system

Abstract

Provide a system. A means for storing debtor information; means for generating messages to the debtor using an automated chat function; a means for analyzing the reply from the debtor; means for generating a next message based on the analysis result; A system comprising a server capable of executing the means.

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] In debt collection operations, traditional manual communication with debtors takes time and effort, making efficient and accurate collection activities a challenge. Large-scale operations dealing with many debtors in particular place a heavy burden on the person in charge, making errors and response delays more likely. Similarly, manual telephone responses require a huge amount of effort, making real-time analysis difficult. There is a need for a way to solve these problems and improve the efficiency and accuracy of debt collection operations. [Means for solving the problem]

[0005] This invention provides a system that includes a means for saving debtor information, a means for generating messages for debtors using an automated chat function, a means for analyzing replies from debtors, a means for generating subsequent messages based on the analysis results, and a server capable of executing the above means. Furthermore, by including a means for saving chat histories with debtors and learning the debtor's tendencies based on subsequent exchanges, and a means for integrating telephone response functions to analyze the content of telephone conversations with debtors in real time and provide appropriate guidance, it is possible to significantly improve the efficiency of debt collection operations and achieve accurate responses.

[0006] "Debtor information" refers to detailed data about the debtor, such as the amount of the debt, contact information, and past payment history.

[0007] "Automated chat function" refers to a system that uses AI to automatically communicate with debtors through text.

[0008] "Means for generating messages" refers to the function of using AI or algorithms to create appropriate reminder messages.

[0009] "Means for analyzing replies from debtors" refers to the function in which AI analyzes messages received from debtors and determines the appropriate response content.

[0010] "Means for generating the next message based on the analysis results" refers to a function that automatically creates the next optimal message based on the content of the previous exchange.

[0011] "Server" refers to the central computer system that executes these means and manages and processes data.

[0012] "Chat history with a debtor" refers to data that records past message exchanges with a debtor.

[0013] "Telephone response function" refers to the function of conducting telephone conversations with debtors and analyzing them in real time.

[0014] "Means of analyzing in real time and providing appropriate guidance" refers to a function in which AI instantly analyzes the content of a conversation during a phone call and shows the person in charge the appropriate response or next step. [Brief explanation of the drawings]

[0015] [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

[0016] 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.

[0017] First, the terms used in the following description will be explained.

[0018] 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).

[0019] 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.

[0020] 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.

[0021] 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.

[0022] 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."

[0023] [First embodiment]

[0024] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0025] 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.

[0026] 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).

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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."

[0036] The present invention is a system for improving the efficiency and accuracy of debt collection operations, and is mainly composed of means for automating interactions with debtors. Specific embodiments of this system are described below.

[0037] System Overview

[0038] The system mainly includes the following components:

[0039] 1. Server: The central processing unit that handles all data management, analysis, and message generation.

[0040] 2. Terminal: The interface operated by the user, which mediates communication with the server.

[0041] 3. User: The person who performs the debt collection work and operates the system.

[0042] System Operation

[0043] Saving debtor information

[0044] The server has a database that stores debtor information, including details such as debtor names, contact details, debt amounts, and payment history.

[0045] Automated chat features

[0046] The system's automated chat function works by allowing users to access the server through their devices and initiate a chat with the debtor. The server uses AI to generate appropriate messages based on pre-set templates and past interactions. These messages are then sent to the debtor via their devices.

[0047] Example: The server generates a message saying, "I'm contacting you about an outstanding payment. Would you like to discuss payment options?" and sends it to the debtor via a terminal.

[0048] Analysis of debtor replies

[0049] When the debtor's reply arrives at the terminal, it is forwarded to the server, which uses AI to analyze the reply and generate the next optimal message. The analysis includes responses based on the debtor's questions and wishes.

[0050] Example: If the debtor replies, "When should I make payment by?", the server generates a message saying, "The payment deadline is (specified date). Please make payment by then." and sends it via the terminal.

[0051] Record and learn from interactions

[0052] All chat history is recorded by the server and used for future analysis and to improve the AI ​​model, allowing the system to respond more accurately over time.

[0053] Future phone call integration

[0054] The system is planned to integrate a telephone response function in the future. When a user starts a call on the device, the server analyzes the call content in real time and provides appropriate guidance.

[0055] Example: During a telephone conversation with a debtor, the server displays a prompt on the user's terminal: "Your current debt balance is (amount). Would you like to schedule your next payment date?"

[0056] Program processing

[0057] The server's main role is to manage and analyze data and generate messages. Specifically, it retrieves debtor information from a database, generates chat messages using an AI engine, analyzes the replies, and then generates the next message based on the analysis results, repeating the entire process.

[0058] The terminal provides an interface for users to operate the system and communicates with the server. Users log in through the terminal, select a debt collection case, and start chat or phone calls. The terminal also receives replies from debtors and forwards them to the server.

[0059] In this way, this system, through cooperation between the server, terminals, and users, streamlines debt collection operations and achieves highly accurate responses.

[0060] The processing flow will be explained below.

[0061] Step 1:

[0062] The server creates a database to store debtor information, including debtor names, contact details, debt amounts, and past payment history.

[0063] Step 2:

[0064] A user logs in to the system from a terminal, which sends the entered user ID and password to the server.

[0065] Step 3:

[0066] The server authenticates the user and, if the correct authentication information is verified, grants the user the corresponding privileges.

[0067] Step 4:

[0068] The user selects a debt collection case through the terminal, and the terminal requests information on the selected case from the server.

[0069] Step 5:

[0070] The server searches for the debtor information and past transaction history for the requested case and sends the relevant data to the terminal.

[0071] Step 6:

[0072] The user activates the chat function on the terminal and instructs it to start an automated chat with the debtor.

[0073] Step 7:

[0074] The device sends a command to start a chat to the server, which uses AI to generate an appropriate prompt message.

[0075] Step 8:

[0076] A message generated by the server (e.g., "Your payment is overdue. Please check.") is sent to the debtor via the terminal.

[0077] Step 9:

[0078] When the debtor replies to the chat, the terminal transfers the reply to the server.

[0079] Step 10:

[0080] The server uses AI to analyze the debtor's reply and generate the most appropriate response message (e.g., "The payment deadline is (specified date). Will you be able to pay by then?").

[0081] Step 11:

[0082] The server generates a response message and sends it to the debtor via the terminal. This process is repeated until the debtor agrees to pay.

[0083] Step 12:

[0084] All chat history is recorded by the server and used for future analysis and to improve the AI ​​model.

[0085] Step 13:

[0086] When a user activates the phone function from their device, the server analyzes the call content in real time and provides appropriate guidance (e.g., "Your current debt balance is (amount). Would you like to schedule your next payment date?").

[0087] Step 14:

[0088] Records of telephone responses are also stored on the server and used as useful data for subsequent debt collection activities.

[0089] In this way, the server, terminal, and user cooperate to progress through each step, improving the efficiency and accuracy of debt collection.

[0090] Example 1

[0091] 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."

[0092] Traditional debt collection operations relied heavily on manual work, resulting in low operational efficiency and a high risk of human error. Furthermore, insufficient recording and analysis of interactions with debtors made it difficult to improve the accuracy and efficiency of the overall system.

[0093] 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.

[0094] In this invention, the server includes a system that includes a means for storing debtor information, a means for generating messages for debtors using an automated chat function, a means for analyzing replies from debtors, and an information processing device capable of executing the above means. This makes it possible to streamline debt collection operations and enable accurate and prompt responses. The system also includes a means for recording a history of interactions with debtors and learning the debtor's response tendencies based on subsequent interactions, allowing the system's response accuracy to improve over time. Furthermore, by integrating telephone response functions and including a means for analyzing the content of telephone conversations with debtors in real time and providing appropriate guidance, the entire dialogue with debtors can be managed in a unified and effective manner.

[0095] "Debtor Information" refers to detailed information such as the debtor's name, contact details, debt amount, and payment history.

[0096] "Automated chat function" refers to a function that uses artificial intelligence to automatically exchange messages with debtors.

[0097] "Message generation means" refers to a function that generates appropriate messages based on pre-set templates and past interactions.

[0098] "Reply analysis means" refers to a function that analyzes a reply message from a debtor and generates the next most appropriate message based on the content of the reply message.

[0099] The term "information processing device" refers to a central processing unit capable of executing the above-mentioned means, and a device that manages and analyzes data.

[0100] "Chat history" refers to a record of past message exchanges with a debtor.

[0101] "Response tendencies" refer to the behavioral patterns and tendencies exhibited by the debtor in past interactions.

[0102] "Telephone response function" refers to the function that integrates telephone conversations with debtors, analyzes the content of the conversations in real time, and provides appropriate guidance.

[0103] This invention is a system for improving the efficiency and accuracy of debt collection operations, and is mainly composed of a means for automating interactions with debtors. This system is mainly composed of a server, terminals, and users.

[0104] Server Roles and Functions

[0105] The Server is the central processing unit responsible for all data management, analysis, and message generation. The Server has the following main responsibilities:

[0106] 1. Storage of debtor information

[0107] The server has a database that stores debtor information, including details such as debtor names, contact details, debt amounts, and payment history.

[0108] 2. Message Creation

[0109] The server utilizes an automated chat function to generate messages for the debtor using an AI engine (e.g., OpenAI® GPT-4®), including the ability to generate messages based on pre-defined templates and past interactions.

[0110] 3. Reply Analysis

[0111] The reply from the debtor is received by the server and analyzed using an AI engine. Based on the results of the analysis, the next optimal message is generated.

[0112] 4. Chat history recording

[0113] All chat history is recorded by the server and used for future analysis and to improve the AI ​​model.

[0114] 5. Telephone integration

[0115] In the future, telephone response functions will also be integrated, making it possible to analyze call content in real time and provide appropriate guidance.

[0116] Device roles and functions

[0117] The terminal provides an interface for users to operate the system and communicates with the server. Specifically, it has the following functions:

[0118] 1. User authentication and login

[0119] A user logs into the system using a terminal and sends authentication information to the server, which verifies this information and authenticates the user.

[0120] 2. Display of debtor information

[0121] When a user searches for specific debtor information, the terminal sends this request to the server, which retrieves the relevant information and displays it on the terminal.

[0122] 3. Start chatting

[0123] The user selects a chat function on the terminal to start chatting with the debtor.

[0124] 4. Sending and receiving messages

[0125] The terminal sends messages generated by the server to the debtor and also forwards replies from the debtor to the server.

[0126] 5. Telephone call assistance

[0127] During a call, the server analyzes the call content in real time and displays information on the device that provides appropriate guidance.

[0128] User operations

[0129] The user operates the system through a terminal, specifically by performing the following steps:

[0130] 1. Log in

[0131] The user logs in from the terminal and starts the debt collection operation.

[0132] 2. Obtaining debtor information

[0133] The user selects a specific debtor on the terminal and retrieves that information from the server.

[0134] 3. Start chatting

[0135] The user initiates a conversation with the debtor using the chat function.

[0136] 4. Chat and phone support

[0137] The user sends a message generated by the server to the debtor and waits for a reply from the debtor. When answering the phone, the user also receives guidance from the server.

[0138] Examples and prompts

[0139] As a concrete example, the server generates a message saying, "I'm contacting you regarding an outstanding payment. Can we discuss payment methods?" and sends it to the debtor via the terminal. If the debtor replies, "By when should I make payment?", the server generates a message saying, "The payment deadline is (specified date). We ask that you make payment by then," and sends it via the terminal.

[0140] Example prompt sentence:

[0141] Generate a "payment confirmation" message for a debt collection case. The debtor asks, "When is the payment due?"

[0142] In this way, this system streamlines debt collection operations by linking servers, terminals, and users, and enables highly accurate responses.

[0143] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0144] Step 1:

[0145] A user logs in to a terminal

[0146] Specific behavior:

[0147] The user opens a login screen through the terminal and enters authentication information, which is then sent to the server by the terminal (user ID and password).

[0148] Data processing / calculation:

[0149] The server compares the received authentication information with the user information in its database. If the comparison is successful, the user is authenticated and the dashboard screen is sent to the terminal.

[0150] Input: User ID, Password

[0151] Output: User's dashboard screen

[0152] Step 2:

[0153] Obtain debtor information

[0154] Specific behavior:

[0155] The user uses the terminal's search function to input a specific debtor ID to search for debtor information. The terminal sends this request to the server.

[0156] Data processing / calculation:

[0157] The server retrieves the relevant debtor information (name, contact details, debt amount, payment history, etc.) from the database based on the debtor ID and sends it to the terminal.

[0158] Input: Debtor ID

[0159] Output: Screen showing debtor details

[0160] Step 3:

[0161] Start a chat

[0162] Specific behavior:

[0163] The user clicks a button on the terminal to start a chat with a particular debtor, and the terminal notifies the server of this action.

[0164] Data processing / calculation:

[0165] The server performs initial settings to start a chat session and displays a chat window on the terminal.

[0166] Input: Request to start chat

[0167] Output: Show chat window

[0168] Step 4:

[0169] AI-generated initial message

[0170] Specific behavior:

[0171] The server uses an AI engine to generate an initial message for the debtor, which it then sends to the terminal, which then sends it to the debtor.

[0172] Data processing / calculation:

[0173] The AI ​​engine (e.g., OpenAI GPT-4) generates optimal messages based on pre-defined templates and past interactions.

[0174] Input: Request for initial message creation

[0175] Output: The generated message (e.g., "I'm contacting you regarding an outstanding payment. Would you like to discuss payment options?")

[0176] Step 5:

[0177] Receiving a reply from the debtor

[0178] Specific behavior:

[0179] When the debtor replies, the message is delivered to the terminal and then forwarded to the server.

[0180] Data processing / calculation:

[0181] The server passes the received reply to the AI ​​engine, which analyzes the content and generates the next optimal message based on the analysis results.

[0182] Input: Debtor's reply message (e.g., "When do I need to make payment?")

[0183] Output: Analysis results and next message

[0184] Step 6:

[0185] AI-based reply analysis and next message generation

[0186] Specific behavior:

[0187] The server uses an AI engine to analyze the reply from the debtor and generate the next message based on the content of the reply, which is then sent to the terminal, which then sends it to the debtor.

[0188] Data processing / calculation:

[0189] The AI ​​engine analyzes the reply content and generates the optimal response message based on the analysis results.

[0190] Input: The reply message to be parsed

[0191] Output: The next message generated (e.g. "Payment is due by (specified date). We ask that you make payment by that date.")

[0192] Step 7:

[0193] Chat history recording

[0194] Specific behavior:

[0195] All messages exchanged through the device are recorded on the server.

[0196] Data processing / calculation:

[0197] The server stores each chat message in a database for subsequent analysis and to improve the AI ​​model.

[0198] Input: Each exchanged message

[0199] Output: Recorded chat history

[0200] Step 8:

[0201] Future phone call integration

[0202] Specific behavior:

[0203] When a user starts answering a call through the terminal, the server analyzes the contents of the call in real time and provides appropriate guidance to the terminal.

[0204] Data processing / calculation:

[0205] The server analyzes the call content in real time, generates guidance using an AI engine, and notifies the device.

[0206] Input: Phone call details

[0207] Output: Real-time guidance (e.g., "Your current debt balance is (amount). Would you like to schedule your next payment date?")

[0208] (Application example 1)

[0209] 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."

[0210] Conventional debt collection systems often require manual interaction with debtors, making it difficult to efficiently manage a large number of debtors. Furthermore, responses via telephone require real-time communication, placing a heavy burden on operators. Furthermore, there is a lack of systems that can flexibly respond using the latest interface devices, such as smartphones and smart glasses, making it necessary to improve the efficiency of debt collection.

[0211] 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.

[0212] In this invention, the server includes a means for storing debtor information, a means for generating messages for debtors using an automated chat function, a means for analyzing replies from debtors, a means for generating subsequent messages based on the analysis results, a means for analyzing telephone conversations with debtors in real time and providing appropriate guidance, and an interface means using a smartphone or smart glasses. This significantly improves the efficiency of interactions with debtors, enables real-time responses and hands-free operation, and enhances the accuracy and efficiency of the entire debt collection process.

[0213] "Means for storing debtor information" refers to a data storage function that accumulates and manages information about debtors (such as names, contact details, debt amounts, payment history, etc.).

[0214] "Means of generating messages to debtors using an automated chat function" refers to a function that utilizes AI technology to refer to pre-set templates and past interactions, and automatically creates and sends appropriate messages to debtors.

[0215] "Means for analyzing replies from debtors" refers to a function that uses AI to analyze the content of replies sent by debtors via chat or email, and understand their meaning and intent.

[0216] The "means for generating the next message based on the analysis result" refers to a function for generating the optimal next message based on the result of analyzing the reply content of the debtor and sending it to the debtor.

[0217] "Means of analyzing the content of telephone conversations with debtors in real time and providing appropriate guidance" refers to a function that uses AI technology to analyze the content of conversations with debtors in real time and immediately instruct the operator on the appropriate response method.

[0218] "Interface means using a smartphone or smart glasses" refers to an operation interface that allows a user to access the system using a smartphone or smart glasses and perform interactions with debtors.

[0219] "Server" refers to a central processing unit that manages data, analyzes data, and generates messages for the entire system, and has computing resources that can execute all means.

[0220] This invention is a system for improving the efficiency and accuracy of debt collection operations, and is mainly composed of means for automating interactions with debtors. Specific embodiments of this system are described below.

[0221] System configuration

[0222] The system mainly includes the following components:

[0223] 1. Server: The central processing unit that manages all data, performs analysis, and generates messages.

[0224] 2. Terminal: The interface operated by the user, which mediates communication with the server.

[0225] 3. User: The person who performs debt collection work and operates the system.

[0226] 4. Smart devices: Interface methods using smartphones and smart glasses.

[0227] System Operation

[0228] Saving debtor information

[0229] The server has a database that stores debtor information, including details such as debtor names, contact details, debt amounts, and payment history.

[0230] Automated chat features

[0231] The system's automated chat function works by allowing users to access the server through their terminal and start a chat with the debtor. The server uses AI to generate an appropriate message based on pre-set templates and past interactions. This message is then sent to the debtor through the terminal. For example, the server generates a message that reads, "I'm contacting you regarding an outstanding payment. Would you like to discuss payment options?" and sends it to the debtor through the terminal.

[0232] Analysis of debtor replies

[0233] When the reply from the debtor arrives at the terminal, the contents are transferred to the server. The server uses AI to analyze the reply and generate the next optimal message. The analysis includes a response based on the debtor's questions and wishes. For example, if the debtor replies, "When should I make payment by?" the server will generate a message saying, "The payment deadline is (specified date). We ask that you make payment by then," and send it via the terminal.

[0234] Record and learn from interactions

[0235] All chat history is recorded by the server and used for future analysis and to improve the AI ​​model, allowing the system to respond more accurately over time.

[0236] Telephone response function

[0237] The server also integrates a telephone response function. When a user starts a telephone response through the terminal, the server analyzes the content of the call in real time and provides appropriate guidance. For example, while on a telephone call with a debtor, the server displays a prompt on the user's terminal saying, "Your current debt balance is (amount). Would you like to schedule your next payment date?"

[0238] Use of smart devices

[0239] The interface using a smartphone or smart glasses allows users to operate hands-free, improving work efficiency. For example, users can respond by voice input while visually checking chat content and debtor information.

[0240] Hardware and software used

[0241] Hardware: Smartphones, smart glasses (e.g., Google® Glass®)

[0242] Software: Online databases (e.g., Firebase), AI message generation engines (e.g., OpenAI GPT-3 (registered trademark))

[0243] Examples of prompt statements

[0244] Debtor information: Name - Common name, Debt amount - 100000 yen, Payment history - ['2023-01-01: 50000', '2023-02-01: 0']

[0245] Message: Please send the following message to your debtor:

[0246] "I'm contacting you regarding an outstanding payment. Can we discuss payment options?"

[0247] In this way, this system works in cooperation with the server, terminals, and users to streamline debt collection operations and achieve highly accurate responses.

[0248] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0249] Step 1:

[0250] Obtaining debtor information

[0251] Input: A user logs in through a terminal and requests information about a particular debtor.

[0252] Data processing / data calculation: The server retrieves details such as the debtor's name, contact details, debt amount, and payment history from a database (e.g., Firebase).

[0253] Output: Send the debtor's information to the user's terminal.

[0254] Specific operation: A user logs in and searches for information on a specific debtor, and the server retrieves the information from the database and displays it on the terminal.

[0255] Step 2:

[0256] Automatic chat message generation

[0257] Input: Debtor information obtained by the server and instructions to start chat.

[0258] Data processing / data calculation: The server uses a generative AI model (e.g., OpenAI GPT-3) to generate a new message based on the debtor's information and past interactions. It creates a prompt and inputs it into the AI ​​model to generate the message.

[0259] Output: The generated message is sent to the debtor via the user's terminal.

