:

[1436] It is a means of receiving feedback from users and updating the knowledge base.

[1437] 8. Emotion Engine Means:

[1438] The emotion engine recognizes emotions from user input and the context of the conversation, allowing the generative AI model to adjust the tone and content of responses, and also incorporates emotional data into the knowledge base.

[1439] A natural language description of the program's operation

[1440] Step 1:

[1441] The user accesses the system to display the login screen on their own terminal. The user enters the required authentication information (user ID and password) on the login screen.

[1442] Step 2:

[1443] The terminal transmits the input authentication information to the server. The authentication information is encrypted before transmission.

[1444] Step 3:

[1445] The server checks the received authentication information against the database, checking whether the authentication is successful by checking against the user information stored in the database.

[1446] Step 4:

[1447] If the authentication is successful, the server creates a user session and sends an authentication success message to the terminal. If the authentication fails, the server sends a failure message to the terminal and asks the terminal to enter authentication information again.

[1448] Step 5:

[1449] After receiving the authentication success message, the terminal displays a chat interface to the user, who then inputs an inquiry into the chat interface and sends it to the system.

[1450] Step 6:

[1451] The server receives the inquiry sent by the user, analyzes the inquiry, and searches the knowledge base for relevant information based on the inquiry.

[1452] Step 7:

[1453] The generative AI model generates an initial response based on the query, which is then sent to the device via the server.

[1454] Step 8:

[1455] The terminal displays the generated initial answer to the user, and the server uses an emotion engine to analyze the user's emotional state.

[1456] Step 9:

[1457] The emotion engine recognizes emotions from the user's input and the context of the conversation, and the generative AI model adjusts the tone and content of the response based on the recognized emotional state. The adjusted response is then sent back to the device.

[1458] Step 10:

[1459] If the user is not satisfied with the initial response, the user submits a request for further investigation through the chat interface.

[1460] Step 11:

[1461] The server receives the request for further investigation and escalates it to the appropriate person, who begins the investigation and provides updates on its progress.

[1462] Step 12:

[1463] The terminal displays the status of the escalation to the user in real time, allowing the user to see how their inquiry is being handled.

[1464] Step 13:

[1465] After answering, users can submit feedback through the chat interface, which helps improve the quality and responsiveness of the answers.

[1466] Step 14:

[1467] The server receives feedback from users and updates the knowledge base to reflect that feedback. The contents of the knowledge base are checked regularly, and any new information or corrections that are needed are updated immediately.

[1468] Specific examples

[1469] Example 1: If you want to know the procedure for resetting your password

[1470] 1. The user types "How do I reset my password?" into the chat interface.

[1471] 2. The server receives the query, parses it, and searches its knowledge base for password reset instructions.

[1472] 3. The generative AI model generates a password reset procedure, which the server sends to the user's device.

[1473] 4. The device displays the generated instructions to the user. The emotion engine measures the user's reaction and adjusts the response accordingly.

[1474] 5. The user follows the instructions they receive to reset their password.

[1475] Example 2: Inquiry about the application flow

[1476] 1. The user types in the chat, "Please tell me the flow for applying for a business trip."

[1477] 2. The server searches the knowledge base for the travel request flow and generates a detailed flow using the generative AI model.

[1478] 3. The server sends the generated flow to the user's terminal, where the user can view it.

[1479] 4. The terminal shows the displayed flow to the user. The emotion engine analyzes the user's emotional state and optimizes the answer.

[1480] 5. The user follows the displayed flow to apply for a business trip.

[1481] This system allows users to instantly access the information they need, significantly reducing the time they spend struggling. In addition, the introduction of an emotion engine enables more flexible and human-like responses, improving the user experience.

[1482] Example 2

[1483] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1484] In modern companies, employees must respond to numerous inquiries every day, and delays and confusion in these responses can reduce work efficiency. While there is a demand for systems that allow employees to instantly access the information they need, existing systems lack the flexibility to respond to user feedback. Furthermore, there is the issue of how difficult it is to effectively incorporate user feedback and continuously improve the system.

[1485] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1486] In this invention, the server includes an authentication means for receiving authentication information from a user and comparing the authentication information with a storage device, a means for providing a dialogue interface to a user terminal if authentication is successful, a means for receiving an inquiry from a user, analyzing the inquiry, and searching for relevant information from a knowledge base, a means for generating an initial answer using a generation AI, a means for transmitting the generated initial answer to the user terminal, a means including an emotion engine for analyzing user input and recognizing the user's emotional state, a means for forwarding a request for further investigation to an appropriate person in an escalation process, and a means for receiving feedback and updating the knowledge base. This not only allows employees to access the information they need immediately, but also enables flexible responses to user emotions, and allows for continuous improvement of the system by effectively incorporating feedback.

[1487] An "authentication means" is a system component that has the function of receiving authentication information from a user and authenticating the user by comparing it with a storage device.

[1488] An "interactive interface" is an interface that provides a screen or window for a user to input a query.

[1489] A "knowledge base" is a database system that stores answers and information to user inquiries.

[1490] "Generative AI" is an artificial intelligence model that automatically generates an initial response based on the content of the inquiry.

[1491] An "emotion engine" is a system component that analyzes user input and recognizes the user's emotional state.

[1492] An "escalation mechanism" is a system component that receives a request for further investigation from a user and transfers it to the appropriate person.

[1493] A "feedback mechanism" is a system component that has the function of receiving feedback from users and updating the knowledge base.

[1494] "Storage" is hardware or software used to store authentication information, knowledge base data, etc.

[1495] This system aims to improve work efficiency by eliminating delays and confusion experienced by corporate employees in responding to inquiries. The system provides a dialogue interface that includes user authentication, automatically generates initial responses using generative AI, and escalates to personnel for further investigation as needed. It also incorporates an emotion engine that recognizes emotions from user input and the context of the dialogue, enabling flexible responses based on the user's emotions.

[1496] Overall system configuration

[1497] The system includes the following elements:

[1498] 1. User authentication method:

[1499] When a user accesses a system, they enter authentication information such as a user ID and password, and the server authenticates them by comparing this information with the stored data.

[1500] 2. Means for providing a dialogue interface:

[1501] If the authentication is successful, the server provides the user terminal with a conversation interface available 24 hours a day, 365 days a year, through which the user can make inquiries.

[1502] 3. Query analysis methods:

[1503] It is a means of receiving an inquiry from a user, analyzing the content of the inquiry, and searching for relevant information from a knowledge base.

[1504] 4. Generative AI model means:

[1505] Generative AI is used to automatically generate an initial response based on the analyzed inquiry content.

[1506] 5. Means of providing answers:

[1507] The generated initial answer is sent to the user terminal and displayed to the user.

[1508] 6. Further investigation escalation procedures:

[1509] If the user is not satisfied with the initial response, it is a means to receive further investigation requests and escalate them to the appropriate personnel.

[1510] 7. How we receive feedback and update our knowledge base:

[1511] It is a means of receiving feedback from users and updating the knowledge base.

[1512] 8. Emotion Engine Means:

[1513] The emotion engine recognizes emotions from user input and the context of the conversation, allowing the generative AI to adjust the tone and content of the response. It also reflects the emotional data in the knowledge base.

[1514] Specific examples

[1515] Example 1: If you want to know the procedure for resetting your password

[1516] 1. The user enters "How do I reset my password?" into the conversational interface.

[1517] 2. The server receives the query, parses it, and searches its knowledge base for password reset instructions.

[1518] 3. The generation AI generates a password reset procedure, and the server sends it to the user's device.

[1519] 4. The device displays the generated instructions to the user. The emotion engine measures the user's reaction and adjusts the response accordingly.

[1520] 5. The user follows the instructions they receive to reset their password.

[1521] Example 2: Inquiry about the application flow

[1522] 1. The user enters "Please tell me the flow for applying for a business trip" into the dialogue interface.

[1523] 2. The server searches the knowledge base for the travel request flow and generates a detailed flow using generation AI.

[1524] 3. The server sends the generated flow to the user's terminal, where the user can view it.

[1525] 4. The terminal shows the displayed flow to the user. The emotion engine analyzes the user's emotional state and optimizes the answer.

