System

Abstract

A system is provided.SOLUTION: A system comprising: means for making optimal suggestions using a generative AI model for payment and reimbursement offers from outstanding customers; means for providing department contact information for offers other than payment or reimbursement; and means for returning responses generated by the generative AI model to a user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] There are many challenges in dealing with unpaid customers, but it is especially important to respond quickly and appropriately when customers request payment or complain that they are having difficulty paying. Furthermore, the need to allocate a large number of personnel to this process places a significant burden on companies. Furthermore, inadequate customer response risks increasing customer anxiety and undermining trust in companies, so efficient and effective solutions are needed in this area. [Means for solving the problem]

[0005] This invention is a system that includes a means for using a generative AI model to make optimal proposals for payments and payment change requests from customers with outstanding accounts, a means for providing contact information for the department in charge for requests other than payments or payment changes, and a means for returning a response generated by the generative AI model to the user. Furthermore, by including a means for analyzing the content of the user's message and classifying it as a payment request, a payment change request, or other requests, and a generative AI model for proposing the optimal payment method for a payment request, this system improves the quality of customer support, reduces the burden on in-house personnel, and alleviates customer anxiety.

[0006] "Delinquent Customer" means a customer who has not paid their bills by the due date.

[0007] "Payment offer" refers to the situation where a customer who has outstanding payments has offered to pay the fees.

[0008] "Payment change request" refers to a situation in which an unpaid customer requests a change in payment terms.

[0009] A "generative AI model" refers to an algorithm or system that uses artificial intelligence techniques to generate optimal responses to user input.

[0010] "Optimal proposal" refers to presenting the most suitable solution or option under specific circumstances or conditions.

[0011] "Contact information" refers to information such as telephone numbers and email addresses for contacting the relevant department or support department.

[0012] "Response" refers to a response that the system returns in response to a message from a user.

[0013] "Analysis" refers to the process of analyzing the content of a user's message to understand its content.

[0014] "Classification" refers to separating a user's messages into specific categories.

[0015] "System" refers to a set of components or programs combined to achieve a specific function. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

[0024] [First embodiment]

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

[0026] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

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

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

[0029] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

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

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

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

[0033] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

[0035] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

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

[0037] This invention provides a system for streamlining the handling of unpaid customers. The system uses a generative AI model to make optimal proposals for unpaid customers' payments and payment change requests, and provides contact information for the appropriate department for other requests. By automating communication with unpaid customers, this system can alleviate customer anxiety and reduce the company's personnel burden.

[0038] Overall system configuration

[0039] The system consists of a user's device, a server, and a generative AI model. The user inputs a message through the device, which is then sent to the server. The server analyzes the message and uses the generative AI model to generate the optimal answer, which is then sent back to the user.

[0040] Program processing and explanation

[0041] 1. The user types and sends a message on the device.

[0042] The user enters a message such as "I want to pay" or "I can't pay" in the chat window on the terminal and clicks the send button. This sends the user's input message to the server.

[0043] 2. The server receives the request and analyzes the message content

[0044] The server receives the message from the user and analyzes its content to determine whether the message contains keywords such as "I want to pay" or "I can't pay."

[0045] 3. Response in the case of a "payment request"

[0046] When a user says, "I want to pay," the server uses a generative AI model to suggest the optimal payment method. For example, if a user says, "I want to pay," the server suggests payment methods such as credit card or bank transfer.

[0047] 4. Response in the case of a "payment change request"

[0048] If the user says they "cannot pay," the server uses a generative AI model to suggest the optimal payment modification method. For example, if the user says they "cannot pay," the server suggests options such as installment payments or extending the payment deadline.

[0049] 5. Response to other requests

[0050] If the user's message is not about a payment or payment change request, the server will provide contact information for the appropriate department. For example, if the user says "How can I contact you?", the server will provide the phone number and email address of the support department.

[0051] Specific example explanation

[0052] 1. Example 1:

[0053] User: "I want to pay, how do I do that?"

[0054] Server: "We accept payments by credit card or bank transfer."

[0055] 2. Example 2:

[0056] User: "I can't pay, what should I do?"

[0057] Server: "You can pay in installments or postpone the payment due date."

[0058] 3. Example 3:

[0059] User: "How can I contact you?"

[0060] Server: "Please contact our support department for any inquiries. Phone: 012-345-6789 Email: support@example.com"

[0061] Through the processing of the system and program described above, the present invention improves the accuracy of handling unpaid customers, increases customer satisfaction, and also has the effect of significantly reducing the burden on company personnel.

[0062] The processing flow will be explained below.

[0063] Step 1:

[0064] The user enters a message in the chat window on the terminal and clicks the send button, which sends the message to the server.

[0065] Step 2:

[0066] The terminal sends the user's input message to the server as an HTTP POST request. The request data includes the user's message.

[0067] Step 3:

[0068] The server receives the POST request and extracts the user message from the JSON data included in the request.

[0069] Step 4:

[0070] The server analyzes the content of the user's message to determine whether the message contains keywords such as "I want to pay" or "I can't pay."

[0071] Step 5:

[0072] The server calls the appropriate function (optimal_payment_method, optimal_payment_change, contact_department) based on the message content. For example, if the user message contains "I want to make a payment," it calls the optimal_payment_method function.

[0073] Step 6:

[0074] When the server calls the optimal_payment_method function, it uses a generative AI model to generate the optimal payment method. The generative AI model receives the user message as input and suggests an appropriate payment method.

[0075] Step 7:

[0076] When the server calls the optimal_payment_change function, it uses a generative AI model to generate the optimal payment change method. The generative AI model receives the user message as input and proposes an appropriate payment change method.

[0077] Step 8:

[0078] When the server calls the contact_department function, it returns fixed contact information (e.g., the phone number and email address of the support department).

[0079] Step 9:

[0080] The server returns the generated response in JSON format to the terminal. The response data contains the generated response message.

[0081] Step 10:

[0082] The terminal receives the response from the server and displays it to the user, who can then view the response in the chat window.

[0083] Example 1

[0084] 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."

[0085] There is a need to respond quickly and appropriately to payments and payment change requests from customers who have not paid, but current systems require a lot of manual processing, which takes time and effort. Furthermore, because customer inquiries are diverse, there is a problem that contact with the appropriate department is delayed, resulting in lower customer satisfaction. The purpose of this invention is to solve these problems, improve the efficiency of interactions with customers who have not paid, and reduce the burden on companies' personnel.

[0086] 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.

[0087] In this invention, the server includes means for a user to input and send a message on a terminal, means for the server to receive a request and analyze the message content, means for proposing an optimal payment method for a payment request, means for proposing an optimal payment change method for a payment change request, means for providing contact information for the department in charge for requests other than payment or payment change, and means for returning a response generated by the generative AI model to the user. This enables prompt and appropriate responses to requests from customers with unpaid bills, improving customer satisfaction and reducing the company's personnel burden.

[0088] "Means for users to input and send messages on a terminal" refers to the interface that allows users to input messages on a terminal to convey their intentions and send them to a server.

[0089] "Means by which the server receives requests and analyzes the message content" refers to a combination of software and hardware that allows the server to receive messages from users and analyze their contents to understand their meaning and intent.

[0090] "Means for proposing the optimal payment method in response to a payment request" refers to the process of analyzing multiple payment methods using a generative AI model and proposing the optimal method from among them when a user indicates their intention to make a payment.

[0091] "Means for proposing optimal payment modification methods in response to payment modification requests" refers to a process that uses a generative AI model to analyze and propose appropriate payment modification options when a user indicates difficulty in making payment.

[0092] "Means for providing contact information for departments in charge for requests other than payments or payment changes" refers to the process for providing contact information for appropriate departments in charge for inquiries or requests from users other than payments.

[0093] "Means for returning the response generated by the generative AI model to the user" refers to the process by which the server formats the answer generated by the generative AI model and sends it to the user in order to return it to the user.

[0094] This invention provides a system for streamlining the handling of unpaid customers, using a generative AI model to make optimal proposals for unpaid customer payments and payment change requests, and also provides contact information for the appropriate department in charge for other requests.

[0095] Overall system configuration

[0096] The system consists of a user's device, a server, and a generative AI model. The user inputs a message through the device, which is then sent to the server. The server analyzes the message and uses the generative AI model to generate the optimal answer, which is then returned to the user.

[0097] Hardware and software used

[0098] 1. User's device

[0099] Using a web browser or dedicated application, users access the chat window and type and send messages.

[0100] 2. Server

[0101] The server that receives and analyzes the messages is a computer system equipped with a high-performance processor and memory, and uses cloud services such as Amazon Web Services (AWS) and Microsoft Azure.

[0102] For natural language processing, software such as NLTK (Natural Language Toolkit) and spaCy is used.

[0103] 3. Generative AI Models

[0104] It uses a generative AI model such as OpenAI GPT-4, which runs on a server and generates optimal answers by inputting appropriate prompts.

[0105] Program processing explanation

[0106] 1. The user types and sends a message on the device.

[0107] The user enters a message such as "I want to pay" or "I can't pay" in the chat window and clicks the send button, which sends the user's input message to the server.

[0108] 2. The server receives the request and analyzes the message content

[0109] The server receives the message from the user, then uses natural language processing software (e.g., NLTK or spaCy) to analyze the message content and identify keywords such as "I want to pay" or "I can't pay."

[0110] 3. Response to requests for payment or payment change

[0111] In the case of a payment request, the server generates and sends a prompt to a generative AI model (e.g., OpenAI GPT-4) to suggest the optimal payment method. An example of a prompt is, "Please provide a payment method that you would suggest if the user says they want to pay." The generative AI model generates an answer, and the server sends that answer to the user. Similarly, in the case of a payment change request, the generative AI model is used to make an appropriate suggestion. An example of a prompt is, "Please provide a payment change option that you would suggest if the user says they cannot pay."

[0112] 4. Response to other requests

[0113] For inquiries other than those for payment or payment change requests, the server provides the contact information of the department in charge. Depending on the inquiry, the contact information of the department in charge (e.g., phone number or email address) is sent to the user. An example of a prompt sentence is "Please provide the contact information to be provided when a user asks for inquiries."

[0114] Specific example explanation

[0115] 1. Example 1:

[0116] User: "I want to pay, how do I do that?"

[0117] Server: "We accept payments by credit card or bank transfer."

[0118] 2. Example 2:

[0119] User: "I can't pay, what should I do?"

[0120] Server: "You can pay in installments or postpone the payment due date."

[0121] 3. Example 3:

[0122] User: "How can I contact you?"

[0123] Server: "Please contact our support department for inquiries. Phone: 012-345-6789 Email: support@example.com."

[0124] Through the processing of the above-described system and program, the present invention improves the accuracy of handling unpaid customers, increases customer satisfaction, and has the effect of significantly reducing the burden on company personnel.

[0125] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0126] Program processing flow

[0127] (Step 1)

[0128] The user types and sends a message on the device.

[0129] Specific actions

[0130] The user enters a message such as "I want to pay" or "I can't pay" in the chat window on the terminal and clicks the send button, which sends the user's input message to the server.

[0131] Input and Output

[0132] Input: The message the user types into the terminal (e.g., "I want to pay")

[0133] Output: User message sent to the server

[0134] (Step 2)

[0135] The server receives the request and analyzes the message content

[0136] Specific actions

[0137] The server receives the message from the user, then uses natural language processing software (e.g., NLTK or spaCy) to analyze the message content and identify keywords such as "I want to pay" or "I can't pay."

[0138] Input and Output

[0139] Input: Message sent by the user

[0140] Output: Parsed message content (recognized keywords)

[0141] (Step 3)

[0142] Propose the best payment method for your payment request

[0143] Specific actions

[0144] If the user's message is analyzed as "I want to make a payment," the server generates and sends a prompt to the generative AI model (e.g., OpenAI GPT-4). The generative AI model generates a proposed payment method and returns it to the server. The server then composes the generated payment method into a message to return to the user and sends it to the user's device. A message such as "Payment can be made by credit card or bank transfer" is displayed on the user's screen.

[0145] Input and Output

[0146] Input: Parsed message content ("I want to pay")

[0147] Output: A reply message to the user containing the payment method suggestions obtained by the generative AI model

[0148] (Step 4)

[0149] Propose the best payment change method for payment change requests

[0150] Specific actions

[0151] If the user's message is analyzed as "I can't pay," the server generates and sends a prompt message to the generative AI model. The generative AI model generates payment change options and returns them to the server. The server then composes the generated payment change methods into a message to return to the user and sends it to the user's terminal. A message such as "Installment payments or an extension of the payment deadline are possible" is displayed on the user's screen.

[0152] Input and Output

[0153] Input: Parsed message content ("I can't pay")

[0154] Output: A reply message to the user containing the proposed payment change method obtained by the generative AI model

[0155] (Step 5)

[0156] For requests other than payments or payment changes, provide contact details for the relevant department.

[0157] Specific actions

[0158] If the user's message is not a request for payment or payment change, the server obtains the contact information of the appropriate department, composes a reply message for the user, and sends it to the user's terminal. For example, a message such as "For inquiries, please contact our support department. Phone: 012-345-6789 Email: support@example.com" may be provided.

[0159] Input and Output

[0160] Input: Parsed message content (other offers)

[0161] Output: A reply message to the user containing the department's contact information.

[0162] By the above steps, the system can quickly and appropriately respond to requests for payment and payment changes from customers who have not paid.

[0163] (Application example 1)

[0164] 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."

[0165] Existing systems for handling unpaid customer accounts make it difficult to efficiently and effectively process unpaid bills and payment change requests. Responding to various customer inquiries is also time-consuming, significantly increasing the workload of companies. Furthermore, there is a lack of a way for customers to easily find out which department they should contact. As a result, customer satisfaction is declining and the burden on companies' personnel is increasing.

[0166] 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.

[0167] In this invention, the server includes means for using a generative AI model to make optimal proposals for payments and payment change requests from customers with outstanding balances, means for providing contact information for the relevant department for requests other than payments or payment changes, means for returning responses generated by the generative AI model to the user, means for analyzing the user's message and classifying it into a payment request, a payment change request, or other requests, and means for using the generative AI model to propose appropriate payment methods and payment change options. This enables efficient and effective responses to unpaid balances and payment change requests, significantly reducing the company's personnel burden and improving customer satisfaction.

[0168] A "defaulting customer" is a customer who has not paid for goods or services by the due date.

[0169] "Payment offer" refers to any action or communication by an unpaid customer that indicates their intention to pay the outstanding amount.

[0170] "Payment Change Request" refers to any action or communication by an unpaid customer requesting a change in payment terms.

[0171] A "generative AI model" refers to a model that is trained to generate information from data using artificial intelligence techniques.

[0172] "Contact information for the relevant department" refers to contact methods such as telephone numbers and email addresses for the department in charge of a specific task or inquiry.

[0173] "User" refers to the customer or user of the system.

[0174] "Message analysis" refers to the process of understanding the content of messages sent by users and classifying them into specific categories.

[0175] "Payment method" refers to payment methods such as credit card, bank transfer, and electronic money.

[0176] "Option" refers to a choice offered to a User.

[0177] "Efficiency" refers to a state in which there is no waste and the goal can be achieved in a short time with little effort.

[0178] "Effective" refers to the state of being able to achieve a desired result or effect.

[0179] The system for implementing this invention consists of a user terminal, a server, and a generative AI model. The user inputs a message through the terminal, and the message is sent to the server. The server analyzes the message and uses the generative AI model to generate an optimal answer, which is then returned to the user.

[0180] Hardware and Software Configuration

[0181] User's device: A device that can connect to the Internet, such as a smartphone or computer, and can type messages into a chat window.

[0182] Server: A server with the processing power to run message analysis and generative AI models. It uses a web framework such as Flask to receive and analyze user messages.

[0183] Generative AI models: Use OpenAI APIs and similar generative AI technologies to generate optimal suggestions and responses to user messages.

[0184] Process Overview

[0185] 1. User message input: The user inputs a message such as "I want to pay" or "I can't pay" in the chat window on the terminal and clicks the send button. This sends the user's input message to the server.

[0186] 2. Message analysis: The server receives the message from the user and analyzes its contents. It determines whether the message contains keywords such as "I want to pay" or "I can't pay," and generates a prompt accordingly.

[0187] 3. Use of generative AI model: The server uses a generative AI model based on the analysis results to generate the optimal answer. If the answer is "I want to pay," the prompt text is "Please suggest a payment method," and if the answer is "I cannot pay," the prompt text is "Please suggest a payment change."

[0188] 4. Answer Response: Automate outstanding customer support by returning generated answers to users.

[0189] Specific examples

[0190] Example 1:

[0191] User: "I want to pay, how do I do that?"

[0192] Server: "We accept payments by credit card or bank transfer."

[0193] Example 2:

[0194] User: "I can't pay, what should I do?"

[0195] Server: "You can pay in installments or postpone the payment due date."

[0196] Example 3:

[0197] User: "How can I contact you?"

[0198] Server: "Please contact our support department for any inquiries. Phone: 012-345-6789 Email: support@example.com"

[0199] These processes use generative AI models to improve the accuracy of handling unpaid customer accounts, increasing customer satisfaction while reducing the burden on companies' personnel.

[0200] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0201] Step 1:

[0202] The user types and sends a message on the device.

[0203] The user enters a message such as "I want to pay" or "I can't pay" in the chat window on the terminal and clicks the send button. This sends the user's input message to the server via the network.

[0204] Input: User message

[0205] Output: Send message to server

[0206] Step 2:

[0207] The server receives the request and analyzes the message content.

[0208] The server takes in the message data received from the user and analyzes its content. Specifically, it uses a text analysis algorithm to determine whether the message contains keywords such as "I want to pay" or "I can't pay."

[0209] Input: Message data from the user

[0210] Output: Keyword analysis results

[0211] Step 3:

[0212] Based on the analysis results, a prompt sentence is generated for the generative AI model.

[0213] The server creates a prompt based on the message analysis results. For example, if the keyword "I want to pay" is detected, it generates the prompt "Please suggest a payment method."

[0214] Input: Keyword analysis results

[0215] Output: prompt statement

[0216] Step 4:

[0217] Generate optimal answers using generative AI models.

[0218] The server generates a prompt and gives it to the AI ​​model, which then uses it to make inferences based on the prompt and generate the optimal answer.

[0219] Input: prompt statement

[0220] Output: Answer from the AI ​​model

[0221] Step 5:

[0222] The generated answer is returned to the user.

[0223] The server receives the answer from the generative AI model and returns it to the user, displaying a chat window with the appropriate payment method, payment change options, or contact information for the relevant department.

[0224] Input: Answer from the AI ​​model

[0225] Output: Message reply to the user

[0226] Specific example operation steps

[0227] Example 1:

[0228] Step 1: The user enters and submits "I would like to make a payment. How do I do this?"

[0229] Step 2: The server detects the keyword "I want to pay."

[0230] Step 3: Generate the prompt "Please suggest a payment method."

[0231] Step 4: The generative AI model generates the answer, "Payment can be made by credit card or bank transfer."

[0232] Step 5: The server returns this answer to the user.

[0233] Example 2:

[0234] Step 1: The user types and submits "I can't pay, what should I do?"

[0235] Step 2: The server detects the keyword "can't pay."

[0236] Step 3: Generate the prompt "Please suggest a payment change."