[0260] Specific operation: A prompt sentence is generated, and based on that, an AI model is asked to generate a message, and the result is sent to the debtor.

[0261] Step 3:

[0262] Receiving and analyzing debtor replies

[0263] Input: Reply message from debtor.

[0264] Data processing / data calculation: The server analyzes the reply message received from the debtor. Using an AI model, it understands the intent of the reply and the question, and then extracts the necessary information.

[0265] Output: The data needed to generate the next best message based on the analyzed results.

[0266] Specific operation: Receives a reply from the debtor, requests the AI ​​model to analyze it, and uses the analysis results to generate the next message.

[0267] Step 4:

[0268] Generate next message

[0269] Input: Instructions for generating the next message based on the analysis results.

[0270] Data processing / data calculation: The server uses the generative AI model again to generate the next appropriate message based on the analysis results of the reply content. The previous prompt and the analysis results are input into the AI ​​model as the new prompt.

[0271] Output: The following generated message is sent to the debtor via the user's terminal.

[0272] Specific operation: A new prompt sentence is generated based on the analysis results, which is then input into the AI ​​model to generate a new message and send it to the debtor.

[0273] Step 5:

[0274] Record and learn from interactions

[0275] Input: Each chat message and its response.

[0276] Data processing / data calculation: All chat history is recorded in a database by the server and stored for future analysis and to improve the AI ​​model.

[0277] Output: Saved chat history data.

[0278] Specific behavior: The entire interaction history is periodically saved to a database and used in the future to learn response trends and improve the accuracy of the model.

[0279] Step 6:

[0280] Phone call integration and real-time analysis

[0281] Input: The instructions and call content for the user to initiate a phone call.

[0282] Data processing / data calculation: The server recognizes the voice of the call in real time and analyzes the content using an AI model. Based on the analysis results, the server provides appropriate guidance to the user.

[0283] Output: Guidance messages generated in real time.

[0284] Specific operation: The contents of the call are converted into text data using a voice recognition engine, and guidance is displayed on the user's device based on the analysis results.

[0285] Step 7:

[0286] Use of smart devices

[0287] Input: User instructions via smartphone or smart glasses.

[0288] Data processing / data calculation: The user inputs instructions through a smart device, and the server provides appropriate messages and information based on those instructions.

[0289] Output: Appropriate response message to the debtor and visual feedback to the user.

[0290] Specific actions: The user interacts with the system using voice input or touch, and relevant information and messages are displayed in real time.

[0291] 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.

[0292] The present invention is a system that combines an emotion engine that recognizes the emotions of users and debtors in order to improve the efficiency of debt collection operations and achieve accurate and effective responses. A specific embodiment of this system will now be described.

[0293] System configuration

[0294] The system consists of the following main components:

[0295] 1. Server: As the central processing unit, it manages data, generates messages, analyzes emotions, and performs other processing.

[0296] 2. Terminal: The interface operated by the user, which mediates communication with the server.

[0297] 3. User: A person who works in debt collection and is responsible for operating the system.

[0298] Program processing

[0299] Saving and managing debtor information

[0300] The server has a database that stores debtor information, including debtor names, contact details, debt amounts, and past payment history.

[0301] Chat function launch and automatic message generation

[0302] The user logs in to the system using a terminal and selects a debt collection case. The terminal then requests information about the selected case from the server, and the server searches for the relevant debtor information and past transaction history and sends it to the terminal.

[0303] When a user activates the chat function, the server uses an automated chat function to generate an appropriate reminder message and transmit it to the debtor via the terminal.

[0304] Example: The server generates a message saying, "I'm contacting you about an outstanding payment. Would you like to discuss payment options?" and sends it to the debtor via a terminal.

[0305] Parsing the reply from the debtor and generating the next message

[0306] When the reply from the debtor arrives at the device, it is forwarded to the server. The server's AI analyzes the reply and generates the optimal response message. At the same time, the emotion engine analyzes the reply and evaluates the debtor's emotional state.

[0307] Example: If a debtor replies, "When should I make payment by?", the server generates a message saying, "The payment deadline is (specified date). We ask that you make payment by then," and sends it through the terminal. If the emotion engine detects feelings of anxiety or confusion from the debtor's message, it can adjust the response accordingly, adding a message that provides reassurance, such as, "Please contact us at any time if you have any questions or concerns."

[0308] Emotion analysis using an emotion engine

[0309] The emotion engine analyzes the emotional state of the user and the debtor in real time. For example, it can detect emotions such as stress, anger, or confusion from the debtor's replies and voice data, and provide appropriate guidance to the server based on the results.

[0310] Record and learn from interactions

[0311] The server records all interactions and uses them for subsequent analysis and to improve the AI ​​model, allowing the system to learn each borrower's tendencies and respond more accurately.

[0312] Call handling integration and real-time sentiment analysis

[0313] As a future extension, when a user initiates a call from their device, the server will analyze the content of the call in real time and use an emotion engine to provide guidance based on the emotional state of the debtor and the user.

[0314] Example: During a telephone conversation with a debtor, the server displays a prompt on the user's device, such as "Your current debt balance is (amount). Would you like to schedule your next payment date?" If the server detects that the debtor is confused or anxious from the content of the call, it generates a message that provides more reassurance, such as "If you need additional assistance, we are here to help."

[0315] In this way, this system streamlines debt collection operations and achieves accurate and effective responses through collaboration between servers, terminals, and users and processing that incorporates an emotion engine.

[0316] The processing flow will be explained below.

[0317] Step 1:

[0318] The server creates a database that stores debtor information, including debtor names, contact details, debt amounts, and past payment history.

[0319] Step 2:

[0320] A user logs in to the system using a terminal, which provides an interface for entering a user ID and password and sends the authentication information to the server.

[0321] Step 3:

[0322] The server authenticates the user, and if the authentication is successful, grants the user the corresponding privileges and provides the information that the user can access.

[0323] Step 4:

[0324] The user selects a debt collection case on the terminal. The terminal requests information on the selected case from the server, and the server obtains the relevant debtor information and transaction history and sends them to the terminal.

[0325] Step 5:

[0326] The user activates the chat function through the terminal, and a command to start chat is sent from the terminal to the server.

[0327] Step 6:

[0328] The server uses an automated chat function to generate the initial reminder message, which is then sent to the debtor via their terminal.

[0329] Example: The server generates a message saying, "The payment deadline has passed. Please confirm." and sends it to the debtor via the terminal.

[0330] Step 7:

[0331] When the debtor replies via chat, the reply message arrives at the terminal, which then forwards the reply to the server.

[0332] Step 8:

[0333] The server uses AI to analyze the debtor's reply, and an emotion engine analyzes the debtor's emotions and determines the appropriate response along with the reply content.

[0334] Example: If the debtor replies, "I will explain why my payment is late," the server generates a message saying, "I understand your post. By when can you make your payment?" and sends it through the terminal. If the emotion engine detects the debtor's anxiety, it generates an additional message saying, "If you have any questions or concerns, please feel free to contact us."

[0335] Step 9:

[0336] The next response message is generated and sent to the debtor via the terminal. This process is repeated until the debtor pays or a solution is found.

[0337] Step 10:

[0338] All chat history is recorded by the server, which allows the system to use it as reference data for subsequent debt collection activities.

[0339] Step 11:

[0340] When a user activates the call handling function on their device, the device sends the instruction to the server, which analyzes the call content in real time and uses an emotion engine to generate a response based on the user's emotional state.

[0341] Example: The server generates a prompt that says, "Your remaining debt is (amount). Would you like to schedule your next payment?" and displays it on the terminal. If the debtor expresses anxiety during the call, the emotion engine generates a message that says, "We're here to help you so you can talk with peace of mind."

[0342] Step 12:

[0343] The entire call conversation is also recorded on the server and stored for future analysis and to improve the AI ​​model.

[0344] In this way, a system can be constructed that realizes debt collection operations more efficiently and accurately through processing that combines the server, terminals, and users in cooperation with an emotion engine.

[0345] Example 2

[0346] 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."

[0347] In debt collection work, communication with debtors is extremely important, but doing it manually takes a lot of time and effort. Furthermore, sending messages mechanically without considering the debtor's emotions can result in ineffective communication. Furthermore, when dealing with debtors over the phone, it is difficult to analyze their emotions in real time and respond appropriately. There is a need for a system that can solve these problems and achieve efficient and effective debt collection.

[0348] 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. In this invention, the server includes a means for saving debtor information, a means for generating a message for the debtor using an automated chat function, a means for analyzing a reply from the debtor, a means for generating a next message based on the analysis result, a means for analyzing the debtor's emotions using an emotion analysis engine and adjusting the response message based on the analysis result, a means operated by the user through a terminal, and a server capable of executing the above means. This enables effective communication that takes the debtor's emotions into consideration, thereby realizing efficient debt collection.

[0349] "Debtor information" is data including the debtor's name, contact information, debt amount, payment history, etc.

[0350] An "automated chat function" is a function that automatically communicates with debtors using messages generated by a server.

[0351] "Means for analyzing replies from debtors" refers to AI models or algorithms used to receive messages from debtors and analyze their contents.

[0352] The "means for generating the next message based on the analysis result" is a function for automatically generating the next response based on the analysis result of the message from the debtor.

[0353] An "emotion analysis engine" is a technology that analyzes emotions from debtors' messages and call content and generates appropriate responses based on the results.

[0354] "User" refers to a person who performs debt collection work and operates the system.

[0355] A "terminal" is an interface device operated by a user, and includes a computer or mobile device for communicating with a server.

[0356] A "server" is a central processing unit that performs data management, message generation, analysis, and other processing.

[0357] This invention is a system that combines an emotion engine that recognizes the emotions of users and debtors in order to streamline debt collection operations and achieve accurate and effective responses. This system operates through the cooperation of a server, terminals, and users.

[0358] System configuration

[0359] The system consists of the following main components:

[0360] 1. Server: A central processing unit that manages data, generates messages, analyzes emotions, and performs other processing. The server has a database for storing debtor information and has the ability to record interactions with debtors.

[0361] 2. Terminal: The interface operated by the user, which mediates communication with the server. The terminal runs software that allows the user to log in and operate the system.

[0362] 3. User: The person in charge of debt collection operations who operates the system.

[0363] System Features

[0364] An embodiment of the system is described in detail below.

[0365] Saving and managing debtor information

[0366] The server has a database that stores debtor information, including names, contact details, debt amounts, payment history, etc. When a user enters information from a terminal, the server stores the information in the database.

[0367] Chat function launch and automatic message generation

[0368] A user logs in to the system using a terminal and selects a debt collection case. The terminal requests case information from the server, which then searches for the relevant debtor information and past communication history and sends them to the terminal. When the user activates the chat function, the server uses an automated chat function to generate an appropriate message and sends it to the debtor via the terminal.

[0369] Examples:

[0370] The server generates a message saying, "We're contacting you regarding an outstanding payment. Would you like to discuss payment options?" and sends it to the debtor via the terminal.

[0371] Parsing the reply from the debtor and generating the next message

[0372] When the debtor's reply arrives at the device, it is forwarded to the server. The server's AI analyzes the reply and generates the optimal response message. At the same time, the emotion engine analyzes the debtor's reply and evaluates their emotional state.

[0373] Examples:

[0374] If the debtor replies, "By when should I make payment?", the server generates a message saying, "The payment deadline is (specified date). Please make payment by then." and sends it through the terminal.

[0375] If the emotion engine detects feelings of anxiety or confusion in the debtor's message, it adds a reassuring message such as, "If you have any questions or concerns, please contact us at any time."

[0376] Emotion analysis using an emotion analysis engine

[0377] The emotion engine analyzes the emotional state of the user and the debtor in real time. For example, it can detect emotions such as stress, anger, or confusion from the debtor's replies and voice data, and provide appropriate guidance to the server based on the results.

[0378] Record and learn from interactions

[0379] The server records all interactions and uses them for subsequent analysis and to improve the AI ​​model, allowing the system to learn each borrower's tendencies and respond more accurately.

[0380] Call handling integration and real-time sentiment analysis

[0381] When a user initiates a call from a terminal, the server analyzes the contents of the call in real time and uses an emotion engine to provide guidance based on the emotional state of the debtor and the user.

[0382] Examples:

[0383] The server displays a prompt on the user's terminal such as "Your current debt balance is (amount). Would you like to schedule your next payment date?"

[0384] If the call detects confusion or anxiety from the debtor, it generates a reassuring message such as, "If you need additional assistance, we're here to help."

[0385] As a result, this system streamlines debt collection operations and achieves accurate and effective responses by linking servers, terminals, and users and incorporating an emotion engine.

[0386] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0387] Step 1: Register and save debtor information

[0388] Input: Information such as debtor's name, contact details, debt amount, and payment history

[0389] Operation: The user enters debtor information into the input form on the terminal and presses the send button.

[0390] Data processing: The server receives the entered information, formats it, and stores it in a database.

[0391] Output: A confirmation message will be printed to the terminal indicating a successful save.

[0392] Step 2: User login and selection of debt collection case

[0393] Input: User name and password, Debt collection case ID to be selected after logging in

[0394] How it works: The user enters their authentication information on the login screen of their device, presses the login button, and then selects the debt collection case.

[0395] Data calculation: The server authenticates the login information and, if authentication is successful, receives the case ID selected by the user.

[0396] Output: A successful authentication message and detailed information about the case will be displayed on the terminal.

[0397] Step 3: Retrieve and display case information

[0398] Input: Debt collection case ID selected by the user

[0399] How it works: The device requests a case ID from the server.

[0400] Data processing: The server searches the database for relevant debtor information and past transaction history, and sends the results to the terminal.

[0401] Output: Debtor information and past transaction history will be displayed on the terminal.

[0402] Step 4: Generate and send automated messages

[0403] Input: Debtor information and past transaction history

[0404] How it works: A user activates the chat feature, and the device sends the request to the server.

[0405] Data calculation: The server uses a generative AI model based on debtor information and transaction history to generate an appropriate dunning message.

[0406] Output: The generated message is sent to the debtor.

[0407] Specific example of operation: The server generates a message saying, "I'm contacting you about an outstanding payment. Would you like to discuss payment options?" and sends it to the debtor via the terminal.

[0408] Step 5: Receive and analyze the debtor's response

[0409] Input: Reply message from debtor

[0410] What happens: The device receives the reply and forwards it to the server.

[0411] Data calculation: The server's AI analyzes the reply content, and the emotion analysis engine evaluates the emotional state.

[0412] Output: Analysis results and emotional states are generated.

[0413] Step 6: Generate and send the next message

[0414] Input: Analysis results and emotional state

[0415] Action: The server generates the following response message based on the analysis results and emotional state.

[0416] Data calculation: The server uses a generative AI model to generate specific response messages and adjusts the content according to the emotional state.

[0417] Output: The generated response message is sent to the debtor through the terminal.

[0418] Specific example of operation: If the debtor replies, "When should I make payment by?", the server generates a message saying, "The payment deadline is (specified date). We ask that you make payment by then," and sends it through the terminal. Also, if the sentiment analysis engine detects anxiety, it adds a reassuring message saying, "Please contact us at any time if you have any questions or concerns."

[0419] Step 7: Record the interaction

[0420] Input: All interaction data

[0421] How it works: The server records chat logs with the debtor and sentiment analysis results.

[0422] Data processing: The server organizes the log data and stores it in a database or log file.

[0423] Output: Creates a database entry that can be used for subsequent analysis and improving the AI ​​model.

[0424] Step 8: Call response activation and real-time analysis

[0425] Input: Call with debtor

[0426] How it works: The user activates the phone function on their device, and the server analyzes the call content in real time.

[0427] Data Computing: The server converts the call audio into text and generates appropriate guidance using a sentiment analysis engine and generative AI model.

[0428] Output: Appropriate guidance is displayed in real time on the user's device.

[0429] Example of specific behavior: During a call, the server displays a prompt saying, "Your current debt balance is (amount). Would you like to schedule your next payment date?" and, if anxiety is detected, generates a message saying, "If you need additional assistance, we're here to help."

[0430] The above are the detailed processing steps of this system.

[0431] (Application example 2)

[0432] 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."

[0433] In traditional debt collection operations, communication with debtors is inefficient, and especially emotional responses are lacking, leading to stress and disputes. Furthermore, there is no system in place for security personnel to detect suspicious individuals or dangerous situations in real time and take appropriate action.

[0434] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for storing debtor information, means for generating messages for the debtor using an automated chat function, means for analyzing replies from the debtor, means for generating the next message based on the analysis results, and means for analyzing emotions in real time and providing appropriate response guidance. This makes it possible to generate accurate and effective messages based on the debtor's emotional state.

[0435] In addition, by including a means for recognizing the emotional state of the other party and detecting dangerous situations based on emotion analysis, it becomes possible for devices used by security personnel to detect suspicious individuals and dangerous situations in real time and provide appropriate response guidance.

[0436] "Debtor Information" means information including the debtor's name, contact details, amount of debt, past payment history, and other data relevant to debt collection activities.

[0437] An "automated chat function" is a function that automatically generates and sends messages to debtors using predefined messages or algorithms.

[0438] "Means for analyzing replies" refers to a program or algorithm for analyzing replies from debtors and understanding their contents and intent.

[0439] "Means for generating messages" refers to a system or algorithm for automatically creating the next appropriate message based on the analysis results.

[0440] "Means for analyzing emotions in real time and providing appropriate response guidance" is a mechanism for analyzing the emotional state of the user or other party and presenting appropriate response methods in real time based on the results.

[0441] "Means for detecting dangerous situations" refers to the function of detecting suspicious behavior or dangerous emotional states from the results of emotion analysis, and issuing a warning or prompting appropriate action.

[0442] Specific Embodiments of the Invention

[0443] The present invention realizes a security support system that combines an emotion recognition engine. Specific embodiments of the present invention are described below.

[0444] System Components

[0445] The system consists of the following main components:

[0446] Server: A central processing unit that manages data, generates messages, analyzes sentiment, and performs other processing.

[0447] Terminal: Smart glasses used by security personnel, equipped with a camera and microphone, which act as a medium for communication with the server.

[0448] Users: Security personnel responsible for operating the system.

[0449] Program processing

[0450] Debtor information and chat function

[0451] The server has a database that stores debtor information, including the debtor's name, contact information, debt amount, past payment history, etc. When a user logs into the system using a terminal and selects a debt collection case, the server activates an automated chat function based on the selection and generates an appropriate collection message.

[0452] Sentiment analysis and guidance

[0453] The device's camera and microphone are used to capture the user's or debtor's facial expressions and tone of voice, which are then sent to a server in real time. The server then uses an emotion analysis engine to analyze this data. For example, if the server detects stress, anger, or confusion, it will provide appropriate guidance based on the results.

[0454] Example: If the sentiment analysis engine detects that the debtor is confused, it generates a reassuring message such as, "Please contact us anytime if you have any questions or concerns."

[0455] Security Support Function

[0456] When security personnel wear smart glasses, the device's camera constantly monitors the surrounding environment. If suspicious behavior or facial emotional states are detected, the information is sent to the server in real time, and appropriate warnings and guidance are displayed on the device based on the server's analysis.

[0457] Example: If the emotion analysis engine determines that a person may be suspicious, a warning message such as "This person may be suspicious. Please be on guard" will be output.

[0458] Hardware and software used

[0459] Hardware: Smart glasses (camera, microphone)

[0460] Software: OpenCV (face detection), Keras (emotion recognition model), pyttsx3 (audio output)

[0461] Prompt Sentence Examples

[0462] "Use the following face images to identify the emotion. Output the most likely emotion label:

[0463] [Image data]

[0464] In this way, the present invention utilizes an emotion recognition engine to realize a system that is useful for improving the efficiency of communication with debtors and supporting security operations.

[0465] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0466] Step 1:

[0467] A user logs in to the system using a terminal and selects security monitoring mode. The data entered at this time is the user ID and password. The terminal sends the entered data to the server, which performs authentication processing and returns a response indicating that the login was successful. If the login is successful, the terminal displays the security monitoring mode screen.

[0468] Step 2:

[0469] The device's camera and microphone are activated and capture the surrounding video and audio in real time. The video and audio data obtained from the camera and microphone are input. This data is sent to the server in real time, and the server performs preprocessing for analysis. Specifically, it detects faces from the video data and extracts the tone of voice from the audio data.

[0470] Step 3:

[0471] The server inputs the preprocessed facial image data into an emotion recognition model (Keras) to predict the emotional state. The input is the detected facial image data, and the output is the emotional state (e.g., anger, anxiety, happiness, etc.). The emotion recognition model analyzes the facial features and outputs the most likely emotion label.

[0472] Step 4:

[0473] The server generates appropriate response guidance based on the analysis results. If the emotion determination result indicates "anger" or "anxiety," the server generates a warning message. On the other hand, if the emotion determination result indicates "happiness" or "calmness," no particular warning is generated. This output warning message and guidance are sent to the device.