[1526] 5. The user follows the displayed flow to apply for a business trip.

[1527] This system allows users to instantly access the information they need, significantly reducing the time they spend struggling. In addition, the introduction of an emotion engine enables more flexible and human-like responses, improving the user experience.

[1528] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1529] Program processing flow

[1530] User authentication step

[1531] Step 1:

[1532] The user displays the login screen on the terminal and enters the user ID and password, thereby entering authentication information.

[1533] Step 2:

[1534] The terminal receives the entered authentication information, encrypts it, and sends it to the server. The input is the user ID and password, and the encrypted data is output and sent to the server.

[1535] Step 3:

[1536] The server checks the received authentication information against the storage device. Specifically, it uses an SQL query to search for the corresponding record in the database, compares the entered authentication information with the information in the database, and performs authentication. If the information matches, it outputs a successful authentication status.

[1537] Step 4:

[1538] If authentication is successful, the server creates a user session and sends a success message to the terminal. The input is the authentication success status, and the output is a success message including the session ID. If authentication fails, the server sends a failure message to the terminal and notifies the user. The input is the authentication failure status, and the output is a failure message.

[1539] Enquiry Processing Steps

[1540] Step 5:

[1541] If authentication is successful, the server displays an interactive interface on the user's device. This is achieved by dynamically generating an interactive window using HTML and JavaScript. The input is a session ID, and the output is an interactive interface.

[1542] Step 6:

[1543] The user inputs the inquiry into the dialogue interface and presses the send button, which sends the input inquiry.

[1544] Step 7:

[1545] The server analyzes the received query. It uses a natural language processing (NLP) engine to extract keywords and analyze intent. The query is input and the analysis results are output.

[1546] Step 8:

[1547] Based on the analysis results, the generative AI generates an initial answer. Specifically, the language model generates text based on the prompt sentence. The analysis results are input, and the generated initial answer is output.

[1548] Step 9:

[1549] The server sends the generated initial answer to the user terminal. The generated answer is input, and data to be sent to the user terminal is output. The user terminal displays the received initial answer to the user.

[1550] Further investigation and escalation steps

[1551] Step 10:

[1552] The terminal displays the received initial response to the user, allowing the user to confirm the initial response.

[1553] Step 11:

[1554] If the user is not satisfied with the initial answer, they submit a further investigation request through the dialogue interface, which takes the further investigation request as input and the submitted data as output.

[1555] Step 12:

[1556] The server receives the further investigation request and escalates it to the appropriate person. As part of the escalation process, the request is forwarded to the help desk system and the person in charge is notified. The further investigation request is input, and notification data for the person in charge is output.

[1557] Emotion recognition and response adjustment steps

[1558] Step 13:

[1559] The server analyzes the user's input and recognizes the user's emotional state using an emotion engine. User input is input and emotional data is output.

[1560] Step 14:

[1561] The generative AI adjusts the tone and content of the response based on the recognized emotional state, enabling it to respond appropriately to the user's emotions. It takes emotional data as input and outputs an adjusted response.

[1562] Feedback and Knowledge Base Update Steps

[1563] Step 15:

[1564] After answering, the user sends feedback through the dialogue interface. The feedback content is input and the transmitted data is output.

[1565] Step 16:

[1566] The server receives the feedback and updates the knowledge base to reflect it. It analyzes the feedback and improves the corresponding knowledge base entry. It takes the feedback as input and outputs the updated knowledge base entry.

[1567] (Application example 2)

[1568] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1569] Conventional inquiry response systems for content distribution services can be slow and confusing, resulting in low user satisfaction. Additionally, there are issues with the quality of initial responses being inconsistent and it being difficult to respond flexibly based on user emotions. Therefore, an efficient system that can solve these issues is needed.

[1570] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1571] In this invention, the server

[1572] authentication means for receiving authentication information from a user and comparing the authentication information with a database;

[1573] means for providing an interface to the terminal upon successful authentication;

[1574] means for receiving an inquiry from a user, analyzing the content of the inquiry, and searching for relevant information from a knowledge base;

[1575] a means for generating an initial answer using a generative AI model;

[1576] means for transmitting the generated initial response to a terminal;

[1577] A means of receiving further investigation requests from users and escalating them to the appropriate personnel;

[1578] a means for receiving feedback and updating the knowledge base;

[1579] an emotion analysis means for recognizing emotions from the content of a user's input and the context of the dialogue, and adjusting the content and tone of a response based on the emotions;

[1580] This will improve the efficiency and quality of inquiries and increase user satisfaction.

[1581] "User authentication means" refers to a means for checking authentication information provided by a user against a database and permitting or denying access to the system.

[1582] The "chat interface providing means" is a means for displaying an interface on the user terminal and allowing the user to input an inquiry if user authentication is successful.

[1583] The "inquiry analysis means" is a means for analyzing the content of an inquiry received from a user and searching for relevant information from a knowledge base.

[1584] A "generative AI model" is an artificial intelligence model that automatically generates an initial response based on the user's inquiry.

[1585] The "answer providing means" is a means for transmitting the initial answer generated by the generative AI model to the user terminal.

[1586] The "detailed investigation escalation means" is a means for receiving a detailed investigation request from a user and escalating it to an appropriate person in charge.

[1587] The "feedback receiving means" is a means for receiving feedback from a user.

[1588] A "knowledge base updater" is a means for updating the knowledge base based on received feedback.

[1589] The "emotion analysis means" is a means for recognizing emotions from the content of a user's input and the context of the dialogue, and adjusting the content and tone of the response based on the emotions.

[1590] The present invention provides a system for efficiently responding to user inquiries about content distribution services. Specifically, the system includes the following means and processing steps.

[1591] System Configuration

[1592] 1. User authentication method

[1593] When a user accesses the system, the server receives authentication information such as the user ID and password and authenticates them by checking them against the database. Only if the user is successfully authenticated can the server proceed to the next step.

[1594] 2. Means of providing a chat interface

[1595] If authentication is successful, the server displays a chat interface on the user's device, through which the user can make inquiries. This chat interface is designed to be available 24 hours a day, 365 days a year.

[1596] 3. Query Analysis Methods

[1597] When a user enters a query into the chat interface, the server analyzes the content and searches for relevant information in the knowledge base using natural language processing technology.

[1598] 4. Generative AI Model Means

[1599] The server uses a generative AI model to generate an initial answer based on the query content. This AI model uses a pre-trained algorithm to automatically generate the best answer to the user's question.

[1600] 5. Means of providing answers

[1601] The generated initial response is sent from the server to the user terminal and displayed to the user through the chat interface.

[1602] 6. Further investigation and escalation procedures

[1603] If the user is not satisfied with the initial response generated, they can submit a request for further investigation, which the server will receive and escalate to the appropriate personnel.

[1604] 7. How we receive feedback and update our knowledge base

[1605] If the user provides feedback after answering, the server receives this feedback, which is reflected in the knowledge base and used to improve the quality of future inquiries.

[1606] 8. Emotion analysis method

[1607] The server recognizes emotions from the user's input and the context of the conversation. This emotion analysis engine allows the generative AI model to adjust the content and tone of the response to enable flexible responses based on the user's emotions.

[1608] Hardware and software used

[1609] Hardware: Smartphone

[1610] software:

[1611] UserAuth: User authentication module

[1612] ChatInterface: Chat interface module

[1613] InquiryParser: Inquiry parsing module (natural language processing)

[1614] AIModel: Answer generation module in generative AI model

[1615] SentimentEngine: Sentiment analysis and response adjustment module

[1616] EscalationManager: Further investigation escalation module

[1617] FeedbackManager: Feedback management module

[1618] Specific examples

[1619] Example 1: Inquiry about how to use the new movie recommendations feature

[1620] 1. The user types, "Please tell me the recommended features for new movies" through the chat interface of the smartphone app.

[1621] 2. The server receives and analyzes the query and retrieves relevant information from a knowledge base.

[1622] 3. The AI ​​model generates initial answers for new movie recommendations.

[1623] 4. The sentiment analysis engine analyzes the emotions expressed by the user's input and adjusts the tone and content of the response.