[0237] Step 4: The generative AI model generates the answer, "Installment payments or postponement of payment due dates are possible."

[0238] Step 5: The server returns this answer to the user.

[0239] Example prompt sentence:

[0240] Please suggest a payment method.

[0241] Please suggest a payment change.

[0242] 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.

[0243] This invention provides a system for streamlining the handling of unpaid customers. The system uses a generative AI model to make optimal proposals for unpaid customer payments and payment change requests, and combines it with an emotion engine that recognizes user emotions to achieve more user-friendly handling. By automating communication with unpaid customers, this system can alleviate customer anxiety and reduce the burden on companies' personnel.

[0244] Overall system configuration

[0245] The system consists of a user's device, a server, a generative AI model, and an emotion engine. The user inputs a message through the device, which is then sent to the server. The server analyzes the message and uses the generative AI model and emotion engine to generate the optimal response, which is then sent back to the user.

[0246] Program processing and explanation

[0247] 1. The user types and sends a message on the device.

[0248] The user enters a message such as "I want to pay" or "I can't pay" in the chat window on the terminal and clicks the send button, which sends the user's input message to the server.

[0249] 2. The server receives the request and analyzes the message content

[0250] The server receives the message from the user and analyzes its content to determine whether the message contains keywords such as "I want to pay" or "I can't pay."

[0251] 3. The emotion engine analyzes the user's emotions

[0252] The server passes the analysis results of the message to the emotion engine, which analyzes the user's emotion and determines the user's emotional state (e.g., positive, negative, neutral).

[0253] 4. Response in the case of a "payment request"

[0254] When a user says, "I want to pay," the server uses the generative AI model to suggest the optimal payment method. For example, if a user says, "I want to pay," the server suggests payment methods such as credit card or bank transfer. The response content is adjusted based on the analysis results of the emotion engine.

[0255] 5. Response in the case of a "payment change request"

[0256] If the user says, "I can't pay," the server uses the generative AI model to suggest the optimal payment modification method. For example, if the user says, "I can't pay," the server will suggest options such as installment payments or extending the payment deadline. The response content is adjusted based on the analysis results of the emotion engine.

[0257] 6. Response to other requests

[0258] If the user's message is not about a payment or payment change, the server will provide the contact information for the relevant department. For example, if the user says, "How can I contact you?", the server will provide the phone number and email address of the support department. The server will then tailor the response based on the analysis results of the emotion engine.

[0259] Specific example explanation

[0260] 1. Example 1:

[0261] User: "I want to pay, how do I do that?"

[0262] Server: "We accept credit card or bank transfer payments." (If the user expresses concern, adjust the response to something like, "Don't worry, we accept credit card or bank transfer payments.")

[0263] 2. Example 2:

[0264] User: "I can't pay, what should I do?"

[0265] Server: "We can offer you a payment plan or postpone the payment date." (If the user expresses sadness or stress, tailor your response to something like, "I'm sorry you're struggling. We offer a payment plan or postponement. Please consider this.")

[0266] 3. Example 3:

[0267] User: "How can I contact you?"

[0268] Server: "Please contact our support department if you have any questions. Phone: 012-345-6789 Email: support@example.com" (If the user expresses a sense of urgency, adjust the response to something like "We'll get back to you shortly. Please contact our support department if you have any questions. Phone: 012-345-6789 Email: support@example.com")

[0269] Through the processing of the system and program described above, this invention improves the accuracy of handling unpaid customers and increases customer satisfaction. It also has the effect of significantly reducing the burden on company personnel. The introduction of an emotion engine enables the system to respond more flexibly and human-like, resulting in more effective customer service.

[0270] The processing flow will be explained below.

[0271] Step 1:

[0272] The user enters a message in the chat window on the terminal and clicks the send button, which sends the message to the server.

[0273] Step 2:

[0274] The device sends an HTTP POST request to the server, including the user's message. The request data contains the user's message in JSON format.

[0275] Step 3:

[0276] The server receives the POST request and extracts the user message from the JSON data included in the request.

[0277] Step 4:

[0278] The server analyzes the content of the user's message to determine whether the message contains keywords such as "I want to pay" or "I can't pay."

[0279] Step 5:

[0280] The server passes the message analysis results to the emotion engine, which analyzes the user's emotion and determines the user's emotional state (e.g., positive, negative, neutral).

[0281] Step 6:

[0282] The server calls the appropriate function (optimal_payment_method, optimal_payment_change, contact_department) based on the message content. For example, if the user message contains "I want to make a payment," it calls the optimal_payment_method function.

[0283] Step 7:

[0284] When the server calls the optimal_payment_method function, it uses a generative AI model to generate the optimal payment method. The generative AI model receives the user message as input and suggests an appropriate payment method.

[0285] Step 8:

[0286] When the server calls the optimal_payment_change function, it uses a generative AI model to generate the optimal payment change method. The generative AI model receives the user message as input and proposes an appropriate payment change method.

[0287] Step 9:

[0288] When the server calls the contact_department function, it returns fixed contact information (e.g., the phone number and email address of the support department).

[0289] Step 10:

[0290] The server adjusts the generated response based on the analysis results of the emotion engine. For example, if the user's emotion is negative, the server generates a response using more polite language.

[0291] Step 11:

[0292] The server returns the generated response in JSON format to the terminal. The response data contains the generated response message.

[0293] Step 12:

[0294] The terminal receives the response from the server and displays it to the user, who can then view the response in the chat window.

[0295] Example 2

[0296] 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."

[0297] When dealing with unpaid customers, it is necessary to reduce the anxiety and stress felt by customers while at the same time significantly reducing the burden on companies' personnel. However, conventional systems make it difficult to respond in a way that takes into account the feelings of customers, and this has prevented companies from achieving sufficient improvements in customer satisfaction and efficiency.

[0298] 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.

[0299] In this invention, the server includes means for using a generative AI model to make optimal proposals for payments and payment change requests from customers with outstanding accounts, means for providing contact information for the relevant department for requests other than payments or payment changes, means for returning a response generated by the generative AI model to the user, and means for adjusting the content of the response using an emotion engine that analyzes the user's emotions, thereby enabling flexible and efficient responses that take customer emotions into consideration.

[0300] "Delinquent Customer" means a customer who has not made a payment by the due date.

[0301] "Payment Offer" means a request or offer by a Customer to pay an outstanding balance.

[0302] "Payment Change Request" means a request or offer by a Customer to change payment terms.

[0303] A "generative AI model" refers to a model that uses artificial intelligence to automatically generate sentences and suggestions.

[0304] "Contact information for the relevant department" refers to contact information such as the telephone number and email address of the relevant department within the company.

[0305] An "emotion engine" is an algorithm that analyzes the emotional state of an input message and determines whether the emotion is positive, negative, neutral, or other.

[0306] "Terminal" refers to an electronic device such as a computer or smartphone used by a user.

[0307] A "server" refers to a computer system on a network that receives requests from users, processes them, and returns the results.

[0308] "Response content" refers to the content of a message sent in response to a user's inquiry.

[0309] A "natural language processing (NLP) library" refers to software that contains programs and methods for understanding and analyzing natural language.

[0310] "Keywords" refer to important words or phrases used in a message to guide specific actions.

[0311] An "HTTP request" refers to a protocol request for sending data from a terminal to a server.

[0312] This invention provides a system for streamlining the handling of unpaid customers and responding to user emotions. The system is composed of a user terminal, a server, a generative AI model, and an emotion engine. Specific embodiments of this system are described below.

[0313] First, the user enters a message into the chat window on their device and clicks the send button. This message is sent to the server as an HTTP request. The device can be a PC, smartphone, or other device.

[0314] The server receives an HTTP request from a user and extracts a message from the request body. The server then uses a natural language processing (NLP) library (e.g., spaCy or NLTK) to analyze the message content and extract keywords.

[0315] The server passes the parsed message to an emotion engine (e.g., Hugging Face Transformers), which determines the user's emotional state (positive, negative, neutral, etc.) based on the message content.

[0316] When a user requests to "make a payment," the server uses a generative AI model (e.g., GPT-4) to suggest the optimal payment method. The server adjusts the suggestion based on the analysis results of the emotion engine. Specifically, it sends a prompt to the generative AI model asking, "If I want to make a payment, what payment method should I suggest?" and adjusts the generated response.

[0317] Similarly, if the user says they "cannot pay," the server uses the generative AI model to suggest the optimal payment change method. It sends the generative AI model a prompt message asking, "If you cannot pay, what payment change method should we suggest?" and adjusts the generated response.

[0318] If the user's message is not about a payment or payment change request, the server will provide the contact information of the department in charge. Specifically, if the message says "How can I contact you?", the server will provide the phone number and email address of the support department.

[0319] Here, a specific example will be given.

[0320] Example 1:

[0321] User: "I want to pay, how do I do that?"

[0322] Server: "We accept credit card or bank transfer payments." (If the user expresses concern, adjust the response to something like, "Don't worry, we accept credit card or bank transfer payments.")

[0323] Example 2:

[0324] User: "I can't pay, what should I do?"

[0325] Server: "We can offer you a payment plan or postpone the payment date." (If the user expresses sadness or stress, tailor your response to something like, "I'm sorry you're struggling. We offer a payment plan or postponement. Please consider this.")

[0326] Example 3:

[0327] User: "How can I contact you?"

[0328] Server: "Please contact our support department if you have any questions. Phone: 012-345-6789 Email: support@example.com" (If the user expresses a sense of urgency, adjust the response to something like "We'll get back to you shortly. Please contact our support department if you have any questions. Phone: 012-345-6789 Email: support@example.com")

[0329] This system enables efficient and flexible responses to unpaid customers, improving customer satisfaction and reducing the burden on companies' personnel. The combination of a generative AI model and an emotion engine enables optimal responses that take into account customer emotions.

[0330] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0331] Step 1:

[0332] The user types a message on the terminal and sends it.

[0333] Specifically, the user enters something like "I want to pay" or "I can't pay" into the chat window and clicks the send button. This action causes the device to compose the user's input message as an HTTP request and send it to the server.

[0334] Input: User input message (e.g. "I want to make a payment")

[0335] Output: HTTP request (including the user's message)

[0336] Step 2:

[0337] The server receives the HTTP request from the user and extracts and parses the message.

[0338] The server receives the HTTP request and extracts the user's message from the request body. The server then analyzes the message content using a natural language processing (NLP) library (e.g., spaCy or NLTK) to extract keywords such as "I want to pay" and "I can't pay."

[0339] Input: HTTP request (including the user's message)

[0340] Output: Extracted messages and keywords

[0341] Step 3:

[0342] The emotion engine analyzes the user's emotions.

[0343] The server passes the analyzed message to the emotion engine, which determines the user's emotional state (positive, negative, neutral, etc.) based on the content of the message. The emotion engine uses Transformers such as Hugging Face.

[0344] Input: Extracted message and keywords

[0345] Output: User's emotional state (e.g., negative)

[0346] Step 4:

[0347] The server uses the generative AI model to generate the optimal proposal (in the case of a payment offer).

[0348] If the user's message is determined to be "I want to pay," the server sends a prompt to the generative AI model (e.g., GPT-4) to generate a response suggesting the optimal payment method. For example, in response to the message "I want to pay," the server sends a prompt such as "If I want to pay, what payment method should I suggest?" to the generative AI model.

[0349] Input: The keyword "I want to pay", the user's emotional state

[0350] Output: Suggestion of the best payment method (e.g. "Payment can be made by credit card or bank transfer.")

[0351] Step 5:

[0352] The server uses the generative AI model to generate the optimal proposal (in the case of a payment change request).

[0353] If the user's message is judged to be "I can't pay," the server sends a prompt to the generative AI model and generates a response proposing the optimal payment change method. For example, in response to the message "I can't pay," the server sends a prompt such as "If I can't pay, what payment change method should I suggest?" to the generative AI model.

[0354] Input: The keyword "can't pay", the user's emotional state

[0355] Output: Suggestion of the best payment modification method (e.g. "You can pay in installments or postpone the payment due date.")

[0356] Step 6:

[0357] The server will provide contact details for the relevant department for any other requests.

[0358] If the user's message is not a request for payment or payment change, the server will provide pre-defined contact information for the relevant department. For example, in response to the message "How can I contact you?", the server will provide a support department's phone number and email address.

[0359] Input: Message from user, emotional state

[0360] Output: Contact information for the department in charge (e.g., "Please contact our support department for inquiries. Phone: 012-345-6789 Email: support@example.com")

[0361] Step 7:

[0362] The server generates a response and sends it back to the user.

[0363] Finally, the server compiles the generated response content and sends it back to the user's device as an HTTP response. Based on the results of the emotion engine, the server adjusts the response content and replies in a way that is appropriate to the user's emotions.

[0364] Input: Best suggestions or contact information, user's emotional state

[0365] Output: HTTP response (response content sent to the user's device)

[0366] Through the above processing steps, the system can realize efficient and optimal responses that take into consideration the user's feelings.

[0367] (Application example 2)

[0368] 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."

[0369] Conventional systems for handling unpaid customer accounts provide uniform responses without considering the customer's feelings, which makes it impossible to alleviate customer anxiety and stress. This can result in lower customer satisfaction and loss of trust for the company. Furthermore, the system's payment method presentation and proposals for changes are not optimized, which can make it difficult to fully respond to customer requests.

[0370] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0371] In this invention, the server includes: means for using a generative AI model to make optimal proposals in response to payment and payment change requests from customers with outstanding payments; means including an emotion analysis engine for analyzing the content of users' messages and determining their emotional state; means for providing contact information for the relevant department in response to requests other than payment or payment change requests; and means for adjusting the response generated by the generative AI model based on the emotion analysis results and returning the response to the user. This enables flexible responses based on customer emotions, improves customer satisfaction, increases the company's credibility, and makes it possible to propose optimal payment methods and payment change methods.

[0372] "Delinquent Customers" refers to customers who have not yet made a payment when the payment deadline has passed.

[0373] "Payment change" refers to changing the payment terms that have already been determined, and specifically includes extending the payment due date or making installment payments.

[0374] A "generative AI model" refers to an artificial intelligence algorithm that provides generative responses to specific tasks by learning from large amounts of data.

[0375] "Emotion analysis engine" refers to software or hardware for recognizing a user's emotional state (e.g., sadness, anxiety, joy, etc.) from text or speech.

[0376] "Department contact information" refers to contact information such as phone numbers and email addresses for departments within a company that handle specific tasks or inquiries.

[0377] A "server" refers to a computer system that processes requests from clients and provides information over a network.

[0378] "Analyzing the content of the user's message" refers to a data processing means for processing the text data input by the user and understanding the intent and content of the message.

[0379] "Optimal proposal" refers to providing the most suitable payment method or payment change method for the user under specific conditions.

[0380] "Adjusting the response" refers to modifying the content of the answer generated by the generative AI model based on the user's emotional state to provide a more effective and friendly response.

[0381] The present invention relates to a system for making optimal proposals for unpaid customer payments and payment change requests, and providing responses that take into account the emotional state of the user. The system includes the following components.

[0382] Overall system configuration

[0383] The system consists of a user's device, a server, a generative AI model, and a sentiment analysis engine. The user inputs a message through the device, which is then sent to the server. The server analyzes the message and uses the generative AI model and sentiment analysis engine to generate the optimal response, which is then sent back to the user.

[0384] Program processing overview

[0385] First, the user enters a message on their device and clicks the send button. This message is sent to the server. The server receives the message from the user and analyzes its content. For example, it determines whether the message contains keywords such as "I want to pay" or "I can't pay." The server then passes the analysis results to a sentiment analysis engine to analyze the user's emotions. The server determines the user's emotional state (e.g., positive, negative, neutral) and uses a generative AI model to suggest the optimal payment method or payment change method.

[0386] Hardware and software used

[0387] The main hardware and software required to implement the present invention are as follows:

[0388] 1. User device (smartphone, tablet, PC, etc.)

[0389] This allows the user to input a message.

[0390] 2. Server

[0391] It receives and analyzes user messages and generates responses using generative AI models and a sentiment analysis engine.

[0392] 3. Generative AI Models

[0393] Use large-scale language models such as OpenAI GPT.

[0394] 4. Sentiment Analysis Engine

[0395] Use emotion analysis software such as EmotionRecognizer.

[0396] Specific examples

[0397] 1. Example 1:

[0398] User: "I want to pay, how do I do that?"

[0399] Server: "We accept credit card or bank transfer payments." (If the user expresses concern, adjust the response to something like "Don't worry, we accept credit card or bank transfer payments.")

[0400] 2. Example 2:

[0401] User: "I can't pay, what should I do?"

[0402] Server: "We can offer you a payment plan or postpone the payment date." (If the user expresses sadness or stress, you could tailor your response to something like, "I'm sorry you're struggling. We offer a payment plan or postponement. Please consider this.")

[0403] 3. Example 3:

[0404] User: "How can I contact you?"

[0405] Server: "Please contact our support department if you have any questions. Phone: 012-345-6789 Email: support@example.com" (If the user expresses a sense of urgency, adjust the response to something like "We'll get back to you shortly. Please contact our support department if you have any questions. Phone: 012-345-6789 Email: support@example.com")

[0406] Prompt Sentence Examples

[0407] "When you receive a message like this, generate a response with the appropriate emotion: Message: 'I want to pay, how do I do that?' Emotion: Anxiety Response: 'Don't worry, you can pay by credit card or bank transfer.'"

[0408] This system will streamline the process of dealing with unpaid customers and provide user-friendly services, thereby reducing the burden on companies' personnel and improving customer satisfaction.

[0409] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0410] Step 1:

[0411] The user inputs and sends a message on the terminal. The user inputs a message such as "I want to pay" or "I can't pay" in the terminal's chat window and clicks the send button. This sends the user's input message to the server. The input data is the user's text message, and the output data is the text message sent to the server.

[0412] Step 2:

[0413] The server receives the request and analyzes the message content. The server receives the message from the user and analyzes its content. Specifically, it analyzes whether the message contains keywords such as "I want to pay" or "I can't pay," and classifies it as a payment request, a payment change request, or other request. The input data is the text message sent to the server, and the output data is the classification result of the analyzed message.

[0414] Step 3:

[0415] The server passes the analysis results to a sentiment analysis engine, which analyzes the user's sentiment. The server passes the message analysis results to a sentiment analysis engine, which determines the user's emotional state (e.g., positive, negative, neutral). The input data are the analyzed message classification results and the text message, and the output data is the user's emotional state.

[0416] Step 4:

[0417] The server uses a generative AI model to generate the optimal proposal. If the user says "I want to pay," the server uses the generative AI model to propose the optimal payment method (e.g., credit card, bank transfer). If the user says "I can't pay," the server uses the generative AI model to propose the optimal payment change method (e.g., installment payments, extension of payment due date). The input data is the user's emotional state and the message classification results, and the output data is the optimal proposal.

[0418] Step 5:

[0419] The server adjusts the response based on the emotion analysis results. Based on the analysis results of the emotion analysis engine, the server adjusts the response generated by the generative AI model. For example, if the user expresses anxiety, the server adds the phrase "Don't worry" to the response. The input data is the user's emotional state and the generated response, and the output data is the adjusted response.