[0474] Step 5:

[0475] The device notifies the user in real time of warning messages and guidance received from the server. Specifically, the device displays the message on the smart glasses display and outputs audio (pyttsx3) as needed. For example, it displays a message saying, "There may be a suspicious person. Please be on guard." and announces it in audio. The input to this step is the guidance received from the server, and the output is a notification to the user.

[0476] 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.

[0477] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (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.

[0478] 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.

[0479] [Second embodiment]

[0480] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0481] 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.

[0482] 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).

[0483] 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.

[0484] 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.

[0485] 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).

[0486] 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.

[0487] 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.

[0488] 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.

[0489] 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.

[0490] 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.

[0491] 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."

[0492] The present invention is a system for improving the efficiency and accuracy of debt collection operations, and is mainly composed of means for automating interactions with debtors. Specific embodiments of this system are described below.

[0493] System Overview

[0494] The system mainly includes the following components:

[0495] 1. Server: The central processing unit that handles all data management, analysis, and message generation.

[0496] 2. Terminal: The interface operated by the user, which mediates communication with the server.

[0497] 3. User: The person who performs the debt collection work and operates the system.

[0498] System Operation

[0499] Saving debtor information

[0500] The server has a database that stores debtor information, including details such as debtor names, contact details, debt amounts, and payment history.

[0501] Automated chat features

[0502] The system's automated chat function works by allowing users to access the server through their devices and initiate a chat with the debtor. The server uses AI to generate appropriate messages based on pre-set templates and past interactions. These messages are then sent to the debtor via their devices.

[0503] Example: The server generates a message saying, "I'm contacting you about an outstanding payment. Would you like to discuss payment options?" and sends it to the debtor via a terminal.

[0504] Analysis of debtor replies

[0505] When the debtor's reply arrives at the terminal, it is forwarded to the server, which uses AI to analyze the reply and generate the next optimal message. The analysis includes responses based on the debtor's questions and wishes.

[0506] Example: If the debtor replies, "When should I make payment by?", the server generates a message saying, "The payment deadline is (specified date). Please make payment by then." and sends it via the terminal.

[0507] Record and learn from interactions

[0508] All chat history is recorded by the server and used for future analysis and to improve the AI ​​model, allowing the system to respond more accurately over time.

[0509] Future phone call integration

[0510] The system is planned to integrate a telephone response function in the future. When a user starts a call on the device, the server analyzes the call content in real time and provides appropriate guidance.

[0511] Example: During a telephone conversation with a debtor, the server displays a prompt on the user's terminal: "Your current debt balance is (amount). Would you like to schedule your next payment date?"

[0512] Program processing

[0513] The server's main role is to manage and analyze data and generate messages. Specifically, it retrieves debtor information from a database, generates chat messages using an AI engine, analyzes the replies, and then generates the next message based on the analysis results, repeating the entire process.

[0514] The terminal provides an interface for users to operate the system and communicates with the server. Users log in through the terminal, select a debt collection case, and start chat or phone calls. The terminal also receives replies from debtors and forwards them to the server.

[0515] In this way, this system, through cooperation between the server, terminals, and users, streamlines debt collection operations and achieves highly accurate responses.

[0516] The processing flow will be explained below.

[0517] Step 1:

[0518] The server creates a database to store debtor information, including debtor names, contact details, debt amounts, and past payment history.

[0519] Step 2:

[0520] A user logs in to the system from a terminal, which sends the entered user ID and password to the server.

[0521] Step 3:

[0522] The server authenticates the user and, if the correct authentication information is verified, grants the user the corresponding privileges.

[0523] Step 4:

[0524] The user selects a debt collection case through the terminal, and the terminal requests information on the selected case from the server.

[0525] Step 5:

[0526] The server searches for the debtor information and past transaction history for the requested case and sends the relevant data to the terminal.

[0527] Step 6:

[0528] The user activates the chat function on the terminal and instructs it to start an automated chat with the debtor.

[0529] Step 7:

[0530] The device sends a command to start a chat to the server, which uses AI to generate an appropriate prompt message.

[0531] Step 8:

[0532] A message generated by the server (e.g., "Your payment is overdue. Please check.") is sent to the debtor via the terminal.

[0533] Step 9:

[0534] When the debtor replies to the chat, the terminal transfers the reply to the server.

[0535] Step 10:

[0536] The server uses AI to analyze the debtor's reply and generate the most appropriate response message (e.g., "The payment deadline is (specified date). Will you be able to pay by then?").

[0537] Step 11:

[0538] The server generates a response message and sends it to the debtor via the terminal. This process is repeated until the debtor agrees to pay.

[0539] Step 12:

[0540] All chat history is recorded by the server and used for future analysis and to improve the AI ​​model.

[0541] Step 13:

[0542] When a user activates the phone function from their device, the server analyzes the call content in real time and provides appropriate guidance (e.g., "Your current debt balance is (amount). Would you like to schedule your next payment date?").

[0543] Step 14:

[0544] Records of telephone responses are also stored on the server and used as useful data for subsequent debt collection activities.

[0545] In this way, the server, terminal, and user cooperate to progress through each step, improving the efficiency and accuracy of debt collection.

[0546] Example 1

[0547] 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."

[0548] Traditional debt collection operations relied heavily on manual work, resulting in low operational efficiency and a high risk of human error. Furthermore, insufficient recording and analysis of interactions with debtors made it difficult to improve the accuracy and efficiency of the overall system.

[0549] 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.

[0550] In this invention, the server includes a system that includes a means for storing debtor information, a means for generating messages for debtors using an automated chat function, a means for analyzing replies from debtors, and an information processing device capable of executing the above means. This makes it possible to streamline debt collection operations and enable accurate and prompt responses. The system also includes a means for recording a history of interactions with debtors and learning the debtor's response tendencies based on subsequent interactions, allowing the system's response accuracy to improve over time. Furthermore, by integrating telephone response functions and including a means for analyzing the content of telephone conversations with debtors in real time and providing appropriate guidance, the entire dialogue with debtors can be managed in a unified and effective manner.

[0551] "Debtor Information" refers to detailed information such as the debtor's name, contact details, debt amount, and payment history.

[0552] "Automated chat function" refers to a function that uses artificial intelligence to automatically exchange messages with debtors.

[0553] "Message generation means" refers to a function that generates appropriate messages based on pre-set templates and past interactions.

[0554] "Reply analysis means" refers to a function that analyzes a reply message from a debtor and generates the next most appropriate message based on the content of the reply message.

[0555] The term "information processing device" refers to a central processing unit capable of executing the above-mentioned means, and a device that manages and analyzes data.

[0556] "Chat history" refers to a record of past message exchanges with a debtor.

[0557] "Response tendencies" refer to the behavioral patterns and tendencies exhibited by the debtor in past interactions.

[0558] "Telephone response function" refers to the function that integrates telephone conversations with debtors, analyzes the content of the conversations in real time, and provides appropriate guidance.

[0559] This invention is a system for improving the efficiency and accuracy of debt collection operations, and is mainly composed of a means for automating interactions with debtors. This system is mainly composed of a server, terminals, and users.

[0560] Server Roles and Functions

[0561] The Server is the central processing unit responsible for all data management, analysis, and message generation. The Server has the following main responsibilities:

[0562] 1. Storage of debtor information

[0563] The server has a database that stores debtor information, including details such as debtor names, contact details, debt amounts, and payment history.

[0564] 2. Message Creation

[0565] The server utilizes an automated chat function that uses an AI engine (e.g., OpenAI GPT-4) to generate messages for the debtor, including messages based on pre-defined templates and past interactions.

[0566] 3. Reply Analysis

[0567] The reply from the debtor is received by the server and analyzed using an AI engine. Based on the results of the analysis, the next optimal message is generated.

[0568] 4. Chat history recording

[0569] All chat history is recorded by the server and used for future analysis and to improve the AI ​​model.

[0570] 5. Telephone integration

[0571] In the future, telephone response functions will also be integrated, making it possible to analyze call content in real time and provide appropriate guidance.

[0572] Device roles and functions

[0573] The terminal provides an interface for users to operate the system and communicates with the server. Specifically, it has the following functions:

[0574] 1. User authentication and login

[0575] A user logs into the system using a terminal and sends authentication information to the server, which verifies this information and authenticates the user.

[0576] 2. Display of debtor information

[0577] When a user searches for specific debtor information, the terminal sends this request to the server, which retrieves the relevant information and displays it on the terminal.

[0578] 3. Start chatting

[0579] The user selects a chat function on the terminal to start chatting with the debtor.

[0580] 4. Sending and receiving messages

[0581] The terminal sends messages generated by the server to the debtor and also forwards replies from the debtor to the server.

[0582] 5. Telephone call assistance

[0583] During a call, the server analyzes the call content in real time and displays information on the device that provides appropriate guidance.

[0584] User operations

[0585] The user operates the system through a terminal, specifically by performing the following steps:

[0586] 1. Log in

[0587] The user logs in from the terminal and starts the debt collection operation.

[0588] 2. Obtaining debtor information

[0589] The user selects a specific debtor on the terminal and retrieves that information from the server.

[0590] 3. Start chatting

[0591] The user initiates a conversation with the debtor using the chat function.

[0592] 4. Chat and phone support

[0593] The user sends a message generated by the server to the debtor and waits for a reply from the debtor. When answering the phone, the user also receives guidance from the server.

[0594] Examples and prompts

[0595] As a concrete example, the server generates a message saying, "I'm contacting you regarding an outstanding payment. Can we discuss payment methods?" and sends it to the debtor via the terminal. If the debtor replies, "By when should I make payment?", the server generates a message saying, "The payment deadline is (specified date). We ask that you make payment by then," and sends it via the terminal.

[0596] Example prompt sentence:

[0597] Generate a "payment confirmation" message for a debt collection case. The debtor asks, "When is the payment due?"

[0598] In this way, this system streamlines debt collection operations by linking servers, terminals, and users, and enables highly accurate responses.

[0599] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0600] Step 1:

[0601] A user logs in to a terminal

[0602] Specific behavior:

[0603] The user opens a login screen through the terminal and enters authentication information, which is then sent to the server by the terminal (user ID and password).

[0604] Data processing / calculation:

[0605] The server compares the received authentication information with the user information in its database. If the comparison is successful, the user is authenticated and the dashboard screen is sent to the terminal.

[0606] Input: User ID, Password

[0607] Output: User's dashboard screen

[0608] Step 2:

[0609] Obtain debtor information

[0610] Specific behavior:

[0611] The user uses the terminal's search function to input a specific debtor ID to search for debtor information. The terminal sends this request to the server.

[0612] Data processing / calculation:

[0613] The server retrieves the relevant debtor information (name, contact details, debt amount, payment history, etc.) from the database based on the debtor ID and sends it to the terminal.

[0614] Input: Debtor ID

[0615] Output: Screen showing debtor details

[0616] Step 3:

[0617] Start a chat

[0618] Specific behavior:

[0619] The user clicks a button on the terminal to start a chat with a particular debtor, and the terminal notifies the server of this action.

[0620] Data processing / calculation:

[0621] The server performs initial settings to start a chat session and displays a chat window on the terminal.

[0622] Input: Request to start chat

[0623] Output: Show chat window

[0624] Step 4:

[0625] AI-generated initial message

[0626] Specific behavior:

[0627] The server uses an AI engine to generate an initial message for the debtor, which it then sends to the terminal, which then sends it to the debtor.

[0628] Data processing / calculation:

[0629] The AI ​​engine (e.g., OpenAI GPT-4) generates optimal messages based on pre-defined templates and past interactions.

[0630] Input: Request for initial message creation

[0631] Output: The generated message (e.g., "I'm contacting you regarding an outstanding payment. Would you like to discuss payment options?")

[0632] Step 5:

[0633] Receiving a reply from the debtor

[0634] Specific behavior:

[0635] When the debtor replies, the message is delivered to the terminal and then forwarded to the server.

[0636] Data processing / calculation:

[0637] The server passes the received reply to the AI ​​engine, which analyzes the content and generates the next optimal message based on the analysis results.

[0638] Input: Debtor's reply message (e.g., "When do I need to make payment?")

[0639] Output: Analysis results and next message

[0640] Step 6:

[0641] AI-based reply analysis and next message generation

[0642] Specific behavior:

[0643] The server uses an AI engine to analyze the reply from the debtor and generate the next message based on the content of the reply, which is then sent to the terminal, which then sends it to the debtor.

[0644] Data processing / calculation:

[0645] The AI ​​engine analyzes the reply content and generates the optimal response message based on the analysis results.

[0646] Input: The reply message to be parsed

[0647] Output: The next message generated (e.g. "Payment is due by (specified date). We ask that you make payment by that date.")

[0648] Step 7:

[0649] Chat history recording

[0650] Specific behavior:

[0651] All messages exchanged through the device are recorded on the server.

[0652] Data processing / calculation:

[0653] The server stores each chat message in a database for subsequent analysis and to improve the AI ​​model.

[0654] Input: Each exchanged message

[0655] Output: Recorded chat history

[0656] Step 8:

[0657] Future phone call integration

[0658] Specific behavior:

[0659] When a user starts answering a call through the terminal, the server analyzes the contents of the call in real time and provides appropriate guidance to the terminal.

[0660] Data processing / calculation:

[0661] The server analyzes the call content in real time, generates guidance using an AI engine, and notifies the device.

[0662] Input: Phone call details

[0663] Output: Real-time guidance (e.g., "Your current debt balance is (amount). Would you like to schedule your next payment date?")

[0664] (Application example 1)

[0665] 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."

[0666] Conventional debt collection systems often require manual interaction with debtors, making it difficult to efficiently manage a large number of debtors. Furthermore, responses via telephone require real-time communication, placing a heavy burden on operators. Furthermore, there is a lack of systems that can flexibly respond using the latest interface devices, such as smartphones and smart glasses, making it necessary to improve the efficiency of debt collection.

[0667] 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.

[0668] In this invention, the server includes a means for storing debtor information, a means for generating messages for debtors using an automated chat function, a means for analyzing replies from debtors, a means for generating subsequent messages based on the analysis results, a means for analyzing telephone conversations with debtors in real time and providing appropriate guidance, and an interface means using a smartphone or smart glasses. This significantly improves the efficiency of interactions with debtors, enables real-time responses and hands-free operation, and enhances the accuracy and efficiency of the entire debt collection process.

[0669] "Means for storing debtor information" refers to a data storage function that accumulates and manages information about debtors (such as names, contact details, debt amounts, payment history, etc.).

[0670] "Means of generating messages to debtors using an automated chat function" refers to a function that utilizes AI technology to refer to pre-set templates and past interactions, and automatically creates and sends appropriate messages to debtors.

[0671] "Means for analyzing replies from debtors" refers to a function that uses AI to analyze the content of replies sent by debtors via chat or email, and understand their meaning and intent.

[0672] The "means for generating the next message based on the analysis result" refers to a function for generating the optimal next message based on the result of analyzing the reply content of the debtor and sending it to the debtor.

[0673] "Means of analyzing the content of telephone conversations with debtors in real time and providing appropriate guidance" refers to a function that uses AI technology to analyze the content of conversations with debtors in real time and immediately instruct the operator on the appropriate response method.

[0674] "Interface means using a smartphone or smart glasses" refers to an operation interface that allows a user to access the system using a smartphone or smart glasses and perform interactions with debtors.

[0675] "Server" refers to a central processing unit that manages data, analyzes data, and generates messages for the entire system, and has computing resources that can execute all means.

[0676] This invention is a system for improving the efficiency and accuracy of debt collection operations, and is mainly composed of means for automating interactions with debtors. Specific embodiments of this system are described below.

[0677] System configuration

[0678] The system mainly includes the following components:

[0679] 1. Server: The central processing unit that manages all data, performs analysis, and generates messages.

[0680] 2. Terminal: The interface operated by the user, which mediates communication with the server.

[0681] 3. User: The person who performs debt collection work and operates the system.

[0682] 4. Smart devices: Interface methods using smartphones and smart glasses.

[0683] System Operation

[0684] Saving debtor information

[0685] The server has a database that stores debtor information, including details such as debtor names, contact details, debt amounts, and payment history.

[0686] Automated chat features

[0687] The system's automated chat function works by allowing users to access the server through their terminal and start a chat with the debtor. The server uses AI to generate an appropriate message based on pre-set templates and past interactions. This message is then sent to the debtor through the terminal. For example, the server generates a message that reads, "I'm contacting you regarding an outstanding payment. Would you like to discuss payment options?" and sends it to the debtor through the terminal.

[0688] Analysis of debtor replies

[0689] When the reply from the debtor arrives at the terminal, the contents are transferred to the server. The server uses AI to analyze the reply and generate the next optimal message. The analysis includes a response based on the debtor's questions and wishes. For example, if the debtor replies, "When should I make payment by?" the server will generate a message saying, "The payment deadline is (specified date). We ask that you make payment by then," and send it via the terminal.

[0690] Record and learn from interactions

[0691] All chat history is recorded by the server and used for future analysis and to improve the AI ​​model, allowing the system to respond more accurately over time.

[0692] Telephone response function

[0693] The server also integrates a telephone response function. When a user starts a telephone response through the terminal, the server analyzes the content of the call in real time and provides appropriate guidance. For example, while on a telephone call with a debtor, the server displays a prompt on the user's terminal saying, "Your current debt balance is (amount). Would you like to schedule your next payment date?"

[0694] Use of smart devices

[0695] The interface using a smartphone or smart glasses allows users to operate hands-free, improving work efficiency. For example, users can respond by voice input while visually checking chat content and debtor information.

[0696] Hardware and software used

[0697] Hardware: Smartphones, smart glasses (e.g., Google Glass)

[0698] Software: Online databases (e.g., Firebase), AI message generation engines (e.g., OpenAI GPT-3)

[0699] Examples of prompt statements

[0700] Debtor information: Name - Common name, Debt amount - 100000 yen, Payment history - ['2023-01-01: 50000', '2023-02-01: 0']

[0701] Message: Please send the following message to your debtor:

[0702] "I'm contacting you regarding an outstanding payment. Can we discuss payment options?"

[0703] In this way, this system works in cooperation with the server, terminals, and users to streamline debt collection operations and achieve highly accurate responses.

[0704] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0705] Step 1:

[0706] Obtaining debtor information

[0707] Input: A user logs in through a terminal and requests information about a particular debtor.

[0708] Data processing / data calculation: The server retrieves details such as the debtor's name, contact details, debt amount, and payment history from a database (e.g., Firebase).

[0709] Output: Send the debtor's information to the user's terminal.

[0710] Specific operation: A user logs in and searches for information on a specific debtor, and the server retrieves the information from the database and displays it on the terminal.

[0711] Step 2:

[0712] Automatic chat message generation

[0713] Input: Debtor information obtained by the server and instructions to start chat.

[0714] Data processing / data calculation: The server uses a generative AI model (e.g., OpenAI GPT-3) to generate a new message based on the debtor's information and past interactions. It creates a prompt and inputs it into the AI ​​model to generate the message.

[0715] Output: The generated message is sent to the debtor via the user's terminal.

[0716] Specific operation: A prompt sentence is generated, and based on that, an AI model is asked to generate a message, and the result is sent to the debtor.

[0717] Step 3:

[0718] Receiving and analyzing debtor replies

[0719] Input: Reply message from debtor.

[0720] Data processing / data calculation: The server analyzes the reply message received from the debtor. Using an AI model, it understands the intent of the reply and the question, and then extracts the necessary information.

[0721] Output: The data needed to generate the next best message based on the analyzed results.

[0722] Specific operation: Receives a reply from the debtor, requests the AI ​​model to analyze it, and uses the analysis results to generate the next message.

[0723] Step 4:

[0724] Generate next message

[0725] Input: Instructions for generating the next message based on the analysis results.

[0726] Data processing / data calculation: The server uses the generative AI model again to generate the next appropriate message based on the analysis results of the reply content. The previous prompt and the analysis results are input into the AI ​​model as the new prompt.

[0727] Output: The following generated message is sent to the debtor via the user's terminal.

[0728] Specific operation: A new prompt sentence is generated based on the analysis results, which is then input into the AI ​​model to generate a new message and send it to the debtor.

[0729] Step 5:

[0730] Record and learn from interactions

[0731] Input: Each chat message and its response.

[0732] Data processing / data calculation: All chat history is recorded in a database by the server and stored for future analysis and to improve the AI ​​model.

[0733] Output: Saved chat history data.

[0734] Specific behavior: The entire interaction history is periodically saved to a database and used in the future to learn response trends and improve the accuracy of the model.

[0735] Step 6:

[0736] Phone call integration and real-time analysis

[0737] Input: The instructions and call content for the user to initiate a phone call.

[0738] Data processing / data calculation: The server recognizes the voice of the call in real time and analyzes the content using an AI model. Based on the analysis results, the server provides appropriate guidance to the user.

