system

Abstract

Provide a system. A means for receiving and recording user status information; A means of receiving and recording complaints; means for generating an appropriate response based on the received complaint; means for providing the generated response to a user; A system including:

Description

[Technical Field]

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

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

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

[0004] In recent years, customer support operators have increasingly been faced with excessive complaints and customer harassment. These situations increase the mental burden on operators, leading to problems such as reduced work efficiency and increased turnover. In particular, when responding to excessive customer complaints, it is difficult for operators to immediately prepare appropriate responses, which can lead to a decline in the quality of support. For this reason, a system that can appropriately respond to excessive complaints and customer harassment is needed. [Means for solving the problem]

[0005] The present invention solves the above-mentioned problems with a system including a means for receiving and recording user status information, a means for receiving and recording complaint details, a means for generating an appropriate response based on the received complaint details, and a means for providing the generated response to the user. Specifically, when an operator inputs status information such as work status, job details, and past complaint history, the system records this. In addition, when the operator inputs the details of a customer complaint, the system generates an appropriate response based on the content using a natural language processing algorithm. The generated response is provided to the operator, who then uses it to respond to the customer. This reduces the mental burden on the operator and improves the quality of the response.

[0006] "User" refers to a person who operates the system and performs customer service duties as an operator.

[0007] "Status information" refers to information related to on-site operations and work, such as the user's work status, work content, and past complaint history.

[0008] "Server" refers to a central computer that receives, records, processes various information, and manages the entire system.

[0009] A "terminal" is a device operated by a user, and refers to a device for inputting user information and complaint details and for confirming responses.

[0010] "Complaint content" refers to information that includes the specific details of customer dissatisfaction, requests, and complaints.

[0011] "Response" refers to the response message or reply generated in response to the complaint content entered.

[0012] "Natural language processing algorithms" refer to computer algorithms that analyze, understand, and generate human language. [Brief explanation of the drawings]

[0013] [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

[0014] 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.

[0015] First, the terms used in the following description will be explained.

[0016] 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).

[0017] 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.

[0018] 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.

[0019] 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.

[0020] 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."

[0021] [First embodiment]

[0022] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0023] 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.

[0024] 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).

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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.

[0029] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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."

[0034] The system of this invention utilizes AI's natural language processing capabilities to provide appropriate responses when complaint handling operators deal with excessive complaints or harassment from customers. Below, we will generate a program for the system and explain its processing in natural language.

[0035] System configuration

[0036] 1. A means of receiving and recording user status information

[0037] User: The operator inputs information about the working situation, work content, past complaint history, etc. into the terminal. This includes the operator's current physical condition and the types of complaints they have handled in the past.

[0038] Terminal: Receives status information entered by the user and sends it to the server.

[0039] Server: Records the status information received from the terminal and manages information for each user.

[0040] 2. How complaints are received and recorded

[0041] User: The operator inputs the specific details of the customer's complaint into the terminal. For example, the operator inputs "The product has not arrived."

[0042] Terminal: Receives the complaint details entered by the user and sends them to the server.

[0043] Server: Records the complaint received from the terminal and prepares it for later processing.

[0044] 3. A means of generating appropriate responses based on the complaint received

[0045] Server: Sends the complaint details to the AI ​​engine, which uses natural language processing algorithms to generate an appropriate response based on the complaint details entered.

[0046] AI Engine: Using natural language processing algorithms, it analyzes incoming complaints and generates corresponding responses.

[0047] Server: Records the responses sent back from the AI ​​engine and manages them for each user.

[0048] 4. A means of providing the generated response to the user

[0049] User: Sends a request from the device to the server, asking for a response.

[0050] Terminal: Receives requests from users and sends them to the server.

[0051] Server: In response to a user request, it retrieves the generated response and sends it to the terminal.

[0052] Terminal: The reply received from the server is displayed to the user, who then checks the reply and uses it to respond to the customer.

[0053] Specific examples

[0054] For example, let's say Operator A comes to work and logs in to the system. Operator A enters his / her current work status and past complaint history, and registers the status information in the system.

[0055] Next, operator A receives a complaint from customer B that the product has not arrived and enters the details into the system. The terminal sends the details of the complaint to the server, which records them.

[0056] The server sends the complaint details to the AI ​​engine, which uses a natural language processing algorithm to generate an appropriate response, such as "We will check the delivery status of the product and contact you again."

[0057] This response is recorded on the server, and when Operator A sends a request for a response, the server generates a response and sends it to the terminal. Operator A checks the response on the terminal and uses it to respond to Customer B.

[0058] As mentioned above, this system is designed to reduce the mental burden on operators and improve the quality of their responses, enabling appropriate responses to excessive complaints and customer harassment.

[0059] The processing flow will be explained below.

[0060] Step 1:

[0061] The user inputs status information such as work status, work content, and past complaint history into the terminal.

[0062] Step 2:

[0063] The terminal receives the input status information and transmits it to the server.

[0064] Step 3:

[0065] The server records the received user status information and manages the information for each user.

[0066] Step 4:

[0067] The user inputs the specific content of the complaint received from the customer into the terminal.

[0068] Step 5:

[0069] The terminal receives the input complaint details and transmits them to the server.

[0070] Step 6:

[0071] The server records the complaint received for later processing.

[0072] Step 7:

[0073] The server sends the complaint details to the AI ​​engine.

[0074] Step 8:

[0075] The AI ​​engine uses natural language processing algorithms to analyze the inputted complaint content and generate an appropriate response.

[0076] Step 9:

[0077] The AI ​​engine sends the generated response back to the server.

[0078] Step 10:

[0079] The server records the responses it receives and manages them for each user.

[0080] Step 11:

[0081] The user sends a request from the terminal to the server, requesting a response.

[0082] Step 12:

[0083] The server responds to the user's request by recalling the generated response and sending it to the terminal.

[0084] Step 13:

[0085] The terminal displays the response received from the server to the user.

[0086] Step 14:

[0087] The user reviews the displayed response and uses it to respond to the customer.

[0088] Example 1

[0089] 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."

[0090] With conventional complaint handling systems, it was difficult for operators to quickly provide effective responses to excessive complaints or harassment from customers. This increased the mental burden on operators and raised concerns that the quality of responses would decline. Furthermore, operators needed time to think of an appropriate response on the spot, which could prolong response times.

[0091] 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.

[0092] In this invention, the server includes means for receiving and recording user status information, means for receiving and recording complaint details, means for transmitting the status information entered by the user to the server, means for transmitting the complaint details to the server, means for generating an appropriate response based on the received complaint details, means for saving the generated response, and means for providing the generated response to the user. This enables the operator to provide a prompt and appropriate response, reducing mental strain and improving the quality of the response.

[0093] "User status information" refers to information such as the user's (operator's) working status, work content, and past complaint history.

[0094] "Complaint details" refers to the details of specific complaints or claims received from customers.

[0095] "Server" refers to a central processing unit that receives and records user status information and complaints, generates appropriate responses, and stores them.

[0096] "Terminal" refers to a device through which a user inputs information and transmits that information to a server.

[0097] "Means for receiving and recording" refers to the function of receiving user status information and complaint details and storing them in a database or the like.

[0098] "Means for generating appropriate responses" refers to the ability to automatically generate responses based on the content of the complaint received using an AI engine or natural language processing algorithm.

[0099] "Means for storing generated responses" refers to the function of storing responses generated by the AI ​​engine in a database and managing them so that they can be retrieved later.

[0100] The "means for providing the generated response to the user" refers to a function for transmitting the response stored by the server to the terminal in response to a request from the user and providing it to the user.

[0101] "Natural language processing algorithms" refers to a set of algorithms that analyze text data, understand its meaning, and generate appropriate responses.

[0102] MODE FOR CARRYING OUT THE INVENTION

[0103] As an embodiment of the present invention, the following describes a system that utilizes the natural language processing capabilities of AI to provide appropriate responses to complaint handling operators in order to deal with excessive complaints and harassment from customers.

[0104] System configuration and hardware / software used

[0105] The system includes the following major components:

[0106] 1. A means of receiving and recording user status information

[0107] User: The operator inputs information such as working status, work content, and past complaint history into the terminal, including the operator's current physical condition and the types of complaints they have handled in the past.

[0108] Terminal: Receives the input status information and sends the data to the server. Terminals used include PCs and tablets.

[0109] Server: Records status information received from the terminal and stores and manages information for each user in a database. The database may be MySQL (registered trademark) or PostgreSQL.

[0110] 2. How complaints are received and recorded

[0111] User: The operator inputs the specific details of the customer's complaint into the terminal, such as "the product has not arrived."