[0420] Step 6:

[0421] The server returns the adjusted response content to the user. The server sends the final adjusted response content to the user's terminal. The user's terminal receives it and displays it in the chat window. The input data is the adjusted response content, and the output data is the final response sent to the user.

[0422] 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.

[0423] 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.

[0424] 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.

[0425] [Second embodiment]

[0426] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0427] 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.

[0428] 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).

[0429] 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.

[0430] 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.

[0431] 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).

[0432] 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.

[0433] 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.

[0434] 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.

[0435] 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.

[0436] In the smart glasses 214, 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.

[0437] 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."

[0438] This invention provides a system for streamlining the handling of unpaid customers. The system uses a generative AI model to make optimal proposals for unpaid customers' payments and payment change requests, and provides contact information for the appropriate department for other requests. By automating communication with unpaid customers, this system can alleviate customer anxiety and reduce the company's personnel burden.

[0439] Overall system configuration

[0440] The system consists of a user's device, a server, and a generative AI model. The user inputs a message through the device, which is then sent to the server. The server analyzes the message and uses the generative AI model to generate the optimal answer, which is then sent back to the user.

[0441] Program processing and explanation

[0442] 1. The user types and sends a message on the device.

[0443] The user enters a message such as "I want to pay" or "I can't pay" in the chat window on the terminal and clicks the send button. This sends the user's input message to the server.

[0444] 2. The server receives the request and analyzes the message content

[0445] The server receives the message from the user and analyzes its content to determine whether the message contains keywords such as "I want to pay" or "I can't pay."

[0446] 3. Response in the case of a "payment request"

[0447] When a user says, "I want to pay," the server uses a generative AI model to suggest the optimal payment method. For example, if a user says, "I want to pay," the server suggests payment methods such as credit card or bank transfer.

[0448] 4. Response in the case of a "payment change request"

[0449] If the user says they "cannot pay," the server uses a generative AI model to suggest the optimal payment modification method. For example, if the user says they "cannot pay," the server suggests options such as installment payments or extending the payment deadline.

[0450] 5. Response to other requests

[0451] If the user's message is not about a payment or payment change request, the server will provide contact information for the appropriate department. For example, if the user says "How can I contact you?", the server will provide the phone number and email address of the support department.

[0452] Specific example explanation

[0453] 1. Example 1:

[0454] User: "I want to pay, how do I do that?"

[0455] Server: "We accept payments by credit card or bank transfer."

[0456] 2. Example 2:

[0457] User: "I can't pay, what should I do?"

[0458] Server: "You can pay in installments or postpone the payment due date."

[0459] 3. Example 3:

[0460] User: "How can I contact you?"

[0461] Server: "Please contact our support department for any inquiries. Phone: 012-345-6789 Email: support@example.com"

[0462] Through the processing of the system and program described above, the present invention improves the accuracy of handling unpaid customers, increases customer satisfaction, and also has the effect of significantly reducing the burden on company personnel.

[0463] The processing flow will be explained below.

[0464] Step 1:

[0465] The user enters a message in the chat window on the terminal and clicks the send button, which sends the message to the server.

[0466] Step 2:

[0467] The terminal sends the user's input message to the server as an HTTP POST request. The request data includes the user's message.

[0468] Step 3:

[0469] The server receives the POST request and extracts the user message from the JSON data included in the request.

[0470] Step 4:

[0471] The server analyzes the content of the user's message to determine whether the message contains keywords such as "I want to pay" or "I can't pay."

[0472] Step 5:

[0473] The server calls the appropriate function (optimal_payment_method, optimal_payment_change, contact_department) based on the message content. For example, if the user message contains "I want to make a payment," it calls the optimal_payment_method function.

[0474] Step 6:

[0475] When the server calls the optimal_payment_method function, it uses a generative AI model to generate the optimal payment method. The generative AI model receives the user message as input and suggests an appropriate payment method.

[0476] Step 7:

[0477] When the server calls the optimal_payment_change function, it uses a generative AI model to generate the optimal payment change method. The generative AI model receives the user message as input and proposes an appropriate payment change method.

[0478] Step 8:

[0479] When the server calls the contact_department function, it returns fixed contact information (e.g., the phone number and email address of the support department).

[0480] Step 9:

[0481] The server returns the generated response in JSON format to the terminal. The response data contains the generated response message.

[0482] Step 10:

[0483] The terminal receives the response from the server and displays it to the user, who can then view the response in the chat window.

[0484] Example 1

[0485] 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."

[0486] There is a need to respond quickly and appropriately to payments and payment change requests from customers who have not paid, but current systems require a lot of manual processing, which takes time and effort. Furthermore, because customer inquiries are diverse, there is a problem that contact with the appropriate department is delayed, resulting in lower customer satisfaction. The purpose of this invention is to solve these problems, improve the efficiency of interactions with customers who have not paid, and reduce the burden on companies' personnel.

[0487] 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.

[0488] In this invention, the server includes means for a user to input and send a message on a terminal, means for the server to receive a request and analyze the message content, means for proposing an optimal payment method for a payment request, means for proposing an optimal payment change method for a payment change request, means for providing contact information for the department in charge for requests other than payment or payment change, and means for returning a response generated by the generative AI model to the user. This enables prompt and appropriate responses to requests from customers with unpaid bills, improving customer satisfaction and reducing the company's personnel burden.

[0489] "Means for users to input and send messages on a terminal" refers to the interface that allows users to input messages on a terminal to convey their intentions and send them to a server.

[0490] "Means by which the server receives requests and analyzes the message content" refers to a combination of software and hardware that allows the server to receive messages from users and analyze their contents to understand their meaning and intent.

[0491] "Means for proposing the optimal payment method in response to a payment request" refers to the process of analyzing multiple payment methods using a generative AI model and proposing the optimal method from among them when a user indicates their intention to make a payment.

[0492] "Means for proposing optimal payment modification methods in response to payment modification requests" refers to a process that uses a generative AI model to analyze and propose appropriate payment modification options when a user indicates difficulty in making payment.

[0493] "Means for providing contact information for departments in charge for requests other than payments or payment changes" refers to the process for providing contact information for appropriate departments in charge for inquiries or requests from users other than payments.

[0494] "Means for returning the response generated by the generative AI model to the user" refers to the process by which the server formats the answer generated by the generative AI model and sends it to the user in order to return it to the user.

[0495] This invention provides a system for streamlining the handling of unpaid customers, using a generative AI model to make optimal proposals for unpaid customer payments and payment change requests, and also provides contact information for the appropriate department in charge for other requests.

[0496] Overall system configuration

[0497] The system consists of a user's device, a server, and a generative AI model. The user inputs a message through the device, which is then sent to the server. The server analyzes the message and uses the generative AI model to generate the optimal answer, which is then returned to the user.

[0498] Hardware and software used

[0499] 1. User's device

[0500] Using a web browser or dedicated application, users access the chat window and type and send messages.

[0501] 2. Server

[0502] The server that receives and analyzes the messages is a computer system equipped with a high-performance processor and memory, and uses cloud services such as Amazon Web Services (AWS) and Microsoft Azure.

[0503] For natural language processing, software such as NLTK (Natural Language Toolkit) and spaCy is used.

[0504] 3. Generative AI Models

[0505] It uses a generative AI model such as OpenAI GPT-4, which runs on a server and generates optimal answers by inputting appropriate prompts.

[0506] Program processing explanation

[0507] 1. The user types and sends a message on the device.

[0508] The user enters a message such as "I want to pay" or "I can't pay" in the chat window and clicks the send button, which sends the user's input message to the server.

[0509] 2. The server receives the request and analyzes the message content

[0510] The server receives the message from the user, then uses natural language processing software (e.g., NLTK or spaCy) to analyze the message content and identify keywords such as "I want to pay" or "I can't pay."

[0511] 3. Response to requests for payment or payment change

[0512] In the case of a payment request, the server generates and sends a prompt to a generative AI model (e.g., OpenAI GPT-4) to suggest the optimal payment method. An example of a prompt is, "Please provide a payment method that you would suggest if the user says they want to pay." The generative AI model generates an answer, and the server sends that answer to the user. Similarly, in the case of a payment change request, the generative AI model is used to make an appropriate suggestion. An example of a prompt is, "Please provide a payment change option that you would suggest if the user says they cannot pay."

[0513] 4. Response to other requests

[0514] For inquiries other than those for payment or payment change requests, the server provides the contact information of the department in charge. Depending on the inquiry, the contact information of the department in charge (e.g., phone number or email address) is sent to the user. An example of a prompt sentence is "Please provide the contact information to be provided when a user asks for inquiries."

[0515] Specific example explanation

[0516] 1. Example 1:

[0517] User: "I want to pay, how do I do that?"

[0518] Server: "We accept payments by credit card or bank transfer."

[0519] 2. Example 2:

[0520] User: "I can't pay, what should I do?"

[0521] Server: "You can pay in installments or postpone the payment due date."

[0522] 3. Example 3:

[0523] User: "How can I contact you?"

[0524] Server: "Please contact our support department for inquiries. Phone: 012-345-6789 Email: support@example.com."

[0525] Through the processing of the above-described system and program, the present invention improves the accuracy of handling unpaid customers, increases customer satisfaction, and has the effect of significantly reducing the burden on company personnel.

[0526] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0527] Program processing flow

[0528] (Step 1)

[0529] The user types and sends a message on the device.

[0530] Specific actions

[0531] The user enters a message such as "I want to pay" or "I can't pay" in the chat window on the terminal and clicks the send button, which sends the user's input message to the server.

[0532] Input and Output

[0533] Input: The message the user types into the terminal (e.g., "I want to pay")

[0534] Output: User message sent to the server

[0535] (Step 2)

[0536] The server receives the request and analyzes the message content

[0537] Specific actions

[0538] The server receives the message from the user, then uses natural language processing software (e.g., NLTK or spaCy) to analyze the message content and identify keywords such as "I want to pay" or "I can't pay."

[0539] Input and Output

[0540] Input: Message sent by the user

[0541] Output: Parsed message content (recognized keywords)

[0542] (Step 3)

[0543] Propose the best payment method for your payment request

[0544] Specific actions

[0545] If the user's message is analyzed as "I want to make a payment," the server generates and sends a prompt to the generative AI model (e.g., OpenAI GPT-4). The generative AI model generates a proposed payment method and returns it to the server. The server then composes the generated payment method into a message to return to the user and sends it to the user's device. A message such as "Payment can be made by credit card or bank transfer" is displayed on the user's screen.

[0546] Input and Output

[0547] Input: Parsed message content ("I want to pay")

[0548] Output: A reply message to the user containing the payment method suggestions obtained by the generative AI model

[0549] (Step 4)

[0550] Propose the best payment change method for payment change requests

[0551] Specific actions

[0552] If the user's message is analyzed as "I can't pay," the server generates and sends a prompt message to the generative AI model. The generative AI model generates payment change options and returns them to the server. The server then composes the generated payment change methods into a message to return to the user and sends it to the user's terminal. A message such as "Installment payments or an extension of the payment deadline are possible" is displayed on the user's screen.

[0553] Input and Output

[0554] Input: Parsed message content ("I can't pay")

[0555] Output: A reply message to the user containing the proposed payment change method obtained by the generative AI model

[0556] (Step 5)

[0557] For requests other than payments or payment changes, provide contact details for the relevant department.

[0558] Specific actions

[0559] If the user's message is not a request for payment or payment change, the server obtains the contact information of the appropriate department, composes a reply message for the user, and sends it to the user's terminal. For example, a message such as "For inquiries, please contact our support department. Phone: 012-345-6789 Email: support@example.com" may be provided.

[0560] Input and Output

[0561] Input: Parsed message content (other offers)

[0562] Output: A reply message to the user containing the department's contact information.

[0563] By the above steps, the system can quickly and appropriately respond to requests for payment and payment changes from customers who have not paid.

[0564] (Application example 1)

[0565] 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."

[0566] Existing systems for handling unpaid customer accounts make it difficult to efficiently and effectively process unpaid bills and payment change requests. Responding to various customer inquiries is also time-consuming, significantly increasing the workload of companies. Furthermore, there is a lack of a way for customers to easily find out which department they should contact. As a result, customer satisfaction is declining and the burden on companies' personnel is increasing.

[0567] 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.

[0568] In this invention, the server includes means for using a generative AI model to make optimal proposals for payments and payment change requests from customers with outstanding balances, means for providing contact information for the relevant department for requests other than payments or payment changes, means for returning responses generated by the generative AI model to the user, means for analyzing the user's message and classifying it into a payment request, a payment change request, or other requests, and means for using the generative AI model to propose appropriate payment methods and payment change options. This enables efficient and effective responses to unpaid balances and payment change requests, significantly reducing the company's personnel burden and improving customer satisfaction.

[0569] A "defaulting customer" is a customer who has not paid for goods or services by the due date.

[0570] "Payment offer" refers to any action or communication by an unpaid customer that indicates their intention to pay the outstanding amount.

[0571] "Payment Change Request" refers to any action or communication by an unpaid customer requesting a change in payment terms.

[0572] A "generative AI model" refers to a model that is trained to generate information from data using artificial intelligence techniques.

[0573] "Contact information for the relevant department" refers to contact methods such as telephone numbers and email addresses for the department in charge of a specific task or inquiry.

[0574] "User" refers to the customer or user of the system.

[0575] "Message analysis" refers to the process of understanding the content of messages sent by users and classifying them into specific categories.

[0576] "Payment method" refers to payment methods such as credit card, bank transfer, and electronic money.

[0577] "Option" refers to a choice offered to a User.

[0578] "Efficiency" refers to a state in which there is no waste and the goal can be achieved in a short time with little effort.

[0579] "Effective" refers to the state of being able to achieve a desired result or effect.

[0580] The system for implementing this invention consists of a user terminal, a server, and a generative AI model. The user inputs a message through the terminal, and the message is sent to the server. The server analyzes the message and uses the generative AI model to generate an optimal answer, which is then returned to the user.

[0581] Hardware and Software Configuration

[0582] User's device: A device that can connect to the Internet, such as a smartphone or computer, and can type messages into a chat window.

[0583] Server: A server with the processing power to run message analysis and generative AI models. It uses a web framework such as Flask to receive and analyze user messages.

[0584] Generative AI models: Use OpenAI APIs and similar generative AI technologies to generate optimal suggestions and responses to user messages.

[0585] Process Overview

[0586] 1. User message input: The user inputs a message such as "I want to pay" or "I can't pay" in the chat window on the terminal and clicks the send button. This sends the user's input message to the server.

[0587] 2. Message analysis: The server receives the message from the user and analyzes its contents. It determines whether the message contains keywords such as "I want to pay" or "I can't pay," and generates a prompt accordingly.

[0588] 3. Use of generative AI model: The server uses a generative AI model based on the analysis results to generate the optimal answer. If the answer is "I want to pay," the prompt text is "Please suggest a payment method," and if the answer is "I cannot pay," the prompt text is "Please suggest a payment change."

[0589] 4. Answer Response: Automate outstanding customer support by returning generated answers to users.

[0590] Specific examples

[0591] Example 1:

[0592] User: "I want to pay, how do I do that?"

[0593] Server: "We accept payments by credit card or bank transfer."

[0594] Example 2:

[0595] User: "I can't pay, what should I do?"

[0596] Server: "You can pay in installments or postpone the payment due date."

[0597] Example 3:

[0598] User: "How can I contact you?"

[0599] Server: "Please contact our support department for any inquiries. Phone: 012-345-6789 Email: support@example.com"

[0600] These processes use generative AI models to improve the accuracy of handling unpaid customer accounts, increasing customer satisfaction while reducing the burden on companies' personnel.

[0601] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0602] Step 1:

[0603] The user types and sends a message on the device.

[0604] The user enters a message such as "I want to pay" or "I can't pay" in the chat window on the terminal and clicks the send button. This sends the user's input message to the server via the network.

[0605] Input: User message

[0606] Output: Send message to server

[0607] Step 2:

[0608] The server receives the request and analyzes the message content.

[0609] The server takes in the message data received from the user and analyzes its content. Specifically, it uses a text analysis algorithm to determine whether the message contains keywords such as "I want to pay" or "I can't pay."

[0610] Input: Message data from the user

[0611] Output: Keyword analysis results

[0612] Step 3:

[0613] Based on the analysis results, a prompt sentence is generated for the generative AI model.

[0614] The server creates a prompt based on the message analysis results. For example, if the keyword "I want to pay" is detected, it generates the prompt "Please suggest a payment method."

[0615] Input: Keyword analysis results

[0616] Output: prompt statement

[0617] Step 4:

[0618] Generate optimal answers using generative AI models.

[0619] The server generates a prompt and gives it to the AI ​​model, which then uses it to make inferences based on the prompt and generate the optimal answer.

[0620] Input: prompt statement

[0621] Output: Answer from the AI ​​model

[0622] Step 5:

[0623] The generated answer is returned to the user.

[0624] The server receives the answer from the generative AI model and returns it to the user, displaying a chat window with the appropriate payment method, payment change options, or contact information for the relevant department.

[0625] Input: Answer from the AI ​​model

[0626] Output: Message reply to the user

[0627] Specific example operation steps

[0628] Example 1:

[0629] Step 1: The user enters and submits "I would like to make a payment. How do I do this?"

[0630] Step 2: The server detects the keyword "I want to pay."

[0631] Step 3: Generate the prompt "Please suggest a payment method."

[0632] Step 4: The generative AI model generates the answer, "Payment can be made by credit card or bank transfer."

[0633] Step 5: The server returns this answer to the user.

[0634] Example 2:

[0635] Step 1: The user types and submits "I can't pay, what should I do?"

[0636] Step 2: The server detects the keyword "can't pay."

[0637] Step 3: Generate the prompt "Please suggest a payment change."

[0638] Step 4: The generative AI model generates the answer, "Installment payments or postponement of payment due dates are possible."

[0639] Step 5: The server returns this answer to the user.

[0640] Example prompt sentence:

[0641] Please suggest a payment method.

[0642] Please suggest a payment change.

[0643] 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.

[0644] This invention provides a system for streamlining the handling of unpaid customers. The system uses a generative AI model to make optimal proposals for unpaid customer payments and payment change requests, and combines it with an emotion engine that recognizes user emotions to achieve more user-friendly handling. By automating communication with unpaid customers, this system can alleviate customer anxiety and reduce the burden on companies' personnel.

[0645] Overall system configuration

[0646] The system consists of a user's device, a server, a generative AI model, and an emotion engine. The user inputs a message through the device, which is then sent to the server. The server analyzes the message and uses the generative AI model and emotion engine to generate the optimal response, which is then sent back to the user.

[0647] Program processing and explanation

[0648] 1. The user types and sends a message on the device.

[0649] The user enters a message such as "I want to pay" or "I can't pay" in the chat window on the terminal and clicks the send button, which sends the user's input message to the server.

[0650] 2. The server receives the request and analyzes the message content

[0651] The server receives the message from the user and analyzes its content to determine whether the message contains keywords such as "I want to pay" or "I can't pay."

[0652] 3. The emotion engine analyzes the user's emotions

[0653] The server passes the analysis results of the message to the emotion engine, which analyzes the user's emotion and determines the user's emotional state (e.g., positive, negative, neutral).