[1624] 5. An initial response is displayed, and if the user is not satisfied, the issue is escalated and handed over to a responsible person.

[1625] 6. Finally, the user leaves feedback, which is added to the knowledge base.

[1626] Example of an input prompt:

[1627] User: "What's the new movie recommendations feature?"

[1628] AI model: "The new movie recommendations feature is automatically generated by an algorithm based on your preferred genres and viewing history. Select the Recommendations tab from the app menu to see a list of new movies."

[1629] Emotion Engine: "If you have any other questions, please feel free to let me know."

[1630] In this way, users can receive prompt and appropriate support, improving the efficiency and quality of inquiries.

[1631] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1632] Step 1:

[1633] The server receives authentication information from the user. The user enters their user ID and password and sends them to the server from their terminal. The server receives this authentication information and authenticates the user by checking it against a database. If authentication is successful, a user session is created.

[1634] Step 2:

[1635] The server displays a chat interface on the terminal of a user who has been successfully authenticated. A chat window opens on the terminal, allowing the user to enter their inquiry. This interface is available 24 hours a day, 365 days a year.

[1636] Step 3:

[1637] The user enters their inquiry into the chat interface and sends it to the server, which then analyzes the received inquiry. Specifically, it uses natural language processing technology to analyze the text and retrieves relevant information from a knowledge base.

[1638] Step 4:

[1639] The server generates an initial answer using a generative AI model based on the analyzed query content. This AI model is a trained algorithm that automatically generates the best answer for the query content. The generated initial answer is stored internally on the server as a prompt text.

[1640] Step 5:

[1641] The server sends the generated initial response to the user's device. The user can view the initial response through the chat interface. In addition, the emotion analysis engine analyzes the user's input and adjusts the tone and content of the response based on the user's emotion. The adjusted response is also displayed on the user's device.

[1642] Step 6:

[1643] If the user is not satisfied with the initial response, they can send a request for further investigation through the chat interface. The server receives this request and escalates it to the appropriate agent. For this purpose, it uses the Escalation Manager module. After the agent takes over, the appropriate action is taken.

[1644] Step 7:

[1645] After answering, users provide feedback, which is sent to the server through the chat interface. The server analyzes the received feedback and updates the knowledge base. The feedback manager module is used to update the knowledge base and improve the quality of future inquiries.

[1646] In this way, users can receive prompt and appropriate support, improving the efficiency and quality of inquiries.

[1647] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1648] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1649] 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 the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1650] [Fourth embodiment]

[1651] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1652] 7, a 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.

[1653] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1654] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1655] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1656] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1657] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1658] The control object 443 includes a display device, LEDs in the eyes, and motors for driving 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1659] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1660] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[1661] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1662] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1663] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1664] The present invention provides a system that improves business efficiency by eliminating delays and confusion in responding to inquiries experienced by corporate employees. The system provides a chat interface with user authentication, automatically generates initial responses using a generative AI model, and escalates to personnel for further investigation as needed.

[1665] Overall system configuration

[1666] The system includes the following elements:

[1667] 1. User authentication method:

[1668] When a user accesses a system, they enter authentication information such as a user ID and password, and the server authenticates them by checking this information against a database.

[1669] 2. Chat interface provided by:

[1670] If the authentication is successful, the server will provide a chat interface on the user's device that is available 24 hours a day, 365 days a year, through which the user can make inquiries.

[1671] 3. Query analysis methods:

[1672] It is a means of receiving an inquiry from a user, analyzing the content of the inquiry, and searching for relevant information from a knowledge base.

[1673] 4. Generative AI model means:

[1674] Using a generative AI model, an initial response is automatically generated based on the analyzed inquiry content.

[1675] 5. Means of providing answers:

[1676] The generated initial answer is sent to the user terminal and displayed to the user.

[1677] 6. Further investigation escalation procedures:

[1678] If the user is not satisfied with the initial response, it is a means to receive further investigation requests and escalate them to the appropriate personnel.

[1679] 7. How we receive feedback and update our knowledge base:

[1680] It is a means of receiving feedback from users and updating the knowledge base.

[1681] A natural language description of the program's operation

[1682] User Authentication

[1683] 1. The terminal displays a login screen to the user, and the user enters their user ID and password.

[1684] 2. The server receives the entered authentication information and authenticates it by checking it against a database.

[1685] 3. If the authentication is successful, the server creates a user session and sends a success message to the terminal.

[1686] 4. If authentication fails, the server sends a failure message to the terminal and notifies the user.

[1687] Launching the chat interface and sending inquiries

[1688] 1. The server displays a chat interface on the user's device after successful authentication.

[1689] 2. The authenticated user enters and sends their inquiry into the chat interface.

[1690] 3. The server analyzes the received query and searches for relevant information from the knowledge base.

[1691] 4. The generative AI model generates an initial answer, which the server sends to the user device.

[1692] Initial response and detailed investigation request

[1693] 1. The device displays the initial answer from the AI ​​to the user.

[1694] 2. If the user is not satisfied with the initial response, they submit a further investigation request through the chat interface.

[1695] 3. The server receives the request for further investigation and escalates it to the appropriate person.

[1696] Feedback and Knowledge Base Updates

[1697] 1. After answering, the user submits feedback through the chat interface.

[1698] 2. The server receives the feedback and updates the knowledge base to reflect it.

[1699] Specific examples

[1700] Example 1: If you want to know the procedure for resetting your password

[1701] 1. The user types "How do I reset my password?" into the chat interface.

[1702] 2. The server receives the query, parses it, and searches its knowledge base for password reset instructions.

[1703] 3. The generative AI model generates a password reset procedure, which the server sends to the user's device.

[1704] 4. The user follows the instructions they receive to reset their password.

[1705] Example 2: Inquiry about the application flow

[1706] 1. The user types in the chat, "Please tell me the flow for applying for a business trip."

[1707] 2. The server searches the knowledge base for the travel request flow and generates a detailed flow using the generative AI model.

[1708] 3. The server sends the generated flow to the user's terminal, where the user can view it.

[1709] 4. The user follows the displayed flow to apply for a business trip.

[1710] This system allows users to instantly access the information they need, significantly reducing the amount of time they spend struggling.

[1711] The processing flow will be explained below.

[1712] Step 1:

[1713] The user accesses the system to display the login screen on their own terminal. The user enters the required authentication information (user ID and password) on the login screen.

[1714] Step 2:

[1715] The terminal transmits the input authentication information to the server. The authentication information is encrypted before transmission.

[1716] Step 3:

[1717] The server checks the received authentication information against the database, checking whether the authentication is successful by checking against the user information stored in the database.

[1718] Step 4:

[1719] If the authentication is successful, the server creates a user session and sends an authentication success message to the terminal. If the authentication fails, the server sends a failure message to the terminal and asks the terminal to enter authentication information again.

[1720] Step 5:

[1721] After receiving the authentication success message, the terminal displays a chat interface to the user, who then inputs an inquiry into the chat interface and sends it to the system.

[1722] Step 6:

[1723] The server receives the inquiry sent by the user, analyzes the inquiry, and searches the knowledge base for relevant information based on the inquiry.

[1724] Step 7:

[1725] The generative AI model generates an initial response based on the query, which is then sent to the device via the server.

[1726] Step 8:

[1727] The terminal displays the generated initial answer to the user, who then confirms the answer.

[1728] Step 9:

[1729] If the user is not satisfied with the initial response, the user submits a request for further investigation through the chat interface.

[1730] Step 10:

[1731] The server receives the request for further investigation and escalates it to the appropriate person, who begins the investigation and provides updates on its progress.

[1732] Step 11:

[1733] The terminal displays the status of the escalation to the user in real time, allowing the user to see how their inquiry is being handled.

[1734] Step 12:

[1735] After answering, users can submit feedback through the chat interface, which helps improve the quality and responsiveness of the answers.

[1736] Step 13:

[1737] The server receives feedback from users and updates the knowledge base to reflect that feedback. The contents of the knowledge base are checked regularly, and any new information or corrections that are needed are updated immediately.