[0112] Terminal: Receives the entered complaint details and sends them to the server.

[0113] Server: Records the complaint details received from the terminal and stores them in a database.

[0114] 3. A means of generating appropriate responses based on the complaint received

[0115] Server: Sends the saved claim details to an AI engine (e.g., OpenAI's GPT-3) in the form of an API request.

[0116] AI engine: Using a natural language processing algorithm, it generates an appropriate response based on the received complaint. For example, it generates a response such as, "We will check the delivery status of the product and contact you again."

[0117] 4. A means of saving the generated response

[0118] Server: The responses sent back from the AI ​​engine are stored in a database and managed for each user.

[0119] 5. A means of providing the generated response to the user

[0120] User: The operator sends a request from the terminal to the server, which asks for a response.

[0121] Terminal: Receives requests from users and sends them to the server.

[0122] Server: Retrieves the stored response and sends it to the device.

[0123] Terminal: Displays the response received from the server to the user. The operator checks the response and responds to the customer.

[0124] Specific examples

[0125] Specific examples of system use

[0126] For example, let's say Operator A comes to work and logs in to the system. Operator A enters his / her current work status and past complaint history, and registers the status information in the system.

[0127] Next, operator A receives a complaint from customer B that the product has not arrived and enters the details into the system. The terminal sends the details of the complaint to the server, which records them.

[0128] The server sends the complaint to an AI engine (e.g., OpenAI's GPT-3), which uses a natural language processing algorithm to generate an appropriate response, such as, "We will check the delivery status of your product and contact you again."

[0129] This response is recorded on the server, and when Operator A sends a request for a response, the server generates a response and sends it to the terminal. Operator A checks the response on the terminal and uses it to respond to Customer B.

[0130] Example prompts to input to the generative AI model

[0131] Example prompt sentence:

[0132] Customer complaint: "The product never arrived."

[0133] Generate an appropriate response for the operator.

[0134] keyword

[0135] Generative AI model, prompt sentence

[0136] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0137] System program processing flow

[0138] Step 1: User Enters Context Information

[0139] explanation

[0140] The user (operator) inputs information such as working conditions, work content, and past complaint history into the terminal, including the user's current physical condition and the types of complaints they have handled in the past.

[0141] Input and Output

[0142] Input: working status, work content, past complaint history, health information

[0143] Output: Situation information data

[0144] Specific actions

[0145] The operator enters the necessary information into the terminal form and sends it to the system.

[0146] Step 2: The device sends status information to the server

[0147] explanation

[0148] The device receives the status information entered by the user and sends the data to the server in a format such as JSON or XML.

[0149] Input and Output

[0150] Input: Situational information data

[0151] Output: Request sent to server

[0152] Specific actions

[0153] The input information is converted into a data format (e.g., JSON) within the terminal and sent to the specified API endpoint.

[0154] Step 3: The server records status information

[0155] explanation

[0156] The server records the status information received from the terminal and stores the information for each user in a database.

[0157] Input and Output

[0158] Input: Situational information data

[0159] Output: Save to database

[0160] Specific actions

[0161] The server parses the received JSON data and executes SQL queries to store the information in the corresponding database tables.

[0162] Step 4: User enters complaint details

[0163] explanation

[0164] The user (operator) inputs the specific details of the customer's complaint into the terminal. This information includes details of the complaint, such as "the product has not arrived."

[0165] Input and Output

[0166] Input: Complaint details

[0167] Output: Claim data

[0168] Specific actions

[0169] The operator enters the details of the complaint into a form on the terminal and sends it to the system.

[0170] Step 5: The device sends the complaint to the server

[0171] explanation

[0172] The terminal receives the complaint details entered by the user and transmits the data to the server.

[0173] Input and Output

[0174] Input: Claim data

[0175] Output: Request sent to server

[0176] Specific actions

[0177] The entered claim content is converted into a data format (e.g., JSON) within the terminal and sent to the specified API endpoint.

[0178] Step 6: The server records the complaint

[0179] explanation

[0180] The server records the complaint details received from the terminal and stores them in a database for later processing.

[0181] Input and Output

[0182] Input: Claim data

[0183] Output: Save to database

[0184] Specific actions

[0185] The server parses the received JSON data and executes SQL queries to store the information in the corresponding database tables.

[0186] Step 7: The server sends the claim to the AI ​​engine

[0187] explanation

[0188] The server retrieves the stored claim and sends it to an AI engine (e.g., OpenAI's GPT-3), usually in the form of an API request.

[0189] Input and Output

[0190] Input: Claim data

[0191] Output: Send request to AI engine

[0192] Specific actions

[0193] The server converts the claim data into API request format and sends it to the AI ​​engine's endpoint.

[0194] Step 8: The AI ​​engine generates a response

[0195] explanation

[0196] The AI ​​engine (generative AI model) analyzes the content of the received complaint and generates an appropriate response using a natural language processing algorithm.

[0197] Input and Output

[0198] Input: Claim data

[0199] Output: The generated response

[0200] Specific actions

[0201] The AI ​​engine runs the complaint through a natural language processing algorithm and generates a response in text format that corresponds to the complaint.

[0202] Step 9: The server stores the generated response

[0203] explanation

[0204] The server receives the response from the AI ​​engine and stores it in a database.

[0205] Input and Output

[0206] Input: Generated response

[0207] Output: Save to database

[0208] Specific actions

[0209] The server executes an SQL query to retrieve and store the received response data in a database.

[0210] Step 10: User submits request for response

[0211] explanation

[0212] The user (operator) sends a request for a response to the complaint from the terminal to the server.

[0213] Input and Output

[0214] Input: Reply Request

[0215] Output: Request sent to server

[0216] Specific actions

[0217] The operator operates the terminal interface to send a reply request to the server.

[0218] Step 11: The server sends the response to the device

[0219] explanation

[0220] The server receives a request from the user, retrieves the necessary response from the database, and sends it to the terminal.

[0221] Input and Output

[0222] Input: Reply Request

[0223] Output: Response data

[0224] Specific actions

[0225] The server retrieves the answer from the database and generates an API response to send to the device.

[0226] Step 12: The device displays the response to the user

[0227] explanation

[0228] The terminal displays the response received from the server to the user, who then checks the response and uses it to respond to the customer.

[0229] Input and Output

[0230] Input: Response data

[0231] Output: What is displayed to the user

[0232] Specific actions

[0233] The terminal displays the received response data on the screen and provides an interface that allows the operator to easily check it.

[0234] (Application example 1)

[0235] 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."

[0236] In recent years, excessive complaints and harassment (customer harassment) from customers and within companies have become an issue. Operators who respond to these complaints are often burdened with psychological stress, raising concerns that the quality of their responses may decline. In particular, there is a need to quickly provide appropriate countermeasures against harassment. However, conventional systems often fail to efficiently handle such responses. Therefore, the present invention aims to solve these issues.

[0237] 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.

[0238] In this invention, the server includes a means for receiving and recording user status information, a means for receiving and recording complaint details, and a means for generating an appropriate response based on the received complaint details, thereby enabling a means for proposing countermeasures using an AI engine.

[0239] "Means for receiving and recording user status information" refers to a device or function that receives and records the work status, work content, past complaint history, and other status information entered by the operator.

[0240] "Means for receiving and recording complaint details" refers to a device or function that receives and records the specific complaint details entered by the operator.

[0241] "Means for generating an appropriate response based on the received complaint content" refers to a device or function that automatically generates an appropriate response based on the received complaint content.

[0242] The "means for providing the generated response to the user" refers to a device or function that presents the generated response to the operator and enables the operator to take action based on the response.

[0243] "Means of proposing countermeasures using an AI engine" refers to devices or functions that use an AI engine to analyze the content of the complaint and the operator's situation information, and propose the most appropriate countermeasures.

[0244] A "natural language processing algorithm" is an algorithm for understanding and analyzing human language, and is a technology used to analyze the content of claims and generate appropriate responses.

[0245] A "generative AI model" is an AI model trained to perform a specific task (e.g., generating appropriate responses), and is a technology that uses this model to generate responses for complaints.

[0246] This invention provides a system that uses AI to generate and propose appropriate responses and countermeasures in response to excessive complaints and harassment (customer harassment) from customers or within a company.

[0247] The overall system configuration is as follows:

[0248] Means for receiving and recording user status information

[0249] The server receives and records status information entered by the operator through the terminal, such as work status, work content, and past complaint history. Specifically, the operator enters their current physical condition and the content of the complaint they are currently handling, and sends this to the server.