[0739] Output: Guidance messages generated in real time.

[0740] Specific operation: The contents of the call are converted into text data using a voice recognition engine, and guidance is displayed on the user's device based on the analysis results.

[0741] Step 7:

[0742] Use of smart devices

[0743] Input: User instructions via smartphone or smart glasses.

[0744] Data processing / data calculation: The user inputs instructions through a smart device, and the server provides appropriate messages and information based on those instructions.

[0745] Output: Appropriate response message to the debtor and visual feedback to the user.

[0746] Specific actions: The user interacts with the system using voice input or touch, and relevant information and messages are displayed in real time.

[0747] 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.

[0748] The present invention is a system that combines an emotion engine that recognizes the emotions of users and debtors in order to improve the efficiency of debt collection operations and achieve accurate and effective responses. A specific embodiment of this system will now be described.

[0749] System configuration

[0750] The system consists of the following main components:

[0751] 1. Server: As the central processing unit, it manages data, generates messages, analyzes emotions, and performs other processing.

[0752] 2. Terminal: The interface operated by the user, which mediates communication with the server.

[0753] 3. User: A person who works in debt collection and is responsible for operating the system.

[0754] Program processing

[0755] Saving and managing debtor information

[0756] The server has a database that stores debtor information, including debtor names, contact details, debt amounts, and past payment history.

[0757] Chat function launch and automatic message generation

[0758] The user logs in to the system using a terminal and selects a debt collection case. The terminal then requests information about the selected case from the server, and the server searches for the relevant debtor information and past transaction history and sends it to the terminal.

[0759] When a user activates the chat function, the server uses an automated chat function to generate an appropriate reminder message and transmit it to the debtor via the terminal.

[0760] Example: The server generates a message saying, "I'm contacting you about an outstanding payment. Would you like to discuss payment options?" and sends it to the debtor via a terminal.

[0761] Parsing the reply from the debtor and generating the next message

[0762] When the reply from the debtor arrives at the device, it is forwarded to the server. The server's AI analyzes the reply and generates the optimal response message. At the same time, the emotion engine analyzes the reply and evaluates the debtor's emotional state.

[0763] Example: If a debtor replies, "When should I make payment by?", the server generates a message saying, "The payment deadline is (specified date). We ask that you make payment by then," and sends it through the terminal. If the emotion engine detects feelings of anxiety or confusion from the debtor's message, it can adjust the response accordingly, adding a message that provides reassurance, such as, "Please contact us at any time if you have any questions or concerns."

[0764] Emotion analysis using an emotion engine

[0765] The emotion engine analyzes the emotional state of the user and the debtor in real time. For example, it can detect emotions such as stress, anger, or confusion from the debtor's replies and voice data, and provide appropriate guidance to the server based on the results.

[0766] Record and learn from interactions

[0767] The server records all interactions and uses them for subsequent analysis and to improve the AI ​​model, allowing the system to learn each borrower's tendencies and respond more accurately.

[0768] Call handling integration and real-time sentiment analysis

[0769] As a future extension, when a user initiates a call from their device, the server will analyze the content of the call in real time and use an emotion engine to provide guidance based on the emotional state of the debtor and the user.

[0770] Example: During a telephone conversation with a debtor, the server displays a prompt on the user's device, such as "Your current debt balance is (amount). Would you like to schedule your next payment date?" If the server detects that the debtor is confused or anxious from the content of the call, it generates a message that provides more reassurance, such as "If you need additional assistance, we are here to help."

[0771] In this way, this system streamlines debt collection operations and achieves accurate and effective responses through collaboration between servers, terminals, and users and processing that incorporates an emotion engine.

[0772] The processing flow will be explained below.

[0773] Step 1:

[0774] The server creates a database that stores debtor information, including debtor names, contact details, debt amounts, and past payment history.

[0775] Step 2:

[0776] A user logs in to the system using a terminal, which provides an interface for entering a user ID and password and sends the authentication information to the server.

[0777] Step 3:

[0778] The server authenticates the user, and if the authentication is successful, grants the user the corresponding privileges and provides the information that the user can access.

[0779] Step 4:

[0780] The user selects a debt collection case on the terminal. The terminal requests information on the selected case from the server, and the server obtains the relevant debtor information and transaction history and sends them to the terminal.

[0781] Step 5:

[0782] The user activates the chat function through the terminal, and a command to start chat is sent from the terminal to the server.

[0783] Step 6:

[0784] The server uses an automated chat function to generate the initial reminder message, which is then sent to the debtor via their terminal.

[0785] Example: The server generates a message saying, "The payment deadline has passed. Please confirm." and sends it to the debtor via the terminal.

[0786] Step 7:

[0787] When the debtor replies via chat, the reply message arrives at the terminal, which then forwards the reply to the server.

[0788] Step 8:

[0789] The server uses AI to analyze the debtor's reply, and an emotion engine analyzes the debtor's emotions and determines the appropriate response along with the reply content.

[0790] Example: If the debtor replies, "I will explain why my payment is late," the server generates a message saying, "I understand your post. By when can you make your payment?" and sends it through the terminal. If the emotion engine detects the debtor's anxiety, it generates an additional message saying, "If you have any questions or concerns, please feel free to contact us."

[0791] Step 9:

[0792] The next response message is generated and sent to the debtor via the terminal. This process is repeated until the debtor pays or a solution is found.

[0793] Step 10:

[0794] All chat history is recorded by the server, which allows the system to use it as reference data for subsequent debt collection activities.

[0795] Step 11:

[0796] When a user activates the call handling function on their device, the device sends the instruction to the server, which analyzes the call content in real time and uses an emotion engine to generate a response based on the user's emotional state.

[0797] Example: The server generates a prompt that says, "Your remaining debt is (amount). Would you like to schedule your next payment?" and displays it on the terminal. If the debtor expresses anxiety during the call, the emotion engine generates a message that says, "We're here to help you so you can talk with peace of mind."

[0798] Step 12:

[0799] The entire call conversation is also recorded on the server and stored for future analysis and to improve the AI ​​model.

[0800] In this way, a system can be constructed that realizes debt collection operations more efficiently and accurately through processing that combines the server, terminals, and users in cooperation with an emotion engine.

[0801] Example 2

[0802] 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."

[0803] In debt collection work, communication with debtors is extremely important, but doing it manually takes a lot of time and effort. Furthermore, sending messages mechanically without considering the debtor's emotions can result in ineffective communication. Furthermore, when dealing with debtors over the phone, it is difficult to analyze their emotions in real time and respond appropriately. There is a need for a system that can solve these problems and achieve efficient and effective debt collection.

[0804] 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. In this invention, the server includes a means for saving debtor information, a means for generating a message for the debtor using an automated chat function, a means for analyzing a reply from the debtor, a means for generating a next message based on the analysis result, a means for analyzing the debtor's emotions using an emotion analysis engine and adjusting the response message based on the analysis result, a means operated by the user through a terminal, and a server capable of executing the above means. This enables effective communication that takes the debtor's emotions into consideration, thereby realizing efficient debt collection.

[0805] "Debtor information" is data including the debtor's name, contact information, debt amount, payment history, etc.

[0806] An "automated chat function" is a function that automatically communicates with debtors using messages generated by a server.

[0807] "Means for analyzing replies from debtors" refers to AI models or algorithms used to receive messages from debtors and analyze their contents.

[0808] The "means for generating the next message based on the analysis result" is a function for automatically generating the next response based on the analysis result of the message from the debtor.

[0809] An "emotion analysis engine" is a technology that analyzes emotions from debtors' messages and call content and generates appropriate responses based on the results.

[0810] "User" refers to a person who performs debt collection work and operates the system.

[0811] A "terminal" is an interface device operated by a user, and includes a computer or mobile device for communicating with a server.

[0812] A "server" is a central processing unit that performs data management, message generation, analysis, and other processing.

[0813] This invention is a system that combines an emotion engine that recognizes the emotions of users and debtors in order to streamline debt collection operations and achieve accurate and effective responses. This system operates through the cooperation of a server, terminals, and users.

[0814] System configuration

[0815] The system consists of the following main components:

[0816] 1. Server: A central processing unit that manages data, generates messages, analyzes emotions, and performs other processing. The server has a database for storing debtor information and has the ability to record interactions with debtors.

[0817] 2. Terminal: The interface operated by the user, which mediates communication with the server. The terminal runs software that allows the user to log in and operate the system.

[0818] 3. User: The person in charge of debt collection operations who operates the system.

[0819] System Features

[0820] An embodiment of the system is described in detail below.

[0821] Saving and managing debtor information

[0822] The server has a database that stores debtor information, including names, contact details, debt amounts, payment history, etc. When a user enters information from a terminal, the server stores the information in the database.

[0823] Chat function launch and automatic message generation

[0824] A user logs in to the system using a terminal and selects a debt collection case. The terminal requests case information from the server, which then searches for the relevant debtor information and past communication history and sends them to the terminal. When the user activates the chat function, the server uses an automated chat function to generate an appropriate message and sends it to the debtor via the terminal.

[0825] Examples:

[0826] The server generates a message saying, "We're contacting you regarding an outstanding payment. Would you like to discuss payment options?" and sends it to the debtor via the terminal.

[0827] Parsing the reply from the debtor and generating the next message

[0828] When the debtor's reply arrives at the device, it is forwarded to the server. The server's AI analyzes the reply and generates the optimal response message. At the same time, the emotion engine analyzes the debtor's reply and evaluates their emotional state.

[0829] Examples:

[0830] If the debtor replies, "By when should I make payment?", the server generates a message saying, "The payment deadline is (specified date). Please make payment by then." and sends it through the terminal.

[0831] If the emotion engine detects feelings of anxiety or confusion in the debtor's message, it adds a reassuring message such as, "If you have any questions or concerns, please contact us at any time."

[0832] Emotion analysis using an emotion analysis engine

[0833] The emotion engine analyzes the emotional state of the user and the debtor in real time. For example, it can detect emotions such as stress, anger, or confusion from the debtor's replies and voice data, and provide appropriate guidance to the server based on the results.

[0834] Record and learn from interactions

[0835] The server records all interactions and uses them for subsequent analysis and to improve the AI ​​model, allowing the system to learn each borrower's tendencies and respond more accurately.

[0836] Call handling integration and real-time sentiment analysis

[0837] When a user initiates a call from a terminal, the server analyzes the contents of the call in real time and uses an emotion engine to provide guidance based on the emotional state of the debtor and the user.

[0838] Examples:

[0839] The server displays a prompt on the user's terminal such as "Your current debt balance is (amount). Would you like to schedule your next payment date?"

[0840] If the call detects confusion or anxiety from the debtor, it generates a reassuring message such as, "If you need additional assistance, we're here to help."

[0841] As a result, this system streamlines debt collection operations and achieves accurate and effective responses by linking servers, terminals, and users and incorporating an emotion engine.

[0842] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0843] Step 1: Register and save debtor information

[0844] Input: Information such as debtor's name, contact details, debt amount, and payment history

[0845] Operation: The user enters debtor information into the input form on the terminal and presses the send button.

[0846] Data processing: The server receives the entered information, formats it, and stores it in a database.

[0847] Output: A confirmation message will be printed to the terminal indicating a successful save.

[0848] Step 2: User login and selection of debt collection case

[0849] Input: User name and password, Debt collection case ID to be selected after logging in

[0850] How it works: The user enters their authentication information on the login screen of their device, presses the login button, and then selects the debt collection case.

[0851] Data calculation: The server authenticates the login information and, if authentication is successful, receives the case ID selected by the user.

[0852] Output: A successful authentication message and detailed information about the case will be displayed on the terminal.

[0853] Step 3: Retrieve and display case information

[0854] Input: Debt collection case ID selected by the user

[0855] How it works: The device requests a case ID from the server.

[0856] Data processing: The server searches the database for relevant debtor information and past transaction history, and sends the results to the terminal.

[0857] Output: Debtor information and past transaction history will be displayed on the terminal.

[0858] Step 4: Generate and send automated messages

[0859] Input: Debtor information and past transaction history

[0860] How it works: A user activates the chat feature, and the device sends the request to the server.

[0861] Data calculation: The server uses a generative AI model based on debtor information and transaction history to generate an appropriate dunning message.

[0862] Output: The generated message is sent to the debtor.

[0863] Specific example of operation: The server generates a message saying, "I'm contacting you about an outstanding payment. Would you like to discuss payment options?" and sends it to the debtor via the terminal.

[0864] Step 5: Receive and analyze the debtor's response

[0865] Input: Reply message from debtor

[0866] What happens: The device receives the reply and forwards it to the server.

[0867] Data calculation: The server's AI analyzes the reply content, and the emotion analysis engine evaluates the emotional state.

[0868] Output: Analysis results and emotional states are generated.

[0869] Step 6: Generate and send the next message

[0870] Input: Analysis results and emotional state

[0871] Action: The server generates the following response message based on the analysis results and emotional state.

[0872] Data calculation: The server uses a generative AI model to generate specific response messages and adjusts the content according to the emotional state.

[0873] Output: The generated response message is sent to the debtor through the terminal.

[0874] Specific example of operation: If the debtor replies, "When should I make payment by?", the server generates a message saying, "The payment deadline is (specified date). We ask that you make payment by then," and sends it through the terminal. Also, if the sentiment analysis engine detects anxiety, it adds a reassuring message saying, "Please contact us at any time if you have any questions or concerns."

[0875] Step 7: Record the interaction

[0876] Input: All interaction data

[0877] How it works: The server records chat logs with the debtor and sentiment analysis results.

[0878] Data processing: The server organizes the log data and stores it in a database or log file.

[0879] Output: Creates a database entry that can be used for subsequent analysis and improving the AI ​​model.

[0880] Step 8: Call response activation and real-time analysis

[0881] Input: Call with debtor

[0882] How it works: The user activates the phone function on their device, and the server analyzes the call content in real time.

[0883] Data Computing: The server converts the call audio into text and generates appropriate guidance using a sentiment analysis engine and generative AI model.

[0884] Output: Appropriate guidance is displayed in real time on the user's device.

[0885] Example of specific behavior: During a call, the server displays a prompt saying, "Your current debt balance is (amount). Would you like to schedule your next payment date?" and, if anxiety is detected, generates a message saying, "If you need additional assistance, we're here to help."

[0886] The above are the detailed processing steps of this system.

[0887] (Application example 2)

[0888] 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."

[0889] In traditional debt collection operations, communication with debtors is inefficient, and especially emotional responses are lacking, leading to stress and disputes. Furthermore, there is no system in place for security personnel to detect suspicious individuals or dangerous situations in real time and take appropriate action.

[0890] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for storing debtor information, means for generating messages for the debtor using an automated chat function, means for analyzing replies from the debtor, means for generating the next message based on the analysis results, and means for analyzing emotions in real time and providing appropriate response guidance. This makes it possible to generate accurate and effective messages based on the debtor's emotional state.

[0891] In addition, by including a means for recognizing the emotional state of the other party and detecting dangerous situations based on emotion analysis, it becomes possible for devices used by security personnel to detect suspicious individuals and dangerous situations in real time and provide appropriate response guidance.

[0892] "Debtor Information" means information including the debtor's name, contact details, amount of debt, past payment history, and other data relevant to debt collection activities.

[0893] An "automated chat function" is a function that automatically generates and sends messages to debtors using predefined messages or algorithms.

[0894] "Means for analyzing replies" refers to a program or algorithm for analyzing replies from debtors and understanding their contents and intent.

[0895] "Means for generating messages" refers to a system or algorithm for automatically creating the next appropriate message based on the analysis results.

[0896] "Means for analyzing emotions in real time and providing appropriate response guidance" is a mechanism for analyzing the emotional state of the user or other party and presenting appropriate response methods in real time based on the results.

[0897] "Means for detecting dangerous situations" refers to the function of detecting suspicious behavior or dangerous emotional states from the results of emotion analysis, and issuing a warning or prompting appropriate action.

[0898] Specific Embodiments of the Invention

[0899] The present invention realizes a security support system that combines an emotion recognition engine. Specific embodiments of the present invention are described below.

[0900] System Components

[0901] The system consists of the following main components:

[0902] Server: A central processing unit that manages data, generates messages, analyzes sentiment, and performs other processing.

[0903] Terminal: Smart glasses used by security personnel, equipped with a camera and microphone, which act as a medium for communication with the server.

[0904] Users: Security personnel responsible for operating the system.

[0905] Program processing

[0906] Debtor information and chat function

[0907] The server has a database that stores debtor information, including the debtor's name, contact information, debt amount, past payment history, etc. When a user logs into the system using a terminal and selects a debt collection case, the server activates an automated chat function based on the selection and generates an appropriate collection message.

[0908] Sentiment analysis and guidance

[0909] The device's camera and microphone are used to capture the user's or debtor's facial expressions and tone of voice, which are then sent to a server in real time. The server then uses an emotion analysis engine to analyze this data. For example, if the server detects stress, anger, or confusion, it will provide appropriate guidance based on the results.

[0910] Example: If the sentiment analysis engine detects that the debtor is confused, it generates a reassuring message such as, "Please contact us anytime if you have any questions or concerns."

[0911] Security Support Function

[0912] When security personnel wear smart glasses, the device's camera constantly monitors the surrounding environment. If suspicious behavior or facial emotional states are detected, the information is sent to the server in real time, and appropriate warnings and guidance are displayed on the device based on the server's analysis.

[0913] Example: If the emotion analysis engine determines that a person may be suspicious, a warning message such as "This person may be suspicious. Please be on guard" will be output.

[0914] Hardware and software used

[0915] Hardware: Smart glasses (camera, microphone)

[0916] Software: OpenCV (face detection), Keras (emotion recognition model), pyttsx3 (audio output)

[0917] Prompt Sentence Examples

[0918] "Use the following face images to identify the emotion. Output the most likely emotion label:

[0919] [Image data]

[0920] In this way, the present invention utilizes an emotion recognition engine to realize a system that is useful for improving the efficiency of communication with debtors and supporting security operations.

[0921] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0922] Step 1:

[0923] A user logs in to the system using a terminal and selects security monitoring mode. The data entered at this time is the user ID and password. The terminal sends the entered data to the server, which performs authentication processing and returns a response indicating that the login was successful. If the login is successful, the terminal displays the security monitoring mode screen.

[0924] Step 2:

[0925] The device's camera and microphone are activated and capture the surrounding video and audio in real time. The video and audio data obtained from the camera and microphone are input. This data is sent to the server in real time, and the server performs preprocessing for analysis. Specifically, it detects faces from the video data and extracts the tone of voice from the audio data.

[0926] Step 3:

[0927] The server inputs the preprocessed facial image data into an emotion recognition model (Keras) to predict the emotional state. The input is the detected facial image data, and the output is the emotional state (e.g., anger, anxiety, happiness, etc.). The emotion recognition model analyzes the facial features and outputs the most likely emotion label.

[0928] Step 4:

[0929] The server generates appropriate response guidance based on the analysis results. If the emotion determination result indicates "anger" or "anxiety," the server generates a warning message. On the other hand, if the emotion determination result indicates "happiness" or "calmness," no particular warning is generated. This output warning message and guidance are sent to the device.

[0930] Step 5:

[0931] The device notifies the user in real time of warning messages and guidance received from the server. Specifically, the device displays the message on the smart glasses display and outputs audio (pyttsx3) as needed. For example, it displays a message saying, "There may be a suspicious person. Please be on guard." and announces it in audio. The input to this step is the guidance received from the server, and the output is a notification to the user.

[0932] 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.

[0933] 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.

[0934] 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.

[0935] [Third embodiment]

[0936] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0937] 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.

[0938] 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).

[0939] 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.

[0940] 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.

[0941] 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).

[0942] 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.

[0943] 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.

[0944] 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.

[0945] 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.

[0946] 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.

[0947] 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."

[0948] The present invention is a system for improving the efficiency and accuracy of debt collection operations, and is mainly composed of means for automating interactions with debtors. Specific embodiments of this system are described below.

[0949] System Overview

[0950] The system mainly includes the following components:

[0951] 1. Server: The central processing unit that handles all data management, analysis, and message generation.

[0952] 2. Terminal: The interface operated by the user, which mediates communication with the server.

[0953] 3. User: The person who performs the debt collection work and operates the system.

[0954] System Operation

[0955] Saving debtor information

[0956] The server has a database that stores debtor information, including details such as debtor names, contact details, debt amounts, and payment history.

[0957] Automated chat features

[0958] The system's automated chat function works by allowing users to access the server through their devices and initiate a chat with the debtor. The server uses AI to generate appropriate messages based on pre-set templates and past interactions. These messages are then sent to the debtor via their devices.

[0959] Example: The server generates a message saying, "I'm contacting you about an outstanding payment. Would you like to discuss payment options?" and sends it to the debtor via a terminal.