[1738] Example 1

[1739] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1740] There is a need to eliminate delays and confusion in responding to inquiries faced by corporate employees and improve work efficiency. Providing information quickly and accurately is particularly important and directly impacts employee productivity. Conventional methods require a lot of time and effort to respond to inquiries, often overwhelming staff. The present invention is intended to solve these problems.

[1741] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1742] In this invention, the server includes means for receiving authentication information from a user and comparing the authentication information with a database, means for providing a chat interface to the user terminal if authentication is successful, means for receiving an inquiry from a user, analyzing the inquiry content, and searching for relevant information from a knowledge base, means for generating an initial answer using a generative AI model, means for transmitting the generated initial answer to the user terminal, means for receiving a request for detailed investigation from the user and escalating the request to an appropriate person in charge, and means for receiving feedback and updating the knowledge base, thereby enabling prompt and accurate response to inquiries.

[1743] "Authentication information" refers to information such as a user ID and password that a user provides when logging in to a system.

[1744] A "database" is an information system for storing authentication information, inquiry details, feedback, etc.

[1745] A "chat interface" is a communication method that allows users to make inquiries in real time through the system.

[1746] "Inquiry content" refers to the specific content of a question or request that a user makes to the system.

[1747] A "knowledge base" is a collection of data that stores information that the system provides in response to user inquiries.

[1748] A "generative AI model" is an algorithm or software that uses artificial intelligence to automatically generate answers based on analysis results.

[1749] An "initial response" is the first response automatically generated by a generative AI model in response to a user's inquiry.

[1750] A "request for further investigation" is a request where the user is not satisfied with the initial response and requests further detailed information or investigation.

[1751] "Escalation" is the process of passing a request for further investigation to the appropriate person.

[1752] "Feedback" refers to ratings and comments that users make on answers provided.

[1753] This system improves business efficiency by eliminating delays and confusion in responding to inquiries from company employees. The system provides a chat interface with user authentication, automatically generates initial responses using a generative AI model, and escalates to personnel for further investigation as needed.

[1754] Hardware and software used

[1755] The system includes the following elements:

[1756] 1. Server: Responsible for verifying user authentication information, analyzing queries, running generative AI models, and managing the knowledge base. The server uses hardware equipped with high-performance processors and large amounts of memory.

[1757] 2. Database: Includes software for storing data such as user information, authentication information, knowledge base, feedback, etc. This database uses an appropriate database management system such as SQL or NoSQL.

[1758] 3. Terminal: A device through which a user accesses the system, such as a PC, tablet, or smartphone. The terminal communicates with the server via a browser or dedicated app.

[1759] 4. Generative AI Model: An artificial intelligence model used to analyze queries and generate initial responses. This model utilizes advanced algorithms, including natural language processing techniques.

[1760] Detailed System Description

[1761] User Authentication

[1762] The device displays a login screen to the user, prompting them to enter their user ID and password. Once the user enters the information, the device sends it to the server. The server compares the received authentication information with its database to see if there is a matching record. If a match is found, a successful authentication message is sent to the device, and the user is able to use the chat interface.

[1763] Providing a chat interface

[1764] If the user is successfully authenticated, the server sends instructions to the terminal to display a chat interface, through which the user can make inquiries and send their details 24 hours a day, 365 days a year.

[1765] Inquiry analysis and initial response generation

[1766] The server analyzes the inquiry received from the user and searches for relevant information in the knowledge base.The generative AI model then generates an initial answer based on the analyzed content, and the server sends it to the user's device.

[1767] Escalation of further investigation

[1768] If the user is not satisfied with the initial response, they can submit a request for further investigation through the chat interface. The server receives this request and escalates it to the appropriate agent. The agent then investigates and returns the results to the server, which then provides a detailed response to the user.

[1769] Feedback and Knowledge Base Updates

[1770] Users can provide feedback on the answers they provide, and the device sends the feedback information to the server, which stores it in a database and updates the knowledge base, allowing the system to continually improve.

[1771] Specific examples

[1772] Example 1: If you want to know the procedure for resetting your password

[1773] 1. The user types "How do I reset my password?" into the chat interface.

[1774] 2. The terminal sends this query to the server.

[1775] 3. The server analyzes the received inquiry and searches the knowledge base for password reset instructions.

[1776] 4. The generative AI model generates an initial answer for the password reset procedure, and the server sends it to the user's device.

[1777] 5. The user follows the instructions they receive to reset their password.

[1778] Example 2: Inquiry about the application flow

[1779] 1. The user types "Please tell me the process for requesting a business trip" into the chat interface.

[1780] 2. The terminal sends this query to the server.

[1781] 3. The server searches the knowledge base for the travel request flow and generates a detailed flow using the generative AI model.

[1782] 4. The server sends the generated flow to the user's device so that the user can view it.

[1783] 5. The user follows the displayed flow to apply for a business trip.

[1784] In this way, the system can respond to inquiries quickly and accurately, significantly improving user convenience and business efficiency.

[1785] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1786] Step 1:

[1787] The terminal displays a login screen to the user and prompts them to enter their user ID and password. The entered authentication information (user ID and password) is sent to the server. The server compares the received authentication information with its database to see if there is a matching record. If authentication is successful, the server creates a user session and sends an authentication success message to the terminal. The input authentication information is the input data, and the database comparison and authentication results are the output data.

[1788] Step 2:

[1789] The server sends an instruction to the terminal that has been successfully authenticated to display a chat interface. Based on this instruction, the terminal displays the chat interface. The chat interface includes a text field for the user to enter their inquiry. The instruction based on successful authentication is input data, and the display of the chat interface is output data.

[1790] Step 3:

[1791] The user enters the inquiry into the chat interface and presses the send button. The terminal sends the entered inquiry to the server. The input of the inquiry is input data, and the transmission of the inquiry is output data.

[1792] Step 4:

[1793] The server analyzes the received inquiry and searches for related information from a knowledge base. This analysis uses natural language processing technology to break down the inquiry into topics and keywords. Based on the analysis results, the server searches for appropriate information from the knowledge base. The inquiry is the input data, and the search results for related information are the output data.

[1794] Step 5:

[1795] The generative AI model generates an initial answer based on the analyzed query content. This AI model uses prompts to generate the optimal answer corresponding to the query content. The analysis results are the input data, and the generated initial answer is the output data.

[1796] Step 6:

[1797] The server receives the initial answer generated by the generative AI model and sends it to the terminal. The terminal displays the received initial answer to the user. The generation of the initial answer is input data, and the displayed answer is output data.

[1798] Step 7:

[1799] If the user is not satisfied with the initial response, he / she inputs and sends a detailed investigation request to the chat interface. The terminal sends the detailed investigation request to the server. The input of the detailed investigation request is input data, and the transmission of the detailed investigation request is output data.

[1800] Step 8:

[1801] The server receives the detailed investigation request and escalates it to the appropriate person. The person in charge conducts the detailed investigation and returns the results to the server. The server receives the detailed response from the person in charge and sends it to the terminal. The receipt of the detailed investigation request is input data, and the provision of the detailed response is output data.

[1802] Step 9:

[1803] The terminal displays the detailed answer to the user. The user can provide feedback on the provided answer. The input of the feedback is input data, and the output of the feedback is output data.

[1804] Step 10:

[1805] The server analyzes the received feedback and updates the knowledge base. The received feedback is the input data, and the updates to the knowledge base are the output data. This allows the system to continuously improve.

[1806] (Application example 1)

[1807] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1808] When employees work at a logistics center, they have a variety of inquiries and things to confirm. However, if employees cannot get prompt and appropriate answers, work delays and efficiency declines. This situation has a negative impact on overall business performance, so a system that can provide prompt and appropriate answers is needed. Another challenge is creating an environment where employees can receive support 24 hours a day, 365 days a year.

[1809] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1810] In this invention, the server includes authentication means for receiving authentication information from a user and comparing the authentication information with a database, means for providing a chat interface to an information device if authentication is successful, means for receiving an inquiry from a user, analyzing the inquiry, and searching for relevant information from a knowledge base, means for generating an initial answer using a generative AI model, means for transmitting the generated initial answer to the information device, means for receiving a request for further investigation from a user and escalating the request to an appropriate person, means for receiving feedback and updating the knowledge base, processing means for responding to inquiries from employees at a logistics center and providing business support, and means for generating prompt sentences for the generative AI model and providing answers. This allows employees to quickly obtain appropriate answers, improving business efficiency and quality.