[0654] 4. Response in the case of a "payment request"

[0655] When a user says, "I want to pay," the server uses the generative AI model to suggest the optimal payment method. For example, if a user says, "I want to pay," the server suggests payment methods such as credit card or bank transfer. The response content is adjusted based on the analysis results of the emotion engine.

[0656] 5. Response in the case of a "payment change request"

[0657] If the user says, "I can't pay," the server uses the generative AI model to suggest the optimal payment modification method. For example, if the user says, "I can't pay," the server will suggest options such as installment payments or extending the payment deadline. The response content is adjusted based on the analysis results of the emotion engine.

[0658] 6. Response to other requests

[0659] If the user's message is not about a payment or payment change, the server will provide the contact information for the relevant department. For example, if the user says, "How can I contact you?", the server will provide the phone number and email address of the support department. The server will then tailor the response based on the analysis results of the emotion engine.

[0660] Specific example explanation

[0661] 1. Example 1:

[0662] User: "I want to pay, how do I do that?"

[0663] Server: "We accept credit card or bank transfer payments." (If the user expresses concern, adjust the response to something like, "Don't worry, we accept credit card or bank transfer payments.")

[0664] 2. Example 2:

[0665] User: "I can't pay, what should I do?"

[0666] Server: "We can offer you a payment plan or postpone the payment date." (If the user expresses sadness or stress, tailor your response to something like, "I'm sorry you're struggling. We offer a payment plan or postponement. Please consider this.")

[0667] 3. Example 3:

[0668] User: "How can I contact you?"

[0669] Server: "Please contact our support department if you have any questions. Phone: 012-345-6789 Email: support@example.com" (If the user expresses a sense of urgency, adjust the response to something like "We'll get back to you shortly. Please contact our support department if you have any questions. Phone: 012-345-6789 Email: support@example.com")

[0670] Through the processing of the system and program described above, this invention improves the accuracy of handling unpaid customers and increases customer satisfaction. It also has the effect of significantly reducing the burden on company personnel. The introduction of an emotion engine enables the system to respond more flexibly and human-like, resulting in more effective customer service.

[0671] The processing flow will be explained below.

[0672] Step 1:

[0673] The user enters a message in the chat window on the terminal and clicks the send button, which sends the message to the server.

[0674] Step 2:

[0675] The device sends an HTTP POST request to the server, including the user's message. The request data contains the user's message in JSON format.

[0676] Step 3:

[0677] The server receives the POST request and extracts the user message from the JSON data included in the request.

[0678] Step 4:

[0679] The server analyzes the content of the user's message to determine whether the message contains keywords such as "I want to pay" or "I can't pay."

[0680] Step 5:

[0681] The server passes the message analysis results to the emotion engine, which analyzes the user's emotion and determines the user's emotional state (e.g., positive, negative, neutral).

[0682] Step 6:

[0683] The server calls the appropriate function (optimal_payment_method, optimal_payment_change, contact_department) based on the message content. For example, if the user message contains "I want to make a payment," it calls the optimal_payment_method function.

[0684] Step 7:

[0685] When the server calls the optimal_payment_method function, it uses a generative AI model to generate the optimal payment method. The generative AI model receives the user message as input and suggests an appropriate payment method.

[0686] Step 8:

[0687] When the server calls the optimal_payment_change function, it uses a generative AI model to generate the optimal payment change method. The generative AI model receives the user message as input and proposes an appropriate payment change method.

[0688] Step 9:

[0689] When the server calls the contact_department function, it returns fixed contact information (e.g., the phone number and email address of the support department).

[0690] Step 10:

[0691] The server adjusts the generated response based on the analysis results of the emotion engine. For example, if the user's emotion is negative, the server generates a response using more polite language.

[0692] Step 11:

[0693] The server returns the generated response in JSON format to the terminal. The response data contains the generated response message.

[0694] Step 12:

[0695] The terminal receives the response from the server and displays it to the user, who can then view the response in the chat window.

[0696] Example 2

[0697] 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."

[0698] When dealing with unpaid customers, it is necessary to reduce the anxiety and stress felt by customers while at the same time significantly reducing the burden on companies' personnel. However, conventional systems make it difficult to respond in a way that takes into account the feelings of customers, and this has prevented companies from achieving sufficient improvements in customer satisfaction and efficiency.

[0699] 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.

[0700] In this invention, the server includes means for using a generative AI model to make optimal proposals for payments and payment change requests from customers with outstanding accounts, means for providing contact information for the relevant department for requests other than payments or payment changes, means for returning a response generated by the generative AI model to the user, and means for adjusting the content of the response using an emotion engine that analyzes the user's emotions, thereby enabling flexible and efficient responses that take customer emotions into consideration.

[0701] "Delinquent Customer" means a customer who has not made a payment by the due date.

[0702] "Payment Offer" means a request or offer by a Customer to pay an outstanding balance.

[0703] "Payment Change Request" means a request or offer by a Customer to change payment terms.

[0704] A "generative AI model" refers to a model that uses artificial intelligence to automatically generate sentences and suggestions.

[0705] "Contact information for the relevant department" refers to contact information such as the telephone number and email address of the relevant department within the company.

[0706] An "emotion engine" is an algorithm that analyzes the emotional state of an input message and determines whether the emotion is positive, negative, neutral, or other.

[0707] "Terminal" refers to an electronic device such as a computer or smartphone used by a user.

[0708] A "server" refers to a computer system on a network that receives requests from users, processes them, and returns the results.

[0709] "Response content" refers to the content of a message sent in response to a user's inquiry.

[0710] A "natural language processing (NLP) library" refers to software that contains programs and methods for understanding and analyzing natural language.

[0711] "Keywords" refer to important words or phrases used in a message to guide specific actions.

[0712] An "HTTP request" refers to a protocol request for sending data from a terminal to a server.

[0713] This invention provides a system for streamlining the handling of unpaid customers and responding to user emotions. The system is composed of a user terminal, a server, a generative AI model, and an emotion engine. Specific embodiments of this system are described below.

[0714] First, the user enters a message into the chat window on their device and clicks the send button. This message is sent to the server as an HTTP request. The device can be a PC, smartphone, or other device.

[0715] The server receives an HTTP request from a user and extracts a message from the request body. The server then uses a natural language processing (NLP) library (e.g., spaCy or NLTK) to analyze the message content and extract keywords.

[0716] The server passes the parsed message to an emotion engine (e.g., Hugging Face Transformers), which determines the user's emotional state (positive, negative, neutral, etc.) based on the message content.

[0717] When a user requests to "make a payment," the server uses a generative AI model (e.g., GPT-4) to suggest the optimal payment method. The server adjusts the suggestion based on the analysis results of the emotion engine. Specifically, it sends a prompt to the generative AI model asking, "If I want to make a payment, what payment method should I suggest?" and adjusts the generated response.

[0718] Similarly, if the user says they "cannot pay," the server uses the generative AI model to suggest the optimal payment change method. It sends the generative AI model a prompt message asking, "If you cannot pay, what payment change method should we suggest?" and adjusts the generated response.

[0719] If the user's message is not about a payment or payment change request, the server will provide the contact information of the department in charge. Specifically, if the message says "How can I contact you?", the server will provide the phone number and email address of the support department.

[0720] Here, a specific example will be given.

[0721] Example 1:

[0722] User: "I want to pay, how do I do that?"

[0723] Server: "We accept credit card or bank transfer payments." (If the user expresses concern, adjust the response to something like, "Don't worry, we accept credit card or bank transfer payments.")

[0724] Example 2:

[0725] User: "I can't pay, what should I do?"

[0726] Server: "We can offer you a payment plan or postpone the payment date." (If the user expresses sadness or stress, tailor your response to something like, "I'm sorry you're struggling. We offer a payment plan or postponement. Please consider this.")

[0727] Example 3:

[0728] User: "How can I contact you?"

[0729] Server: "Please contact our support department if you have any questions. Phone: 012-345-6789 Email: support@example.com" (If the user expresses a sense of urgency, adjust the response to something like "We'll get back to you shortly. Please contact our support department if you have any questions. Phone: 012-345-6789 Email: support@example.com")

[0730] This system enables efficient and flexible responses to unpaid customers, improving customer satisfaction and reducing the burden on companies' personnel. The combination of a generative AI model and an emotion engine enables optimal responses that take into account customer emotions.

[0731] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0732] Step 1:

[0733] The user types a message on the terminal and sends it.

[0734] Specifically, the user enters something like "I want to pay" or "I can't pay" into the chat window and clicks the send button. This action causes the device to compose the user's input message as an HTTP request and send it to the server.

[0735] Input: User input message (e.g. "I want to make a payment")

[0736] Output: HTTP request (including the user's message)

[0737] Step 2:

[0738] The server receives the HTTP request from the user and extracts and parses the message.

[0739] The server receives the HTTP request and extracts the user's message from the request body. The server then analyzes the message content using a natural language processing (NLP) library (e.g., spaCy or NLTK) to extract keywords such as "I want to pay" and "I can't pay."

[0740] Input: HTTP request (including the user's message)

[0741] Output: Extracted messages and keywords

[0742] Step 3:

[0743] The emotion engine analyzes the user's emotions.

[0744] The server passes the analyzed message to the emotion engine, which determines the user's emotional state (positive, negative, neutral, etc.) based on the content of the message. The emotion engine uses Transformers such as Hugging Face.

[0745] Input: Extracted message and keywords

[0746] Output: User's emotional state (e.g., negative)

[0747] Step 4:

[0748] The server uses the generative AI model to generate the optimal proposal (in the case of a payment offer).

[0749] If the user's message is determined to be "I want to pay," the server sends a prompt to the generative AI model (e.g., GPT-4) to generate a response suggesting the optimal payment method. For example, in response to the message "I want to pay," the server sends a prompt such as "If I want to pay, what payment method should I suggest?" to the generative AI model.

[0750] Input: The keyword "I want to pay", the user's emotional state

[0751] Output: Suggestion of the best payment method (e.g. "Payment can be made by credit card or bank transfer.")

[0752] Step 5:

[0753] The server uses the generative AI model to generate the optimal proposal (in the case of a payment change request).

[0754] If the user's message is judged to be "I can't pay," the server sends a prompt to the generative AI model and generates a response proposing the optimal payment change method. For example, in response to the message "I can't pay," the server sends a prompt such as "If I can't pay, what payment change method should I suggest?" to the generative AI model.

[0755] Input: The keyword "can't pay", the user's emotional state

[0756] Output: Suggestion of the best payment modification method (e.g. "You can pay in installments or postpone the payment due date.")

[0757] Step 6:

[0758] The server will provide contact details for the relevant department for any other requests.

[0759] If the user's message is not a request for payment or payment change, the server will provide pre-defined contact information for the relevant department. For example, in response to the message "How can I contact you?", the server will provide a support department's phone number and email address.

[0760] Input: Message from user, emotional state

[0761] Output: Contact information for the department in charge (e.g., "Please contact our support department for inquiries. Phone: 012-345-6789 Email: support@example.com")

[0762] Step 7:

[0763] The server generates a response and sends it back to the user.

[0764] Finally, the server compiles the generated response content and sends it back to the user's device as an HTTP response. Based on the results of the emotion engine, the server adjusts the response content and replies in a way that is appropriate to the user's emotions.

[0765] Input: Best suggestions or contact information, user's emotional state

[0766] Output: HTTP response (response content sent to the user's device)

[0767] Through the above processing steps, the system can realize efficient and optimal responses that take into consideration the user's feelings.

[0768] (Application example 2)

[0769] 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."

[0770] Conventional systems for handling unpaid customer accounts provide uniform responses without considering the customer's feelings, which makes it impossible to alleviate customer anxiety and stress. This can result in lower customer satisfaction and loss of trust for the company. Furthermore, the system's payment method presentation and proposals for changes are not optimized, which can make it difficult to fully respond to customer requests.

[0771] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0772] In this invention, the server includes: means for using a generative AI model to make optimal proposals in response to payment and payment change requests from customers with outstanding payments; means including an emotion analysis engine for analyzing the content of users' messages and determining their emotional state; means for providing contact information for the relevant department in response to requests other than payment or payment change requests; and means for adjusting the response generated by the generative AI model based on the emotion analysis results and returning the response to the user. This enables flexible responses based on customer emotions, improves customer satisfaction, increases the company's credibility, and makes it possible to propose optimal payment methods and payment change methods.

[0773] "Delinquent Customers" refers to customers who have not yet made a payment when the payment deadline has passed.

[0774] "Payment change" refers to changing the payment terms that have already been determined, and specifically includes extending the payment due date or making installment payments.

[0775] A "generative AI model" refers to an artificial intelligence algorithm that provides generative responses to specific tasks by learning from large amounts of data.

[0776] "Emotion analysis engine" refers to software or hardware for recognizing a user's emotional state (e.g., sadness, anxiety, joy, etc.) from text or speech.

[0777] "Department contact information" refers to contact information such as phone numbers and email addresses for departments within a company that handle specific tasks or inquiries.

[0778] A "server" refers to a computer system that processes requests from clients and provides information over a network.

[0779] "Analyzing the content of the user's message" refers to a data processing means for processing the text data input by the user and understanding the intent and content of the message.

[0780] "Optimal proposal" refers to providing the most suitable payment method or payment change method for the user under specific conditions.

[0781] "Adjusting the response" refers to modifying the content of the answer generated by the generative AI model based on the user's emotional state to provide a more effective and friendly response.

[0782] The present invention relates to a system for making optimal proposals for unpaid customer payments and payment change requests, and providing responses that take into account the emotional state of the user. The system includes the following components.

[0783] Overall system configuration

[0784] The system consists of a user's device, a server, a generative AI model, and a sentiment analysis engine. The user inputs a message through the device, which is then sent to the server. The server analyzes the message and uses the generative AI model and sentiment analysis engine to generate the optimal response, which is then sent back to the user.

[0785] Program processing overview

[0786] First, the user enters a message on their device and clicks the send button. This message is sent to the server. The server receives the message from the user and analyzes its content. For example, it determines whether the message contains keywords such as "I want to pay" or "I can't pay." The server then passes the analysis results to a sentiment analysis engine to analyze the user's emotions. The server determines the user's emotional state (e.g., positive, negative, neutral) and uses a generative AI model to suggest the optimal payment method or payment change method.

[0787] Hardware and software used

[0788] The main hardware and software required to implement the present invention are as follows:

[0789] 1. User device (smartphone, tablet, PC, etc.)

[0790] This allows the user to input a message.

[0791] 2. Server

[0792] It receives and analyzes user messages and generates responses using generative AI models and a sentiment analysis engine.

[0793] 3. Generative AI Models

[0794] Use large-scale language models such as OpenAI GPT.

[0795] 4. Sentiment Analysis Engine

[0796] Use emotion analysis software such as EmotionRecognizer.

[0797] Specific examples

[0798] 1. Example 1:

[0799] User: "I want to pay, how do I do that?"

[0800] Server: "We accept credit card or bank transfer payments." (If the user expresses concern, adjust the response to something like "Don't worry, we accept credit card or bank transfer payments.")

[0801] 2. Example 2:

[0802] User: "I can't pay, what should I do?"

[0803] Server: "We can offer you a payment plan or postpone the payment date." (If the user expresses sadness or stress, you could tailor your response to something like, "I'm sorry you're struggling. We offer a payment plan or postponement. Please consider this.")

[0804] 3. Example 3:

[0805] User: "How can I contact you?"

[0806] Server: "Please contact our support department if you have any questions. Phone: 012-345-6789 Email: support@example.com" (If the user expresses a sense of urgency, adjust the response to something like "We'll get back to you shortly. Please contact our support department if you have any questions. Phone: 012-345-6789 Email: support@example.com")

[0807] Prompt Sentence Examples

[0808] "When you receive a message like this, generate a response with the appropriate emotion: Message: 'I want to pay, how do I do that?' Emotion: Anxiety Response: 'Don't worry, you can pay by credit card or bank transfer.'"

[0809] This system will streamline the process of dealing with unpaid customers and provide user-friendly services, thereby reducing the burden on companies' personnel and improving customer satisfaction.

[0810] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0811] Step 1:

[0812] The user inputs and sends a message on the terminal. The user inputs a message such as "I want to pay" or "I can't pay" in the terminal's chat window and clicks the send button. This sends the user's input message to the server. The input data is the user's text message, and the output data is the text message sent to the server.

[0813] Step 2:

[0814] The server receives the request and analyzes the message content. The server receives the message from the user and analyzes its content. Specifically, it analyzes whether the message contains keywords such as "I want to pay" or "I can't pay," and classifies it as a payment request, a payment change request, or other request. The input data is the text message sent to the server, and the output data is the classification result of the analyzed message.

[0815] Step 3:

[0816] The server passes the analysis results to a sentiment analysis engine, which analyzes the user's sentiment. The server passes the message analysis results to a sentiment analysis engine, which determines the user's emotional state (e.g., positive, negative, neutral). The input data are the analyzed message classification results and the text message, and the output data is the user's emotional state.

[0817] Step 4:

[0818] The server uses a generative AI model to generate the optimal proposal. If the user says "I want to pay," the server uses the generative AI model to propose the optimal payment method (e.g., credit card, bank transfer). If the user says "I can't pay," the server uses the generative AI model to propose the optimal payment change method (e.g., installment payments, extension of payment due date). The input data is the user's emotional state and the message classification results, and the output data is the optimal proposal.

[0819] Step 5:

[0820] The server adjusts the response based on the emotion analysis results. Based on the analysis results of the emotion analysis engine, the server adjusts the response generated by the generative AI model. For example, if the user expresses anxiety, the server adds the phrase "Don't worry" to the response. The input data is the user's emotional state and the generated response, and the output data is the adjusted response.

[0821] Step 6:

[0822] The server returns the adjusted response content to the user. The server sends the final adjusted response content to the user's terminal. The user's terminal receives it and displays it in the chat window. The input data is the adjusted response content, and the output data is the final response sent to the user.

[0823] 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.

[0824] 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.

[0825] 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.

[0826] [Third embodiment]

[0827] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0828] 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.

[0829] 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).

[0830] 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.

[0831] 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.

[0832] 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).

[0833] 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.

[0834] 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.

[0835] 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.

[0836] 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.

[0837] 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.

[0838] 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."

[0839] This invention provides a system for streamlining the handling of unpaid customers. The system uses a generative AI model to make optimal proposals for unpaid customers' payments and payment change requests, and provides contact information for the appropriate department for other requests. By automating communication with unpaid customers, this system can alleviate customer anxiety and reduce the company's personnel burden.

[0840] Overall system configuration

[0841] The system consists of a user's device, a server, and a generative AI model. The user inputs a message through the device, which is then sent to the server. The server analyzes the message and uses the generative AI model to generate the optimal answer, which is then sent back to the user.

[0842] Program processing and explanation

[0843] 1. The user types and sends a message on the device.

[0844] The user enters a message such as "I want to pay" or "I can't pay" in the chat window on the terminal and clicks the send button. This sends the user's input message to the server.

[0845] 2. The server receives the request and analyzes the message content

[0846] The server receives the message from the user and analyzes its content to determine whether the message contains keywords such as "I want to pay" or "I can't pay."

[0847] 3. Response in the case of a "payment request"

[0848] When a user says, "I want to pay," the server uses a generative AI model to suggest the optimal payment method. For example, if a user says, "I want to pay," the server suggests payment methods such as credit card or bank transfer.