[0960] Analysis of debtor replies

[0961] When the debtor's reply arrives at the terminal, it is forwarded to the server, which uses AI to analyze the reply and generate the next optimal message. The analysis includes responses based on the debtor's questions and wishes.

[0962] Example: If the debtor replies, "When should I make payment by?", the server generates a message saying, "The payment deadline is (specified date). Please make payment by then." and sends it via the terminal.

[0963] Record and learn from interactions

[0964] All chat history is recorded by the server and used for future analysis and to improve the AI ​​model, allowing the system to respond more accurately over time.

[0965] Future phone call integration

[0966] The system is planned to integrate a telephone response function in the future. When a user starts a call on the device, the server analyzes the call content in real time and provides appropriate guidance.

[0967] Example: During a telephone conversation with a debtor, the server displays a prompt on the user's terminal: "Your current debt balance is (amount). Would you like to schedule your next payment date?"

[0968] Program processing

[0969] The server's main role is to manage and analyze data and generate messages. Specifically, it retrieves debtor information from a database, generates chat messages using an AI engine, analyzes the replies, and then generates the next message based on the analysis results, repeating the entire process.

[0970] The terminal provides an interface for users to operate the system and communicates with the server. Users log in through the terminal, select a debt collection case, and start chat or phone calls. The terminal also receives replies from debtors and forwards them to the server.

[0971] In this way, this system, through cooperation between the server, terminals, and users, streamlines debt collection operations and achieves highly accurate responses.

[0972] The processing flow will be explained below.

[0973] Step 1:

[0974] The server creates a database to store debtor information, including debtor names, contact details, debt amounts, and past payment history.

[0975] Step 2:

[0976] A user logs in to the system from a terminal, which sends the entered user ID and password to the server.

[0977] Step 3:

[0978] The server authenticates the user and, if the correct authentication information is verified, grants the user the corresponding privileges.

[0979] Step 4:

[0980] The user selects a debt collection case through the terminal, and the terminal requests information on the selected case from the server.

[0981] Step 5:

[0982] The server searches for the debtor information and past transaction history for the requested case and sends the relevant data to the terminal.

[0983] Step 6:

[0984] The user activates the chat function on the terminal and instructs it to start an automated chat with the debtor.

[0985] Step 7:

[0986] The device sends a command to start a chat to the server, which uses AI to generate an appropriate prompt message.

[0987] Step 8:

[0988] A message generated by the server (e.g., "Your payment is overdue. Please check.") is sent to the debtor via the terminal.

[0989] Step 9:

[0990] When the debtor replies to the chat, the terminal transfers the reply to the server.

[0991] Step 10:

[0992] The server uses AI to analyze the debtor's reply and generate the most appropriate response message (e.g., "The payment deadline is (specified date). Will you be able to pay by then?").

[0993] Step 11:

[0994] The server generates a response message and sends it to the debtor via the terminal. This process is repeated until the debtor agrees to pay.

[0995] Step 12:

[0996] All chat history is recorded by the server and used for future analysis and to improve the AI ​​model.

[0997] Step 13:

[0998] When a user activates the phone function from their device, the server analyzes the call content in real time and provides appropriate guidance (e.g., "Your current debt balance is (amount). Would you like to schedule your next payment date?").

[0999] Step 14:

[1000] Records of telephone responses are also stored on the server and used as useful data for subsequent debt collection activities.

[1001] In this way, the server, terminal, and user cooperate to progress through each step, improving the efficiency and accuracy of debt collection.

[1002] Example 1

[1003] 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."

[1004] Traditional debt collection operations relied heavily on manual work, resulting in low operational efficiency and a high risk of human error. Furthermore, insufficient recording and analysis of interactions with debtors made it difficult to improve the accuracy and efficiency of the overall system.

[1005] 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.

[1006] In this invention, the server includes a system that includes a means for storing debtor information, a means for generating messages for debtors using an automated chat function, a means for analyzing replies from debtors, and an information processing device capable of executing the above means. This makes it possible to streamline debt collection operations and enable accurate and prompt responses. The system also includes a means for recording a history of interactions with debtors and learning the debtor's response tendencies based on subsequent interactions, allowing the system's response accuracy to improve over time. Furthermore, by integrating telephone response functions and including a means for analyzing the content of telephone conversations with debtors in real time and providing appropriate guidance, the entire dialogue with debtors can be managed in a unified and effective manner.

[1007] "Debtor Information" refers to detailed information such as the debtor's name, contact details, debt amount, and payment history.

[1008] "Automated chat function" refers to a function that uses artificial intelligence to automatically exchange messages with debtors.

[1009] "Message generation means" refers to a function that generates appropriate messages based on pre-set templates and past interactions.

[1010] "Reply analysis means" refers to a function that analyzes a reply message from a debtor and generates the next most appropriate message based on the content of the reply message.

[1011] The term "information processing device" refers to a central processing unit capable of executing the above-mentioned means, and a device that manages and analyzes data.

[1012] "Chat history" refers to a record of past message exchanges with a debtor.

[1013] "Response tendencies" refer to the behavioral patterns and tendencies exhibited by the debtor in past interactions.

[1014] "Telephone response function" refers to the function that integrates telephone conversations with debtors, analyzes the content of the conversations in real time, and provides appropriate guidance.

[1015] This invention is a system for improving the efficiency and accuracy of debt collection operations, and is mainly composed of a means for automating interactions with debtors. This system is mainly composed of a server, terminals, and users.

[1016] Server Roles and Functions

[1017] The Server is the central processing unit responsible for all data management, analysis, and message generation. The Server has the following main responsibilities:

[1018] 1. Storage of debtor information

[1019] The server has a database that stores debtor information, including details such as debtor names, contact details, debt amounts, and payment history.

[1020] 2. Message Creation

[1021] The server utilizes an automated chat function that uses an AI engine (e.g., OpenAI GPT-4) to generate messages for the debtor, including messages based on pre-defined templates and past interactions.

[1022] 3. Reply Analysis

[1023] The reply from the debtor is received by the server and analyzed using an AI engine. Based on the results of the analysis, the next optimal message is generated.

[1024] 4. Chat history recording

[1025] All chat history is recorded by the server and used for future analysis and to improve the AI ​​model.

[1026] 5. Telephone integration

[1027] In the future, telephone response functions will also be integrated, making it possible to analyze call content in real time and provide appropriate guidance.

[1028] Device roles and functions

[1029] The terminal provides an interface for users to operate the system and communicates with the server. Specifically, it has the following functions:

[1030] 1. User authentication and login

[1031] A user logs into the system using a terminal and sends authentication information to the server, which verifies this information and authenticates the user.

[1032] 2. Display of debtor information

[1033] When a user searches for specific debtor information, the terminal sends this request to the server, which retrieves the relevant information and displays it on the terminal.

[1034] 3. Start chatting

[1035] The user selects a chat function on the terminal to start chatting with the debtor.

[1036] 4. Sending and receiving messages

[1037] The terminal sends messages generated by the server to the debtor and also forwards replies from the debtor to the server.

[1038] 5. Telephone call assistance

[1039] During a call, the server analyzes the call content in real time and displays information on the device that provides appropriate guidance.

[1040] User operations

[1041] The user operates the system through a terminal, specifically by performing the following steps:

[1042] 1. Log in

[1043] The user logs in from the terminal and starts the debt collection operation.

[1044] 2. Obtaining debtor information

[1045] The user selects a specific debtor on the terminal and retrieves that information from the server.

[1046] 3. Start chatting

[1047] The user initiates a conversation with the debtor using the chat function.

[1048] 4. Chat and phone support

[1049] The user sends a message generated by the server to the debtor and waits for a reply from the debtor. When answering the phone, the user also receives guidance from the server.

[1050] Examples and prompts

[1051] As a concrete example, the server generates a message saying, "I'm contacting you regarding an outstanding payment. Can we discuss payment methods?" and sends it to the debtor via the terminal. If the debtor replies, "By when should I make payment?", the server generates a message saying, "The payment deadline is (specified date). We ask that you make payment by then," and sends it via the terminal.

[1052] Example prompt sentence:

[1053] Generate a "payment confirmation" message for a debt collection case. The debtor asks, "When is the payment due?"

[1054] In this way, this system streamlines debt collection operations by linking servers, terminals, and users, and enables highly accurate responses.

[1055] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1056] Step 1:

[1057] A user logs in to a terminal

[1058] Specific behavior:

[1059] The user opens a login screen through the terminal and enters authentication information, which is then sent to the server by the terminal (user ID and password).

[1060] Data processing / calculation:

[1061] The server compares the received authentication information with the user information in its database. If the comparison is successful, the user is authenticated and the dashboard screen is sent to the terminal.

[1062] Input: User ID, Password

[1063] Output: User's dashboard screen

[1064] Step 2:

[1065] Obtain debtor information

[1066] Specific behavior:

[1067] The user uses the terminal's search function to input a specific debtor ID to search for debtor information. The terminal sends this request to the server.

[1068] Data processing / calculation:

[1069] The server retrieves the relevant debtor information (name, contact details, debt amount, payment history, etc.) from the database based on the debtor ID and sends it to the terminal.

[1070] Input: Debtor ID

[1071] Output: Screen showing debtor details

[1072] Step 3:

[1073] Start a chat

[1074] Specific behavior:

[1075] The user clicks a button on the terminal to start a chat with a particular debtor, and the terminal notifies the server of this action.

[1076] Data processing / calculation:

[1077] The server performs initial settings to start a chat session and displays a chat window on the terminal.

[1078] Input: Request to start chat

[1079] Output: Show chat window

[1080] Step 4:

[1081] AI-generated initial message

[1082] Specific behavior:

[1083] The server uses an AI engine to generate an initial message for the debtor, which it then sends to the terminal, which then sends it to the debtor.

[1084] Data processing / calculation:

[1085] The AI ​​engine (e.g., OpenAI GPT-4) generates optimal messages based on pre-defined templates and past interactions.

[1086] Input: Request for initial message creation

[1087] Output: The generated message (e.g., "I'm contacting you regarding an outstanding payment. Would you like to discuss payment options?")

[1088] Step 5:

[1089] Receiving a reply from the debtor

[1090] Specific behavior:

[1091] When the debtor replies, the message is delivered to the terminal and then forwarded to the server.

[1092] Data processing / calculation:

[1093] The server passes the received reply to the AI ​​engine, which analyzes the content and generates the next optimal message based on the analysis results.

[1094] Input: Debtor's reply message (e.g., "When do I need to make payment?")

[1095] Output: Analysis results and next message

[1096] Step 6:

[1097] AI-based reply analysis and next message generation

[1098] Specific behavior:

[1099] The server uses an AI engine to analyze the reply from the debtor and generate the next message based on the content of the reply, which is then sent to the terminal, which then sends it to the debtor.

[1100] Data processing / calculation:

[1101] The AI ​​engine analyzes the reply content and generates the optimal response message based on the analysis results.

[1102] Input: The reply message to be parsed

[1103] Output: The next message generated (e.g. "Payment is due by (specified date). We ask that you make payment by that date.")

[1104] Step 7:

[1105] Chat history recording

[1106] Specific behavior:

[1107] All messages exchanged through the device are recorded on the server.

[1108] Data processing / calculation:

[1109] The server stores each chat message in a database for subsequent analysis and to improve the AI ​​model.

[1110] Input: Each exchanged message

[1111] Output: Recorded chat history

[1112] Step 8:

[1113] Future phone call integration

[1114] Specific behavior:

[1115] When a user starts answering a call through the terminal, the server analyzes the contents of the call in real time and provides appropriate guidance to the terminal.

[1116] Data processing / calculation:

[1117] The server analyzes the call content in real time, generates guidance using an AI engine, and notifies the device.

[1118] Input: Phone call details

[1119] Output: Real-time guidance (e.g., "Your current debt balance is (amount). Would you like to schedule your next payment date?")

[1120] (Application example 1)

[1121] 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."

[1122] Conventional debt collection systems often require manual interaction with debtors, making it difficult to efficiently manage a large number of debtors. Furthermore, responses via telephone require real-time communication, placing a heavy burden on operators. Furthermore, there is a lack of systems that can flexibly respond using the latest interface devices, such as smartphones and smart glasses, making it necessary to improve the efficiency of debt collection.

[1123] 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.

[1124] In this invention, the server includes a means for storing debtor information, a means for generating messages for debtors using an automated chat function, a means for analyzing replies from debtors, a means for generating subsequent messages based on the analysis results, a means for analyzing telephone conversations with debtors in real time and providing appropriate guidance, and an interface means using a smartphone or smart glasses. This significantly improves the efficiency of interactions with debtors, enables real-time responses and hands-free operation, and enhances the accuracy and efficiency of the entire debt collection process.

[1125] "Means for storing debtor information" refers to a data storage function that accumulates and manages information about debtors (such as names, contact details, debt amounts, payment history, etc.).

[1126] "Means of generating messages to debtors using an automated chat function" refers to a function that utilizes AI technology to refer to pre-set templates and past interactions, and automatically creates and sends appropriate messages to debtors.

[1127] "Means for analyzing replies from debtors" refers to a function that uses AI to analyze the content of replies sent by debtors via chat or email, and understand their meaning and intent.

[1128] The "means for generating the next message based on the analysis result" refers to a function for generating the optimal next message based on the result of analyzing the reply content of the debtor and sending it to the debtor.

[1129] "Means of analyzing the content of telephone conversations with debtors in real time and providing appropriate guidance" refers to a function that uses AI technology to analyze the content of conversations with debtors in real time and immediately instruct the operator on the appropriate response method.

[1130] "Interface means using a smartphone or smart glasses" refers to an operation interface that allows a user to access the system using a smartphone or smart glasses and perform interactions with debtors.

[1131] "Server" refers to a central processing unit that manages data, analyzes data, and generates messages for the entire system, and has computing resources that can execute all means.

[1132] This invention is a system for improving the efficiency and accuracy of debt collection operations, and is mainly composed of means for automating interactions with debtors. Specific embodiments of this system are described below.

[1133] System configuration

[1134] The system mainly includes the following components:

[1135] 1. Server: The central processing unit that manages all data, performs analysis, and generates messages.

[1136] 2. Terminal: The interface operated by the user, which mediates communication with the server.

[1137] 3. User: The person who performs debt collection work and operates the system.

[1138] 4. Smart devices: Interface methods using smartphones and smart glasses.

[1139] System Operation

[1140] Saving debtor information

[1141] The server has a database that stores debtor information, including details such as debtor names, contact details, debt amounts, and payment history.

[1142] Automated chat features

[1143] The system's automated chat function works by allowing users to access the server through their terminal and start a chat with the debtor. The server uses AI to generate an appropriate message based on pre-set templates and past interactions. This message is then sent to the debtor through the terminal. For example, the server generates a message that reads, "I'm contacting you regarding an outstanding payment. Would you like to discuss payment options?" and sends it to the debtor through the terminal.

[1144] Analysis of debtor replies

[1145] When the reply from the debtor arrives at the terminal, the contents are transferred to the server. The server uses AI to analyze the reply and generate the next optimal message. The analysis includes a response based on the debtor's questions and wishes. For example, if the debtor replies, "When should I make payment by?" the server will generate a message saying, "The payment deadline is (specified date). We ask that you make payment by then," and send it via the terminal.

[1146] Record and learn from interactions

[1147] All chat history is recorded by the server and used for future analysis and to improve the AI ​​model, allowing the system to respond more accurately over time.

[1148] Telephone response function

[1149] The server also integrates a telephone response function. When a user starts a telephone response through the terminal, the server analyzes the content of the call in real time and provides appropriate guidance. For example, while on a telephone call with a debtor, the server displays a prompt on the user's terminal saying, "Your current debt balance is (amount). Would you like to schedule your next payment date?"

[1150] Use of smart devices

[1151] The interface using a smartphone or smart glasses allows users to operate hands-free, improving work efficiency. For example, users can respond by voice input while visually checking chat content and debtor information.

[1152] Hardware and software used

[1153] Hardware: Smartphones, smart glasses (e.g., Google Glass)

[1154] Software: Online databases (e.g., Firebase), AI message generation engines (e.g., OpenAI GPT-3)

[1155] Examples of prompt statements

[1156] Debtor information: Name - Common name, Debt amount - 100000 yen, Payment history - ['2023-01-01: 50000', '2023-02-01: 0']

[1157] Message: Please send the following message to your debtor:

[1158] "I'm contacting you regarding an outstanding payment. Can we discuss payment options?"

[1159] In this way, this system works in cooperation with the server, terminals, and users to streamline debt collection operations and achieve highly accurate responses.

[1160] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1161] Step 1:

[1162] Obtaining debtor information

[1163] Input: A user logs in through a terminal and requests information about a particular debtor.

[1164] Data processing / data calculation: The server retrieves details such as the debtor's name, contact details, debt amount, and payment history from a database (e.g., Firebase).

[1165] Output: Send the debtor's information to the user's terminal.

[1166] Specific operation: A user logs in and searches for information on a specific debtor, and the server retrieves the information from the database and displays it on the terminal.

[1167] Step 2:

[1168] Automatic chat message generation

[1169] Input: Debtor information obtained by the server and instructions to start chat.

[1170] Data processing / data calculation: The server uses a generative AI model (e.g., OpenAI GPT-3) to generate a new message based on the debtor's information and past interactions. It creates a prompt and inputs it into the AI ​​model to generate the message.

[1171] Output: The generated message is sent to the debtor via the user's terminal.

[1172] Specific operation: A prompt sentence is generated, and based on that, an AI model is asked to generate a message, and the result is sent to the debtor.

[1173] Step 3:

[1174] Receiving and analyzing debtor replies

[1175] Input: Reply message from debtor.

[1176] Data processing / data calculation: The server analyzes the reply message received from the debtor. Using an AI model, it understands the intent of the reply and the question, and then extracts the necessary information.

[1177] Output: The data needed to generate the next best message based on the analyzed results.

[1178] Specific operation: Receives a reply from the debtor, requests the AI ​​model to analyze it, and uses the analysis results to generate the next message.

[1179] Step 4:

[1180] Generate next message

[1181] Input: Instructions for generating the next message based on the analysis results.

[1182] Data processing / data calculation: The server uses the generative AI model again to generate the next appropriate message based on the analysis results of the reply content. The previous prompt and the analysis results are input into the AI ​​model as the new prompt.

[1183] Output: The following generated message is sent to the debtor via the user's terminal.

[1184] Specific operation: A new prompt sentence is generated based on the analysis results, which is then input into the AI ​​model to generate a new message and send it to the debtor.

[1185] Step 5:

[1186] Record and learn from interactions

[1187] Input: Each chat message and its response.

[1188] Data processing / data calculation: All chat history is recorded in a database by the server and stored for future analysis and to improve the AI ​​model.

[1189] Output: Saved chat history data.

[1190] Specific behavior: The entire interaction history is periodically saved to a database and used in the future to learn response trends and improve the accuracy of the model.

[1191] Step 6:

[1192] Phone call integration and real-time analysis

[1193] Input: The instructions and call content for the user to initiate a phone call.

[1194] Data processing / data calculation: The server recognizes the voice of the call in real time and analyzes the content using an AI model. Based on the analysis results, the server provides appropriate guidance to the user.

[1195] Output: Guidance messages generated in real time.

[1196] Specific operation: The contents of the call are converted into text data using a voice recognition engine, and guidance is displayed on the user's device based on the analysis results.

[1197] Step 7:

[1198] Use of smart devices

[1199] Input: User instructions via smartphone or smart glasses.

[1200] Data processing / data calculation: The user inputs instructions through a smart device, and the server provides appropriate messages and information based on those instructions.

[1201] Output: Appropriate response message to the debtor and visual feedback to the user.

[1202] Specific actions: The user interacts with the system using voice input or touch, and relevant information and messages are displayed in real time.

[1203] 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.

[1204] The present invention is a system that combines an emotion engine that recognizes the emotions of users and debtors in order to improve the efficiency of debt collection operations and achieve accurate and effective responses. A specific embodiment of this system will now be described.

[1205] System configuration

[1206] The system consists of the following main components:

[1207] 1. Server: As the central processing unit, it manages data, generates messages, analyzes emotions, and performs other processing.

[1208] 2. Terminal: The interface operated by the user, which mediates communication with the server.

[1209] 3. User: A person who works in debt collection and is responsible for operating the system.

[1210] Program processing

[1211] Saving and managing debtor information

[1212] The server has a database that stores debtor information, including debtor names, contact details, debt amounts, and past payment history.

[1213] Chat function launch and automatic message generation

[1214] The user logs in to the system using a terminal and selects a debt collection case. The terminal then requests information about the selected case from the server, and the server searches for the relevant debtor information and past transaction history and sends it to the terminal.

[1215] When a user activates the chat function, the server uses an automated chat function to generate an appropriate reminder message and transmit it to the debtor via the terminal.