[1811] A "user authentication means" is a means by which a user enters authentication information such as a user ID and password when accessing a system, and the server compares this information with a database to perform authentication.

[1812] The "chat interface providing means" is a means for providing a chat interface that allows real-time interaction to the user terminal when authentication is successful.

[1813] The "inquiry analysis means" is a means for receiving an inquiry from a user, analyzing the content of the inquiry, and searching for relevant information from the knowledge base.

[1814] A "generative AI model means" is a means for using a generative AI model to automatically generate an initial response based on the analyzed inquiry content.

[1815] The "answer providing means" is a means for transmitting the generated initial answer to the user terminal and displaying it to the user.

[1816] The "detailed investigation escalation means" is a means for receiving a detailed investigation request and escalating it to an appropriate person in case the user is not satisfied with the initial response.

[1817] The "feedback receiving and knowledge base updating means" is a means for receiving feedback from users and updating the knowledge base based on that feedback.

[1818] A "logistics center" is a facility where goods are stored, sorted, shipped, etc., and where a wide variety of operations are carried out all at once.

[1819] "Information device" refers to a hardware device that allows a user to access a system, specifically a smartphone, tablet, or computer.

[1820] A "prompt sentence" is an instruction sentence used to generate a specific answer for a generative AI model, and is input information that enables the generative AI model to generate an appropriate answer based on the instruction sentence.

[1821] The present invention is a system for supporting efficient work of employees in a logistics center. The system includes the following components:

[1822] Hardware and Software Configuration

[1823] Server: This performs the central processing for user authentication, providing a chat interface, analyzing inquiries, generating answers using generative AI models, escalating detailed investigations, receiving feedback, and updating the knowledge base. The server is built using a web framework such as Flask.

[1824] Database: Use a database system such as SQLite to manage user authentication information and knowledge base information.

[1825] Information devices: Users use devices such as smartphones, tablets, and computers to access information.

[1826] System processing flow

[1827] 1. User authentication method:

[1828] The terminal displays a login screen to the user, and the user enters their user ID and password.

[1829] The server receives the entered authentication information and authenticates it by checking it against an SQLite database.

[1830] If the authentication is successful, the server creates a user session and sends a success message to the terminal.

[1831] 2. Chat interface provided by:

[1832] A chat interface is displayed on the user terminal on which authentication has been successful.

[1833] The terminal receives a user's inquiry through a chat interface.

[1834] 3. Query analysis methods:

[1835] The server analyzes the received query and searches for relevant information from a knowledge base.

[1836] 4. Generative AI model means:

[1837] A generative AI model is used to generate an initial answer based on the analyzed query content, specifically using OpenAI's API.

[1838] Example: The prompt sentence "Question about operations at a distribution center: I would like to know the current inventory status. How can I check it?" is input into the generative AI model.

[1839] 5. Means of providing answers:

[1840] The generated initial answer is sent to the terminal and displayed to the user.

[1841] Example: Show the user the initial response "You can check your current inventory status by logging into your Warehouse Management System (WMS) and clicking the 'Check Inventory' tab."

[1842] 6. Further investigation escalation procedures:

[1843] If the user is not satisfied with the initial response, they submit a request for further investigation through the chat interface.

[1844] The server receives the request for further investigation and escalates it to the appropriate person.

[1845] 7. How we receive feedback and update our knowledge base:

[1846] After the user answers, they submit their feedback through the chat interface.

[1847] The server receives the feedback and updates the knowledge base.

[1848] Specific use cases

[1849] Example 1: Inventory management inquiry

[1850] User: "I'd like to know the current inventory status. How can I check it?"

[1851] Prompt generated by the generative AI model: "Question about operations at a distribution center: I would like to know the current inventory status. How can I check it?"

[1852] Example answer: "You can check your current inventory by logging into your Warehouse Management System (WMS) and clicking on the 'Check Inventory' tab."

[1853] In this way, the present invention is a system that enables employees at a logistics center to quickly obtain appropriate answers, thereby improving work efficiency and quality.

[1854] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1855] Step 1:

[1856] A user opens a login screen using an information device and inputs a user ID and password. The input information includes the user ID and password.

[1857] Input: User ID and password

[1858] Operation: The device sends the input information to the server.

[1859] Step 2:

[1860] The server performs authentication by checking the received authentication information against the SQLite database. The server queries the database for the user ID and password.

[1861] Input: User ID and password

[1862] Data processing: The server converts the user ID and password into an SQL query and queries the database.

[1863] Output: Authentication success or failure

[1864] Operation: Generates an authentication result based on the database matching result, and if successful, creates a user session and sends a success message to the terminal.

[1865] Step 3:

[1866] If the authentication is successful, the server provides a chat interface to the information device.

[1867] Input: Successful authentication result

[1868] Operation: The server sends data to the user's device to display a real-time chat interface.

[1869] Step 4:

[1870] The user uses the chat interface to input and send the inquiry.

[1871] Input: Inquiry details

[1872] Operation: The device sends the query to the server.

[1873] Step 5:

[1874] The server analyzes the received query and searches for relevant information from a knowledge base using natural language processing algorithms.

[1875] Input: Inquiry details

[1876] Data processing: Analyze the inquiry content and extract related keywords.

[1877] Output: Search results (information in the knowledge base)

[1878] Operation: Based on the analysis results, the server searches the knowledge base for relevant information.

[1879] Step 6:

[1880] A generative AI model is used to generate an initial answer based on the analyzed query content. Specifically, a prompt sentence is input into the generative AI model to obtain an answer.

[1881] Input: Enquiry and Knowledge Base search results

[1882] Data processing: Convert the query content into a prompt sentence and input it into the generative AI model.

[1883] Output: Initial answer

[1884] How it works: The server receives an initial answer from the generative AI model.

[1885] Step 7:

[1886] The server transmits the generated initial answer to the user terminal and displays it to the user.

[1887] Input: Initial answer

[1888] Action: The server sends an initial response to the device, which displays it in the user's chat interface.

[1889] Step 8:

[1890] If the user is not satisfied with the initial response, they submit a request for further investigation through the chat interface.

[1891] Input: Further investigation request

[1892] Action: The device sends a detailed investigation request to the server.

[1893] Step 9:

[1894] The server receives the request for further investigation and escalates it to the appropriate person.

[1895] Input: Further investigation request

[1896] Behavior: The server generates and sends data to escalate the request to the appropriate person.

[1897] Step 10:

[1898] After the user answers, they submit their feedback through the chat interface.

[1899] Input: Feedback

[1900] Action: The device sends feedback to the server.

[1901] Step 11:

[1902] The server receives the feedback and updates the knowledge base.

[1903] Input: Feedback

[1904] Data processing: Analyze the feedback and generate data to be reflected in the knowledge base.

[1905] Action: The server updates the knowledge base to reflect the new information.

[1906] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1907] This invention provides a system that eliminates delays and confusion experienced by corporate employees in responding to inquiries and improves operational efficiency. This system provides a chat interface with user authentication, automatically generates initial responses using a generative AI model, and escalates to a human for further investigation as needed. It also incorporates an emotion engine that recognizes emotions from user input and the context of the conversation, enabling flexible responses based on the user's emotions.

[1908] Overall system configuration

[1909] The system includes the following elements:

[1910] 1. User authentication method:

[1911] When a user accesses a system, they enter authentication information such as a user ID and password, and the server authenticates them by checking this information against a database.

[1912] 2. Chat interface provided by:

[1913] If the authentication is successful, the server will provide a chat interface on the user's device that is available 24 hours a day, 365 days a year, through which the user can make inquiries.

[1914] 3. Query analysis methods:

[1915] It is a means of receiving an inquiry from a user, analyzing the content of the inquiry, and searching for relevant information from a knowledge base.