[0250] A means of receiving and recording complaints

[0251] The operator enters specific details of complaints received from customers or from within the company into the terminal, which then sends the information to the server, which records it and prepares it for analysis.

[0252] A means of generating appropriate responses based on the complaint received

[0253] The server sends the received complaint content to an existing AI engine, which uses a natural language processing algorithm to analyze the content of the complaint and generate an appropriate response. The server records the generated response and manages it so that it can be called up as needed.

[0254] A means of providing the generated response to the user

[0255] When an operator sends a request for a generated answer from a terminal, the server retrieves the corresponding answer from the answer database it manages and sends it to the terminal. The operator checks the answer on the terminal and uses it to respond to the customer.

[0256] A means of proposing countermeasures using an AI engine

[0257] The server sends the content of the complaint and the user's situation information to the AI ​​engine, which then uses the generative AI model to propose appropriate countermeasures and responses, enabling the operator to respond quickly and appropriately.

[0258] As a concrete example, consider the case where an operator receives a complaint that "I can't log in due to a system error." The operator enters this complaint into the terminal and sends it to the server. The server passes this complaint on to the AI ​​engine, which then generates a response such as, "Regarding the login problem, please first clear the cache and try again. If that doesn't resolve it, please contact your system administrator." This is recorded on the server, and when the operator calls it, it is immediately displayed on the terminal.

[0259] Here are some example prompts to input to the generative AI model:

[0260] Operator: Yamada Taro

[0261] Status: Feeling good today. Two complaints from yesterday have been dealt with.

[0262] Complaint: Unable to log in due to a system error.

[0263] Appropriate response:

[0264] By inputting this prompt into a generative AI model, an appropriate response is generated, thereby improving the quality of customer service and reducing the mental burden on operators.

[0265] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0266] Step 1:

[0267] The server receives and records information about the operator's status from the terminal. The operator enters information such as their work status, physical condition, and past complaint history into the terminal, which then sends this information to the server. The server stores the received data in a database and manages detailed status information for each user. The input data is work status, job content, and past complaint history, and the output is to save this information in an organized format in the database.

[0268] Step 2:

[0269] The user inputs specific complaints received from customers or from within the company into the terminal. The terminal then sends the complaints to the server. The server records the received complaints in a database for later processing. The input data is the specific complaints from customers or from within the company, and the output is to save these complaints in an organized format in the database.

[0270] Step 3:

[0271] The server sends the recorded complaint content to the AI ​​engine. The AI ​​engine uses a natural language processing algorithm to analyze the input complaint content and generate an appropriate response. The generated response is sent back to the server, which records it in a database. The input data is the complaint content, and the output is an appropriate response. The specific operation is the process of analyzing the complaint content and generating a response.

[0272] Step 4:

[0273] When a user sends a request for a reply from a terminal, the server retrieves the corresponding reply from the reply database it manages and sends it to the terminal. The terminal displays the reply to the user, who can then confirm it and use it to handle the complaint. The input data is the reply request, and the output is a display of the appropriate reply. The specific operation is to retrieve the reply data from the database and display it.

[0274] Step 5:

[0275] The server uses an AI engine to analyze the content of the complaint and the user's situation information, and then uses a generative AI model to propose appropriate countermeasures. This allows the user to respond quickly and appropriately. The input data is the content of the complaint and situation information, and the output is the proposed countermeasures. The specific operation involves analysis by the AI ​​engine and generation of countermeasures.

[0276] As a concrete example, if an operator receives a complaint that "I can't log in because of a system error," the complaint entered into the terminal is sent to the server, and the AI ​​engine generates a response such as, "Regarding the login problem, please first clear the cache and try again. If that doesn't resolve it, please contact your system administrator." This response is recorded on the server and displayed upon user request.

[0277] Here are some example prompts to input to a generative AI model:

[0278] Operator: Yamada Taro

[0279] Status: Feeling good today. Two complaints from yesterday have been dealt with.

[0280] Complaint: Unable to log in due to a system error.

[0281] Appropriate response:

[0282] By taking the above steps, it is possible to improve the quality of complaint handling and reduce the mental burden on operators.

[0283] 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.

[0284] The system of this invention utilizes AI's natural language processing capabilities and emotion recognition engine to provide appropriate responses when complaint handling operators deal with excessive complaints or harassment from customers. Below, we will generate a program for the system and explain its processing in natural language.

[0285] System configuration

[0286] 1. A means of receiving and recording user status information

[0287] User: The operator inputs status information such as work status, job content, and past complaint history into the terminal.

[0288] Terminal: Receives the status information entered by the user and sends it to the server.

[0289] Server: Records the status information received from the terminal and manages information for each user.

[0290] 2. How complaints are received and recorded

[0291] User: The operator inputs the specific details of the complaint received from the customer into the terminal.

[0292] Terminal: Receives the complaint content entered by the user and sends it to the server.

[0293] Server: Records the complaint received from the terminal and prepares it for later processing.

[0294] 3. A means of generating appropriate responses based on the complaint received

[0295] Server: Sends the complaint details to the AI ​​engine, which uses natural language processing algorithms to generate an appropriate response based on the complaint details entered.

[0296] AI Engine: Using natural language processing algorithms, it analyzes incoming complaints and generates corresponding responses.

[0297] Server: Records the responses sent back from the AI ​​engine and manages them for each user.

[0298] 4. A means of providing the generated response to the user

[0299] User: Sends a request from the device to the server, asking for a response.

[0300] Terminal: Receives requests from users and sends them to the server.

[0301] Server: In response to a user request, it invokes the generated response and sends it to the terminal.

[0302] Terminal: The reply received from the server is displayed to the user, who then checks the reply and uses it to respond to the customer.

[0303] 5. Includes an emotion engine that recognizes user emotions

[0304] User: When an operator inputs work status or complaint details, the tone of voice, facial expression, and input content are analyzed by the emotion engine.

[0305] Emotion engine: Analyzes voice tone, facial expressions, input content, etc. to recognize the user's emotional state in real time. This information is sent to the server.

[0306] Server: Based on the emotion information received from the emotion engine, the server adjusts the generated response and provides the most appropriate response for the user.

[0307] Specific examples

[0308] For example, let's say Operator A comes to work and logs in to the system. Operator A enters his / her current working status and past complaint history, and registers the status information in the system. At this time, the emotion engine analyzes Operator A's tone of voice and facial expressions, and sends the current emotional state to the server.

[0309] Next, operator A receives a complaint from customer B that the product has not arrived and enters the details into the system. The terminal sends the details of the complaint and operator A's emotional information at the time of input to the server.

[0310] The server sends the complaint details to the AI ​​engine, which uses a natural language processing algorithm to generate an appropriate response. For example, a response such as "We will check the delivery status of the product and contact you again." The response is then adjusted taking into account the emotional information from the emotion engine.

[0311] This response is recorded on the server, and when Operator A sends a request for a response, the server generates a response and sends it to the terminal. Operator A checks the response on the terminal and uses it to respond to Customer B.

[0312] This system reduces the mental burden on operators, improves the quality of customer service, and enables more appropriate and effective complaint handling.

[0313] The processing flow will be explained below.

[0314] Step 1:

[0315] The user inputs status information such as work status, work content, past complaint history, etc. into the terminal. The user logs in to the terminal at the start of work and inputs their physical condition and past complaint handling history.

[0316] Step 2:

[0317] The terminal receives the input status information and transmits it to the server. The terminal accurately records the information input by the user and transmits it to the server in real time.

[0318] Step 3:

[0319] The server records the received user status information and manages the information for each user. The server saves the received information in a database and makes it available for use.

[0320] Step 4:

[0321] The user inputs the specific complaint received from the customer into the terminal. To input the received complaint in detail, the user writes information along specific fields.

[0322] Step 5:

[0323] The terminal receives the complaint details entered and sends them to the server. The terminal accurately records the complaint details entered by the user and immediately sends them to the server.

[0324] Step 6:

[0325] The server records the received complaint for later processing. The received complaint is stored in a database along with other related information.

[0326] Step 7:

[0327] The server sends the content of the claim to the AI ​​engine. The received claim data is sent to the AI ​​engine for analysis.

[0328] Step 8:

[0329] The AI ​​engine uses natural language processing algorithms to analyze the content of the complaint entered and generate an appropriate response. The AI ​​engine understands the content of the complaint and compares it with past data to create the optimal response.

[0330] Step 9:

[0331] The AI ​​engine sends the generated response back to the server, where it is ready to be served to the user.

[0332] Step 10:

[0333] The server records the responses received and manages them for each user. Responses are saved with appropriate tags and metadata for easy access later.

[0334] Step 11:

[0335] The user sends a request for a response from the terminal to the server, and the user performs an operation to request a response generated by the system for a specific claim.