[0849] 4. Response in the case of a "payment change request"

[0850] If the user says they "cannot pay," the server uses a generative AI model to suggest the optimal payment modification method. For example, if the user says they "cannot pay," the server suggests options such as installment payments or extending the payment deadline.

[0851] 5. Response to other requests

[0852] If the user's message is not about a payment or payment change request, the server will provide contact information for the appropriate department. For example, if the user says "How can I contact you?", the server will provide the phone number and email address of the support department.

[0853] Specific example explanation

[0854] 1. Example 1:

[0855] User: "I want to pay, how do I do that?"

[0856] Server: "We accept payments by credit card or bank transfer."

[0857] 2. Example 2:

[0858] User: "I can't pay, what should I do?"

[0859] Server: "You can pay in installments or postpone the payment due date."

[0860] 3. Example 3:

[0861] User: "How can I contact you?"

[0862] Server: "Please contact our support department for any inquiries. Phone: 012-345-6789 Email: support@example.com"

[0863] Through the processing of the system and program described above, the present invention improves the accuracy of handling unpaid customers, increases customer satisfaction, and also has the effect of significantly reducing the burden on company personnel.

[0864] The processing flow will be explained below.

[0865] Step 1:

[0866] The user enters a message in the chat window on the terminal and clicks the send button, which sends the message to the server.

[0867] Step 2:

[0868] The terminal sends the user's input message to the server as an HTTP POST request. The request data includes the user's message.

[0869] Step 3:

[0870] The server receives the POST request and extracts the user message from the JSON data included in the request.

[0871] Step 4:

[0872] The server analyzes the content of the user's message to determine whether the message contains keywords such as "I want to pay" or "I can't pay."

[0873] Step 5:

[0874] The server calls the appropriate function (optimal_payment_method, optimal_payment_change, contact_department) based on the message content. For example, if the user message contains "I want to make a payment," it calls the optimal_payment_method function.

[0875] Step 6:

[0876] When the server calls the optimal_payment_method function, it uses a generative AI model to generate the optimal payment method. The generative AI model receives the user message as input and suggests an appropriate payment method.

[0877] Step 7:

[0878] When the server calls the optimal_payment_change function, it uses a generative AI model to generate the optimal payment change method. The generative AI model receives the user message as input and proposes an appropriate payment change method.

[0879] Step 8:

[0880] When the server calls the contact_department function, it returns fixed contact information (e.g., the phone number and email address of the support department).

[0881] Step 9:

[0882] The server returns the generated response in JSON format to the terminal. The response data contains the generated response message.

[0883] Step 10:

[0884] The terminal receives the response from the server and displays it to the user, who can then view the response in the chat window.

[0885] Example 1

[0886] 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."

[0887] There is a need to respond quickly and appropriately to payments and payment change requests from customers who have not paid, but current systems require a lot of manual processing, which takes time and effort. Furthermore, because customer inquiries are diverse, there is a problem that contact with the appropriate department is delayed, resulting in lower customer satisfaction. The purpose of this invention is to solve these problems, improve the efficiency of interactions with customers who have not paid, and reduce the burden on companies' personnel.

[0888] 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.

[0889] In this invention, the server includes means for a user to input and send a message on a terminal, means for the server to receive a request and analyze the message content, means for proposing an optimal payment method for a payment request, means for proposing an optimal payment change method for a payment change request, means for providing contact information for the department in charge for requests other than payment or payment change, and means for returning a response generated by the generative AI model to the user. This enables prompt and appropriate responses to requests from customers with unpaid bills, improving customer satisfaction and reducing the company's personnel burden.

[0890] "Means for users to input and send messages on a terminal" refers to the interface that allows users to input messages on a terminal to convey their intentions and send them to a server.

[0891] "Means by which the server receives requests and analyzes the message content" refers to a combination of software and hardware that allows the server to receive messages from users and analyze their contents to understand their meaning and intent.

[0892] "Means for proposing the optimal payment method in response to a payment request" refers to the process of analyzing multiple payment methods using a generative AI model and proposing the optimal method from among them when a user indicates their intention to make a payment.

[0893] "Means for proposing optimal payment modification methods in response to payment modification requests" refers to a process that uses a generative AI model to analyze and propose appropriate payment modification options when a user indicates difficulty in making payment.

[0894] "Means for providing contact information for departments in charge for requests other than payments or payment changes" refers to the process for providing contact information for appropriate departments in charge for inquiries or requests from users other than payments.

[0895] "Means for returning the response generated by the generative AI model to the user" refers to the process by which the server formats the answer generated by the generative AI model and sends it to the user in order to return it to the user.

[0896] This invention provides a system for streamlining the handling of unpaid customers, using a generative AI model to make optimal proposals for unpaid customer payments and payment change requests, and also provides contact information for the appropriate department in charge for other requests.

[0897] Overall system configuration

[0898] The system consists of a user's device, a server, and a generative AI model. The user inputs a message through the device, which is then sent to the server. The server analyzes the message and uses the generative AI model to generate the optimal answer, which is then returned to the user.

[0899] Hardware and software used

[0900] 1. User's device

[0901] Using a web browser or dedicated application, users access the chat window and type and send messages.

[0902] 2. Server

[0903] The server that receives and analyzes the messages is a computer system equipped with a high-performance processor and memory, and uses cloud services such as Amazon Web Services (AWS) and Microsoft Azure.

[0904] For natural language processing, software such as NLTK (Natural Language Toolkit) and spaCy is used.

[0905] 3. Generative AI Models

[0906] It uses a generative AI model such as OpenAI GPT-4, which runs on a server and generates optimal answers by inputting appropriate prompts.

[0907] Program processing explanation

[0908] 1. The user types and sends a message on the device.

[0909] The user enters a message such as "I want to pay" or "I can't pay" in the chat window and clicks the send button, which sends the user's input message to the server.

[0910] 2. The server receives the request and analyzes the message content

[0911] The server receives the message from the user, then uses natural language processing software (e.g., NLTK or spaCy) to analyze the message content and identify keywords such as "I want to pay" or "I can't pay."

[0912] 3. Response to requests for payment or payment change

[0913] In the case of a payment request, the server generates and sends a prompt to a generative AI model (e.g., OpenAI GPT-4) to suggest the optimal payment method. An example of a prompt is, "Please provide a payment method that you would suggest if the user says they want to pay." The generative AI model generates an answer, and the server sends that answer to the user. Similarly, in the case of a payment change request, the generative AI model is used to make an appropriate suggestion. An example of a prompt is, "Please provide a payment change option that you would suggest if the user says they cannot pay."

[0914] 4. Response to other requests

[0915] For inquiries other than those for payment or payment change requests, the server provides the contact information of the department in charge. Depending on the inquiry, the contact information of the department in charge (e.g., phone number or email address) is sent to the user. An example of a prompt sentence is "Please provide the contact information to be provided when a user asks for inquiries."

[0916] Specific example explanation

[0917] 1. Example 1:

[0918] User: "I want to pay, how do I do that?"

[0919] Server: "We accept payments by credit card or bank transfer."

[0920] 2. Example 2:

[0921] User: "I can't pay, what should I do?"

[0922] Server: "You can pay in installments or postpone the payment due date."

[0923] 3. Example 3:

[0924] User: "How can I contact you?"

[0925] Server: "Please contact our support department for inquiries. Phone: 012-345-6789 Email: support@example.com."

[0926] Through the processing of the above-described system and program, the present invention improves the accuracy of handling unpaid customers, increases customer satisfaction, and has the effect of significantly reducing the burden on company personnel.

[0927] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0928] Program processing flow

[0929] (Step 1)

[0930] The user types and sends a message on the device.

[0931] Specific actions

[0932] The user enters a message such as "I want to pay" or "I can't pay" in the chat window on the terminal and clicks the send button, which sends the user's input message to the server.

[0933] Input and Output

[0934] Input: The message the user types into the terminal (e.g., "I want to pay")

[0935] Output: User message sent to the server

[0936] (Step 2)

[0937] The server receives the request and analyzes the message content

[0938] Specific actions

[0939] The server receives the message from the user, then uses natural language processing software (e.g., NLTK or spaCy) to analyze the message content and identify keywords such as "I want to pay" or "I can't pay."

[0940] Input and Output

[0941] Input: Message sent by the user

[0942] Output: Parsed message content (recognized keywords)

[0943] (Step 3)

[0944] Propose the best payment method for your payment request

[0945] Specific actions

[0946] If the user's message is analyzed as "I want to make a payment," the server generates and sends a prompt to the generative AI model (e.g., OpenAI GPT-4). The generative AI model generates a proposed payment method and returns it to the server. The server then composes the generated payment method into a message to return to the user and sends it to the user's device. A message such as "Payment can be made by credit card or bank transfer" is displayed on the user's screen.

[0947] Input and Output

[0948] Input: Parsed message content ("I want to pay")

[0949] Output: A reply message to the user containing the payment method suggestions obtained by the generative AI model

[0950] (Step 4)

[0951] Propose the best payment change method for payment change requests

[0952] Specific actions

[0953] If the user's message is analyzed as "I can't pay," the server generates and sends a prompt message to the generative AI model. The generative AI model generates payment change options and returns them to the server. The server then composes the generated payment change methods into a message to return to the user and sends it to the user's terminal. A message such as "Installment payments or an extension of the payment deadline are possible" is displayed on the user's screen.

[0954] Input and Output

[0955] Input: Parsed message content ("I can't pay")

[0956] Output: A reply message to the user containing the proposed payment change method obtained by the generative AI model

[0957] (Step 5)

[0958] For requests other than payments or payment changes, provide contact details for the relevant department.

[0959] Specific actions

[0960] If the user's message is not a request for payment or payment change, the server obtains the contact information of the appropriate department, composes a reply message for the user, and sends it to the user's terminal. For example, a message such as "For inquiries, please contact our support department. Phone: 012-345-6789 Email: support@example.com" may be provided.

[0961] Input and Output

[0962] Input: Parsed message content (other offers)

[0963] Output: A reply message to the user containing the department's contact information.

[0964] By the above steps, the system can quickly and appropriately respond to requests for payment and payment changes from customers who have not paid.

[0965] (Application example 1)

[0966] 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."

[0967] Existing systems for handling unpaid customer accounts make it difficult to efficiently and effectively process unpaid bills and payment change requests. Responding to various customer inquiries is also time-consuming, significantly increasing the workload of companies. Furthermore, there is a lack of a way for customers to easily find out which department they should contact. As a result, customer satisfaction is declining and the burden on companies' personnel is increasing.

[0968] 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.

[0969] In this invention, the server includes means for using a generative AI model to make optimal proposals for payments and payment change requests from customers with outstanding balances, means for providing contact information for the relevant department for requests other than payments or payment changes, means for returning responses generated by the generative AI model to the user, means for analyzing the user's message and classifying it into a payment request, a payment change request, or other requests, and means for using the generative AI model to propose appropriate payment methods and payment change options. This enables efficient and effective responses to unpaid balances and payment change requests, significantly reducing the company's personnel burden and improving customer satisfaction.

[0970] A "defaulting customer" is a customer who has not paid for goods or services by the due date.

[0971] "Payment offer" refers to any action or communication by an unpaid customer that indicates their intention to pay the outstanding amount.

[0972] "Payment Change Request" refers to any action or communication by an unpaid customer requesting a change in payment terms.

[0973] A "generative AI model" refers to a model that is trained to generate information from data using artificial intelligence techniques.

[0974] "Contact information for the relevant department" refers to contact methods such as telephone numbers and email addresses for the department in charge of a specific task or inquiry.

[0975] "User" refers to the customer or user of the system.

[0976] "Message analysis" refers to the process of understanding the content of messages sent by users and classifying them into specific categories.

[0977] "Payment method" refers to payment methods such as credit card, bank transfer, and electronic money.

[0978] "Option" refers to a choice offered to a User.

[0979] "Efficiency" refers to a state in which there is no waste and the goal can be achieved in a short time with little effort.

[0980] "Effective" refers to the state of being able to achieve a desired result or effect.

[0981] The system for implementing this invention consists of a user terminal, a server, and a generative AI model. The user inputs a message through the terminal, and the message is sent to the server. The server analyzes the message and uses the generative AI model to generate an optimal answer, which is then returned to the user.

[0982] Hardware and Software Configuration

[0983] User's device: A device that can connect to the Internet, such as a smartphone or computer, and can type messages into a chat window.

[0984] Server: A server with the processing power to run message analysis and generative AI models. It uses a web framework such as Flask to receive and analyze user messages.

[0985] Generative AI models: Use OpenAI APIs and similar generative AI technologies to generate optimal suggestions and responses to user messages.

[0986] Process Overview

[0987] 1. User message input: The user inputs a message such as "I want to pay" or "I can't pay" in the chat window on the terminal and clicks the send button. This sends the user's input message to the server.

[0988] 2. Message analysis: The server receives the message from the user and analyzes its contents. It determines whether the message contains keywords such as "I want to pay" or "I can't pay," and generates a prompt accordingly.

[0989] 3. Use of generative AI model: The server uses a generative AI model based on the analysis results to generate the optimal answer. If the answer is "I want to pay," the prompt text is "Please suggest a payment method," and if the answer is "I cannot pay," the prompt text is "Please suggest a payment change."

[0990] 4. Answer Response: Automate outstanding customer support by returning generated answers to users.

[0991] Specific examples

[0992] Example 1:

[0993] User: "I want to pay, how do I do that?"

[0994] Server: "We accept payments by credit card or bank transfer."

[0995] Example 2:

[0996] User: "I can't pay, what should I do?"

[0997] Server: "You can pay in installments or postpone the payment due date."

[0998] Example 3:

[0999] User: "How can I contact you?"

[1000] Server: "Please contact our support department for any inquiries. Phone: 012-345-6789 Email: support@example.com"

[1001] These processes use generative AI models to improve the accuracy of handling unpaid customer accounts, increasing customer satisfaction while reducing the burden on companies' personnel.

[1002] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1003] Step 1:

[1004] The user types and sends a message on the device.

[1005] The user enters a message such as "I want to pay" or "I can't pay" in the chat window on the terminal and clicks the send button. This sends the user's input message to the server via the network.

[1006] Input: User message

[1007] Output: Send message to server

[1008] Step 2:

[1009] The server receives the request and analyzes the message content.

[1010] The server takes in the message data received from the user and analyzes its content. Specifically, it uses a text analysis algorithm to determine whether the message contains keywords such as "I want to pay" or "I can't pay."

[1011] Input: Message data from the user

[1012] Output: Keyword analysis results

[1013] Step 3:

[1014] Based on the analysis results, a prompt sentence is generated for the generative AI model.

[1015] The server creates a prompt based on the message analysis results. For example, if the keyword "I want to pay" is detected, it generates the prompt "Please suggest a payment method."

[1016] Input: Keyword analysis results

[1017] Output: prompt statement

[1018] Step 4:

[1019] Generate optimal answers using generative AI models.

[1020] The server generates a prompt and gives it to the AI ​​model, which then uses it to make inferences based on the prompt and generate the optimal answer.

[1021] Input: prompt statement

[1022] Output: Answer from the AI ​​model

[1023] Step 5:

[1024] The generated answer is returned to the user.

[1025] The server receives the answer from the generative AI model and returns it to the user, displaying a chat window with the appropriate payment method, payment change options, or contact information for the relevant department.

[1026] Input: Answer from the AI ​​model

[1027] Output: Message reply to the user

[1028] Specific example operation steps

[1029] Example 1:

[1030] Step 1: The user enters and submits "I would like to make a payment. How do I do this?"

[1031] Step 2: The server detects the keyword "I want to pay."

[1032] Step 3: Generate the prompt "Please suggest a payment method."

[1033] Step 4: The generative AI model generates the answer, "Payment can be made by credit card or bank transfer."

[1034] Step 5: The server returns this answer to the user.

[1035] Example 2:

[1036] Step 1: The user types and submits "I can't pay, what should I do?"

[1037] Step 2: The server detects the keyword "can't pay."

[1038] Step 3: Generate the prompt "Please suggest a payment change."

[1039] Step 4: The generative AI model generates the answer, "Installment payments or postponement of payment due dates are possible."

[1040] Step 5: The server returns this answer to the user.

[1041] Example prompt sentence:

[1042] Please suggest a payment method.

[1043] Please suggest a payment change.

[1044] 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.

[1045] This invention provides a system for streamlining the handling of unpaid customers. The system uses a generative AI model to make optimal proposals for unpaid customer payments and payment change requests, and combines it with an emotion engine that recognizes user emotions to achieve more user-friendly handling. By automating communication with unpaid customers, this system can alleviate customer anxiety and reduce the burden on companies' personnel.

[1046] Overall system configuration

[1047] The system consists of a user's device, a server, a generative AI model, and an emotion engine. The user inputs a message through the device, which is then sent to the server. The server analyzes the message and uses the generative AI model and emotion engine to generate the optimal response, which is then sent back to the user.

[1048] Program processing and explanation

[1049] 1. The user types and sends a message on the device.

[1050] The user enters a message such as "I want to pay" or "I can't pay" in the chat window on the terminal and clicks the send button, which sends the user's input message to the server.

[1051] 2. The server receives the request and analyzes the message content

[1052] The server receives the message from the user and analyzes its content to determine whether the message contains keywords such as "I want to pay" or "I can't pay."

[1053] 3. The emotion engine analyzes the user's emotions

[1054] The server passes the analysis results of the message to the emotion engine, which analyzes the user's emotion and determines the user's emotional state (e.g., positive, negative, neutral).

[1055] 4. Response in the case of a "payment request"

[1056] When a user says, "I want to pay," the server uses the generative AI model to suggest the optimal payment method. For example, if a user says, "I want to pay," the server suggests payment methods such as credit card or bank transfer. The response content is adjusted based on the analysis results of the emotion engine.

[1057] 5. Response in the case of a "payment change request"

[1058] If the user says, "I can't pay," the server uses the generative AI model to suggest the optimal payment modification method. For example, if the user says, "I can't pay," the server will suggest options such as installment payments or extending the payment deadline. The response content is adjusted based on the analysis results of the emotion engine.

[1059] 6. Response to other requests

[1060] If the user's message is not about a payment or payment change, the server will provide the contact information for the relevant department. For example, if the user says, "How can I contact you?", the server will provide the phone number and email address of the support department. The server will then tailor the response based on the analysis results of the emotion engine.

[1061] Specific example explanation

[1062] 1. Example 1:

[1063] User: "I want to pay, how do I do that?"

[1064] Server: "We accept credit card or bank transfer payments." (If the user expresses concern, adjust the response to something like, "Don't worry, we accept credit card or bank transfer payments.")

[1065] 2. Example 2:

[1066] User: "I can't pay, what should I do?"

[1067] Server: "We can offer you a payment plan or postpone the payment date." (If the user expresses sadness or stress, tailor your response to something like, "I'm sorry you're struggling. We offer a payment plan or postponement. Please consider this.")

[1068] 3. Example 3:

[1069] User: "How can I contact you?"