[1916] 4. Generative AI model means:

[1917] Using a generative AI model, an initial response is automatically generated based on the analyzed inquiry content.

[1918] 5. Means of providing answers:

[1919] The generated initial answer is sent to the user terminal and displayed to the user.

[1920] 6. Further investigation escalation procedures:

[1921] If the user is not satisfied with the initial response, it is a means to receive further investigation requests and escalate them to the appropriate personnel.

[1922] 7. How we receive feedback and update our knowledge base:

[1923] It is a means of receiving feedback from users and updating the knowledge base.

[1924] 8. Emotion Engine Means:

[1925] The emotion engine recognizes emotions from user input and the context of the conversation, allowing the generative AI model to adjust the tone and content of responses, and also incorporates emotional data into the knowledge base.

[1926] A natural language description of the program's operation

[1927] User Authentication

[1928] 1. The terminal displays a login screen to the user, and the user enters their user ID and password.

[1929] 2. The server receives the entered authentication information and authenticates it by checking it against a database.

[1930] 3. If the authentication is successful, the server creates a user session and sends a success message to the terminal.

[1931] 4. If authentication fails, the server sends a failure message to the terminal and notifies the user.

[1932] Launching the chat interface and sending inquiries

[1933] 1. The server displays a chat interface on the user's device after successful authentication.

[1934] 2. The authenticated user enters and sends their inquiry into the chat interface.

[1935] 3. The server analyzes the received query and searches the knowledge base for relevant information.

[1936] 4. The generative AI model generates an initial answer, which the server sends to the user device.

[1937] Initial response and detailed investigation request

[1938] 1. The device displays the initial answer from the AI ​​to the user.

[1939] 2. If the user is not satisfied with the initial response, they submit a further investigation request through the chat interface.

[1940] 3. The server receives the request for further investigation and escalates it to the appropriate person.

[1941] Emotion Engine Operation

[1942] 1. The server analyzes the user's input and recognizes the user's emotional state using an emotion engine.

[1943] 2. The generative AI model adjusts the tone and content of responses based on the perceived emotional state.

[1944] 3. The recognized emotion data is recorded for future user improvement.

[1945] Feedback and Knowledge Base Updates

[1946] 1. After answering, the user submits feedback through the chat interface.

[1947] 2. The server receives the feedback and updates the knowledge base to reflect it.

[1948] Specific examples

[1949] Example 1: If you want to know the procedure for resetting your password

[1950] 1. The user types "How do I reset my password?" into the chat interface.

[1951] 2. The server receives the query, parses it, and searches its knowledge base for password reset instructions.

[1952] 3. The generative AI model generates a password reset procedure, which the server sends to the user's device.

[1953] 4. The device displays the generated instructions to the user. The emotion engine measures the user's reaction and adjusts the response accordingly.

[1954] 5. The user follows the instructions they receive to reset their password.

[1955] Example 2: Inquiry about the application flow

[1956] 1. The user types in the chat, "Please tell me the flow for applying for a business trip."

[1957] 2. The server searches the knowledge base for the travel request flow and generates a detailed flow using the generative AI model.

[1958] 3. The server sends the generated flow to the user's terminal, where the user can view it.

[1959] 4. The terminal shows the displayed flow to the user. The emotion engine analyzes the user's emotional state and optimizes the answer.

[1960] 5. The user follows the displayed flow to apply for a business trip.

[1961] This system allows users to instantly access the information they need, significantly reducing the time they spend struggling. In addition, the introduction of an emotion engine enables more flexible and human-like responses, improving the user experience.

[1962] The processing flow will be explained below.

[1963] This invention provides a system that eliminates delays and confusion experienced by corporate employees in responding to inquiries and improves operational efficiency. This system provides a chat interface with user authentication, automatically generates initial responses using a generative AI model, and escalates to a human for further investigation as needed. It also incorporates an emotion engine that recognizes emotions from user input and the context of the conversation, enabling flexible responses based on the user's emotions.

[1964] Overall system configuration

[1965] The system includes the following elements:

[1966] 1. User authentication method:

[1967] When a user accesses a system, they enter authentication information such as a user ID and password, and the server authenticates them by checking this information against a database.

[1968] 2. Chat interface provided by:

[1969] If the authentication is successful, the server will provide a chat interface on the user's device that is available 24 hours a day, 365 days a year, through which the user can make inquiries.

[1970] 3. Query analysis methods:

[1971] It is a means of receiving an inquiry from a user, analyzing the content of the inquiry, and searching for relevant information from a knowledge base.

[1972] 4. Generative AI model means:

[1973] Using a generative AI model, an initial response is automatically generated based on the analyzed inquiry content.

[1974] 5. Means of providing answers:

[1975] The generated initial answer is sent to the user terminal and displayed to the user.

[1976] 6. Further investigation escalation procedures:

[1977] If the user is not satisfied with the initial response, it is a means to receive further investigation requests and escalate them to the appropriate personnel.

[1978] 7. How we receive feedback and update our knowledge base:

[1979] It is a means of receiving feedback from users and updating the knowledge base.

[1980] 8. Emotion Engine Means:

[1981] The emotion engine recognizes emotions from user input and the context of the conversation, allowing the generative AI model to adjust the tone and content of responses, and also incorporates emotional data into the knowledge base.

[1982] A natural language description of the program's operation

[1983] Step 1:

[1984] The user accesses the system to display the login screen on their own terminal. The user enters the required authentication information (user ID and password) on the login screen.

[1985] Step 2:

[1986] The terminal transmits the input authentication information to the server. The authentication information is encrypted before transmission.

[1987] Step 3:

[1988] The server checks the received authentication information against the database, checking whether the authentication is successful by checking against the user information stored in the database.

[1989] Step 4:

[1990] If the authentication is successful, the server creates a user session and sends an authentication success message to the terminal. If the authentication fails, the server sends a failure message to the terminal and asks the terminal to enter authentication information again.

[1991] Step 5:

[1992] After receiving the authentication success message, the terminal displays a chat interface to the user, who then inputs an inquiry into the chat interface and sends it to the system.

[1993] Step 6:

[1994] The server receives the inquiry sent by the user, analyzes the inquiry, and searches the knowledge base for relevant information based on the inquiry.

[1995] Step 7:

[1996] The generative AI model generates an initial response based on the query, which is then sent to the device via the server.

[1997] Step 8:

[1998] The terminal displays the generated initial answer to the user, and the server uses an emotion engine to analyze the user's emotional state.

[1999] Step 9:

[2000] The emotion engine recognizes emotions from the user's input and the context of the conversation, and the generative AI model adjusts the tone and content of the response based on the recognized emotional state. The adjusted response is then sent back to the device.

[2001] Step 10:

[2002] If the user is not satisfied with the initial response, the user submits a request for further investigation through the chat interface.

[2003] Step 11:

[2004] The server receives the request for further investigation and escalates it to the appropriate person, who begins the investigation and provides updates on its progress.

[2005] Step 12:

[2006] The terminal displays the status of the escalation to the user in real time, allowing the user to see how their inquiry is being handled.

[2007] Step 13:

[2008] After answering, users can submit feedback through the chat interface, which helps improve the quality and responsiveness of the answers.

[2009] Step 14:

[2010] The server receives feedback from users and updates the knowledge base to reflect that feedback. The contents of the knowledge base are checked regularly, and any new information or corrections that are needed are updated immediately.

[2011] Specific examples

[2012] Example 1: If you want to know the procedure for resetting your password

[2013] 1. The user types "How do I reset my password?" into the chat interface.

[2014] 2. The server receives the query, parses it, and searches its knowledge base for password reset instructions.

[2015] 3. The generative AI model generates a password reset procedure, which the server sends to the user's device.

[2016] 4. The device displays the generated instructions to the user. The emotion engine measures the user's reaction and adjusts the response accordingly.

[2017] 5. The user follows the instructions they receive to reset their password.

[2018] Example 2: Inquiry about the application flow

[2019] 1. The user types in the chat, "Please tell me the flow for applying for a business trip."

[2020] 2. The server searches the knowledge base for the travel request flow and generates a detailed flow using the generative AI model.