[0336] Step 12:

[0337] In response to a user request, the server retrieves a pre-generated response and sends it to the terminal. The server retrieves the appropriate response from the database and sends it to the user's terminal.

[0338] Step 13:

[0339] The terminal displays the response received from the server to the user. The terminal displays the response sent back from the server so that the user can check it.

[0340] Step 14:

[0341] The user checks the displayed response and responds to the customer using it. The user responds appropriately to the customer based on the confirmed response.

[0342] Step 15:

[0343] When a user inputs situation information or complaint details, the terminal sends the emotion to the emotion engine, which analyzes the user's tone of voice, facial expression, and input content.

[0344] Step 16:

[0345] The emotion engine sends the analysis results to the server to recognize the user's emotional state. The emotion engine sends the analysis results to the server to update the user's emotional state in real time.

[0346] Step 17:

[0347] The server adjusts the generated response based on the received emotional information to provide the most appropriate response for the user. The server incorporates the emotional information to adjust the tone and content of the response and display it to the user.

[0348] Example 2

[0349] 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."

[0350] In today's customer service environment, dealing with excessive customer complaints and malicious harassment (kasahara) places a heavy mental burden on operators. Responding appropriately and quickly requires high levels of judgment and emotional balance, but maintaining these skills is extremely difficult. Furthermore, the appropriate response must be considered for each complaint, making the process cumbersome. The present invention aims to provide a system that solves these problems, reduces the burden on operators, and improves the quality of customer service.

[0351] 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.

[0352] In this invention, the server includes means for receiving and recording user status information, means for receiving and recording complaint details, means for generating an appropriate response based on the received complaint details and adjusting the response based on emotion analysis, and means for providing the generated response to the user. This allows the operator to record their working status and work content in real time, and appropriate responses are generated accordingly, with adjustments made taking into account their emotional state, reducing their mental burden and enabling higher quality customer service.

[0353] "User" refers to the person who operates the system and inputs information. Specifically, this includes operators who handle customer complaints.

[0354] "Status information" refers to information including the current business status and past data, such as the user's working status, work content, and past complaint history.

[0355] "Complaint content" refers to the specific claims or dissatisfaction received from customers.

[0356] A "response" refers to an answer or response message that is generated based on the received complaint and that the user provides to the customer.

[0357] "Natural language processing algorithms" refer to computer programs that analyze text data and understand its meaning, allowing them to generate responses tailored to the content of a claim.

[0358] A "generative AI model" refers to an AI program that is trained on a huge amount of data and generates an appropriate output (response) for a specific input.

[0359] "Emotion analysis" refers to the process of analyzing a user's tone of voice, facial expression, input content, etc. to determine their emotional state.

[0360] "Server" refers to a computer system that receives and processes user input data, stores the data, executes algorithms, and generates and manages responses.

[0361] "Terminal" refers to a computer device that allows a user to input information and transmits the input data to a server.

[0362] The system of this invention utilizes AI's natural language processing capabilities and sentiment analysis engine to provide appropriate responses when a complaint handling operator deals with excessive complaints or harassment from customers. Specific embodiments of the system are described in detail below.

[0363] System configuration

[0364] 1. A means of receiving and recording user status information

[0365] User: The operator inputs status information such as work status, work content, past complaint history, etc. into the terminal. For example, when an operator starts a shift, they input their current work status and past complaint handling history.

[0366] Terminal: Receives status information entered by the user and sends it to the server. Specifically, it converts the data entered in the input form into JSON format and sends it to the server via the network.

[0367] Server: Records the status information received from the terminal and stores information for each user in a database.

[0368] 2. How complaints are received and recorded

[0369] User: The operator inputs the specific details of the complaint received from the customer into the terminal. For example, the operator inputs the details of the customer's complaint in real time while on the phone.

[0370] Terminal: Receives the complaint data entered by the user and sends it to the server. This data is also converted to JSON format.

[0371] Server: Records the complaint details received from the terminal and stores them in a database for later processing.

[0372] 3. A means to generate appropriate responses based on the complaints received and adjust them based on sentiment analysis

[0373] Server: Uses an API request to send the claim details to the AI ​​engine.

[0374] AI Engine: Using natural language processing algorithms and generative AI models, it analyzes incoming complaints and generates appropriate responses.

[0375] Server: Records the responses sent back by the AI ​​engine and adjusts the responses taking into account information from the sentiment analysis engine.

[0376] 4. A means of providing the generated response to the user

[0377] User: The operator sends a request for a response from the terminal to the server, for example by clicking "Generate response."

[0378] Terminal: Receives requests from users and sends them to the server.

[0379] Server: In response to a user request, it invokes the generated response and sends it to the terminal.

[0380] Terminal: The reply received from the server is displayed to the user, who then checks the reply and uses it to respond to the customer.

[0381] Specific examples

[0382] Operator A comes to work and logs in to the system. Operator A enters his / her current working status and past complaint history into the system. At this time, the emotion analysis engine analyzes Operator A's tone of voice and facial expressions and sends his / her emotional state to the server.

[0383] Next, operator A receives a complaint from customer B that the product has not arrived and enters the details into the system. The terminal then sends the details of the complaint and operator A's emotional information to the server.

[0384] The server sends the complaint details to the AI ​​engine, which uses a natural language processing algorithm and a generative AI model to generate an appropriate response. For example, a response such as, "We will check the delivery status of the product and contact you again." The response is adjusted based on information from the sentiment analysis engine.

[0385] This response is recorded on the server, and when Operator A sends a request for a response, the server sends the response to the terminal. Operator A checks the response on the terminal and uses it to respond to Customer B.

[0386] Prompt Sentence Examples

[0387] Operator: "Please enter your current work status and past claim history."

[0388] Terminal: "Please enter the details of the customer's complaint."

[0389] Server: "Generate an appropriate response based on the complaint."

[0390] Emotion Engine: "Provide emotional information by analyzing the tone of voice and facial expressions during input."

[0391] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0392] Step 1: Entering and receiving user context information

[0393] User: The operator enters information such as work status, job content, and past complaint history into the terminal.

[0394] How it works: At the start of a shift, an operator uses the system interface to fill out a form with their name, shift time, and complaint history for the past 24 hours.

[0395] Input: working status, work content, past complaint history

[0396] Terminal: Receives the status information entered by the user and sends it to the server.

[0397] Specific operation: The terminal converts the input data into JSON format and sends it to the server via the network.

[0398] Output: JSON format status information data

[0399] Step 2: Enter and receive your complaint

[0400] User: The operator inputs the specific details of the complaint received from the customer into the terminal.

[0401] Specific operation: The operator enters the details of the customer's complaint in real time while on the phone.

[0402] Input: Customer complaint details

[0403] Terminal: Receives the complaint content entered by the user and sends it to the server.

[0404] Specific operation: The terminal saves the content of the claim in real time, converts it into JSON format, and sends it to the server.

[0405] Output: Claim content in JSON format

[0406] Step 3: Sentiment analysis and information reception

[0407] User: The emotion engine analyzes the tone of voice, facial expressions, and input content when the operator enters the details of the complaint.

[0408] Specific operation: Data input by the operator through a microphone or camera is sent to the emotion engine.

[0409] Input: Operator tone of voice, facial expressions, input content

[0410] Emotion Engine: Analyzes the emotional state in real time based on the received data. This information is sent to the server.

[0411] Specific operation: The emotion engine uses a deep learning model to analyze the emotional state as numerical data and send it to the server.

[0412] Output: Numerical data of emotional information

[0413] Step 4: Generate and tailor an appropriate response

[0414] Server: Sends the received complaint to the AI ​​engine, which uses natural language processing algorithms and generative AI models to generate an appropriate response.

[0415] Specific operation: The server sends the claim content to the AI ​​engine as an API request.

[0416] Input: Complaint details

[0417] AI Engine: Generates responses using natural language processing algorithms and generative AI models.

[0418] Specific operation: The AI ​​engine analyzes the content of the complaint and generates an appropriate response.

[0419] Output: Generated response data

[0420] Step 5: Adjust your response based on emotional information

[0421] Server: Compares the response data returned from the AI ​​engine with emotional information and adjusts the response as necessary.

[0422] Specific operation: The server fine-tunes the response text based on information from the emotion engine.

[0423] Input: Generated response data, emotion information

[0424] Output: Adjusted response data

[0425] Step 6: Providing generated responses

[0426] User: Sends a request from the device to the server, asking for a response.

[0427] Specific action: The operator presses the button to generate a response.