[1216] Example: The server generates a message saying, "I'm contacting you about an outstanding payment. Would you like to discuss payment options?" and sends it to the debtor via a terminal.

[1217] Parsing the reply from the debtor and generating the next message

[1218] When the reply from the debtor arrives at the device, it is forwarded to the server. The server's AI analyzes the reply and generates the optimal response message. At the same time, the emotion engine analyzes the reply and evaluates the debtor's emotional state.

[1219] Example: If a debtor replies, "When should I make payment by?", the server generates a message saying, "The payment deadline is (specified date). We ask that you make payment by then," and sends it through the terminal. If the emotion engine detects feelings of anxiety or confusion from the debtor's message, it can adjust the response accordingly, adding a message that provides reassurance, such as, "Please contact us at any time if you have any questions or concerns."

[1220] Emotion analysis using an emotion engine

[1221] The emotion engine analyzes the emotional state of the user and the debtor in real time. For example, it can detect emotions such as stress, anger, or confusion from the debtor's replies and voice data, and provide appropriate guidance to the server based on the results.

[1222] Record and learn from interactions

[1223] The server records all interactions and uses them for subsequent analysis and to improve the AI ​​model, allowing the system to learn each borrower's tendencies and respond more accurately.

[1224] Call handling integration and real-time sentiment analysis

[1225] As a future extension, when a user initiates a call from their device, the server will analyze the content of the call in real time and use an emotion engine to provide guidance based on the emotional state of the debtor and the user.

[1226] Example: During a telephone conversation with a debtor, the server displays a prompt on the user's device, such as "Your current debt balance is (amount). Would you like to schedule your next payment date?" If the server detects that the debtor is confused or anxious from the content of the call, it generates a message that provides more reassurance, such as "If you need additional assistance, we are here to help."

[1227] In this way, this system streamlines debt collection operations and achieves accurate and effective responses through collaboration between servers, terminals, and users and processing that incorporates an emotion engine.

[1228] The processing flow will be explained below.

[1229] Step 1:

[1230] The server creates a database that stores debtor information, including debtor names, contact details, debt amounts, and past payment history.

[1231] Step 2:

[1232] A user logs in to the system using a terminal, which provides an interface for entering a user ID and password and sends the authentication information to the server.

[1233] Step 3:

[1234] The server authenticates the user, and if the authentication is successful, grants the user the corresponding privileges and provides the information that the user can access.

[1235] Step 4:

[1236] The user selects a debt collection case on the terminal. The terminal requests information on the selected case from the server, and the server obtains the relevant debtor information and transaction history and sends them to the terminal.

[1237] Step 5:

[1238] The user activates the chat function through the terminal, and a command to start chat is sent from the terminal to the server.

[1239] Step 6:

[1240] The server uses an automated chat function to generate the initial reminder message, which is then sent to the debtor via their terminal.

[1241] Example: The server generates a message saying, "The payment deadline has passed. Please confirm." and sends it to the debtor via the terminal.

[1242] Step 7:

[1243] When the debtor replies via chat, the reply message arrives at the terminal, which then forwards the reply to the server.

[1244] Step 8:

[1245] The server uses AI to analyze the debtor's reply, and an emotion engine analyzes the debtor's emotions and determines the appropriate response along with the reply content.

[1246] Example: If the debtor replies, "I will explain why my payment is late," the server generates a message saying, "I understand your post. By when can you make your payment?" and sends it through the terminal. If the emotion engine detects the debtor's anxiety, it generates an additional message saying, "If you have any questions or concerns, please feel free to contact us."

[1247] Step 9:

[1248] The next response message is generated and sent to the debtor via the terminal. This process is repeated until the debtor pays or a solution is found.

[1249] Step 10:

[1250] All chat history is recorded by the server, which allows the system to use it as reference data for subsequent debt collection activities.

[1251] Step 11:

[1252] When a user activates the call handling function on their device, the device sends the instruction to the server, which analyzes the call content in real time and uses an emotion engine to generate a response based on the user's emotional state.

[1253] Example: The server generates a prompt that says, "Your remaining debt is (amount). Would you like to schedule your next payment?" and displays it on the terminal. If the debtor expresses anxiety during the call, the emotion engine generates a message that says, "We're here to help you so you can talk with peace of mind."

[1254] Step 12:

[1255] The entire call conversation is also recorded on the server and stored for future analysis and to improve the AI ​​model.

[1256] In this way, a system can be constructed that realizes debt collection operations more efficiently and accurately through processing that combines the server, terminals, and users in cooperation with an emotion engine.

[1257] Example 2

[1258] 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."

[1259] In debt collection work, communication with debtors is extremely important, but doing it manually takes a lot of time and effort. Furthermore, sending messages mechanically without considering the debtor's emotions can result in ineffective communication. Furthermore, when dealing with debtors over the phone, it is difficult to analyze their emotions in real time and respond appropriately. There is a need for a system that can solve these problems and achieve efficient and effective debt collection.

[1260] 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. In this invention, the server includes a means for saving debtor information, a means for generating a message for the debtor using an automated chat function, a means for analyzing a reply from the debtor, a means for generating a next message based on the analysis result, a means for analyzing the debtor's emotions using an emotion analysis engine and adjusting the response message based on the analysis result, a means operated by the user through a terminal, and a server capable of executing the above means. This enables effective communication that takes the debtor's emotions into consideration, thereby realizing efficient debt collection.

[1261] "Debtor information" is data including the debtor's name, contact information, debt amount, payment history, etc.

[1262] An "automated chat function" is a function that automatically communicates with debtors using messages generated by a server.

[1263] "Means for analyzing replies from debtors" refers to AI models or algorithms used to receive messages from debtors and analyze their contents.

[1264] The "means for generating the next message based on the analysis result" is a function for automatically generating the next response based on the analysis result of the message from the debtor.

[1265] An "emotion analysis engine" is a technology that analyzes emotions from debtors' messages and call content and generates appropriate responses based on the results.

[1266] "User" refers to a person who performs debt collection work and operates the system.

[1267] A "terminal" is an interface device operated by a user, and includes a computer or mobile device for communicating with a server.

[1268] A "server" is a central processing unit that performs data management, message generation, analysis, and other processing.

[1269] This invention is a system that combines an emotion engine that recognizes the emotions of users and debtors in order to streamline debt collection operations and achieve accurate and effective responses. This system operates through the cooperation of a server, terminals, and users.

[1270] System configuration

[1271] The system consists of the following main components:

[1272] 1. Server: A central processing unit that manages data, generates messages, analyzes emotions, and performs other processing. The server has a database for storing debtor information and has the ability to record interactions with debtors.

[1273] 2. Terminal: The interface operated by the user, which mediates communication with the server. The terminal runs software that allows the user to log in and operate the system.

[1274] 3. User: The person in charge of debt collection operations who operates the system.

[1275] System Features

[1276] An embodiment of the system is described in detail below.

[1277] Saving and managing debtor information

[1278] The server has a database that stores debtor information, including names, contact details, debt amounts, payment history, etc. When a user enters information from a terminal, the server stores the information in the database.

[1279] Chat function launch and automatic message generation

[1280] A user logs in to the system using a terminal and selects a debt collection case. The terminal requests case information from the server, which then searches for the relevant debtor information and past communication history and sends them to the terminal. When the user activates the chat function, the server uses an automated chat function to generate an appropriate message and sends it to the debtor via the terminal.

[1281] Examples:

[1282] The server generates a message saying, "We're contacting you regarding an outstanding payment. Would you like to discuss payment options?" and sends it to the debtor via the terminal.

[1283] Parsing the reply from the debtor and generating the next message

[1284] When the debtor's reply arrives at the device, it is forwarded to the server. The server's AI analyzes the reply and generates the optimal response message. At the same time, the emotion engine analyzes the debtor's reply and evaluates their emotional state.

[1285] Examples:

[1286] If the debtor replies, "By when should I make payment?", the server generates a message saying, "The payment deadline is (specified date). Please make payment by then." and sends it through the terminal.

[1287] If the emotion engine detects feelings of anxiety or confusion in the debtor's message, it adds a reassuring message such as, "If you have any questions or concerns, please contact us at any time."

[1288] Emotion analysis using an emotion analysis engine

[1289] The emotion engine analyzes the emotional state of the user and the debtor in real time. For example, it can detect emotions such as stress, anger, or confusion from the debtor's replies and voice data, and provide appropriate guidance to the server based on the results.

[1290] Record and learn from interactions

[1291] The server records all interactions and uses them for subsequent analysis and to improve the AI ​​model, allowing the system to learn each borrower's tendencies and respond more accurately.

[1292] Call handling integration and real-time sentiment analysis

[1293] When a user initiates a call from a terminal, the server analyzes the contents of the call in real time and uses an emotion engine to provide guidance based on the emotional state of the debtor and the user.

[1294] Examples:

[1295] The server displays a prompt on the user's terminal such as "Your current debt balance is (amount). Would you like to schedule your next payment date?"

[1296] If the call detects confusion or anxiety from the debtor, it generates a reassuring message such as, "If you need additional assistance, we're here to help."

[1297] As a result, this system streamlines debt collection operations and achieves accurate and effective responses by linking servers, terminals, and users and incorporating an emotion engine.

[1298] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1299] Step 1: Register and save debtor information

[1300] Input: Information such as debtor's name, contact details, debt amount, and payment history

[1301] Operation: The user enters debtor information into the input form on the terminal and presses the send button.

[1302] Data processing: The server receives the entered information, formats it, and stores it in a database.

[1303] Output: A confirmation message will be printed to the terminal indicating a successful save.

[1304] Step 2: User login and selection of debt collection case

[1305] Input: User name and password, Debt collection case ID to be selected after logging in

[1306] How it works: The user enters their authentication information on the login screen of their device, presses the login button, and then selects the debt collection case.

[1307] Data calculation: The server authenticates the login information and, if authentication is successful, receives the case ID selected by the user.

[1308] Output: A successful authentication message and detailed information about the case will be displayed on the terminal.

[1309] Step 3: Retrieve and display case information

[1310] Input: Debt collection case ID selected by the user

[1311] How it works: The device requests a case ID from the server.

[1312] Data processing: The server searches the database for relevant debtor information and past transaction history, and sends the results to the terminal.

[1313] Output: Debtor information and past transaction history will be displayed on the terminal.

[1314] Step 4: Generate and send automated messages

[1315] Input: Debtor information and past transaction history

[1316] How it works: A user activates the chat feature, and the device sends the request to the server.

[1317] Data calculation: The server uses a generative AI model based on debtor information and transaction history to generate an appropriate dunning message.

[1318] Output: The generated message is sent to the debtor.

[1319] Specific example of operation: The server generates a message saying, "I'm contacting you about an outstanding payment. Would you like to discuss payment options?" and sends it to the debtor via the terminal.

[1320] Step 5: Receive and analyze the debtor's response

[1321] Input: Reply message from debtor

[1322] What happens: The device receives the reply and forwards it to the server.

[1323] Data calculation: The server's AI analyzes the reply content, and the emotion analysis engine evaluates the emotional state.

[1324] Output: Analysis results and emotional states are generated.

[1325] Step 6: Generate and send the next message

[1326] Input: Analysis results and emotional state

[1327] Action: The server generates the following response message based on the analysis results and emotional state.

[1328] Data calculation: The server uses a generative AI model to generate specific response messages and adjusts the content according to the emotional state.

[1329] Output: The generated response message is sent to the debtor through the terminal.

[1330] Specific example of operation: If the debtor replies, "When should I make payment by?", the server generates a message saying, "The payment deadline is (specified date). We ask that you make payment by then," and sends it through the terminal. Also, if the sentiment analysis engine detects anxiety, it adds a reassuring message saying, "Please contact us at any time if you have any questions or concerns."

[1331] Step 7: Record the interaction

[1332] Input: All interaction data

[1333] How it works: The server records chat logs with the debtor and sentiment analysis results.

[1334] Data processing: The server organizes the log data and stores it in a database or log file.

[1335] Output: Creates a database entry that can be used for subsequent analysis and improving the AI ​​model.

[1336] Step 8: Call response activation and real-time analysis

[1337] Input: Call with debtor

[1338] How it works: The user activates the phone function on their device, and the server analyzes the call content in real time.

[1339] Data Computing: The server converts the call audio into text and generates appropriate guidance using a sentiment analysis engine and generative AI model.

[1340] Output: Appropriate guidance is displayed in real time on the user's device.

[1341] Example of specific behavior: During a call, the server displays a prompt saying, "Your current debt balance is (amount). Would you like to schedule your next payment date?" and, if anxiety is detected, generates a message saying, "If you need additional assistance, we're here to help."

[1342] The above are the detailed processing steps of this system.

[1343] (Application example 2)

[1344] 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."

[1345] In traditional debt collection operations, communication with debtors is inefficient, and especially emotional responses are lacking, leading to stress and disputes. Furthermore, there is no system in place for security personnel to detect suspicious individuals or dangerous situations in real time and take appropriate action.

[1346] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for storing debtor information, means for generating messages for the debtor using an automated chat function, means for analyzing replies from the debtor, means for generating the next message based on the analysis results, and means for analyzing emotions in real time and providing appropriate response guidance. This makes it possible to generate accurate and effective messages based on the debtor's emotional state.

[1347] In addition, by including a means for recognizing the emotional state of the other party and detecting dangerous situations based on emotion analysis, it becomes possible for devices used by security personnel to detect suspicious individuals and dangerous situations in real time and provide appropriate response guidance.

[1348] "Debtor Information" means information including the debtor's name, contact details, amount of debt, past payment history, and other data relevant to debt collection activities.

[1349] An "automated chat function" is a function that automatically generates and sends messages to debtors using predefined messages or algorithms.

[1350] "Means for analyzing replies" refers to a program or algorithm for analyzing replies from debtors and understanding their contents and intent.

[1351] "Means for generating messages" refers to a system or algorithm for automatically creating the next appropriate message based on the analysis results.

[1352] "Means for analyzing emotions in real time and providing appropriate response guidance" is a mechanism for analyzing the emotional state of the user or other party and presenting appropriate response methods in real time based on the results.

[1353] "Means for detecting dangerous situations" refers to the function of detecting suspicious behavior or dangerous emotional states from the results of emotion analysis, and issuing a warning or prompting appropriate action.

[1354] Specific Embodiments of the Invention

[1355] The present invention realizes a security support system that combines an emotion recognition engine. Specific embodiments of the present invention are described below.

[1356] System Components

[1357] The system consists of the following main components:

[1358] Server: A central processing unit that manages data, generates messages, analyzes sentiment, and performs other processing.

[1359] Terminal: Smart glasses used by security personnel, equipped with a camera and microphone, which act as a medium for communication with the server.

[1360] Users: Security personnel responsible for operating the system.

[1361] Program processing

[1362] Debtor information and chat function

[1363] The server has a database that stores debtor information, including the debtor's name, contact information, debt amount, past payment history, etc. When a user logs into the system using a terminal and selects a debt collection case, the server activates an automated chat function based on the selection and generates an appropriate collection message.

[1364] Sentiment analysis and guidance

[1365] The device's camera and microphone are used to capture the user's or debtor's facial expressions and tone of voice, which are then sent to a server in real time. The server then uses an emotion analysis engine to analyze this data. For example, if the server detects stress, anger, or confusion, it will provide appropriate guidance based on the results.

[1366] Example: If the sentiment analysis engine detects that the debtor is confused, it generates a reassuring message such as, "Please contact us anytime if you have any questions or concerns."

[1367] Security Support Function

[1368] When security personnel wear smart glasses, the device's camera constantly monitors the surrounding environment. If suspicious behavior or facial emotional states are detected, the information is sent to the server in real time, and appropriate warnings and guidance are displayed on the device based on the server's analysis.

[1369] Example: If the emotion analysis engine determines that a person may be suspicious, a warning message such as "This person may be suspicious. Please be on guard" will be output.

[1370] Hardware and software used

[1371] Hardware: Smart glasses (camera, microphone)

[1372] Software: OpenCV (face detection), Keras (emotion recognition model), pyttsx3 (audio output)

[1373] Prompt Sentence Examples

[1374] "Use the following face images to identify the emotion. Output the most likely emotion label:

[1375] [Image data]

[1376] In this way, the present invention utilizes an emotion recognition engine to realize a system that is useful for improving the efficiency of communication with debtors and supporting security operations.

[1377] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1378] Step 1:

[1379] A user logs in to the system using a terminal and selects security monitoring mode. The data entered at this time is the user ID and password. The terminal sends the entered data to the server, which performs authentication processing and returns a response indicating that the login was successful. If the login is successful, the terminal displays the security monitoring mode screen.

[1380] Step 2:

[1381] The device's camera and microphone are activated and capture the surrounding video and audio in real time. The video and audio data obtained from the camera and microphone are input. This data is sent to the server in real time, and the server performs preprocessing for analysis. Specifically, it detects faces from the video data and extracts the tone of voice from the audio data.

[1382] Step 3:

[1383] The server inputs the preprocessed facial image data into an emotion recognition model (Keras) to predict the emotional state. The input is the detected facial image data, and the output is the emotional state (e.g., anger, anxiety, happiness, etc.). The emotion recognition model analyzes the facial features and outputs the most likely emotion label.

[1384] Step 4:

[1385] The server generates appropriate response guidance based on the analysis results. If the emotion determination result indicates "anger" or "anxiety," the server generates a warning message. On the other hand, if the emotion determination result indicates "happiness" or "calmness," no particular warning is generated. This output warning message and guidance are sent to the device.

[1386] Step 5:

[1387] The device notifies the user in real time of warning messages and guidance received from the server. Specifically, the device displays the message on the smart glasses display and outputs audio (pyttsx3) as needed. For example, it displays a message saying, "There may be a suspicious person. Please be on guard." and announces it in audio. The input to this step is the guidance received from the server, and the output is a notification to the user.

[1388] 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.

[1389] 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.

[1390] 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.

[1391] [Fourth embodiment]

[1392] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1393] 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.

[1394] 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).

[1395] 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.

[1396] 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.

[1397] 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).

[1398] 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.

[1399] 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.

[1400] 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.

[1401] 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.

[1402] 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.

[1403] 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.

[1404] 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."

[1405] The present invention is a system for improving the efficiency and accuracy of debt collection operations, and is mainly composed of means for automating interactions with debtors. Specific embodiments of this system are described below.

[1406] System Overview

[1407] The system mainly includes the following components:

[1408] 1. Server: The central processing unit that handles all data management, analysis, and message generation.

[1409] 2. Terminal: The interface operated by the user, which mediates communication with the server.

[1410] 3. User: The person who performs the debt collection work and operates the system.

[1411] System Operation

[1412] Saving debtor information

[1413] The server has a database that stores debtor information, including details such as debtor names, contact details, debt amounts, and payment history.

[1414] Automated chat features

[1415] The system's automated chat function works by allowing users to access the server through their devices and initiate a chat with the debtor. The server uses AI to generate appropriate messages based on pre-set templates and past interactions. These messages are then sent to the debtor via their devices.

[1416] Example: The server generates a message saying, "I'm contacting you about an outstanding payment. Would you like to discuss payment options?" and sends it to the debtor via a terminal.

[1417] Analysis of debtor replies

[1418] When the debtor's reply arrives at the terminal, it is forwarded to the server, which uses AI to analyze the reply and generate the next optimal message. The analysis includes responses based on the debtor's questions and wishes.

[1419] Example: If the debtor replies, "When should I make payment by?", the server generates a message saying, "The payment deadline is (specified date). Please make payment by then." and sends it via the terminal.

[1420] Record and learn from interactions

[1421] All chat history is recorded by the server and used for future analysis and to improve the AI ​​model, allowing the system to respond more accurately over time.

[1422] Future phone call integration

[1423] The system is planned to integrate a telephone response function in the future. When a user starts a call on the device, the server analyzes the call content in real time and provides appropriate guidance.

[1424] Example: During a telephone conversation with a debtor, the server displays a prompt on the user's terminal: "Your current debt balance is (amount). Would you like to schedule your next payment date?"

[1425] Program processing

[1426] The server's main role is to manage and analyze data and generate messages. Specifically, it retrieves debtor information from a database, generates chat messages using an AI engine, analyzes the replies, and then generates the next message based on the analysis results, repeating the entire process.

[1427] The terminal provides an interface for users to operate the system and communicates with the server. Users log in through the terminal, select a debt collection case, and start chat or phone calls. The terminal also receives replies from debtors and forwards them to the server.

[1428] In this way, this system, through cooperation between the server, terminals, and users, streamlines debt collection operations and achieves highly accurate responses.

[1429] The processing flow will be explained below.

[1430] Step 1:

[1431] The server creates a database to store debtor information, including debtor names, contact details, debt amounts, and past payment history.

[1432] Step 2:

[1433] A user logs in to the system from a terminal, which sends the entered user ID and password to the server.

[1434] Step 3:

[1435] The server authenticates the user and, if the correct authentication information is verified, grants the user the corresponding privileges.