[1070] Server: "Please contact our support department if you have any questions. Phone: 012-345-6789 Email: support@example.com" (If the user expresses a sense of urgency, adjust the response to something like "We'll get back to you shortly. Please contact our support department if you have any questions. Phone: 012-345-6789 Email: support@example.com")

[1071] Through the processing of the system and program described above, this invention improves the accuracy of handling unpaid customers and increases customer satisfaction. It also has the effect of significantly reducing the burden on company personnel. The introduction of an emotion engine enables the system to respond more flexibly and human-like, resulting in more effective customer service.

[1072] The processing flow will be explained below.

[1073] Step 1:

[1074] The user enters a message in the chat window on the terminal and clicks the send button, which sends the message to the server.

[1075] Step 2:

[1076] The device sends an HTTP POST request to the server, including the user's message. The request data contains the user's message in JSON format.

[1077] Step 3:

[1078] The server receives the POST request and extracts the user message from the JSON data included in the request.

[1079] Step 4:

[1080] The server analyzes the content of the user's message to determine whether the message contains keywords such as "I want to pay" or "I can't pay."

[1081] Step 5:

[1082] The server passes the message analysis results to the emotion engine, which analyzes the user's emotion and determines the user's emotional state (e.g., positive, negative, neutral).

[1083] Step 6:

[1084] The server calls the appropriate function (optimal_payment_method, optimal_payment_change, contact_department) based on the message content. For example, if the user message contains "I want to make a payment," it calls the optimal_payment_method function.

[1085] Step 7:

[1086] When the server calls the optimal_payment_method function, it uses a generative AI model to generate the optimal payment method. The generative AI model receives the user message as input and suggests an appropriate payment method.

[1087] Step 8:

[1088] When the server calls the optimal_payment_change function, it uses a generative AI model to generate the optimal payment change method. The generative AI model receives the user message as input and proposes an appropriate payment change method.

[1089] Step 9:

[1090] When the server calls the contact_department function, it returns fixed contact information (e.g., the phone number and email address of the support department).

[1091] Step 10:

[1092] The server adjusts the generated response based on the analysis results of the emotion engine. For example, if the user's emotion is negative, the server generates a response using more polite language.

[1093] Step 11:

[1094] The server returns the generated response in JSON format to the terminal. The response data contains the generated response message.

[1095] Step 12:

[1096] The terminal receives the response from the server and displays it to the user, who can then view the response in the chat window.

[1097] Example 2

[1098] 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."

[1099] When dealing with unpaid customers, it is necessary to reduce the anxiety and stress felt by customers while at the same time significantly reducing the burden on companies' personnel. However, conventional systems make it difficult to respond in a way that takes into account the feelings of customers, and this has prevented companies from achieving sufficient improvements in customer satisfaction and efficiency.

[1100] 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.

[1101] In this invention, the server includes means for using a generative AI model to make optimal proposals for payments and payment change requests from customers with outstanding accounts, means for providing contact information for the relevant department for requests other than payments or payment changes, means for returning a response generated by the generative AI model to the user, and means for adjusting the content of the response using an emotion engine that analyzes the user's emotions, thereby enabling flexible and efficient responses that take customer emotions into consideration.

[1102] "Delinquent Customer" means a customer who has not made a payment by the due date.

[1103] "Payment Offer" means a request or offer by a Customer to pay an outstanding balance.

[1104] "Payment Change Request" means a request or offer by a Customer to change payment terms.

[1105] A "generative AI model" refers to a model that uses artificial intelligence to automatically generate sentences and suggestions.

[1106] "Contact information for the relevant department" refers to contact information such as the telephone number and email address of the relevant department within the company.

[1107] An "emotion engine" is an algorithm that analyzes the emotional state of an input message and determines whether the emotion is positive, negative, neutral, or other.

[1108] "Terminal" refers to an electronic device such as a computer or smartphone used by a user.

[1109] A "server" refers to a computer system on a network that receives requests from users, processes them, and returns the results.

[1110] "Response content" refers to the content of a message sent in response to a user's inquiry.

[1111] A "natural language processing (NLP) library" refers to software that contains programs and methods for understanding and analyzing natural language.

[1112] "Keywords" refer to important words or phrases used in a message to guide specific actions.

[1113] An "HTTP request" refers to a protocol request for sending data from a terminal to a server.

[1114] This invention provides a system for streamlining the handling of unpaid customers and responding to user emotions. The system is composed of a user terminal, a server, a generative AI model, and an emotion engine. Specific embodiments of this system are described below.

[1115] First, the user enters a message into the chat window on their device and clicks the send button. This message is sent to the server as an HTTP request. The device can be a PC, smartphone, or other device.

[1116] The server receives an HTTP request from a user and extracts a message from the request body. The server then uses a natural language processing (NLP) library (e.g., spaCy or NLTK) to analyze the message content and extract keywords.

[1117] The server passes the parsed message to an emotion engine (e.g., Hugging Face Transformers), which determines the user's emotional state (positive, negative, neutral, etc.) based on the message content.

[1118] When a user requests to "make a payment," the server uses a generative AI model (e.g., GPT-4) to suggest the optimal payment method. The server adjusts the suggestion based on the analysis results of the emotion engine. Specifically, it sends a prompt to the generative AI model asking, "If I want to make a payment, what payment method should I suggest?" and adjusts the generated response.

[1119] Similarly, if the user says they "cannot pay," the server uses the generative AI model to suggest the optimal payment change method. It sends the generative AI model a prompt message asking, "If you cannot pay, what payment change method should we suggest?" and adjusts the generated response.

[1120] If the user's message is not about a payment or payment change request, the server will provide the contact information of the department in charge. Specifically, if the message says "How can I contact you?", the server will provide the phone number and email address of the support department.

[1121] Here, a specific example will be given.

[1122] Example 1:

[1123] User: "I want to pay, how do I do that?"

[1124] Server: "We accept credit card or bank transfer payments." (If the user expresses concern, adjust the response to something like, "Don't worry, we accept credit card or bank transfer payments.")

[1125] Example 2:

[1126] User: "I can't pay, what should I do?"

[1127] Server: "We can offer you a payment plan or postpone the payment date." (If the user expresses sadness or stress, tailor your response to something like, "I'm sorry you're struggling. We offer a payment plan or postponement. Please consider this.")

[1128] Example 3:

[1129] User: "How can I contact you?"

[1130] Server: "Please contact our support department if you have any questions. Phone: 012-345-6789 Email: support@example.com" (If the user expresses a sense of urgency, adjust the response to something like "We'll get back to you shortly. Please contact our support department if you have any questions. Phone: 012-345-6789 Email: support@example.com")

[1131] This system enables efficient and flexible responses to unpaid customers, improving customer satisfaction and reducing the burden on companies' personnel. The combination of a generative AI model and an emotion engine enables optimal responses that take into account customer emotions.

[1132] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1133] Step 1:

[1134] The user types a message on the terminal and sends it.

[1135] Specifically, the user enters something like "I want to pay" or "I can't pay" into the chat window and clicks the send button. This action causes the device to compose the user's input message as an HTTP request and send it to the server.

[1136] Input: User input message (e.g. "I want to make a payment")

[1137] Output: HTTP request (including the user's message)

[1138] Step 2:

[1139] The server receives the HTTP request from the user and extracts and parses the message.

[1140] The server receives the HTTP request and extracts the user's message from the request body. The server then analyzes the message content using a natural language processing (NLP) library (e.g., spaCy or NLTK) to extract keywords such as "I want to pay" and "I can't pay."

[1141] Input: HTTP request (including the user's message)

[1142] Output: Extracted messages and keywords

[1143] Step 3:

[1144] The emotion engine analyzes the user's emotions.

[1145] The server passes the analyzed message to the emotion engine, which determines the user's emotional state (positive, negative, neutral, etc.) based on the content of the message. The emotion engine uses Transformers such as Hugging Face.

[1146] Input: Extracted message and keywords

[1147] Output: User's emotional state (e.g., negative)

[1148] Step 4:

[1149] The server uses the generative AI model to generate the optimal proposal (in the case of a payment offer).

[1150] If the user's message is determined to be "I want to pay," the server sends a prompt to the generative AI model (e.g., GPT-4) to generate a response suggesting the optimal payment method. For example, in response to the message "I want to pay," the server sends a prompt such as "If I want to pay, what payment method should I suggest?" to the generative AI model.

[1151] Input: The keyword "I want to pay", the user's emotional state

[1152] Output: Suggestion of the best payment method (e.g. "Payment can be made by credit card or bank transfer.")

[1153] Step 5:

[1154] The server uses the generative AI model to generate the optimal proposal (in the case of a payment change request).

[1155] If the user's message is judged to be "I can't pay," the server sends a prompt to the generative AI model and generates a response proposing the optimal payment change method. For example, in response to the message "I can't pay," the server sends a prompt such as "If I can't pay, what payment change method should I suggest?" to the generative AI model.

[1156] Input: The keyword "can't pay", the user's emotional state

[1157] Output: Suggestion of the best payment modification method (e.g. "You can pay in installments or postpone the payment due date.")

[1158] Step 6:

[1159] The server will provide contact details for the relevant department for any other requests.

[1160] If the user's message is not a request for payment or payment change, the server will provide pre-defined contact information for the relevant department. For example, in response to the message "How can I contact you?", the server will provide a support department's phone number and email address.

[1161] Input: Message from user, emotional state

[1162] Output: Contact information for the department in charge (e.g., "Please contact our support department for inquiries. Phone: 012-345-6789 Email: support@example.com")

[1163] Step 7:

[1164] The server generates a response and sends it back to the user.

[1165] Finally, the server compiles the generated response content and sends it back to the user's device as an HTTP response. Based on the results of the emotion engine, the server adjusts the response content and replies in a way that is appropriate to the user's emotions.

[1166] Input: Best suggestions or contact information, user's emotional state

[1167] Output: HTTP response (response content sent to the user's device)

[1168] Through the above processing steps, the system can realize efficient and optimal responses that take into consideration the user's feelings.

[1169] (Application example 2)

[1170] 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."

[1171] Conventional systems for handling unpaid customer accounts provide uniform responses without considering the customer's feelings, which makes it impossible to alleviate customer anxiety and stress. This can result in lower customer satisfaction and loss of trust for the company. Furthermore, the system's payment method presentation and proposals for changes are not optimized, which can make it difficult to fully respond to customer requests.

[1172] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1173] In this invention, the server includes: means for using a generative AI model to make optimal proposals in response to payment and payment change requests from customers with outstanding payments; means including an emotion analysis engine for analyzing the content of users' messages and determining their emotional state; means for providing contact information for the relevant department in response to requests other than payment or payment change requests; and means for adjusting the response generated by the generative AI model based on the emotion analysis results and returning the response to the user. This enables flexible responses based on customer emotions, improves customer satisfaction, increases the company's credibility, and makes it possible to propose optimal payment methods and payment change methods.

[1174] "Delinquent Customers" refers to customers who have not yet made a payment when the payment deadline has passed.

[1175] "Payment change" refers to changing the payment terms that have already been determined, and specifically includes extending the payment due date or making installment payments.

[1176] A "generative AI model" refers to an artificial intelligence algorithm that provides generative responses to specific tasks by learning from large amounts of data.

[1177] "Emotion analysis engine" refers to software or hardware for recognizing a user's emotional state (e.g., sadness, anxiety, joy, etc.) from text or speech.

[1178] "Department contact information" refers to contact information such as phone numbers and email addresses for departments within a company that handle specific tasks or inquiries.

[1179] A "server" refers to a computer system that processes requests from clients and provides information over a network.

[1180] "Analyzing the content of the user's message" refers to a data processing means for processing the text data input by the user and understanding the intent and content of the message.

[1181] "Optimal proposal" refers to providing the most suitable payment method or payment change method for the user under specific conditions.

[1182] "Adjusting the response" refers to modifying the content of the answer generated by the generative AI model based on the user's emotional state to provide a more effective and friendly response.

[1183] The present invention relates to a system for making optimal proposals for unpaid customer payments and payment change requests, and providing responses that take into account the emotional state of the user. The system includes the following components.

[1184] Overall system configuration

[1185] The system consists of a user's device, a server, a generative AI model, and a sentiment analysis engine. The user inputs a message through the device, which is then sent to the server. The server analyzes the message and uses the generative AI model and sentiment analysis engine to generate the optimal response, which is then sent back to the user.

[1186] Program processing overview

[1187] First, the user enters a message on their device and clicks the send button. This message is sent to the server. The server receives the message from the user and analyzes its content. For example, it determines whether the message contains keywords such as "I want to pay" or "I can't pay." The server then passes the analysis results to a sentiment analysis engine to analyze the user's emotions. The server determines the user's emotional state (e.g., positive, negative, neutral) and uses a generative AI model to suggest the optimal payment method or payment change method.

[1188] Hardware and software used

[1189] The main hardware and software required to implement the present invention are as follows:

[1190] 1. User device (smartphone, tablet, PC, etc.)

[1191] This allows the user to input a message.

[1192] 2. Server

[1193] It receives and analyzes user messages and generates responses using generative AI models and a sentiment analysis engine.

[1194] 3. Generative AI Models

[1195] Use large-scale language models such as OpenAI GPT.

[1196] 4. Sentiment Analysis Engine

[1197] Use emotion analysis software such as EmotionRecognizer.

[1198] Specific examples

[1199] 1. Example 1:

[1200] User: "I want to pay, how do I do that?"

[1201] Server: "We accept credit card or bank transfer payments." (If the user expresses concern, adjust the response to something like "Don't worry, we accept credit card or bank transfer payments.")

[1202] 2. Example 2:

[1203] User: "I can't pay, what should I do?"

[1204] Server: "We can offer you a payment plan or postpone the payment date." (If the user expresses sadness or stress, you could tailor your response to something like, "I'm sorry you're struggling. We offer a payment plan or postponement. Please consider this.")

[1205] 3. Example 3:

[1206] User: "How can I contact you?"

[1207] Server: "Please contact our support department if you have any questions. Phone: 012-345-6789 Email: support@example.com" (If the user expresses a sense of urgency, adjust the response to something like "We'll get back to you shortly. Please contact our support department if you have any questions. Phone: 012-345-6789 Email: support@example.com")

[1208] Prompt Sentence Examples

[1209] "When you receive a message like this, generate a response with the appropriate emotion: Message: 'I want to pay, how do I do that?' Emotion: Anxiety Response: 'Don't worry, you can pay by credit card or bank transfer.'"

[1210] This system will streamline the process of dealing with unpaid customers and provide user-friendly services, thereby reducing the burden on companies' personnel and improving customer satisfaction.

[1211] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1212] Step 1:

[1213] The user inputs and sends a message on the terminal. The user inputs a message such as "I want to pay" or "I can't pay" in the terminal's chat window and clicks the send button. This sends the user's input message to the server. The input data is the user's text message, and the output data is the text message sent to the server.

[1214] Step 2:

[1215] The server receives the request and analyzes the message content. The server receives the message from the user and analyzes its content. Specifically, it analyzes whether the message contains keywords such as "I want to pay" or "I can't pay," and classifies it as a payment request, a payment change request, or other request. The input data is the text message sent to the server, and the output data is the classification result of the analyzed message.

[1216] Step 3:

[1217] The server passes the analysis results to a sentiment analysis engine, which analyzes the user's sentiment. The server passes the message analysis results to a sentiment analysis engine, which determines the user's emotional state (e.g., positive, negative, neutral). The input data are the analyzed message classification results and the text message, and the output data is the user's emotional state.

[1218] Step 4:

[1219] The server uses a generative AI model to generate the optimal proposal. If the user says "I want to pay," the server uses the generative AI model to propose the optimal payment method (e.g., credit card, bank transfer). If the user says "I can't pay," the server uses the generative AI model to propose the optimal payment change method (e.g., installment payments, extension of payment due date). The input data is the user's emotional state and the message classification results, and the output data is the optimal proposal.

[1220] Step 5:

[1221] The server adjusts the response based on the emotion analysis results. Based on the analysis results of the emotion analysis engine, the server adjusts the response generated by the generative AI model. For example, if the user expresses anxiety, the server adds the phrase "Don't worry" to the response. The input data is the user's emotional state and the generated response, and the output data is the adjusted response.

[1222] Step 6:

[1223] The server returns the adjusted response content to the user. The server sends the final adjusted response content to the user's terminal. The user's terminal receives it and displays it in the chat window. The input data is the adjusted response content, and the output data is the final response sent to the user.

[1224] 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.

[1225] 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.

[1226] 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.

[1227] [Fourth embodiment]

[1228] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1229] 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.

[1230] 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).

[1231] 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.

[1232] 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.

[1233] 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).

[1234] 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.

[1235] 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.

[1236] 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.

[1237] 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.

[1238] 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.

[1239] 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.

[1240] 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."

[1241] This invention provides a system for streamlining the handling of unpaid customers. The system uses a generative AI model to make optimal proposals for unpaid customers' payments and payment change requests, and provides contact information for the appropriate department for other requests. By automating communication with unpaid customers, this system can alleviate customer anxiety and reduce the company's personnel burden.

[1242] Overall system configuration

[1243] The system consists of a user's device, a server, and a generative AI model. The user inputs a message through the device, which is then sent to the server. The server analyzes the message and uses the generative AI model to generate the optimal answer, which is then sent back to the user.

[1244] Program processing and explanation

[1245] 1. The user types and sends a message on the device.

[1246] The user enters a message such as "I want to pay" or "I can't pay" in the chat window on the terminal and clicks the send button. This sends the user's input message to the server.

[1247] 2. The server receives the request and analyzes the message content

[1248] The server receives the message from the user and analyzes its content to determine whether the message contains keywords such as "I want to pay" or "I can't pay."

[1249] 3. Response in the case of a "payment request"

[1250] When a user says, "I want to pay," the server uses a generative AI model to suggest the optimal payment method. For example, if a user says, "I want to pay," the server suggests payment methods such as credit card or bank transfer.

[1251] 4. Response in the case of a "payment change request"

[1252] If the user says they "cannot pay," the server uses a generative AI model to suggest the optimal payment modification method. For example, if the user says they "cannot pay," the server suggests options such as installment payments or extending the payment deadline.

[1253] 5. Response to other requests

[1254] If the user's message is not about a payment or payment change request, the server will provide contact information for the appropriate department. For example, if the user says "How can I contact you?", the server will provide the phone number and email address of the support department.

[1255] Specific example explanation

[1256] 1. Example 1:

[1257] User: "I want to pay, how do I do that?"

[1258] Server: "We accept payments by credit card or bank transfer."

[1259] 2. Example 2:

[1260] User: "I can't pay, what should I do?"

[1261] Server: "You can pay in installments or postpone the payment due date."

[1262] 3. Example 3:

[1263] User: "How can I contact you?"

[1264] Server: "Please contact our support department for any inquiries. Phone: 012-345-6789 Email: support@example.com"

[1265] Through the processing of the system and program described above, the present invention improves the accuracy of handling unpaid customers, increases customer satisfaction, and also has the effect of significantly reducing the burden on company personnel.

[1266] The processing flow will be explained below.

[1267] Step 1:

[1268] The user enters a message in the chat window on the terminal and clicks the send button, which sends the message to the server.

[1269] Step 2:

[1270] The terminal sends the user's input message to the server as an HTTP POST request. The request data includes the user's message.

[1271] Step 3:

[1272] The server receives the POST request and extracts the user message from the JSON data included in the request.

[1273] Step 4:

[1274] The server analyzes the content of the user's message to determine whether the message contains keywords such as "I want to pay" or "I can't pay."