[0428] Input: Reply request

[0429] Terminal: Receives requests from users and sends them to the server.

[0430] Specific operation: The terminal forwards the request to the server.

[0431] Output: Request data

[0432] Step 7: Capture and send response data

[0433] Server: In response to a user request, retrieves a pre-generated response from the database and sends it to the terminal.

[0434] Specific operation: The server retrieves the response from the database and sends it back to the terminal.

[0435] Input: Request data

[0436] Output: Response data

[0437] Step 8: Displaying the response on the terminal

[0438] Terminal: The reply received from the server is displayed to the user, who then checks the reply and uses it to respond to the customer.

[0439] Specific operation: The terminal displays the response data on the UI and the operator confirms it.

[0440] Input: Response data

[0441] Output: The response displayed in the UI

[0442] (Application example 2)

[0443] 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."

[0444] In conventional security services, employees face a heavy mental burden when dealing with threats or harassment, making it difficult for them to respond appropriately. Furthermore, there is a lack of systems that can grasp employees' emotional states in real time and provide appropriate countermeasures. Therefore, there is a need for a system that can reduce the mental burden on security operators and support more effective responses.

[0445] 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.

[0446] In this invention, the server includes means for receiving and recording user status information, means for receiving and recording complaint details, means for generating an appropriate response based on the received complaint details, means for providing the generated response to the user, means including an emotion engine for recognizing the user's emotions, and means for adjusting the response based on the user's emotion information. This reduces the mental burden on security operators and enables appropriate responses according to their emotional states in real time.

[0447] "User status information" refers to information such as an employee's working status, work content, and past complaint history.

[0448] "Complaint content" is information about specific dissatisfaction or problems reported by customers or users.

[0449] An "appropriate response" is a response that is generated based on the received complaint and is designed to solve the problem.

[0450] The "emotion engine" is a function that analyzes voice tone, facial expressions, input content, etc. to recognize the user's emotional state in real time.

[0451] A "natural language processing algorithm" is a technology that analyzes input text data, understands its meaning, and generates appropriate responses.

[0452] "Emotion information" is information about the user's emotional state analyzed by the emotion engine.

[0453] The "means for adjusting a response" is a function for optimizing the content of a response by taking into consideration the emotional information of the user.

[0454] System Program Overview

[0455] The system of this invention receives and records the user's situation information and complaint details, and generates appropriate responses using natural language processing algorithms. Furthermore, it can recognize the user's emotions in real time and adjust responses based on the emotional information. The system operates using a combination of smartphones, security robots, and a cloud server.

[0456] Hardware and software used

[0457] Hardware

[0458] Smartphone (iOS / ANDROID (registered trademark))

[0459] Security robot (general-purpose robot)

[0460] software

[0461] Natural language processing engine (GPT-4 (registered trademark), BERT, etc.)

[0462] Emotion recognition engine (Microsoft® Azure® Cognitive Services, IBM Watson®, etc.)

[0463] Cloud server (Amazon AWS (registered trademark), Google (registered trademark) Cloud)

[0464] Program processing overview

[0465] 1. Receiving and recording user status information

[0466] The server receives and records the work status, work content, and past complaint history sent from the smartphone or security robot, making it possible to understand what work an employee is in charge of and what problems they have faced in the past.

[0467] 2. Receiving and recording complaints

[0468] The server receives and records specific complaints sent by the smartphone or security robot, including specific information about the problem the user is facing.

[0469] 3. Generating appropriate responses

[0470] The server sends the received complaint to a natural language processing engine to generate a corresponding response. Using a natural language processing algorithm, a response that provides the best solution based on the input complaint content is created.

[0471] 4. Recognition of emotional information

[0472] The server uses an emotion recognition engine to analyze and record the user's emotional state based on their tone of voice, facial expressions, and input content, making it possible to respond according to the user's mental state.

[0473] 5. Tailor your response

[0474] The server adjusts the generated response based on the recognized emotion information, thereby providing a response that takes the user's emotions into consideration.

[0475] Specific examples

[0476] For example, a security operator may input a complaint to the system that he or she was "threatened by a suspicious individual." At this time, the emotion engine analyzes the operator's emotional state and sends it to the server. The server then takes the emotion recognition information into consideration and uses a natural language processing engine to generate an appropriate response, such as "We will inquire about the specific characteristics of the suspicious individual and take immediate action." This response also corresponds to the operator's emotional state.

[0477] Prompt Sentence Examples

[0478] Examples of prompts for this system include:

[0479] "Today's task: Patrol."

[0480] "I was threatened by a suspicious person"

[0481] This invention reduces the mental burden on security operators and enables them to respond effectively to various threats and harassment.

[0482] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0483] Step 1:

[0484] Receiving and recording user status information

[0485] The server receives and records employee work status, work content, and past complaint history sent from the device (smartphone or security robot). The server stores this information in a database and manages the information for each user. The data input here is the information entered by the user at the start of work, and the output is the recorded database entry.

[0486] Step 2:

[0487] Receiving and recording complaints

[0488] The server receives the specific complaint details (e.g., "I was threatened by a suspicious person") entered by the user (security operator) into the terminal and records this information in a database. The input data is the detailed complaint details, and the output is the recorded complaint data. This data is used for further processing.

[0489] Step 3:

[0490] Generating appropriate responses

[0491] The server sends the received complaint content to a natural language processing engine (such as GPT-4 or BERT) to generate an appropriate response. At this time, the text data of the complaint content is input. The natural language processing engine performs data analysis and calculations based on this input to generate an optimal response text. The generated response text is the output.

[0492] Step 4:

[0493] Recognition of emotional information

[0494] The server uses an emotion recognition engine (such as Microsoft Azure Cognitive Services or IBM Watson) to analyze the user's tone of voice, facial expression, and input content when the user enters a complaint, and recognizes the user's emotional state. The input is the user's voice and text data, and the output is analyzed emotional information. This information is used to tailor the response later.

[0495] Step 5:

[0496] Adjusting responses

[0497] The server adjusts the generated response based on the recognized emotion information. Emotion information is input, and the natural language processing engine optimizes the response text generated based on that information. The output is the adjusted response text.

[0498] Step 6:

[0499] Providing a response

[0500] The server responds to the user's request by sending a tailored response to the device (smartphone or security robot) and displaying it to the user. The user then checks the displayed response and responds to the customer based on it. The input is the response request, and the output is the response text provided to the user.

[0501] 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.

[0502] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0503] 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.

[0504] [Second embodiment]

[0505] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0506] 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.

[0507] 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).

[0508] 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.

[0509] 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.

[0510] 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).

[0511] 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.

[0512] 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.

[0513] 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.

[0514] 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.

[0515] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0516] 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."

[0517] The system of this invention utilizes AI's natural language processing capabilities to provide appropriate responses when complaint handling operators deal with excessive complaints or harassment from customers. Below, we will generate a program for the system and explain its processing in natural language.

[0518] System configuration

[0519] 1. A means of receiving and recording user status information

[0520] User: The operator inputs information about the working situation, work content, past complaint history, etc. into the terminal. This includes the operator's current physical condition and the types of complaints they have handled in the past.

[0521] Terminal: Receives status information entered by the user and sends it to the server.

[0522] Server: Records the status information received from the terminal and manages information for each user.

[0523] 2. How complaints are received and recorded

[0524] User: The operator inputs the specific details of the customer's complaint into the terminal. For example, the operator inputs "The product has not arrived."

[0525] Terminal: Receives the complaint details entered by the user and sends them to the server.

[0526] Server: Records the complaint received from the terminal and prepares it for later processing.

[0527] 3. A means of generating appropriate responses based on the complaint received

[0528] Server: Sends the complaint details to the AI ​​engine, which uses natural language processing algorithms to generate an appropriate response based on the complaint details entered.

[0529] AI Engine: Using natural language processing algorithms, it analyzes incoming complaints and generates corresponding responses.

[0530] Server: Records the responses sent back from the AI ​​engine and manages them for each user.

[0531] 4. A means of providing the generated response to the user

[0532] User: Sends a request from the device to the server, asking for a response.

[0533] Terminal: Receives requests from users and sends them to the server.

[0534] Server: In response to a user request, it retrieves the generated response and sends it to the terminal.

[0535] Terminal: The reply received from the server is displayed to the user, who then checks the reply and uses it to respond to the customer.

[0536] Specific examples

[0537] For example, let's say Operator A comes to work and logs in to the system. Operator A enters his / her current work status and past complaint history, and registers the status information in the system.

[0538] Next, operator A receives a complaint from customer B that the product has not arrived and enters the details into the system. The terminal sends the details of the complaint to the server, which records them.