[1436] Step 4:

[1437] The user selects a debt collection case through the terminal, and the terminal requests information on the selected case from the server.

[1438] Step 5:

[1439] The server searches for the debtor information and past transaction history for the requested case and sends the relevant data to the terminal.

[1440] Step 6:

[1441] The user activates the chat function on the terminal and instructs it to start an automated chat with the debtor.

[1442] Step 7:

[1443] The device sends a command to start a chat to the server, which uses AI to generate an appropriate prompt message.

[1444] Step 8:

[1445] A message generated by the server (e.g., "Your payment is overdue. Please check.") is sent to the debtor via the terminal.

[1446] Step 9:

[1447] When the debtor replies to the chat, the terminal transfers the reply to the server.

[1448] Step 10:

[1449] The server uses AI to analyze the debtor's reply and generate the most appropriate response message (e.g., "The payment deadline is (specified date). Will you be able to pay by then?").

[1450] Step 11:

[1451] The server generates a response message and sends it to the debtor via the terminal. This process is repeated until the debtor agrees to pay.

[1452] Step 12:

[1453] All chat history is recorded by the server and used for future analysis and to improve the AI ​​model.

[1454] Step 13:

[1455] When a user activates the phone function from their device, the server analyzes the call content in real time and provides appropriate guidance (e.g., "Your current debt balance is (amount). Would you like to schedule your next payment date?").

[1456] Step 14:

[1457] Records of telephone responses are also stored on the server and used as useful data for subsequent debt collection activities.

[1458] In this way, the server, terminal, and user cooperate to progress through each step, improving the efficiency and accuracy of debt collection.

[1459] Example 1

[1460] 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."

[1461] Traditional debt collection operations relied heavily on manual work, resulting in low operational efficiency and a high risk of human error. Furthermore, insufficient recording and analysis of interactions with debtors made it difficult to improve the accuracy and efficiency of the overall system.

[1462] 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.

[1463] In this invention, the server includes a system that includes a means for storing debtor information, a means for generating messages for debtors using an automated chat function, a means for analyzing replies from debtors, and an information processing device capable of executing the above means. This makes it possible to streamline debt collection operations and enable accurate and prompt responses. The system also includes a means for recording a history of interactions with debtors and learning the debtor's response tendencies based on subsequent interactions, allowing the system's response accuracy to improve over time. Furthermore, by integrating telephone response functions and including a means for analyzing the content of telephone conversations with debtors in real time and providing appropriate guidance, the entire dialogue with debtors can be managed in a unified and effective manner.

[1464] "Debtor Information" refers to detailed information such as the debtor's name, contact details, debt amount, and payment history.

[1465] "Automated chat function" refers to a function that uses artificial intelligence to automatically exchange messages with debtors.

[1466] "Message generation means" refers to a function that generates appropriate messages based on pre-set templates and past interactions.

[1467] "Reply analysis means" refers to a function that analyzes a reply message from a debtor and generates the next most appropriate message based on the content of the reply message.

[1468] The term "information processing device" refers to a central processing unit capable of executing the above-mentioned means, and a device that manages and analyzes data.

[1469] "Chat history" refers to a record of past message exchanges with a debtor.

[1470] "Response tendencies" refer to the behavioral patterns and tendencies exhibited by the debtor in past interactions.

[1471] "Telephone response function" refers to the function that integrates telephone conversations with debtors, analyzes the content of the conversations in real time, and provides appropriate guidance.

[1472] This invention is a system for improving the efficiency and accuracy of debt collection operations, and is mainly composed of a means for automating interactions with debtors. This system is mainly composed of a server, terminals, and users.

[1473] Server Roles and Functions

[1474] The Server is the central processing unit responsible for all data management, analysis, and message generation. The Server has the following main responsibilities:

[1475] 1. Storage of debtor information

[1476] The server has a database that stores debtor information, including details such as debtor names, contact details, debt amounts, and payment history.

[1477] 2. Message Creation

[1478] The server utilizes an automated chat function that uses an AI engine (e.g., OpenAI GPT-4) to generate messages for the debtor, including messages based on pre-defined templates and past interactions.

[1479] 3. Reply Analysis

[1480] The reply from the debtor is received by the server and analyzed using an AI engine. Based on the results of the analysis, the next optimal message is generated.

[1481] 4. Chat history recording

[1482] All chat history is recorded by the server and used for future analysis and to improve the AI ​​model.

[1483] 5. Telephone integration

[1484] In the future, telephone response functions will also be integrated, making it possible to analyze call content in real time and provide appropriate guidance.

[1485] Device roles and functions

[1486] The terminal provides an interface for users to operate the system and communicates with the server. Specifically, it has the following functions:

[1487] 1. User authentication and login

[1488] A user logs into the system using a terminal and sends authentication information to the server, which verifies this information and authenticates the user.

[1489] 2. Display of debtor information

[1490] When a user searches for specific debtor information, the terminal sends this request to the server, which retrieves the relevant information and displays it on the terminal.

[1491] 3. Start chatting

[1492] The user selects a chat function on the terminal to start chatting with the debtor.

[1493] 4. Sending and receiving messages

[1494] The terminal sends messages generated by the server to the debtor and also forwards replies from the debtor to the server.

[1495] 5. Telephone call assistance

[1496] During a call, the server analyzes the call content in real time and displays information on the device that provides appropriate guidance.

[1497] User operations

[1498] The user operates the system through a terminal, specifically by performing the following steps:

[1499] 1. Log in

[1500] The user logs in from the terminal and starts the debt collection operation.

[1501] 2. Obtaining debtor information

[1502] The user selects a specific debtor on the terminal and retrieves that information from the server.

[1503] 3. Start chatting

[1504] The user initiates a conversation with the debtor using the chat function.

[1505] 4. Chat and phone support

[1506] The user sends a message generated by the server to the debtor and waits for a reply from the debtor. When answering the phone, the user also receives guidance from the server.

[1507] Examples and prompts

[1508] As a concrete example, the server generates a message saying, "I'm contacting you regarding an outstanding payment. Can we discuss payment methods?" and sends it to the debtor via the terminal. If the debtor replies, "By when should I make payment?", the server generates a message saying, "The payment deadline is (specified date). We ask that you make payment by then," and sends it via the terminal.

[1509] Example prompt sentence:

[1510] Generate a "payment confirmation" message for a debt collection case. The debtor asks, "When is the payment due?"

[1511] In this way, this system streamlines debt collection operations by linking servers, terminals, and users, and enables highly accurate responses.

[1512] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1513] Step 1:

[1514] A user logs in to a terminal

[1515] Specific behavior:

[1516] The user opens a login screen through the terminal and enters authentication information, which is then sent to the server by the terminal (user ID and password).

[1517] Data processing / calculation:

[1518] The server compares the received authentication information with the user information in its database. If the comparison is successful, the user is authenticated and the dashboard screen is sent to the terminal.

[1519] Input: User ID, Password

[1520] Output: User's dashboard screen

[1521] Step 2:

[1522] Obtain debtor information

[1523] Specific behavior:

[1524] The user uses the terminal's search function to input a specific debtor ID to search for debtor information. The terminal sends this request to the server.

[1525] Data processing / calculation:

[1526] The server retrieves the relevant debtor information (name, contact details, debt amount, payment history, etc.) from the database based on the debtor ID and sends it to the terminal.

[1527] Input: Debtor ID

[1528] Output: Screen showing debtor details

[1529] Step 3:

[1530] Start a chat

[1531] Specific behavior:

[1532] The user clicks a button on the terminal to start a chat with a particular debtor, and the terminal notifies the server of this action.

[1533] Data processing / calculation:

[1534] The server performs initial settings to start a chat session and displays a chat window on the terminal.

[1535] Input: Request to start chat

[1536] Output: Show chat window

[1537] Step 4:

[1538] AI-generated initial message

[1539] Specific behavior:

[1540] The server uses an AI engine to generate an initial message for the debtor, which it then sends to the terminal, which then sends it to the debtor.

[1541] Data processing / calculation:

[1542] The AI ​​engine (e.g., OpenAI GPT-4) generates optimal messages based on pre-defined templates and past interactions.

[1543] Input: Request for initial message creation

[1544] Output: The generated message (e.g., "I'm contacting you regarding an outstanding payment. Would you like to discuss payment options?")

[1545] Step 5:

[1546] Receiving a reply from the debtor

[1547] Specific behavior:

[1548] When the debtor replies, the message is delivered to the terminal and then forwarded to the server.

[1549] Data processing / calculation:

[1550] The server passes the received reply to the AI ​​engine, which analyzes the content and generates the next optimal message based on the analysis results.

[1551] Input: Debtor's reply message (e.g., "When do I need to make payment?")

[1552] Output: Analysis results and next message

[1553] Step 6:

[1554] AI-based reply analysis and next message generation

[1555] Specific behavior:

[1556] The server uses an AI engine to analyze the reply from the debtor and generate the next message based on the content of the reply, which is then sent to the terminal, which then sends it to the debtor.

[1557] Data processing / calculation:

[1558] The AI ​​engine analyzes the reply content and generates the optimal response message based on the analysis results.

[1559] Input: The reply message to be parsed

[1560] Output: The next message generated (e.g. "Payment is due by (specified date). We ask that you make payment by that date.")

[1561] Step 7:

[1562] Chat history recording

[1563] Specific behavior:

[1564] All messages exchanged through the device are recorded on the server.

[1565] Data processing / calculation:

[1566] The server stores each chat message in a database for subsequent analysis and to improve the AI ​​model.

[1567] Input: Each exchanged message

[1568] Output: Recorded chat history

[1569] Step 8:

[1570] Future phone call integration

[1571] Specific behavior:

[1572] When a user starts answering a call through the terminal, the server analyzes the contents of the call in real time and provides appropriate guidance to the terminal.

[1573] Data processing / calculation:

[1574] The server analyzes the call content in real time, generates guidance using an AI engine, and notifies the device.

[1575] Input: Phone call details

[1576] Output: Real-time guidance (e.g., "Your current debt balance is (amount). Would you like to schedule your next payment date?")

[1577] (Application example 1)

[1578] 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."

[1579] Conventional debt collection systems often require manual interaction with debtors, making it difficult to efficiently manage a large number of debtors. Furthermore, responses via telephone require real-time communication, placing a heavy burden on operators. Furthermore, there is a lack of systems that can flexibly respond using the latest interface devices, such as smartphones and smart glasses, making it necessary to improve the efficiency of debt collection.

[1580] 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.

[1581] In this invention, the server includes a means for storing debtor information, a means for generating messages for debtors using an automated chat function, a means for analyzing replies from debtors, a means for generating subsequent messages based on the analysis results, a means for analyzing telephone conversations with debtors in real time and providing appropriate guidance, and an interface means using a smartphone or smart glasses. This significantly improves the efficiency of interactions with debtors, enables real-time responses and hands-free operation, and enhances the accuracy and efficiency of the entire debt collection process.

[1582] "Means for storing debtor information" refers to a data storage function that accumulates and manages information about debtors (such as names, contact details, debt amounts, payment history, etc.).

[1583] "Means of generating messages to debtors using an automated chat function" refers to a function that utilizes AI technology to refer to pre-set templates and past interactions, and automatically creates and sends appropriate messages to debtors.

[1584] "Means for analyzing replies from debtors" refers to a function that uses AI to analyze the content of replies sent by debtors via chat or email, and understand their meaning and intent.

[1585] The "means for generating the next message based on the analysis result" refers to a function for generating the optimal next message based on the result of analyzing the reply content of the debtor and sending it to the debtor.

[1586] "Means of analyzing the content of telephone conversations with debtors in real time and providing appropriate guidance" refers to a function that uses AI technology to analyze the content of conversations with debtors in real time and immediately instruct the operator on the appropriate response method.

[1587] "Interface means using a smartphone or smart glasses" refers to an operation interface that allows a user to access the system using a smartphone or smart glasses and perform interactions with debtors.

[1588] "Server" refers to a central processing unit that manages data, analyzes data, and generates messages for the entire system, and has computing resources that can execute all means.

[1589] This invention is a system for improving the efficiency and accuracy of debt collection operations, and is mainly composed of means for automating interactions with debtors. Specific embodiments of this system are described below.

[1590] System configuration

[1591] The system mainly includes the following components:

[1592] 1. Server: The central processing unit that manages all data, performs analysis, and generates messages.

[1593] 2. Terminal: The interface operated by the user, which mediates communication with the server.

[1594] 3. User: The person who performs debt collection work and operates the system.

[1595] 4. Smart devices: Interface methods using smartphones and smart glasses.

[1596] System Operation

[1597] Saving debtor information

[1598] The server has a database that stores debtor information, including details such as debtor names, contact details, debt amounts, and payment history.

[1599] Automated chat features

[1600] The system's automated chat function works by allowing users to access the server through their terminal and start a chat with the debtor. The server uses AI to generate an appropriate message based on pre-set templates and past interactions. This message is then sent to the debtor through the terminal. For example, the server generates a message that reads, "I'm contacting you regarding an outstanding payment. Would you like to discuss payment options?" and sends it to the debtor through the terminal.

[1601] Analysis of debtor replies

[1602] When the reply from the debtor arrives at the terminal, the contents are transferred to the server. The server uses AI to analyze the reply and generate the next optimal message. The analysis includes a response based on the debtor's questions and wishes. For example, if the debtor replies, "When should I make payment by?" the server will generate a message saying, "The payment deadline is (specified date). We ask that you make payment by then," and send it via the terminal.

[1603] Record and learn from interactions

[1604] All chat history is recorded by the server and used for future analysis and to improve the AI ​​model, allowing the system to respond more accurately over time.

[1605] Telephone response function

[1606] The server also integrates a telephone response function. When a user starts a telephone response through the terminal, the server analyzes the content of the call in real time and provides appropriate guidance. For example, while on a telephone call with a debtor, the server displays a prompt on the user's terminal saying, "Your current debt balance is (amount). Would you like to schedule your next payment date?"

[1607] Use of smart devices

[1608] The interface using a smartphone or smart glasses allows users to operate hands-free, improving work efficiency. For example, users can respond by voice input while visually checking chat content and debtor information.

[1609] Hardware and software used

[1610] Hardware: Smartphones, smart glasses (e.g., Google Glass)

[1611] Software: Online databases (e.g., Firebase), AI message generation engines (e.g., OpenAI GPT-3)

[1612] Examples of prompt statements

[1613] Debtor information: Name - Common name, Debt amount - 100000 yen, Payment history - ['2023-01-01: 50000', '2023-02-01: 0']

[1614] Message: Please send the following message to your debtor:

[1615] "I'm contacting you regarding an outstanding payment. Can we discuss payment options?"

[1616] In this way, this system works in cooperation with the server, terminals, and users to streamline debt collection operations and achieve highly accurate responses.

[1617] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1618] Step 1:

[1619] Obtaining debtor information

[1620] Input: A user logs in through a terminal and requests information about a particular debtor.

[1621] Data processing / data calculation: The server retrieves details such as the debtor's name, contact details, debt amount, and payment history from a database (e.g., Firebase).

[1622] Output: Send the debtor's information to the user's terminal.

[1623] Specific operation: A user logs in and searches for information on a specific debtor, and the server retrieves the information from the database and displays it on the terminal.

[1624] Step 2:

[1625] Automatic chat message generation

[1626] Input: Debtor information obtained by the server and instructions to start chat.

[1627] Data processing / data calculation: The server uses a generative AI model (e.g., OpenAI GPT-3) to generate a new message based on the debtor's information and past interactions. It creates a prompt and inputs it into the AI ​​model to generate the message.

[1628] Output: The generated message is sent to the debtor via the user's terminal.

[1629] Specific operation: A prompt sentence is generated, and based on that, an AI model is asked to generate a message, and the result is sent to the debtor.

[1630] Step 3:

[1631] Receiving and analyzing debtor replies

[1632] Input: Reply message from debtor.

[1633] Data processing / data calculation: The server analyzes the reply message received from the debtor. Using an AI model, it understands the intent of the reply and the question, and then extracts the necessary information.

[1634] Output: The data needed to generate the next best message based on the analyzed results.

[1635] Specific operation: Receives a reply from the debtor, requests the AI ​​model to analyze it, and uses the analysis results to generate the next message.

[1636] Step 4:

[1637] Generate next message

[1638] Input: Instructions for generating the next message based on the analysis results.

[1639] Data processing / data calculation: The server uses the generative AI model again to generate the next appropriate message based on the analysis results of the reply content. The previous prompt and the analysis results are input into the AI ​​model as the new prompt.

[1640] Output: The following generated message is sent to the debtor via the user's terminal.

[1641] Specific operation: A new prompt sentence is generated based on the analysis results, which is then input into the AI ​​model to generate a new message and send it to the debtor.

[1642] Step 5:

[1643] Record and learn from interactions

[1644] Input: Each chat message and its response.

[1645] Data processing / data calculation: All chat history is recorded in a database by the server and stored for future analysis and to improve the AI ​​model.

[1646] Output: Saved chat history data.

[1647] Specific behavior: The entire interaction history is periodically saved to a database and used in the future to learn response trends and improve the accuracy of the model.

[1648] Step 6:

[1649] Phone call integration and real-time analysis

[1650] Input: The instructions and call content for the user to initiate a phone call.

[1651] Data processing / data calculation: The server recognizes the voice of the call in real time and analyzes the content using an AI model. Based on the analysis results, the server provides appropriate guidance to the user.

[1652] Output: Guidance messages generated in real time.

[1653] Specific operation: The contents of the call are converted into text data using a voice recognition engine, and guidance is displayed on the user's device based on the analysis results.

[1654] Step 7:

[1655] Use of smart devices

[1656] Input: User instructions via smartphone or smart glasses.

[1657] Data processing / data calculation: The user inputs instructions through a smart device, and the server provides appropriate messages and information based on those instructions.

[1658] Output: Appropriate response message to the debtor and visual feedback to the user.

[1659] Specific actions: The user interacts with the system using voice input or touch, and relevant information and messages are displayed in real time.

[1660] 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.

[1661] The present invention is a system that combines an emotion engine that recognizes the emotions of users and debtors in order to improve the efficiency of debt collection operations and achieve accurate and effective responses. A specific embodiment of this system will now be described.

[1662] System configuration

[1663] The system consists of the following main components:

[1664] 1. Server: As the central processing unit, it manages data, generates messages, analyzes emotions, and performs other processing.

[1665] 2. Terminal: The interface operated by the user, which mediates communication with the server.

[1666] 3. User: A person who works in debt collection and is responsible for operating the system.

[1667] Program processing

[1668] Saving and managing debtor information

[1669] The server has a database that stores debtor information, including debtor names, contact details, debt amounts, and past payment history.

[1670] Chat function launch and automatic message generation

[1671] The user logs in to the system using a terminal and selects a debt collection case. The terminal then requests information about the selected case from the server, and the server searches for the relevant debtor information and past transaction history and sends it to the terminal.

[1672] When a user activates the chat function, the server uses an automated chat function to generate an appropriate reminder message and transmit it to the debtor via the terminal.

[1673] Example: The server generates a message saying, "I'm contacting you about an outstanding payment. Would you like to discuss payment options?" and sends it to the debtor via a terminal.

[1674] Parsing the reply from the debtor and generating the next message

[1675] When the reply from the debtor arrives at the device, it is forwarded to the server. The server's AI analyzes the reply and generates the optimal response message. At the same time, the emotion engine analyzes the reply and evaluates the debtor's emotional state.

[1676] Example: If a debtor replies, "When should I make payment by?", the server generates a message saying, "The payment deadline is (specified date). We ask that you make payment by then," and sends it through the terminal. If the emotion engine detects feelings of anxiety or confusion from the debtor's message, it can adjust the response accordingly, adding a message that provides reassurance, such as, "Please contact us at any time if you have any questions or concerns."

[1677] Emotion analysis using an emotion engine

[1678] The emotion engine analyzes the emotional state of the user and the debtor in real time. For example, it can detect emotions such as stress, anger, or confusion from the debtor's replies and voice data, and provide appropriate guidance to the server based on the results.

[1679] Record and learn from interactions

[1680] The server records all interactions and uses them for subsequent analysis and to improve the AI ​​model, allowing the system to learn each borrower's tendencies and respond more accurately.

[1681] Call handling integration and real-time sentiment analysis

[1682] As a future extension, when a user initiates a call from their device, the server will analyze the content of the call in real time and use an emotion engine to provide guidance based on the emotional state of the debtor and the user.

[1683] Example: During a telephone conversation with a debtor, the server displays a prompt on the user's device, such as "Your current debt balance is (amount). Would you like to schedule your next payment date?" If the server detects that the debtor is confused or anxious from the content of the call, it generates a message that provides more reassurance, such as "If you need additional assistance, we are here to help."