[1275] Step 5:

[1276] The server calls the appropriate function (optimal_payment_method, optimal_payment_change, contact_department) based on the message content. For example, if the user message contains "I want to make a payment," it calls the optimal_payment_method function.

[1277] Step 6:

[1278] When the server calls the optimal_payment_method function, it uses a generative AI model to generate the optimal payment method. The generative AI model receives the user message as input and suggests an appropriate payment method.

[1279] Step 7:

[1280] When the server calls the optimal_payment_change function, it uses a generative AI model to generate the optimal payment change method. The generative AI model receives the user message as input and proposes an appropriate payment change method.

[1281] Step 8:

[1282] When the server calls the contact_department function, it returns fixed contact information (e.g., the phone number and email address of the support department).

[1283] Step 9:

[1284] The server returns the generated response in JSON format to the terminal. The response data contains the generated response message.

[1285] Step 10:

[1286] The terminal receives the response from the server and displays it to the user, who can then view the response in the chat window.

[1287] Example 1

[1288] 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."

[1289] There is a need to respond quickly and appropriately to payments and payment change requests from customers who have not paid, but current systems require a lot of manual processing, which takes time and effort. Furthermore, because customer inquiries are diverse, there is a problem that contact with the appropriate department is delayed, resulting in lower customer satisfaction. The purpose of this invention is to solve these problems, improve the efficiency of interactions with customers who have not paid, and reduce the burden on companies' personnel.

[1290] 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.

[1291] In this invention, the server includes means for a user to input and send a message on a terminal, means for the server to receive a request and analyze the message content, means for proposing an optimal payment method for a payment request, means for proposing an optimal payment change method for a payment change request, means for providing contact information for the department in charge for requests other than payment or payment change, and means for returning a response generated by the generative AI model to the user. This enables prompt and appropriate responses to requests from customers with unpaid bills, improving customer satisfaction and reducing the company's personnel burden.

[1292] "Means for users to input and send messages on a terminal" refers to the interface that allows users to input messages on a terminal to convey their intentions and send them to a server.

[1293] "Means by which the server receives requests and analyzes the message content" refers to a combination of software and hardware that allows the server to receive messages from users and analyze their contents to understand their meaning and intent.

[1294] "Means for proposing the optimal payment method in response to a payment request" refers to the process of analyzing multiple payment methods using a generative AI model and proposing the optimal method from among them when a user indicates their intention to make a payment.

[1295] "Means for proposing optimal payment modification methods in response to payment modification requests" refers to a process that uses a generative AI model to analyze and propose appropriate payment modification options when a user indicates difficulty in making payment.

[1296] "Means for providing contact information for departments in charge for requests other than payments or payment changes" refers to the process for providing contact information for appropriate departments in charge for inquiries or requests from users other than payments.

[1297] "Means for returning the response generated by the generative AI model to the user" refers to the process by which the server formats the answer generated by the generative AI model and sends it to the user in order to return it to the user.

[1298] This invention provides a system for streamlining the handling of unpaid customers, using a generative AI model to make optimal proposals for unpaid customer payments and payment change requests, and also provides contact information for the appropriate department in charge for other requests.

[1299] Overall system configuration

[1300] The system consists of a user's device, a server, and a generative AI model. The user inputs a message through the device, which is then sent to the server. The server analyzes the message and uses the generative AI model to generate the optimal answer, which is then returned to the user.

[1301] Hardware and software used

[1302] 1. User's device

[1303] Using a web browser or dedicated application, users access the chat window and type and send messages.

[1304] 2. Server

[1305] The server that receives and analyzes the messages is a computer system equipped with a high-performance processor and memory, and uses cloud services such as Amazon Web Services (AWS) and Microsoft Azure.

[1306] For natural language processing, software such as NLTK (Natural Language Toolkit) and spaCy is used.

[1307] 3. Generative AI Models

[1308] It uses a generative AI model such as OpenAI GPT-4, which runs on a server and generates optimal answers by inputting appropriate prompts.

[1309] Program processing explanation

[1310] 1. The user types and sends a message on the device.

[1311] The user enters a message such as "I want to pay" or "I can't pay" in the chat window and clicks the send button, which sends the user's input message to the server.

[1312] 2. The server receives the request and analyzes the message content

[1313] The server receives the message from the user, then uses natural language processing software (e.g., NLTK or spaCy) to analyze the message content and identify keywords such as "I want to pay" or "I can't pay."

[1314] 3. Response to requests for payment or payment change

[1315] In the case of a payment request, the server generates and sends a prompt to a generative AI model (e.g., OpenAI GPT-4) to suggest the optimal payment method. An example of a prompt is, "Please provide a payment method that you would suggest if the user says they want to pay." The generative AI model generates an answer, and the server sends that answer to the user. Similarly, in the case of a payment change request, the generative AI model is used to make an appropriate suggestion. An example of a prompt is, "Please provide a payment change option that you would suggest if the user says they cannot pay."

[1316] 4. Response to other requests

[1317] For inquiries other than those for payment or payment change requests, the server provides the contact information of the department in charge. Depending on the inquiry, the contact information of the department in charge (e.g., phone number or email address) is sent to the user. An example of a prompt sentence is "Please provide the contact information to be provided when a user asks for inquiries."

[1318] Specific example explanation

[1319] 1. Example 1:

[1320] User: "I want to pay, how do I do that?"

[1321] Server: "We accept payments by credit card or bank transfer."

[1322] 2. Example 2:

[1323] User: "I can't pay, what should I do?"

[1324] Server: "You can pay in installments or postpone the payment due date."

[1325] 3. Example 3:

[1326] User: "How can I contact you?"

[1327] Server: "Please contact our support department for inquiries. Phone: 012-345-6789 Email: support@example.com."

[1328] Through the processing of the above-described system and program, the present invention improves the accuracy of handling unpaid customers, increases customer satisfaction, and has the effect of significantly reducing the burden on company personnel.

[1329] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1330] Program processing flow

[1331] (Step 1)

[1332] The user types and sends a message on the device.

[1333] Specific actions

[1334] The user enters a message such as "I want to pay" or "I can't pay" in the chat window on the terminal and clicks the send button, which sends the user's input message to the server.

[1335] Input and Output

[1336] Input: The message the user types into the terminal (e.g., "I want to pay")

[1337] Output: User message sent to the server

[1338] (Step 2)

[1339] The server receives the request and analyzes the message content

[1340] Specific actions

[1341] The server receives the message from the user, then uses natural language processing software (e.g., NLTK or spaCy) to analyze the message content and identify keywords such as "I want to pay" or "I can't pay."

[1342] Input and Output

[1343] Input: Message sent by the user

[1344] Output: Parsed message content (recognized keywords)

[1345] (Step 3)

[1346] Propose the best payment method for your payment request

[1347] Specific actions

[1348] If the user's message is analyzed as "I want to make a payment," the server generates and sends a prompt to the generative AI model (e.g., OpenAI GPT-4). The generative AI model generates a proposed payment method and returns it to the server. The server then composes the generated payment method into a message to return to the user and sends it to the user's device. A message such as "Payment can be made by credit card or bank transfer" is displayed on the user's screen.

[1349] Input and Output

[1350] Input: Parsed message content ("I want to pay")

[1351] Output: A reply message to the user containing the payment method suggestions obtained by the generative AI model

[1352] (Step 4)

[1353] Propose the best payment change method for payment change requests

[1354] Specific actions

[1355] If the user's message is analyzed as "I can't pay," the server generates and sends a prompt message to the generative AI model. The generative AI model generates payment change options and returns them to the server. The server then composes the generated payment change methods into a message to return to the user and sends it to the user's terminal. A message such as "Installment payments or an extension of the payment deadline are possible" is displayed on the user's screen.

[1356] Input and Output

[1357] Input: Parsed message content ("I can't pay")

[1358] Output: A reply message to the user containing the proposed payment change method obtained by the generative AI model

[1359] (Step 5)

[1360] For requests other than payments or payment changes, provide contact details for the relevant department.

[1361] Specific actions

[1362] If the user's message is not a request for payment or payment change, the server obtains the contact information of the appropriate department, composes a reply message for the user, and sends it to the user's terminal. For example, a message such as "For inquiries, please contact our support department. Phone: 012-345-6789 Email: support@example.com" may be provided.

[1363] Input and Output

[1364] Input: Parsed message content (other offers)

[1365] Output: A reply message to the user containing the department's contact information.

[1366] By the above steps, the system can quickly and appropriately respond to requests for payment and payment changes from customers who have not paid.

[1367] (Application example 1)

[1368] 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."

[1369] Existing systems for handling unpaid customer accounts make it difficult to efficiently and effectively process unpaid bills and payment change requests. Responding to various customer inquiries is also time-consuming, significantly increasing the workload of companies. Furthermore, there is a lack of a way for customers to easily find out which department they should contact. As a result, customer satisfaction is declining and the burden on companies' personnel is increasing.

[1370] 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.

[1371] In this invention, the server includes means for using a generative AI model to make optimal proposals for payments and payment change requests from customers with outstanding balances, means for providing contact information for the relevant department for requests other than payments or payment changes, means for returning responses generated by the generative AI model to the user, means for analyzing the user's message and classifying it into a payment request, a payment change request, or other requests, and means for using the generative AI model to propose appropriate payment methods and payment change options. This enables efficient and effective responses to unpaid balances and payment change requests, significantly reducing the company's personnel burden and improving customer satisfaction.

[1372] A "defaulting customer" is a customer who has not paid for goods or services by the due date.

[1373] "Payment offer" refers to any action or communication by an unpaid customer that indicates their intention to pay the outstanding amount.

[1374] "Payment Change Request" refers to any action or communication by an unpaid customer requesting a change in payment terms.

[1375] A "generative AI model" refers to a model that is trained to generate information from data using artificial intelligence techniques.

[1376] "Contact information for the relevant department" refers to contact methods such as telephone numbers and email addresses for the department in charge of a specific task or inquiry.

[1377] "User" refers to the customer or user of the system.

[1378] "Message analysis" refers to the process of understanding the content of messages sent by users and classifying them into specific categories.

[1379] "Payment method" refers to payment methods such as credit card, bank transfer, and electronic money.

[1380] "Option" refers to a choice offered to a User.

[1381] "Efficiency" refers to a state in which there is no waste and the goal can be achieved in a short time with little effort.

[1382] "Effective" refers to the state of being able to achieve a desired result or effect.

[1383] The system for implementing this invention consists of a user terminal, a server, and a generative AI model. The user inputs a message through the terminal, and the message is sent to the server. The server analyzes the message and uses the generative AI model to generate an optimal answer, which is then returned to the user.

[1384] Hardware and Software Configuration

[1385] User's device: A device that can connect to the Internet, such as a smartphone or computer, and can type messages into a chat window.

[1386] Server: A server with the processing power to run message analysis and generative AI models. It uses a web framework such as Flask to receive and analyze user messages.

[1387] Generative AI models: Use OpenAI APIs and similar generative AI technologies to generate optimal suggestions and responses to user messages.

[1388] Process Overview

[1389] 1. User message input: The user inputs a message such as "I want to pay" or "I can't pay" in the chat window on the terminal and clicks the send button. This sends the user's input message to the server.

[1390] 2. Message analysis: The server receives the message from the user and analyzes its contents. It determines whether the message contains keywords such as "I want to pay" or "I can't pay," and generates a prompt accordingly.

[1391] 3. Use of generative AI model: The server uses a generative AI model based on the analysis results to generate the optimal answer. If the answer is "I want to pay," the prompt text is "Please suggest a payment method," and if the answer is "I cannot pay," the prompt text is "Please suggest a payment change."

[1392] 4. Answer Response: Automate outstanding customer support by returning generated answers to users.

[1393] Specific examples

[1394] Example 1:

[1395] User: "I want to pay, how do I do that?"

[1396] Server: "We accept payments by credit card or bank transfer."

[1397] Example 2:

[1398] User: "I can't pay, what should I do?"

[1399] Server: "You can pay in installments or postpone the payment due date."

[1400] Example 3:

[1401] User: "How can I contact you?"

[1402] Server: "Please contact our support department for any inquiries. Phone: 012-345-6789 Email: support@example.com"

[1403] These processes use generative AI models to improve the accuracy of handling unpaid customer accounts, increasing customer satisfaction while reducing the burden on companies' personnel.

[1404] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1405] Step 1:

[1406] The user types and sends a message on the device.

[1407] The user enters a message such as "I want to pay" or "I can't pay" in the chat window on the terminal and clicks the send button. This sends the user's input message to the server via the network.

[1408] Input: User message

[1409] Output: Send message to server

[1410] Step 2:

[1411] The server receives the request and analyzes the message content.

[1412] The server takes in the message data received from the user and analyzes its content. Specifically, it uses a text analysis algorithm to determine whether the message contains keywords such as "I want to pay" or "I can't pay."

[1413] Input: Message data from the user

[1414] Output: Keyword analysis results

[1415] Step 3:

[1416] Based on the analysis results, a prompt sentence is generated for the generative AI model.

[1417] The server creates a prompt based on the message analysis results. For example, if the keyword "I want to pay" is detected, it generates the prompt "Please suggest a payment method."

[1418] Input: Keyword analysis results

[1419] Output: prompt statement

[1420] Step 4:

[1421] Generate optimal answers using generative AI models.

[1422] The server generates a prompt and gives it to the AI ​​model, which then uses it to make inferences based on the prompt and generate the optimal answer.

[1423] Input: prompt statement

[1424] Output: Answer from the AI ​​model

[1425] Step 5:

[1426] The generated answer is returned to the user.

[1427] The server receives the answer from the generative AI model and returns it to the user, displaying a chat window with the appropriate payment method, payment change options, or contact information for the relevant department.

[1428] Input: Answer from the AI ​​model

[1429] Output: Message reply to the user

[1430] Specific example operation steps

[1431] Example 1:

[1432] Step 1: The user enters and submits "I would like to make a payment. How do I do this?"

[1433] Step 2: The server detects the keyword "I want to pay."

[1434] Step 3: Generate the prompt "Please suggest a payment method."

[1435] Step 4: The generative AI model generates the answer, "Payment can be made by credit card or bank transfer."

[1436] Step 5: The server returns this answer to the user.

[1437] Example 2:

[1438] Step 1: The user types and submits "I can't pay, what should I do?"

[1439] Step 2: The server detects the keyword "can't pay."

[1440] Step 3: Generate the prompt "Please suggest a payment change."

[1441] Step 4: The generative AI model generates the answer, "Installment payments or postponement of payment due dates are possible."

[1442] Step 5: The server returns this answer to the user.

[1443] Example prompt sentence:

[1444] Please suggest a payment method.

[1445] Please suggest a payment change.

[1446] 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.

[1447] This invention provides a system for streamlining the handling of unpaid customers. The system uses a generative AI model to make optimal proposals for unpaid customer payments and payment change requests, and combines it with an emotion engine that recognizes user emotions to achieve more user-friendly handling. By automating communication with unpaid customers, this system can alleviate customer anxiety and reduce the burden on companies' personnel.

[1448] Overall system configuration

[1449] The system consists of a user's device, a server, a generative AI model, and an emotion engine. The user inputs a message through the device, which is then sent to the server. The server analyzes the message and uses the generative AI model and emotion engine to generate the optimal response, which is then sent back to the user.

[1450] Program processing and explanation

[1451] 1. The user types and sends a message on the device.

[1452] The user enters a message such as "I want to pay" or "I can't pay" in the chat window on the terminal and clicks the send button, which sends the user's input message to the server.

[1453] 2. The server receives the request and analyzes the message content

[1454] The server receives the message from the user and analyzes its content to determine whether the message contains keywords such as "I want to pay" or "I can't pay."

[1455] 3. The emotion engine analyzes the user's emotions

[1456] The server passes the analysis results of the message to the emotion engine, which analyzes the user's emotion and determines the user's emotional state (e.g., positive, negative, neutral).

[1457] 4. Response in the case of a "payment request"

[1458] When a user says, "I want to pay," the server uses the generative AI model to suggest the optimal payment method. For example, if a user says, "I want to pay," the server suggests payment methods such as credit card or bank transfer. The response content is adjusted based on the analysis results of the emotion engine.

[1459] 5. Response in the case of a "payment change request"

[1460] If the user says, "I can't pay," the server uses the generative AI model to suggest the optimal payment modification method. For example, if the user says, "I can't pay," the server will suggest options such as installment payments or extending the payment deadline. The response content is adjusted based on the analysis results of the emotion engine.

[1461] 6. Response to other requests

[1462] If the user's message is not about a payment or payment change, the server will provide the contact information for the relevant department. For example, if the user says, "How can I contact you?", the server will provide the phone number and email address of the support department. The server will then tailor the response based on the analysis results of the emotion engine.

[1463] Specific example explanation

[1464] 1. Example 1:

[1465] User: "I want to pay, how do I do that?"

[1466] Server: "We accept credit card or bank transfer payments." (If the user expresses concern, adjust the response to something like, "Don't worry, we accept credit card or bank transfer payments.")

[1467] 2. Example 2:

[1468] User: "I can't pay, what should I do?"

[1469] Server: "We can offer you a payment plan or postpone the payment date." (If the user expresses sadness or stress, tailor your response to something like, "I'm sorry you're struggling. We offer a payment plan or postponement. Please consider this.")

[1470] 3. Example 3:

[1471] User: "How can I contact you?"

[1472] Server: "Please contact our support department if you have any questions. Phone: 012-345-6789 Email: support@example.com" (If the user expresses a sense of urgency, adjust the response to something like "We'll get back to you shortly. Please contact our support department if you have any questions. Phone: 012-345-6789 Email: support@example.com")

[1473] Through the processing of the system and program described above, this invention improves the accuracy of handling unpaid customers and increases customer satisfaction. It also has the effect of significantly reducing the burden on company personnel. The introduction of an emotion engine enables the system to respond more flexibly and human-like, resulting in more effective customer service.

[1474] The processing flow will be explained below.

[1475] Step 1:

[1476] The user enters a message in the chat window on the terminal and clicks the send button, which sends the message to the server.

[1477] Step 2:

[1478] The device sends an HTTP POST request to the server, including the user's message. The request data contains the user's message in JSON format.

[1479] Step 3:

[1480] The server receives the POST request and extracts the user message from the JSON data included in the request.

[1481] Step 4:

[1482] The server analyzes the content of the user's message to determine whether the message contains keywords such as "I want to pay" or "I can't pay."

[1483] Step 5:

[1484] The server passes the message analysis results to the emotion engine, which analyzes the user's emotion and determines the user's emotional state (e.g., positive, negative, neutral).

[1485] Step 6:

[1486] The server calls the appropriate function (optimal_payment_method, optimal_payment_change, contact_department) based on the message content. For example, if the user message contains "I want to make a payment," it calls the optimal_payment_method function.

[1487] Step 7:

[1488] When the server calls the optimal_payment_method function, it uses a generative AI model to generate the optimal payment method. The generative AI model receives the user message as input and suggests an appropriate payment method.

[1489] Step 8:

[1490] When the server calls the optimal_payment_change function, it uses a generative AI model to generate the optimal payment change method. The generative AI model receives the user message as input and proposes an appropriate payment change method.