[2021] 3. The server sends the generated flow to the user's terminal, where the user can view it.

[2022] 4. The terminal shows the displayed flow to the user. The emotion engine analyzes the user's emotional state and optimizes the answer.

[2023] 5. The user follows the displayed flow to apply for a business trip.

[2024] This system allows users to instantly access the information they need, significantly reducing the time they spend struggling. In addition, the introduction of an emotion engine enables more flexible and human-like responses, improving the user experience.

[2025] Example 2

[2026] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2027] In modern companies, employees must respond to numerous inquiries every day, and delays and confusion in these responses can reduce work efficiency. While there is a demand for systems that allow employees to instantly access the information they need, existing systems lack the flexibility to respond to user feedback. Furthermore, there is the issue of how difficult it is to effectively incorporate user feedback and continuously improve the system.

[2028] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[2029] In this invention, the server includes an authentication means for receiving authentication information from a user and comparing the authentication information with a storage device, a means for providing a dialogue interface to a user terminal if authentication is successful, a means for receiving an inquiry from a user, analyzing the inquiry, and searching for relevant information from a knowledge base, a means for generating an initial answer using a generation AI, a means for transmitting the generated initial answer to the user terminal, a means including an emotion engine for analyzing user input and recognizing the user's emotional state, a means for forwarding a request for further investigation to an appropriate person in an escalation process, and a means for receiving feedback and updating the knowledge base. This not only allows employees to access the information they need immediately, but also enables flexible responses to user emotions, and allows for continuous improvement of the system by effectively incorporating feedback.

[2030] An "authentication means" is a system component that has the function of receiving authentication information from a user and authenticating the user by comparing it with a storage device.

[2031] An "interactive interface" is an interface that provides a screen or window for a user to input a query.

[2032] A "knowledge base" is a database system that stores answers and information to user inquiries.

[2033] "Generative AI" is an artificial intelligence model that automatically generates an initial response based on the content of the inquiry.

[2034] An "emotion engine" is a system component that analyzes user input and recognizes the user's emotional state.

[2035] An "escalation mechanism" is a system component that receives a request for further investigation from a user and transfers it to the appropriate person.

[2036] A "feedback mechanism" is a system component that has the function of receiving feedback from users and updating the knowledge base.

[2037] "Storage" is hardware or software used to store authentication information, knowledge base data, etc.

[2038] This system aims to improve work efficiency by eliminating delays and confusion experienced by corporate employees in responding to inquiries. The system provides a dialogue interface that includes user authentication, automatically generates initial responses using generative AI, and escalates to personnel for further investigation as needed. It also incorporates an emotion engine that recognizes emotions from user input and the context of the dialogue, enabling flexible responses based on the user's emotions.

[2039] Overall system configuration

[2040] The system includes the following elements:

[2041] 1. User authentication method:

[2042] When a user accesses a system, they enter authentication information such as a user ID and password, and the server authenticates them by comparing this information with the stored data.

[2043] 2. Means for providing a dialogue interface:

[2044] If the authentication is successful, the server provides the user terminal with a conversation interface available 24 hours a day, 365 days a year, through which the user can make inquiries.

[2045] 3. Query analysis methods:

[2046] It is a means of receiving an inquiry from a user, analyzing the content of the inquiry, and searching for relevant information from a knowledge base.

[2047] 4. Generative AI model means:

[2048] Generative AI is used to automatically generate an initial response based on the analyzed inquiry content.

[2049] 5. Means of providing answers:

[2050] The generated initial answer is sent to the user terminal and displayed to the user.

[2051] 6. Further investigation escalation procedures:

[2052] If the user is not satisfied with the initial response, it is a means to receive further investigation requests and escalate them to the appropriate personnel.

[2053] 7. How we receive feedback and update our knowledge base:

[2054] It is a means of receiving feedback from users and updating the knowledge base.

[2055] 8. Emotion Engine Means:

[2056] The emotion engine recognizes emotions from user input and the context of the conversation, allowing the generative AI to adjust the tone and content of the response. It also reflects the emotional data in the knowledge base.

[2057] Specific examples

[2058] Example 1: If you want to know the procedure for resetting your password

[2059] 1. The user enters "How do I reset my password?" into the conversational interface.

[2060] 2. The server receives the query, parses it, and searches its knowledge base for password reset instructions.

[2061] 3. The generation AI generates a password reset procedure, and the server sends it to the user's device.

[2062] 4. The device displays the generated instructions to the user. The emotion engine measures the user's reaction and adjusts the response accordingly.

[2063] 5. The user follows the instructions they receive to reset their password.

[2064] Example 2: Inquiry about the application flow

[2065] 1. The user enters "Please tell me the flow for applying for a business trip" into the dialogue interface.

[2066] 2. The server searches the knowledge base for the travel request flow and generates a detailed flow using generation AI.

[2067] 3. The server sends the generated flow to the user's terminal, where the user can view it.

[2068] 4. The terminal shows the displayed flow to the user. The emotion engine analyzes the user's emotional state and optimizes the answer.

[2069] 5. The user follows the displayed flow to apply for a business trip.

[2070] This system allows users to instantly access the information they need, significantly reducing the time they spend struggling. In addition, the introduction of an emotion engine enables more flexible and human-like responses, improving the user experience.

[2071] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2072] Program processing flow

[2073] User authentication step

[2074] Step 1:

[2075] The user displays the login screen on the terminal and enters the user ID and password, thereby entering authentication information.

[2076] Step 2:

[2077] The terminal receives the entered authentication information, encrypts it, and sends it to the server. The input is the user ID and password, and the encrypted data is output and sent to the server.

[2078] Step 3:

[2079] The server checks the received authentication information against the storage device. Specifically, it uses an SQL query to search for the corresponding record in the database, compares the entered authentication information with the information in the database, and performs authentication. If the information matches, it outputs a successful authentication status.

[2080] Step 4:

[2081] If authentication is successful, the server creates a user session and sends a success message to the terminal. The input is the authentication success status, and the output is a success message including the session ID. If authentication fails, the server sends a failure message to the terminal and notifies the user. The input is the authentication failure status, and the output is a failure message.

[2082] Enquiry Processing Steps

[2083] Step 5:

[2084] If authentication is successful, the server displays an interactive interface on the user's device. This is achieved by dynamically generating an interactive window using HTML and JavaScript. The input is a session ID, and the output is an interactive interface.

[2085] Step 6:

[2086] The user inputs the inquiry into the dialogue interface and presses the send button, which sends the input inquiry.

[2087] Step 7:

[2088] The server analyzes the received query. It uses a natural language processing (NLP) engine to extract keywords and analyze intent. The query is input and the analysis results are output.

[2089] Step 8:

[2090] Based on the analysis results, the generative AI generates an initial answer. Specifically, the language model generates text based on the prompt sentence. The analysis results are input, and the generated initial answer is output.

[2091] Step 9:

[2092] The server sends the generated initial answer to the user terminal. The generated answer is input, and data to be sent to the user terminal is output. The user terminal displays the received initial answer to the user.

[2093] Further investigation and escalation steps

[2094] Step 10:

[2095] The terminal displays the received initial response to the user, allowing the user to confirm the initial response.

[2096] Step 11:

[2097] If the user is not satisfied with the initial answer, they submit a further investigation request through the dialogue interface, which takes the further investigation request as input and the submitted data as output.

[2098] Step 12:

[2099] The server receives the further investigation request and escalates it to the appropriate person. As part of the escalation process, the request is forwarded to the help desk system and the person in charge is notified. The further investigation request is input, and notification data for the person in charge is output.

[2100] Emotion recognition and response adjustment steps

[2101] Step 13:

[2102] The server analyzes the user's input and recognizes the user's emotional state using an emotion engine. User input is input and emotional data is output.

[2103] Step 14:

[2104] The generative AI adjusts the tone and content of the response based on the recognized emotional state, enabling it to respond appropriately to the user's emotions. It takes emotional data as input and outputs an adjusted response.

[2105] Feedback and Knowledge Base Update Steps

[2106] Step 15:

[2107] After answering, the user sends feedback through the dialogue interface. The feedback content is input and the transmitted data is output.