[1684] In this way, this system streamlines debt collection operations and achieves accurate and effective responses through collaboration between servers, terminals, and users and processing that incorporates an emotion engine.

[1685] The processing flow will be explained below.

[1686] Step 1:

[1687] The server creates a database that stores debtor information, including debtor names, contact details, debt amounts, and past payment history.

[1688] Step 2:

[1689] A user logs in to the system using a terminal, which provides an interface for entering a user ID and password and sends the authentication information to the server.

[1690] Step 3:

[1691] The server authenticates the user, and if the authentication is successful, grants the user the corresponding privileges and provides the information that the user can access.

[1692] Step 4:

[1693] The user selects a debt collection case on the terminal. The terminal requests information on the selected case from the server, and the server obtains the relevant debtor information and transaction history and sends them to the terminal.

[1694] Step 5:

[1695] The user activates the chat function through the terminal, and a command to start chat is sent from the terminal to the server.

[1696] Step 6:

[1697] The server uses an automated chat function to generate the initial reminder message, which is then sent to the debtor via their terminal.

[1698] Example: The server generates a message saying, "The payment deadline has passed. Please confirm." and sends it to the debtor via the terminal.

[1699] Step 7:

[1700] When the debtor replies via chat, the reply message arrives at the terminal, which then forwards the reply to the server.

[1701] Step 8:

[1702] The server uses AI to analyze the debtor's reply, and an emotion engine analyzes the debtor's emotions and determines the appropriate response along with the reply content.

[1703] Example: If the debtor replies, "I will explain why my payment is late," the server generates a message saying, "I understand your post. By when can you make your payment?" and sends it through the terminal. If the emotion engine detects the debtor's anxiety, it generates an additional message saying, "If you have any questions or concerns, please feel free to contact us."

[1704] Step 9:

[1705] The next response message is generated and sent to the debtor via the terminal. This process is repeated until the debtor pays or a solution is found.

[1706] Step 10:

[1707] All chat history is recorded by the server, which allows the system to use it as reference data for subsequent debt collection activities.

[1708] Step 11:

[1709] When a user activates the call handling function on their device, the device sends the instruction to the server, which analyzes the call content in real time and uses an emotion engine to generate a response based on the user's emotional state.

[1710] Example: The server generates a prompt that says, "Your remaining debt is (amount). Would you like to schedule your next payment?" and displays it on the terminal. If the debtor expresses anxiety during the call, the emotion engine generates a message that says, "We're here to help you so you can talk with peace of mind."

[1711] Step 12:

[1712] The entire call conversation is also recorded on the server and stored for future analysis and to improve the AI ​​model.

[1713] In this way, a system can be constructed that realizes debt collection operations more efficiently and accurately through processing that combines the server, terminals, and users in cooperation with an emotion engine.

[1714] Example 2

[1715] 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."

[1716] In debt collection work, communication with debtors is extremely important, but doing it manually takes a lot of time and effort. Furthermore, sending messages mechanically without considering the debtor's emotions can result in ineffective communication. Furthermore, when dealing with debtors over the phone, it is difficult to analyze their emotions in real time and respond appropriately. There is a need for a system that can solve these problems and achieve efficient and effective debt collection.

[1717] 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. In this invention, the server includes a means for saving debtor information, a means for generating a message for the debtor using an automated chat function, a means for analyzing a reply from the debtor, a means for generating a next message based on the analysis result, a means for analyzing the debtor's emotions using an emotion analysis engine and adjusting the response message based on the analysis result, a means operated by the user through a terminal, and a server capable of executing the above means. This enables effective communication that takes the debtor's emotions into consideration, thereby realizing efficient debt collection.

[1718] "Debtor information" is data including the debtor's name, contact information, debt amount, payment history, etc.

[1719] An "automated chat function" is a function that automatically communicates with debtors using messages generated by a server.

[1720] "Means for analyzing replies from debtors" refers to AI models or algorithms used to receive messages from debtors and analyze their contents.

[1721] The "means for generating the next message based on the analysis result" is a function for automatically generating the next response based on the analysis result of the message from the debtor.

[1722] An "emotion analysis engine" is a technology that analyzes emotions from debtors' messages and call content and generates appropriate responses based on the results.

[1723] "User" refers to a person who performs debt collection work and operates the system.

[1724] A "terminal" is an interface device operated by a user, and includes a computer or mobile device for communicating with a server.

[1725] A "server" is a central processing unit that performs data management, message generation, analysis, and other processing.

[1726] This invention is a system that combines an emotion engine that recognizes the emotions of users and debtors in order to streamline debt collection operations and achieve accurate and effective responses. This system operates through the cooperation of a server, terminals, and users.

[1727] System configuration

[1728] The system consists of the following main components:

[1729] 1. Server: A central processing unit that manages data, generates messages, analyzes emotions, and performs other processing. The server has a database for storing debtor information and has the ability to record interactions with debtors.

[1730] 2. Terminal: The interface operated by the user, which mediates communication with the server. The terminal runs software that allows the user to log in and operate the system.

[1731] 3. User: The person in charge of debt collection operations who operates the system.

[1732] System Features

[1733] An embodiment of the system is described in detail below.

[1734] Saving and managing debtor information

[1735] The server has a database that stores debtor information, including names, contact details, debt amounts, payment history, etc. When a user enters information from a terminal, the server stores the information in the database.

[1736] Chat function launch and automatic message generation

[1737] A user logs in to the system using a terminal and selects a debt collection case. The terminal requests case information from the server, which then searches for the relevant debtor information and past communication history and sends them to the terminal. When the user activates the chat function, the server uses an automated chat function to generate an appropriate message and sends it to the debtor via the terminal.

[1738] Examples:

[1739] The server generates a message saying, "We're contacting you regarding an outstanding payment. Would you like to discuss payment options?" and sends it to the debtor via the terminal.

[1740] Parsing the reply from the debtor and generating the next message

[1741] When the debtor's reply arrives at the device, it is forwarded to the server. The server's AI analyzes the reply and generates the optimal response message. At the same time, the emotion engine analyzes the debtor's reply and evaluates their emotional state.

[1742] Examples:

[1743] If the debtor replies, "By when should I make payment?", the server generates a message saying, "The payment deadline is (specified date). Please make payment by then." and sends it through the terminal.

[1744] If the emotion engine detects feelings of anxiety or confusion in the debtor's message, it adds a reassuring message such as, "If you have any questions or concerns, please contact us at any time."

[1745] Emotion analysis using an emotion analysis engine

[1746] The emotion engine analyzes the emotional state of the user and the debtor in real time. For example, it can detect emotions such as stress, anger, or confusion from the debtor's replies and voice data, and provide appropriate guidance to the server based on the results.

[1747] Record and learn from interactions

[1748] The server records all interactions and uses them for subsequent analysis and to improve the AI ​​model, allowing the system to learn each borrower's tendencies and respond more accurately.

[1749] Call handling integration and real-time sentiment analysis

[1750] When a user initiates a call from a terminal, the server analyzes the contents of the call in real time and uses an emotion engine to provide guidance based on the emotional state of the debtor and the user.

[1751] Examples:

[1752] The server displays a prompt on the user's terminal such as "Your current debt balance is (amount). Would you like to schedule your next payment date?"

[1753] If the call detects confusion or anxiety from the debtor, it generates a reassuring message such as, "If you need additional assistance, we're here to help."

[1754] As a result, this system streamlines debt collection operations and achieves accurate and effective responses by linking servers, terminals, and users and incorporating an emotion engine.

[1755] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1756] Step 1: Register and save debtor information

[1757] Input: Information such as debtor's name, contact details, debt amount, and payment history

[1758] Operation: The user enters debtor information into the input form on the terminal and presses the send button.

[1759] Data processing: The server receives the entered information, formats it, and stores it in a database.

[1760] Output: A confirmation message will be printed to the terminal indicating a successful save.

[1761] Step 2: User login and selection of debt collection case

[1762] Input: User name and password, Debt collection case ID to be selected after logging in

[1763] How it works: The user enters their authentication information on the login screen of their device, presses the login button, and then selects the debt collection case.

[1764] Data calculation: The server authenticates the login information and, if authentication is successful, receives the case ID selected by the user.

[1765] Output: A successful authentication message and detailed information about the case will be displayed on the terminal.

[1766] Step 3: Retrieve and display case information

[1767] Input: Debt collection case ID selected by the user

[1768] How it works: The device requests a case ID from the server.

[1769] Data processing: The server searches the database for relevant debtor information and past transaction history, and sends the results to the terminal.

[1770] Output: Debtor information and past transaction history will be displayed on the terminal.

[1771] Step 4: Generate and send automated messages

[1772] Input: Debtor information and past transaction history

[1773] How it works: A user activates the chat feature, and the device sends the request to the server.

[1774] Data calculation: The server uses a generative AI model based on debtor information and transaction history to generate an appropriate dunning message.

[1775] Output: The generated message is sent to the debtor.

[1776] Specific example of operation: The server generates a message saying, "I'm contacting you about an outstanding payment. Would you like to discuss payment options?" and sends it to the debtor via the terminal.

[1777] Step 5: Receive and analyze the debtor's response

[1778] Input: Reply message from debtor

[1779] What happens: The device receives the reply and forwards it to the server.

[1780] Data calculation: The server's AI analyzes the reply content, and the emotion analysis engine evaluates the emotional state.

[1781] Output: Analysis results and emotional states are generated.

[1782] Step 6: Generate and send the next message

[1783] Input: Analysis results and emotional state

[1784] Action: The server generates the following response message based on the analysis results and emotional state.

[1785] Data calculation: The server uses a generative AI model to generate specific response messages and adjusts the content according to the emotional state.

[1786] Output: The generated response message is sent to the debtor through the terminal.

[1787] Specific example of operation: If the debtor replies, "When should I make payment by?", the server generates a message saying, "The payment deadline is (specified date). We ask that you make payment by then," and sends it through the terminal. Also, if the sentiment analysis engine detects anxiety, it adds a reassuring message saying, "Please contact us at any time if you have any questions or concerns."

[1788] Step 7: Record the interaction

[1789] Input: All interaction data

[1790] How it works: The server records chat logs with the debtor and sentiment analysis results.

[1791] Data processing: The server organizes the log data and stores it in a database or log file.

[1792] Output: Creates a database entry that can be used for subsequent analysis and improving the AI ​​model.

[1793] Step 8: Call response activation and real-time analysis

[1794] Input: Call with debtor

[1795] How it works: The user activates the phone function on their device, and the server analyzes the call content in real time.

[1796] Data Computing: The server converts the call audio into text and generates appropriate guidance using a sentiment analysis engine and generative AI model.

[1797] Output: Appropriate guidance is displayed in real time on the user's device.

[1798] Example of specific behavior: During a call, the server displays a prompt saying, "Your current debt balance is (amount). Would you like to schedule your next payment date?" and, if anxiety is detected, generates a message saying, "If you need additional assistance, we're here to help."

[1799] The above are the detailed processing steps of this system.

[1800] (Application example 2)

[1801] 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."

[1802] In traditional debt collection operations, communication with debtors is inefficient, and especially emotional responses are lacking, leading to stress and disputes. Furthermore, there is no system in place for security personnel to detect suspicious individuals or dangerous situations in real time and take appropriate action.

[1803] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for storing debtor information, means for generating messages for the debtor using an automated chat function, means for analyzing replies from the debtor, means for generating the next message based on the analysis results, and means for analyzing emotions in real time and providing appropriate response guidance. This makes it possible to generate accurate and effective messages based on the debtor's emotional state.

[1804] In addition, by including a means for recognizing the emotional state of the other party and detecting dangerous situations based on emotion analysis, it becomes possible for devices used by security personnel to detect suspicious individuals and dangerous situations in real time and provide appropriate response guidance.

[1805] "Debtor Information" means information including the debtor's name, contact details, amount of debt, past payment history, and other data relevant to debt collection activities.

[1806] An "automated chat function" is a function that automatically generates and sends messages to debtors using predefined messages or algorithms.

[1807] "Means for analyzing replies" refers to a program or algorithm for analyzing replies from debtors and understanding their contents and intent.

[1808] "Means for generating messages" refers to a system or algorithm for automatically creating the next appropriate message based on the analysis results.

[1809] "Means for analyzing emotions in real time and providing appropriate response guidance" is a mechanism for analyzing the emotional state of the user or other party and presenting appropriate response methods in real time based on the results.

[1810] "Means for detecting dangerous situations" refers to the function of detecting suspicious behavior or dangerous emotional states from the results of emotion analysis, and issuing a warning or prompting appropriate action.

[1811] Specific Embodiments of the Invention

[1812] The present invention realizes a security support system that combines an emotion recognition engine. Specific embodiments of the present invention are described below.

[1813] System Components

[1814] The system consists of the following main components:

[1815] Server: A central processing unit that manages data, generates messages, analyzes sentiment, and performs other processing.

[1816] Terminal: Smart glasses used by security personnel, equipped with a camera and microphone, which act as a medium for communication with the server.

[1817] Users: Security personnel responsible for operating the system.

[1818] Program processing

[1819] Debtor information and chat function

[1820] The server has a database that stores debtor information, including the debtor's name, contact information, debt amount, past payment history, etc. When a user logs into the system using a terminal and selects a debt collection case, the server activates an automated chat function based on the selection and generates an appropriate collection message.

[1821] Sentiment analysis and guidance

[1822] The device's camera and microphone are used to capture the user's or debtor's facial expressions and tone of voice, which are then sent to a server in real time. The server then uses an emotion analysis engine to analyze this data. For example, if the server detects stress, anger, or confusion, it will provide appropriate guidance based on the results.

[1823] Example: If the sentiment analysis engine detects that the debtor is confused, it generates a reassuring message such as, "Please contact us anytime if you have any questions or concerns."

[1824] Security Support Function

[1825] When security personnel wear smart glasses, the device's camera constantly monitors the surrounding environment. If suspicious behavior or facial emotional states are detected, the information is sent to the server in real time, and appropriate warnings and guidance are displayed on the device based on the server's analysis.

[1826] Example: If the emotion analysis engine determines that a person may be suspicious, a warning message such as "This person may be suspicious. Please be on guard" will be output.

[1827] Hardware and software used

[1828] Hardware: Smart glasses (camera, microphone)

[1829] Software: OpenCV (face detection), Keras (emotion recognition model), pyttsx3 (audio output)

[1830] Prompt Sentence Examples

[1831] "Use the following face images to identify the emotion. Output the most likely emotion label:

[1832] [Image data]

[1833] In this way, the present invention utilizes an emotion recognition engine to realize a system that is useful for improving the efficiency of communication with debtors and supporting security operations.

[1834] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1835] Step 1:

[1836] A user logs in to the system using a terminal and selects security monitoring mode. The data entered at this time is the user ID and password. The terminal sends the entered data to the server, which performs authentication processing and returns a response indicating that the login was successful. If the login is successful, the terminal displays the security monitoring mode screen.

[1837] Step 2:

[1838] The device's camera and microphone are activated and capture the surrounding video and audio in real time. The video and audio data obtained from the camera and microphone are input. This data is sent to the server in real time, and the server performs preprocessing for analysis. Specifically, it detects faces from the video data and extracts the tone of voice from the audio data.

[1839] Step 3:

[1840] The server inputs the preprocessed facial image data into an emotion recognition model (Keras) to predict the emotional state. The input is the detected facial image data, and the output is the emotional state (e.g., anger, anxiety, happiness, etc.). The emotion recognition model analyzes the facial features and outputs the most likely emotion label.

[1841] Step 4:

[1842] The server generates appropriate response guidance based on the analysis results. If the emotion determination result indicates "anger" or "anxiety," the server generates a warning message. On the other hand, if the emotion determination result indicates "happiness" or "calmness," no particular warning is generated. This output warning message and guidance are sent to the device.

[1843] Step 5:

[1844] The device notifies the user in real time of warning messages and guidance received from the server. Specifically, the device displays the message on the smart glasses display and outputs audio (pyttsx3) as needed. For example, it displays a message saying, "There may be a suspicious person. Please be on guard." and announces it in audio. The input to this step is the guidance received from the server, and the output is a notification to the user.

[1845] 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.

[1846] 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.

[1847] 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 robot 414.

[1848] 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.

[1849] 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.

[1850] 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.

[1851] 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).

[1852] 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 emotions belonging to the "situation" domain, where situational awareness is dominant.

[1853] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1854] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1855] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1856] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1857] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1858] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1859] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1860] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1861] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1862] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1863] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1864] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1865] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1866] The following is further disclosed regarding the above embodiment.

[1867] (Claim 1)

[1868] a means for storing debtor information;

[1869] means for generating messages to the debtor using an automated chat function;

[1870] a means for analyzing the reply from the debtor;

[1871] means for generating a next message based on the analysis result;

[1872] A system comprising a server capable of executing the means.

[1873] (Claim 2)

[1874] 10. The system of claim 1, further comprising means for storing chat histories with debtors and learning the debtor's behavioral tendencies based on subsequent interactions.

[1875] (Claim 3)

[1876] 2. The system according to claim 1, further comprising a means for integrating a telephone response function, analyzing the contents of telephone conversations with debtors in real time, and providing appropriate guidance.

[1877] "Example 1"

[1878] (Claim 1)

[1879] a means for storing debtor information;

[1880] means for generating messages to the debtor using an automated chat function;

[1881] a means for analyzing the reply from the debtor;

[1882] means for generating a next message based on the analysis result;

[1883] A means of recording the history of your interactions with the debtor;

[1884] A system comprising an information processing device capable of executing the above means.

[1885] (Claim 2)

[1886] 10. The system of claim 1, further comprising means for storing chat histories with debtors and learning the debtor's behavioral tendencies based on subsequent interactions.

[1887] (Claim 3)

[1888] 2. The system according to claim 1, further comprising a means for integrating a telephone response function, analyzing the contents of telephone conversations with debtors in real time, and providing appropriate guidance.

[1889] "Application Example 1"

[1890] (Claim 1)

[1891] a means for storing debtor information;

[1892] means for generating messages to the debtor using an automated chat function;

[1893] a means for analyzing the reply from the debtor;

[1894] means for generating a next message based on the analysis result;

[1895] A means to analyze the contents of telephone conversations with debtors in real time and provide appropriate guidance;

[1896] an interface means using a smartphone or smart glasses;

[1897] A system comprising a server capable of executing the means.

[1898] (Claim 2)

[1899] 10. The system of claim 1, further comprising means for storing chat histories with debtors and learning the debtor's behavioral tendencies based on subsequent interactions.

[1900] (Claim 3)

[1901] 10. The system of claim 1, including means for visually presenting interactions with debtors in real time and enabling hands-free operation.

[1902] "Example 2: Combining Emotion Engines"

[1903] (Claim 1)

[1904] a means for storing debtor information;

[1905] means for generating messages to the debtor using an automated chat function;

[1906] a means for analyzing the reply from the debtor;

[1907] means for generating a next message based on the analysis result;

[1908] a means for analyzing the debtor's emotions using a sentiment analysis engine and adjusting a response message based on the results of the analysis;

[1909] A means for a user to operate through a terminal;

[1910] A system comprising a server capable of executing the means.

[1911] (Claim 2)

[1912] 10. The system of claim 1, further comprising means for storing chat histories with debtors and learning the debtor's behavioral tendencies based on subsequent interactions.

[1913] (Claim 3)

[1914] 2. The system according to claim 1, further comprising a means for integrating a telephone response function, analyzing the contents of telephone conversations with debtors in real time, and providing appropriate guidance.

[1915] "Application example 2 when combining emotion engines"

[1916] (Claim 1)

[1917] a means for storing debtor information;

[1918] means for generating messages to the debtor using an automated chat function;

[1919] a means for analyzing the reply from the debtor;

[1920] means for generating a next message based on the analysis result;

[1921] A means of analyzing emotions in real time and providing appropriate response guidance,

[1922] A system comprising a server capable of executing the means.

[1923] (Claim 2)

[1924] 10. The system of claim 1, further comprising means for storing chat histories with debtors and learning the debtor's behavioral tendencies based on subsequent interactions.

[1925] (Claim 3)

[1926] 2. The system according to claim 1, further comprising a means for integrating a telephone response function, analyzing the contents of telephone conversations with debtors in real time, and providing appropriate guidance.

[1927] (Claim 4)

[1928] 10. The system of claim 1, further comprising means for recognizing the emotional state of other parties and detecting dangerous situations based on emotion analysis. [Explanation of symbols]

[1929] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a means for storing debtor information; means for generating messages to the debtor using an automated chat function; a means for analyzing the reply from the debtor; means for generating a next message based on the analysis result; A system comprising a server capable of executing the means.

2. 10. The system of claim 1, further comprising means for storing chat histories with debtors and learning the debtor's tendencies based on subsequent interactions.

3. 2. The system according to claim 1, further comprising means for integrating a telephone response function, analyzing the contents of telephone conversations with debtors in real time, and providing appropriate guidance.

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