[1491] Step 9:

[1492] When the server calls the contact_department function, it returns fixed contact information (e.g., the phone number and email address of the support department).

[1493] Step 10:

[1494] The server adjusts the generated response based on the analysis results of the emotion engine. For example, if the user's emotion is negative, the server generates a response using more polite language.

[1495] Step 11:

[1496] The server returns the generated response in JSON format to the terminal. The response data contains the generated response message.

[1497] Step 12:

[1498] The terminal receives the response from the server and displays it to the user, who can then view the response in the chat window.

[1499] Example 2

[1500] 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."

[1501] When dealing with unpaid customers, it is necessary to reduce the anxiety and stress felt by customers while at the same time significantly reducing the burden on companies' personnel. However, conventional systems make it difficult to respond in a way that takes into account the feelings of customers, and this has prevented companies from achieving sufficient improvements in customer satisfaction and efficiency.

[1502] 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.

[1503] In this invention, the server includes means for using a generative AI model to make optimal proposals for payments and payment change requests from customers with outstanding accounts, means for providing contact information for the relevant department for requests other than payments or payment changes, means for returning a response generated by the generative AI model to the user, and means for adjusting the content of the response using an emotion engine that analyzes the user's emotions, thereby enabling flexible and efficient responses that take customer emotions into consideration.

[1504] "Delinquent Customer" means a customer who has not made a payment by the due date.

[1505] "Payment Offer" means a request or offer by a Customer to pay an outstanding balance.

[1506] "Payment Change Request" means a request or offer by a Customer to change payment terms.

[1507] A "generative AI model" refers to a model that uses artificial intelligence to automatically generate sentences and suggestions.

[1508] "Contact information for the relevant department" refers to contact information such as the telephone number and email address of the relevant department within the company.

[1509] An "emotion engine" is an algorithm that analyzes the emotional state of an input message and determines whether the emotion is positive, negative, neutral, or other.

[1510] "Terminal" refers to an electronic device such as a computer or smartphone used by a user.

[1511] A "server" refers to a computer system on a network that receives requests from users, processes them, and returns the results.

[1512] "Response content" refers to the content of a message sent in response to a user's inquiry.

[1513] A "natural language processing (NLP) library" refers to software that contains programs and methods for understanding and analyzing natural language.

[1514] "Keywords" refer to important words or phrases used in a message to guide specific actions.

[1515] An "HTTP request" refers to a protocol request for sending data from a terminal to a server.

[1516] This invention provides a system for streamlining the handling of unpaid customers and responding to user emotions. The system is composed of a user terminal, a server, a generative AI model, and an emotion engine. Specific embodiments of this system are described below.

[1517] First, the user enters a message into the chat window on their device and clicks the send button. This message is sent to the server as an HTTP request. The device can be a PC, smartphone, or other device.

[1518] The server receives an HTTP request from a user and extracts a message from the request body. The server then uses a natural language processing (NLP) library (e.g., spaCy or NLTK) to analyze the message content and extract keywords.

[1519] The server passes the parsed message to an emotion engine (e.g., Hugging Face Transformers), which determines the user's emotional state (positive, negative, neutral, etc.) based on the message content.

[1520] When a user requests to "make a payment," the server uses a generative AI model (e.g., GPT-4) to suggest the optimal payment method. The server adjusts the suggestion based on the analysis results of the emotion engine. Specifically, it sends a prompt to the generative AI model asking, "If I want to make a payment, what payment method should I suggest?" and adjusts the generated response.

[1521] Similarly, if the user says they "cannot pay," the server uses the generative AI model to suggest the optimal payment change method. It sends the generative AI model a prompt message asking, "If you cannot pay, what payment change method should we suggest?" and adjusts the generated response.

[1522] If the user's message is not about a payment or payment change request, the server will provide the contact information of the department in charge. Specifically, if the message says "How can I contact you?", the server will provide the phone number and email address of the support department.

[1523] Here, a specific example will be given.

[1524] Example 1:

[1525] User: "I want to pay, how do I do that?"

[1526] Server: "We accept credit card or bank transfer payments." (If the user expresses concern, adjust the response to something like, "Don't worry, we accept credit card or bank transfer payments.")

[1527] Example 2:

[1528] User: "I can't pay, what should I do?"

[1529] Server: "We can offer you a payment plan or postpone the payment date." (If the user expresses sadness or stress, tailor your response to something like, "I'm sorry you're struggling. We offer a payment plan or postponement. Please consider this.")

[1530] Example 3:

[1531] User: "How can I contact you?"

[1532] Server: "Please contact our support department if you have any questions. Phone: 012-345-6789 Email: support@example.com" (If the user expresses a sense of urgency, adjust the response to something like "We'll get back to you shortly. Please contact our support department if you have any questions. Phone: 012-345-6789 Email: support@example.com")

[1533] This system enables efficient and flexible responses to unpaid customers, improving customer satisfaction and reducing the burden on companies' personnel. The combination of a generative AI model and an emotion engine enables optimal responses that take into account customer emotions.

[1534] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1535] Step 1:

[1536] The user types a message on the terminal and sends it.

[1537] Specifically, the user enters something like "I want to pay" or "I can't pay" into the chat window and clicks the send button. This action causes the device to compose the user's input message as an HTTP request and send it to the server.

[1538] Input: User input message (e.g. "I want to make a payment")

[1539] Output: HTTP request (including the user's message)

[1540] Step 2:

[1541] The server receives the HTTP request from the user and extracts and parses the message.

[1542] The server receives the HTTP request and extracts the user's message from the request body. The server then analyzes the message content using a natural language processing (NLP) library (e.g., spaCy or NLTK) to extract keywords such as "I want to pay" and "I can't pay."

[1543] Input: HTTP request (including the user's message)

[1544] Output: Extracted messages and keywords

[1545] Step 3:

[1546] The emotion engine analyzes the user's emotions.

[1547] The server passes the analyzed message to the emotion engine, which determines the user's emotional state (positive, negative, neutral, etc.) based on the content of the message. The emotion engine uses Transformers such as Hugging Face.

[1548] Input: Extracted message and keywords

[1549] Output: User's emotional state (e.g., negative)

[1550] Step 4:

[1551] The server uses the generative AI model to generate the optimal proposal (in the case of a payment offer).

[1552] If the user's message is determined to be "I want to pay," the server sends a prompt to the generative AI model (e.g., GPT-4) to generate a response suggesting the optimal payment method. For example, in response to the message "I want to pay," the server sends a prompt such as "If I want to pay, what payment method should I suggest?" to the generative AI model.

[1553] Input: The keyword "I want to pay", the user's emotional state

[1554] Output: Suggestion of the best payment method (e.g. "Payment can be made by credit card or bank transfer.")

[1555] Step 5:

[1556] The server uses the generative AI model to generate the optimal proposal (in the case of a payment change request).

[1557] If the user's message is judged to be "I can't pay," the server sends a prompt to the generative AI model and generates a response proposing the optimal payment change method. For example, in response to the message "I can't pay," the server sends a prompt such as "If I can't pay, what payment change method should I suggest?" to the generative AI model.

[1558] Input: The keyword "can't pay", the user's emotional state

[1559] Output: Suggestion of the best payment modification method (e.g. "You can pay in installments or postpone the payment due date.")

[1560] Step 6:

[1561] The server will provide contact details for the relevant department for any other requests.

[1562] If the user's message is not a request for payment or payment change, the server will provide pre-defined contact information for the relevant department. For example, in response to the message "How can I contact you?", the server will provide a support department's phone number and email address.

[1563] Input: Message from user, emotional state

[1564] Output: Contact information for the department in charge (e.g., "Please contact our support department for inquiries. Phone: 012-345-6789 Email: support@example.com")

[1565] Step 7:

[1566] The server generates a response and sends it back to the user.

[1567] Finally, the server compiles the generated response content and sends it back to the user's device as an HTTP response. Based on the results of the emotion engine, the server adjusts the response content and replies in a way that is appropriate to the user's emotions.

[1568] Input: Best suggestions or contact information, user's emotional state

[1569] Output: HTTP response (response content sent to the user's device)

[1570] Through the above processing steps, the system can realize efficient and optimal responses that take into consideration the user's feelings.

[1571] (Application example 2)

[1572] 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."

[1573] Conventional systems for handling unpaid customer accounts provide uniform responses without considering the customer's feelings, which makes it impossible to alleviate customer anxiety and stress. This can result in lower customer satisfaction and loss of trust for the company. Furthermore, the system's payment method presentation and proposals for changes are not optimized, which can make it difficult to fully respond to customer requests.

[1574] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1575] In this invention, the server includes: means for using a generative AI model to make optimal proposals in response to payment and payment change requests from customers with outstanding payments; means including an emotion analysis engine for analyzing the content of users' messages and determining their emotional state; means for providing contact information for the relevant department in response to requests other than payment or payment change requests; and means for adjusting the response generated by the generative AI model based on the emotion analysis results and returning the response to the user. This enables flexible responses based on customer emotions, improves customer satisfaction, increases the company's credibility, and makes it possible to propose optimal payment methods and payment change methods.

[1576] "Delinquent Customers" refers to customers who have not yet made a payment when the payment deadline has passed.

[1577] "Payment change" refers to changing the payment terms that have already been determined, and specifically includes extending the payment due date or making installment payments.

[1578] A "generative AI model" refers to an artificial intelligence algorithm that provides generative responses to specific tasks by learning from large amounts of data.

[1579] "Emotion analysis engine" refers to software or hardware for recognizing a user's emotional state (e.g., sadness, anxiety, joy, etc.) from text or speech.

[1580] "Department contact information" refers to contact information such as phone numbers and email addresses for departments within a company that handle specific tasks or inquiries.

[1581] A "server" refers to a computer system that processes requests from clients and provides information over a network.

[1582] "Analyzing the content of the user's message" refers to a data processing means for processing the text data input by the user and understanding the intent and content of the message.

[1583] "Optimal proposal" refers to providing the most suitable payment method or payment change method for the user under specific conditions.

[1584] "Adjusting the response" refers to modifying the content of the answer generated by the generative AI model based on the user's emotional state to provide a more effective and friendly response.

[1585] The present invention relates to a system for making optimal proposals for unpaid customer payments and payment change requests, and providing responses that take into account the emotional state of the user. The system includes the following components.

[1586] Overall system configuration

[1587] The system consists of a user's device, a server, a generative AI model, and a sentiment analysis engine. The user inputs a message through the device, which is then sent to the server. The server analyzes the message and uses the generative AI model and sentiment analysis engine to generate the optimal response, which is then sent back to the user.

[1588] Program processing overview

[1589] First, the user enters a message on their device and clicks the send button. This message is sent to the server. The server receives the message from the user and analyzes its content. For example, it determines whether the message contains keywords such as "I want to pay" or "I can't pay." The server then passes the analysis results to a sentiment analysis engine to analyze the user's emotions. The server determines the user's emotional state (e.g., positive, negative, neutral) and uses a generative AI model to suggest the optimal payment method or payment change method.

[1590] Hardware and software used

[1591] The main hardware and software required to implement the present invention are as follows:

[1592] 1. User device (smartphone, tablet, PC, etc.)

[1593] This allows the user to input a message.

[1594] 2. Server

[1595] It receives and analyzes user messages and generates responses using generative AI models and a sentiment analysis engine.

[1596] 3. Generative AI Models

[1597] Use large-scale language models such as OpenAI GPT.

[1598] 4. Sentiment Analysis Engine

[1599] Use emotion analysis software such as EmotionRecognizer.

[1600] Specific examples

[1601] 1. Example 1:

[1602] User: "I want to pay, how do I do that?"

[1603] Server: "We accept credit card or bank transfer payments." (If the user expresses concern, adjust the response to something like "Don't worry, we accept credit card or bank transfer payments.")

[1604] 2. Example 2:

[1605] User: "I can't pay, what should I do?"

[1606] Server: "We can offer you a payment plan or postpone the payment date." (If the user expresses sadness or stress, you could tailor your response to something like, "I'm sorry you're struggling. We offer a payment plan or postponement. Please consider this.")

[1607] 3. Example 3:

[1608] User: "How can I contact you?"

[1609] Server: "Please contact our support department if you have any questions. Phone: 012-345-6789 Email: support@example.com" (If the user expresses a sense of urgency, adjust the response to something like "We'll get back to you shortly. Please contact our support department if you have any questions. Phone: 012-345-6789 Email: support@example.com")

[1610] Prompt Sentence Examples

[1611] "When you receive a message like this, generate a response with the appropriate emotion: Message: 'I want to pay, how do I do that?' Emotion: Anxiety Response: 'Don't worry, you can pay by credit card or bank transfer.'"

[1612] This system will streamline the process of dealing with unpaid customers and provide user-friendly services, thereby reducing the burden on companies' personnel and improving customer satisfaction.

[1613] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1614] Step 1:

[1615] The user inputs and sends a message on the terminal. The user inputs a message such as "I want to pay" or "I can't pay" in the terminal's chat window and clicks the send button. This sends the user's input message to the server. The input data is the user's text message, and the output data is the text message sent to the server.

[1616] Step 2:

[1617] The server receives the request and analyzes the message content. The server receives the message from the user and analyzes its content. Specifically, it analyzes whether the message contains keywords such as "I want to pay" or "I can't pay," and classifies it as a payment request, a payment change request, or other request. The input data is the text message sent to the server, and the output data is the classification result of the analyzed message.

[1618] Step 3:

[1619] The server passes the analysis results to a sentiment analysis engine, which analyzes the user's sentiment. The server passes the message analysis results to a sentiment analysis engine, which determines the user's emotional state (e.g., positive, negative, neutral). The input data are the analyzed message classification results and the text message, and the output data is the user's emotional state.

[1620] Step 4:

[1621] The server uses a generative AI model to generate the optimal proposal. If the user says "I want to pay," the server uses the generative AI model to propose the optimal payment method (e.g., credit card, bank transfer). If the user says "I can't pay," the server uses the generative AI model to propose the optimal payment change method (e.g., installment payments, extension of payment due date). The input data is the user's emotional state and the message classification results, and the output data is the optimal proposal.

[1622] Step 5:

[1623] The server adjusts the response based on the emotion analysis results. Based on the analysis results of the emotion analysis engine, the server adjusts the response generated by the generative AI model. For example, if the user expresses anxiety, the server adds the phrase "Don't worry" to the response. The input data is the user's emotional state and the generated response, and the output data is the adjusted response.

[1624] Step 6:

[1625] The server returns the adjusted response content to the user. The server sends the final adjusted response content to the user's terminal. The user's terminal receives it and displays it in the chat window. The input data is the adjusted response content, and the output data is the final response sent to the user.

[1626] 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.

[1627] 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.

[1628] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1629] 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.

[1630] 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.

[1631] 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.

[1632] 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).

[1633] 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.

[1634] 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."

[1635] 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.

[1636] 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).

[1637] 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.

[1638] 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.

[1639] 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.

[1640] 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.

[1641] 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.

[1642] 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.

[1643] 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.

[1644] 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.

[1645] 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.

[1646] 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.

[1647] The following is further disclosed regarding the above embodiment.

[1648] (Claim 1)

[1649] A means for making optimal proposals using a generative AI model in response to payments and payment change requests from unpaid customers;

[1650] A means of providing contact details for departments responsible for requests other than payments or payment changes;

[1651] A means for returning a response generated by the generative AI model to the user;

[1652] A system including:

[1653] (Claim 2)

[1654] 10. The system of claim 1, further comprising means for analyzing the content of a user's message and classifying it as a payment offer, a payment change offer, or other offer.

[1655] (Claim 3)

[1656] 10. The system of claim 1, comprising a generative AI model for providing optimal payment method recommendations for payment proposals.

[1657] "Example 1"

[1658] (Claim 1)

[1659] a means for a user to input and send messages at the terminal;

[1660] a means for the server to receive the request and parse the message content;

[1661] A means for suggesting the most suitable payment method for a payment request;

[1662] A means for proposing an optimal payment change method in response to a payment change request;

[1663] A means of providing contact details for departments responsible for requests other than payments or payment changes;

[1664] A means for returning a response generated by the generative AI model to the user;

[1665] A system including:

[1666] (Claim 2)

[1667] 10. The system of claim 1, further comprising means for analyzing the content of a user's message and classifying it as a payment offer, a payment change offer, or other offer.

[1668] (Claim 3)

[1669] 10. The system of claim 1, comprising a generative AI model for providing optimal payment method recommendations for payment proposals.

[1670] "Application Example 1"

[1671] (Claim 1)

[1672] A means for making optimal proposals using a generative AI model in response to payments and payment change requests from unpaid customers;

[1673] A means of providing contact details for departments responsible for requests other than payments or payment changes;

[1674] A means for returning the response generated by the generative AI model to the user;

[1675] means for analyzing and classifying user messages into payment requests, payment change requests, and other requests;

[1676] a means for using a generative AI model to suggest appropriate payment methods and payment modification options;

[1677] A system including:

[1678] (Claim 2)

[1679] 2. The system of claim 1, further comprising means for analyzing a message input by a user at a terminal and determining whether the message is a payment request, a payment change request, or some other request.

[1680] (Claim 3)

[1681] 10. The system of claim 1, further comprising means for using a generative AI model to suggest an optimal payment method, such as a credit card or bank transfer, in response to a payment request.

[1682] "Example 2: Combining Emotion Engines"

[1683] (Claim 1)

[1684] A means for making optimal proposals using a generative AI model in response to payments and payment change requests from unpaid customers;

[1685] A means of providing contact details for departments responsible for requests other than payments or payment changes;

[1686] A means for returning a response generated by the generative AI model to the user;

[1687] a means for adjusting the response content using an emotion engine that analyzes the user's emotion;

[1688] A system including:

[1689] (Claim 2)

[1690] 10. The system of claim 1, further comprising means for analyzing the content of a user's message and classifying it as a payment offer, a payment change offer, or other offer.

[1691] (Claim 3)

[1692] 10. The system of claim 1, comprising a generative AI model for providing optimal payment method recommendations for payment proposals.

[1693] "Application example 2 when combining emotion engines"

[1694] (Claim 1)

[1695] A means for making optimal proposals using a generative AI model in response to payments and payment change requests from unpaid customers;

[1696] means including an emotion analysis engine for analyzing the content of a user's message and determining the user's emotional state;

[1697] A means of providing contact details for departments responsible for requests other than payments or payment changes;

[1698] a means for adjusting the response generated by the generative AI model based on the sentiment analysis results and replying to the user;

[1699] A system including:

[1700] (Claim 2)

[1701] 10. The system of claim 1, further comprising means for analyzing the content of a user's message and classifying it as a payment offer, a payment change offer, or other offer.

[1702] (Claim 3)

[1703] 10. The system of claim 1, comprising a generative AI model for providing optimal payment method recommendations for payment proposals. [Explanation of symbols]

[1704] 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 making optimal proposals using a generative AI model in response to requests for payments and payment changes from customers who have not paid; A means of providing contact details for departments responsible for requests other than payments or payment changes; A means for returning a response generated by the generative AI model to the user; A system including:

2. 2. The system of claim 1, further comprising means for analyzing the content of a user's message and classifying it as a payment offer, a payment change offer, or other offer.

3. 10. The system of claim 1, comprising a generative AI model for providing optimal payment method recommendations for payment proposals.

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