[2108] Step 16:

[2109] The server receives the feedback and updates the knowledge base to reflect it. It analyzes the feedback and improves the corresponding knowledge base entry. It takes the feedback as input and outputs the updated knowledge base entry.

[2110] (Application example 2)

[2111] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2112] Conventional inquiry response systems for content distribution services can be slow and confusing, resulting in low user satisfaction. Additionally, there are issues with the quality of initial responses being inconsistent and it being difficult to respond flexibly based on user emotions. Therefore, an efficient system that can solve these issues is needed.

[2113] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[2114] In this invention, the server

[2115] authentication means for receiving authentication information from a user and comparing the authentication information with a database;

[2116] means for providing an interface to the terminal upon successful authentication;

[2117] means for receiving an inquiry from a user, analyzing the content of the inquiry, and searching for relevant information from a knowledge base;

[2118] a means for generating an initial answer using a generative AI model;

[2119] means for transmitting the generated initial response to a terminal;

[2120] A means of receiving further investigation requests from users and escalating them to the appropriate personnel;

[2121] a means for receiving feedback and updating the knowledge base;

[2122] an emotion analysis means for recognizing emotions from the content of a user's input and the context of the dialogue, and adjusting the content and tone of a response based on the emotions;

[2123] This will improve the efficiency and quality of inquiries and increase user satisfaction.

[2124] "User authentication means" refers to a means for checking authentication information provided by a user against a database and permitting or denying access to the system.

[2125] The "chat interface providing means" is a means for displaying an interface on the user terminal and allowing the user to input an inquiry if user authentication is successful.

[2126] The "inquiry analysis means" is a means for analyzing the content of an inquiry received from a user and searching for relevant information from a knowledge base.

[2127] A "generative AI model" is an artificial intelligence model that automatically generates an initial response based on the user's inquiry.

[2128] The "answer providing means" is a means for transmitting the initial answer generated by the generative AI model to the user terminal.

[2129] The "detailed investigation escalation means" is a means for receiving a detailed investigation request from a user and escalating it to an appropriate person in charge.

[2130] The "feedback receiving means" is a means for receiving feedback from a user.

[2131] A "knowledge base updater" is a means for updating the knowledge base based on received feedback.

[2132] The "emotion analysis means" is a means for recognizing emotions from the content of a user's input and the context of the dialogue, and adjusting the content and tone of the response based on the emotions.

[2133] The present invention provides a system for efficiently responding to user inquiries about content distribution services. Specifically, the system includes the following means and processing steps.

[2134] System Configuration

[2135] 1. User authentication method

[2136] When a user accesses the system, the server receives authentication information such as the user ID and password and authenticates them by checking them against the database. Only if the user is successfully authenticated can the server proceed to the next step.

[2137] 2. Means of providing a chat interface

[2138] If authentication is successful, the server displays a chat interface on the user's device, through which the user can make inquiries. This chat interface is designed to be available 24 hours a day, 365 days a year.

[2139] 3. Query Analysis Methods

[2140] When a user enters a query into the chat interface, the server analyzes the content and searches for relevant information in the knowledge base using natural language processing technology.

[2141] 4. Generative AI Model Means

[2142] The server uses a generative AI model to generate an initial answer based on the query content. This AI model uses a pre-trained algorithm to automatically generate the best answer to the user's question.

[2143] 5. Means of providing answers

[2144] The generated initial response is sent from the server to the user terminal and displayed to the user through the chat interface.

[2145] 6. Further investigation and escalation procedures

[2146] If the user is not satisfied with the initial response generated, they can submit a request for further investigation, which the server will receive and escalate to the appropriate personnel.

[2147] 7. How we receive feedback and update our knowledge base

[2148] If the user provides feedback after answering, the server receives this feedback, which is reflected in the knowledge base and used to improve the quality of future inquiries.

[2149] 8. Emotion analysis method

[2150] The server recognizes emotions from the user's input and the context of the conversation. This emotion analysis engine allows the generative AI model to adjust the content and tone of the response to enable flexible responses based on the user's emotions.

[2151] Hardware and software used

[2152] Hardware: Smartphone

[2153] software:

[2154] UserAuth: User authentication module

[2155] ChatInterface: Chat interface module

[2156] InquiryParser: Inquiry parsing module (natural language processing)

[2157] AIModel: Answer generation module in generative AI model

[2158] SentimentEngine: Sentiment analysis and response adjustment module

[2159] EscalationManager: Further investigation escalation module

[2160] FeedbackManager: Feedback management module

[2161] Specific examples

[2162] Example 1: Inquiry about how to use the new movie recommendations feature

[2163] 1. The user types, "Please tell me the recommended features for new movies" through the chat interface of the smartphone app.

[2164] 2. The server receives and analyzes the query and retrieves relevant information from a knowledge base.

[2165] 3. The AI ​​model generates initial answers for new movie recommendations.

[2166] 4. The sentiment analysis engine analyzes the emotions expressed by the user's input and adjusts the tone and content of the response.

[2167] 5. An initial response is displayed, and if the user is not satisfied, the issue is escalated and handed over to a responsible person.

[2168] 6. Finally, the user leaves feedback, which is added to the knowledge base.

[2169] Example of an input prompt:

[2170] User: "What's the new movie recommendations feature?"

[2171] AI model: "The new movie recommendations feature is automatically generated by an algorithm based on your preferred genres and viewing history. Select the Recommendations tab from the app menu to see a list of new movies."

[2172] Emotion Engine: "If you have any other questions, please feel free to let me know."

[2173] In this way, users can receive prompt and appropriate support, improving the efficiency and quality of inquiries.

[2174] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2175] Step 1:

[2176] The server receives authentication information from the user. The user enters their user ID and password and sends them to the server from their terminal. The server receives this authentication information and authenticates the user by checking it against a database. If authentication is successful, a user session is created.

[2177] Step 2:

[2178] The server displays a chat interface on the terminal of a user who has been successfully authenticated. A chat window opens on the terminal, allowing the user to enter their inquiry. This interface is available 24 hours a day, 365 days a year.

[2179] Step 3:

[2180] The user enters their inquiry into the chat interface and sends it to the server, which then analyzes the received inquiry. Specifically, it uses natural language processing technology to analyze the text and retrieves relevant information from a knowledge base.

[2181] Step 4:

[2182] The server generates an initial answer using a generative AI model based on the analyzed query content. This AI model is a trained algorithm that automatically generates the best answer for the query content. The generated initial answer is stored internally on the server as a prompt text.

[2183] Step 5:

[2184] The server sends the generated initial response to the user's device. The user can view the initial response through the chat interface. In addition, the emotion analysis engine analyzes the user's input and adjusts the tone and content of the response based on the user's emotion. The adjusted response is also displayed on the user's device.

[2185] Step 6:

[2186] If the user is not satisfied with the initial response, they can send a request for further investigation through the chat interface. The server receives this request and escalates it to the appropriate agent. For this purpose, it uses the Escalation Manager module. After the agent takes over, the appropriate action is taken.

[2187] Step 7:

[2188] After answering, users provide feedback, which is sent to the server through the chat interface. The server analyzes the received feedback and updates the knowledge base. The feedback manager module is used to update the knowledge base and improve the quality of future inquiries.

[2189] In this way, users can receive prompt and appropriate support, improving the efficiency and quality of inquiries.

[2190] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[2191] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[2192] 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 the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[2193] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[2194] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[2195] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[2196] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[2197] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists em...

Claims

1. authentication means for receiving authentication information from a user and comparing the authentication information with a database; means for providing a chat interface to the user terminal if authentication is successful; means for receiving an inquiry from a user, analyzing the content of the inquiry, and searching for relevant information from a knowledge base; a means for generating an initial answer using a generative AI model; means for transmitting the generated initial response to a user terminal; A means of receiving further investigation requests from users and escalating them to the appropriate personnel; A means to receive feedback and update the knowledge base; A system including:

2. 10. The system of claim 1, further comprising means for providing a 24 / 7 chat interface after authenticating the user.

3. The system of claim 1 , further comprising means for automatically generating an initial response based on the query content using a generative AI model.

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