[0539] The server sends the complaint details to the AI ​​engine, which uses a natural language processing algorithm to generate an appropriate response, such as "We will check the delivery status of the product and contact you again."

[0540] This response is recorded on the server, and when Operator A sends a request for a response, the server generates a response and sends it to the terminal. Operator A checks the response on the terminal and uses it to respond to Customer B.

[0541] As mentioned above, this system is designed to reduce the mental burden on operators and improve the quality of their responses, enabling appropriate responses to excessive complaints and customer harassment.

[0542] The processing flow will be explained below.

[0543] Step 1:

[0544] The user inputs status information such as work status, work content, and past complaint history into the terminal.

[0545] Step 2:

[0546] The terminal receives the input status information and transmits it to the server.

[0547] Step 3:

[0548] The server records the received user status information and manages the information for each user.

[0549] Step 4:

[0550] The user inputs the specific content of the complaint received from the customer into the terminal.

[0551] Step 5:

[0552] The terminal receives the input complaint details and transmits them to the server.

[0553] Step 6:

[0554] The server records the complaint received for later processing.

[0555] Step 7:

[0556] The server sends the complaint details to the AI ​​engine.

[0557] Step 8:

[0558] The AI ​​engine uses natural language processing algorithms to analyze the inputted complaint content and generate an appropriate response.

[0559] Step 9:

[0560] The AI ​​engine sends the generated response back to the server.

[0561] Step 10:

[0562] The server records the responses it receives and manages them for each user.

[0563] Step 11:

[0564] The user sends a request from the terminal to the server, requesting a response.

[0565] Step 12:

[0566] The server responds to the user's request by recalling the generated response and sending it to the terminal.

[0567] Step 13:

[0568] The terminal displays the response received from the server to the user.

[0569] Step 14:

[0570] The user reviews the displayed response and uses it to respond to the customer.

[0571] Example 1

[0572] 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."

[0573] With conventional complaint handling systems, it was difficult for operators to quickly provide effective responses to excessive complaints or harassment from customers. This increased the mental burden on operators and raised concerns that the quality of responses would decline. Furthermore, operators needed time to think of an appropriate response on the spot, which could prolong response times.

[0574] 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.

[0575] In this invention, the server includes means for receiving and recording user status information, means for receiving and recording complaint details, means for transmitting the status information entered by the user to the server, means for transmitting the complaint details to the server, means for generating an appropriate response based on the received complaint details, means for saving the generated response, and means for providing the generated response to the user. This enables the operator to provide a prompt and appropriate response, reducing mental strain and improving the quality of the response.

[0576] "User status information" refers to information such as the user's (operator's) working status, work content, and past complaint history.

[0577] "Complaint details" refers to the details of specific complaints or claims received from customers.

[0578] "Server" refers to a central processing unit that receives and records user status information and complaints, generates appropriate responses, and stores them.

[0579] "Terminal" refers to a device through which a user inputs information and transmits that information to a server.

[0580] "Means for receiving and recording" refers to the function of receiving user status information and complaint details and storing them in a database or the like.

[0581] "Means for generating appropriate responses" refers to the ability to automatically generate responses based on the content of the complaint received using an AI engine or natural language processing algorithm.

[0582] "Means for storing generated responses" refers to the function of storing responses generated by the AI ​​engine in a database and managing them so that they can be retrieved later.

[0583] The "means for providing the generated response to the user" refers to a function for transmitting the response stored by the server to the terminal in response to a request from the user and providing it to the user.

[0584] "Natural language processing algorithms" refers to a set of algorithms that analyze text data, understand its meaning, and generate appropriate responses.

[0585] MODE FOR CARRYING OUT THE INVENTION

[0586] As an embodiment of the present invention, the following describes a system that utilizes the natural language processing capabilities of AI to provide appropriate responses to complaint handling operators in order to deal with excessive complaints and harassment from customers.

[0587] System configuration and hardware / software used

[0588] The system includes the following major components:

[0589] 1. A means of receiving and recording user status information

[0590] User: The operator inputs information such as working status, work content, and past complaint history into the terminal, including the operator's current physical condition and the types of complaints they have handled in the past.

[0591] Terminal: Receives the input status information and sends the data to the server. Terminals used include PCs and tablets.

[0592] Server: Records the status information received from the terminal and stores and manages information for each user in a database. The database can be MySQL or PostgreSQL.

[0593] 2. How complaints are received and recorded

[0594] User: The operator inputs the specific details of the customer's complaint into the terminal, such as "the product has not arrived."

[0595] Terminal: Receives the entered complaint details and sends them to the server.

[0596] Server: Records the complaint details received from the terminal and stores them in a database.

[0597] 3. A means of generating appropriate responses based on the complaint received

[0598] Server: Sends the saved claim details to an AI engine (e.g., OpenAI's GPT-3) in the form of an API request.

[0599] AI engine: Using a natural language processing algorithm, it generates an appropriate response based on the received complaint. For example, it generates a response such as, "We will check the delivery status of the product and contact you again."

[0600] 4. A means of saving the generated response

[0601] Server: The responses sent back from the AI ​​engine are stored in a database and managed for each user.

[0602] 5. A means of providing the generated response to the user

[0603] User: The operator sends a request from the terminal to the server, which asks for a response.

[0604] Terminal: Receives requests from users and sends them to the server.

[0605] Server: Retrieves the stored response and sends it to the device.

[0606] Terminal: Displays the response received from the server to the user. The operator checks the response and responds to the customer.

[0607] Specific examples

[0608] Specific examples of system use

[0609] For example, let's say Operator A comes to work and logs in to the system. Operator A enters his / her current work status and past complaint history, and registers the status information in the system.

[0610] Next, operator A receives a complaint from customer B that the product has not arrived and enters the details into the system. The terminal sends the details of the complaint to the server, which records them.

[0611] The server sends the complaint to an AI engine (e.g., OpenAI's GPT-3), which uses a natural language processing algorithm to generate an appropriate response, such as, "We will check the delivery status of your product and contact you again."

[0612] This response is recorded on the server, and when Operator A sends a request for a response, the server generates a response and sends it to the terminal. Operator A checks the response on the terminal and uses it to respond to Customer B.

[0613] Example prompts to input to the generative AI model

[0614] Example prompt sentence:

[0615] Customer complaint: "The product never arrived."

[0616] Generate an appropriate response for the operator.

[0617] keyword

[0618] Generative AI model, prompt sentence

[0619] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0620] System program processing flow

[0621] Step 1: User Enters Context Information

[0622] explanation

[0623] The user (operator) inputs information such as working conditions, work content, and past complaint history into the terminal, including the user's current physical condition and the types of complaints they have handled in the past.

[0624] Input and Output

[0625] Input: working status, work content, past complaint history, health information

[0626] Output: Situation information data

[0627] Specific actions

[0628] The operator enters the necessary information into the terminal form and sends it to the system.

[0629] Step 2: The device sends status information to the server

[0630] explanation

[0631] The device receives the status information entered by the user and sends the data to the server in a format such as JSON or XML.

[0632] Input and Output

[0633] Input: Situational information data

[0634] Output: Request sent to server

[0635] Specific actions

[0636] The input information is converted into a data format (e.g., JSON) within the terminal and sent to the specified API endpoint.

[0637] Step 3: The server records status information

[0638] explanation

[0639] The server records the status information received from the terminal and stores the information for each user in a database.

[0640] Input and Output

[0641] Input: Situational information data

[0642] Output: Save to database

[0643] Specific actions

[0644] The server parses the received JSON data and executes SQL queries to store the information in the corresponding database tables.

[0645] Step 4: User enters complaint details

[0646] explanation

[0647] The user (operator) inputs the specific details of the customer's complaint into the terminal. This information includes details of the complaint, such as "the product has not arrived."

[0648] Input and Output

[0649] Input: Complaint details

[0650] Output: Claim data

[0651] Specific actions

[0652] The operator enters the details of the complaint into a form on the terminal and sends it to the system.

[0653] Step 5: The device sends the complaint to the server

[0654] explanation

[0655] The terminal receives the complaint details entered by the user and transmits the data to the server.

[0656] Input and Output

[0657] Input: Claim data

[0658] Output: Request sent to server

[0659] Specific actions

[0660] The entered claim content is converted into a data format (e.g., JSON) within the terminal and sent to the specified API endpoint.

[0661] Step 6: The server records the complaint

[0662] explanation

[0663] The server records the complaint details received from the terminal and stores them in a database for later processing.

[0664] Input and Output

[0665] Input: Claim data

[0666] Output: Save to database

[0667] Specific actions

[0668] The server parses the received JSON data and executes SQL queries to store the information in the corresponding database tables.

[0669] Step 7: The server sends the claim to the AI ​​engine

[0670] explanation

[0671] The server retrieves the stored claim and sends it to an AI engine (e.g., OpenAI's GPT-3), usually in the form of an API request.

[0672] Input and Output

[0673] Input: Claim data

[0674] Output: Send request to AI engine

[0675] Specific actions

[0676] The server converts the claim data into API request format and sends it to the AI ​​engine's endpoint.

[0677] Step 8: The AI ​​engine generates a response

[0678] explanation

[0679] The AI ​​engine (generative AI model) analyzes the content of the received complaint and generates an appropriate response using a natural language processing algorithm.

[0680] Input and Output

[0681] Input: Claim data

[0682] Output: The generated response

[0683] Specific actions

[0684] The AI ​​engine runs the complaint through a natural language processing algorithm and generates a response in text format that corresponds to the complaint.

[0685] Step 9: The server stores the generated response

[0686] explanation

[0687] The server receives the response from the AI ​​engine and stores it in a database.

[0688] Input and Output

[0689] Input: Generated response

[0690] Output: Save to database

[0691] Specific actions

[0692] The server executes an SQL query to retrieve and store the received response data in a database.

[0693] Step 10: User submits request for response

[0694] explanation

[0695] The user (operator) sends a request for a response to the complaint from the terminal to the server.

[0696] Input and Output

[0697] Input: Reply Request

[0698] Output: Request sent to server

[0699] Specific actions

[0700] The operator operates the terminal interface to send a reply request to the server.

[0701] Step 11: The server sends the response to the device

[0702] explanation

[0703] The server receives a request from the user, retrieves the necessary response from the database, and sends it to the terminal.

[0704] Input and Output

[0705] Input: Reply Request

[0706] Output: Response data

[0707] Specific actions

[0708] The server retrieves the answer from the database and generates an API response to send to the device.

[0709] Step 12: The device displays the response to the user

[0710] explanation

[0711] The terminal displays the response received from the server to the user, who then checks the response and uses it to respond to the customer.

[0712] Input and Output

[0713] Input: Response data

[0714] Output: What is displayed to the user

[0715] Specific actions

[0716] The terminal displays the received response data on the screen and provides an interface that allows the operator to easily check it.

[0717] (Application example 1)

[0718] 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."

[0719] In recent years, excessive complaints and harassment (customer harassment) from customers and within companies have become an issue. Operators who respond to these complaints are often burdened with psychological stress, raising concerns that the quality of their responses may decline. In particular, there is a need to quickly provide appropriate countermeasures against harassment. However, conventional systems often fail to efficiently handle such responses. Therefore, the present invention aims to solve these issues.

[0720] 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.

[0721] In this invention, the server includes a means for receiving and recording user status information, a means for receiving and recording complaint details, and a means for generating an appropriate response based on the received complaint details, thereby enabling a means for proposing countermeasures using an AI engine.

[0722] "Means for receiving and recording user status information" refers to a device or function that receives and records the work status, work content, past complaint history, and other status information entered by the operator.

[0723] "Means for receiving and recording complaint details" refers to a device or function that receives and records the specific complaint details entered by the operator.

[0724] "Means for generating an appropriate response based on the received complaint content" refers to a device or function that automatically generates an appropriate response based on the received complaint content.

[0725] The "means for providing the generated response to the user" refers to a device or function that presents the generated response to the operator and enables the operator to take action based on the response.

[0726] "Means of proposing countermeasures using an AI engine" refers to devices or functions that use an AI engine to analyze the content of the complaint and the operator's situation information, and propose the most appropriate countermeasures.

[0727] A "natural language processing algorithm" is an algorithm for understanding and analyzing human language, and is a technology used to analyze the content of claims and generate appropriate responses.

[0728] A "generative AI model" is an AI model trained to perform a specific task (e.g., generating appropriate responses), and is a technology that uses this model to generate responses for complaints.

[0729] This invention provides a system that uses AI to generate and propose appropriate responses and countermeasures in response to excessive complaints and harassment (customer harassment) from customers or within a company.

[0730] The overall system configuration is as follows:

[0731] Means for receiving and recording user status information

[0732] The server receives and records status information entered by the operator through the terminal, such as work status, work content, and past complaint history. Specifically, the operator enters their current physical condition and the content of the complaint they are currently handling, and sends this to the server.

[0733] A means of receiving and recording complaints

[0734] The operator enters specific details of complaints received from customers or from within the company into the terminal, which then sends the information to the server, which records it and prepares it for analysis.

[0735] A means of generating appropriate responses based on the complaint received

[0736] The server sends the received complaint content to an existing AI engine, which uses a natural language processing algorithm to analyze the content of the complaint and generate an appropriate response. The server records the generated response and manages it so that it can be called up as needed.

[0737] A means of providing the generated response to the user

[0738] When an operator sends a request for a generated answer from a terminal, the server retrieves the corresponding answer from the answer database it manages and sends it to the terminal. The operator checks the answer on the terminal and uses it to respond to the customer.

[0739] A means of proposing countermeasures using an AI engine

[0740] The server sends the content of the complaint and the user's situation information to the AI ​​engine, which then uses the generative AI model to propose appropriate countermeasures and responses, enabling the operator to respond quickly and appropriately.

[0741] As a concrete example, consider the case where an operator receives a complaint that "I can't log in due to a system error." The operator enters this complaint into the terminal and sends it to the server. The server passes this complaint on to the AI ​​engine, which then generates a response such as, "Regarding the login problem, please first clear the cache and try again. If that doesn't resolve it, please contact your system administrator." This is recorded on the server, and when the operator calls it, it is immediately displayed on the terminal.

[0742] Here are some example prompts to input to the generative AI model:

[0743] Operator: Yamada Taro

[0744] Status: Feeling good today. Two complaints from yesterday have been dealt with.

[0745] Complaint: Unable to log in due to a system error.

[0746] Appropriate response:

[0747] By inputting this prompt into a generative AI model, an appropriate response is generated, thereby improving the quality of customer service and reducing the mental burden on operators.

[0748] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0749] Step 1:

[0750] The server receives and records information about the operator's status from the terminal. The operator enters information such as their work status, physical condition, and past complaint history into the terminal, which then sends this information to the server. The server stores the received data in a database and manages detailed status information for each user. The input data is work status, job content, and past complaint history, and the output is to save this information in an organized format in the database.

[0751] Step 2:

[0752] The user inputs specific complaints received from customers or from within the company into the terminal. The terminal then sends the complaints to the server. The server records the received complaints in a database for later processing. The input data is the specific complaints from customers or from within the company, and the output is to save these complaints in an organized format in the database.

[0753] Step 3:

[0754] The server sends the recorded complaint content to the AI ​​engine. The AI ​​engine uses a natural language processing algorithm to analyze the input complaint content and generate an appropriate response. The generated response is sent back to the server, which records it in a database. The input data is the complaint content, and the output is an appropriate response. The specific operation is the process of analyzing the complaint content and generating a response.

[0755] Step 4:

[0756] When a user sends a request for a reply from a terminal, the server retrieves the corresponding reply from the reply database it manages and sends it to the terminal. The terminal displays the reply to the user, who can then confirm it and use it to handle the complaint. The input data is the reply request, and the output is a display of the appropriate reply. The specific operation is to retrieve the reply data from the database and display it.

[0757] Step 5:

[0758] The server uses an AI engine to analyze the content of the complaint and the user's situation information, and then uses a generative AI model to propose appropriate countermeasures. This allows the user to respond quickly and appropriately. The input data is the content of the complaint and situation information, and the output is the proposed countermeasures. The specific operation involves analysis by the AI ​​engine and generation of countermeasures.

[0759] As a concrete example, if an operator receives a complaint that "I can't log in because of a system error," the complaint entered into the terminal is sent to the server, and the AI ​​engine generates a response such as, "Regarding the login problem, please first clear the cache and try again. If that doesn't resolve it, please contact your system administrator." This response is recorded on the server and displayed upon user request.

[0760] Here are some example prompts to input to a generative AI model:

[0761] Operator: Yamada Taro

[0762] Status: Feeling good today. Two complaints from yesterday have been dealt with.

[0763] Complaint: Unable to log in due to a system error.

[0764] Appropriate response:

[0765] By taking the above steps, it is possible to improve the quality of complaint handling and reduce the mental burden on operators.

[0766] 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.

[0767] The system of this invention utilizes AI's natural language processing capabilities and emotion recognition engine to provide appropriate responses when complaint handling operators deal with excessive complaints or harassment from customers. Below, we will generate a program for the system and explain its processing in natural language.

[0768] System configuration

[0769] 1. A means of receiving and recording user status information

[0770] User: The operator inputs status information such as work status, job content, and past complaint history into the terminal.

[0771] Terminal: Receives the status information entered by the user and sends it to the server.

[0772] Server: Records the status information received from the terminal and manages information for each user.

[0773] 2. How complaints are received and recorded

[0774] User: The operator inputs the specific details of the complaint received from the customer into the terminal.

[0775] Terminal: Receives the complaint content entered by the user and sends it to the server.

[0776] Server: Records the complaint received from the terminal and prepares it for later processing.

[0777] 3. A means of generating appropriate responses based on the complaint received

[0778] Server: Sends the complaint details to the AI ​​engine, which uses natural language processing algorithms to generate an appropriate response based on the complaint details entered.

[0779] AI Engine: Using natural language processing algorithms, it analyzes incoming complaints and generates corresponding responses.

[0780] Server: Records the responses sent back from the AI ​​engine and manages them for each user.

[0781] 4. A means of providing the generated response to the user

[0782] User: Sends a request from the device to the server, asking for a response.

[0783] Terminal: Receives requests from users and sends them to the server.

[0784] Server: In response to a user request, it invokes the generated response and sends it to the terminal.

[0785] Terminal: The reply received from the server is displayed to the user, who then checks the reply and uses it to respond to the customer.

[0786] 5. Includes an emotion engine that recognizes user emotions

[0787] User: When an operator inputs work status or complaint details, the tone of voice, facial expression, and input content are analyzed by the emotion engine.

[0788] Emotion engine: Analyzes voice tone, facial expressions, input content, etc. to recognize the user's emotional state in real time. This information is sent to the server.

[0789] Server: Based on the emotion information received from the emotion engine, the server adjusts the generated response and provides the most appropriate response for the user.

[0790] Specific examples

[0791] For example, let's say Operator A comes to work and logs in to the system. Operator A enters his / her current working status and past complaint history, and registers the status information in the system. At this time, the emotion engine analyzes Operator A's tone of voice and facial expressions, and sends the current emotional state to the server.

[0792] Next, operator A receives a complaint from customer B that the product has not arrived and enters the details into the system. The terminal sends the details of the complaint and operator A's emotional information at the time of input to the server.

[0793] The server sends the complaint details to the AI ​​engine, which uses a natural language processing algorithm to generate an appropriate response. For example, a response such as "We will check the delivery status of the product and contact you again." The response is then adjusted taking into account the emotional information from the emotion engine.

[0794] This response is recorded on the server, and when Operator A sends a request for a response, the server generates a response and sends it to the terminal. Operator A checks the response on the terminal and uses it to respond to Customer B.

[0795] This system reduces the mental burden on operators, improves the quality of customer service, and enables more appropriate and effective complaint handling.

[0796] The processing flow will be explained below.

[0797] Step 1:

[0798] The user inputs status information such as work status, work content, past complaint history, etc. into the terminal. The user logs in to the terminal at the start of work and inputs their physical condition and past complaint handling history.

[0799] Step 2:

[0800] The terminal receives the input status information and transmits it to the server. The terminal accurately records the information input by the user and transmits it to the server in real time.

[0801] Step 3:

[0802] The server records the received user status information and manages the information for each user. The server saves the received information in a database and makes it available for use.

[0803] Step 4:

[0804] The user inputs the specific complaint received from the customer into the terminal. To input the received complaint in detail, the user writes information along specific fields.

[0805] Step 5:

[0806] The terminal receives the complaint details entered and sends them to the server. The terminal accurately records the complaint details entered by the user and immediately sends them to the server.

[0807] Step 6:

[0808] The server records the received complaint for later processing. The received complaint is stored in a database along with other related information.

[0809] Step 7:

[0810] The server sends the content of the claim to the AI ​​engine. The received claim data is sent to the AI ​​engine for analysis.

[0811] Step 8:

[0812] The AI ​​engine uses natural language processing algorithms to analyze the content of the complaint entered and generate an appropriate response. The AI ​​engine understands the content of the complaint and compares it with past data to create the optimal response.

[0813] Step 9:

[0814] The AI ​​engine sends the generated response back to the server, where it is ready to be served to the user.

[0815] Step 10:

[0816] The server records the responses received and manages them for each user. Responses are saved with appropriate tags and metadata for easy access later.

[0817] Step 11:

[0818] The user sends a request for a response from the terminal to the server, and the user performs an operation to request a response generated by the system for a specific claim.

[0819] Step 12:

[0820] In response to a user request, the server retrieves a pre-generated response and sends it to the terminal. The server retrieves the appropriate response from the database and sends it to the user's terminal.

[0821] Step 13:

[0822] The terminal displays the response received from the server to the user. The terminal displays the response sent back from the server so that the user can check it.

[0823] Step 14:

[0824] The user checks the displayed response and responds to the customer using it. The user responds appropriately to the customer based on the confirmed response.

[0825] Step 15:

[0826] When a user inputs situation information or complaint details, the terminal sends the emotion to the emotion engine, which analyzes the user's tone of voice, facial expression, and input content.

[0827] Step 16:

[0828] The emotion engine sends the analysis results to the server to recognize the user's emotional state. The emotion engine sends the analysis results to the server to update the user's emotional state in real time.

[0829] Step 17:

[0830] The server adjusts the generated response based on the received emotional information to provide the most appropriate response for the user. The server incorporates the emotional information to adjust the tone and content of the response and display it to the user.

[0831] Example 2

[0832] 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."

[0833] In today's customer service environment, dealing with excessive customer complaints and malicious harassment (kasahara) places a heavy mental burden on operators. Responding appropriately and quickly requires high levels of judgment and emotional balance, but maintaining these skills is extremely difficult. Furthermore, the appropriate response must be considered for each complaint, making the process cumbersome. The present invention aims to provide a system that solves these problems, reduces the burden on operators, and improves the quality of customer service.

[0834] 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.

[0835] In this invention, the server includes means for receiving and recording user status information, means for receiving and recording complaint details, means for generating an appropriate response based on the received complaint details and adjusting the response based on emotion analysis, and means for providing the generated response to the user. This allows the operator to record their working status and work content in real time, and appropriate responses are generated accordingly, with adjustments made taking into account their emotional state, reducing their mental burden and enabling higher quality customer service.

[0836] "User" refers to the person who operates the system and inputs information. Specifically, this includes operators who handle customer complaints.

[0837] "Status information" refers to information including the current business status and past data, such as the user's working status, work content, and past complaint history.

[0838] "Complaint content" refers to the specific claims or dissatisfaction received from customers.

[0839] A "response" refers to an answer or response message that is generated based on the received complaint and that the user provides to the customer.

[0840] "Natural language processing algorithms" refer to computer programs that analyze text data and understand its meaning, allowing them to generate responses tailored to the content of a claim.

[0841] A "generative AI model" refers to an AI program that is trained on a huge amount of data and generates an appropriate output (response) for a specific input.

[0842] "Emotion analysis" refers to the process of analyzing a user's tone of voice, facial expression, input content, etc. to determine their emotional state.

[0843] "Server" refers to a computer system that receives and processes user input data, stores the data, executes algorithms, and generates and manages responses.

[0844] "Terminal" refers to a computer device that allows a user to input information and transmits the input data to a server.

[0845] The system of this invention utilizes AI's natural language processing capabilities and sentiment analysis engine to provide appropriate responses when a complaint handling operator deals with excessive complaints or harassment from customers. Specific embodiments of the system are described in detail below.

[0846] System configuration

[0847] 1. A means of receiving and recording user status information

[0848] User: The operator inputs status information such as work status, work content, past complaint history, etc. into the terminal. For example, when an operator starts a shift, they input their current work status and past complaint handling history.

[0849] Terminal: Receives status information entered by the user and sends it to the server. Specifically, it converts the data entered in the input form into JSON format and sends it to the server via the network.

[0850] Server: Records the status information received from the terminal and stores information for each user in a database.

[0851] 2. How complaints are received and recorded

[0852] User: The operator inputs the specific details of the complaint received from the customer into the terminal. For example, the operator inputs the details of the customer's complaint in real time while on the phone.

[0853] Terminal: Receives the complaint data entered by the user and sends it to the server. This data is also converted to JSON format.

[0854] Server: Records the complaint details received from the terminal and stores them in a database for later processing.

[0855] 3. A means to generate appropriate responses based on the complaints received and adjust them based on sentiment analysis

[0856] Server: Uses an API request to send the claim details to the AI ​​engine.

[0857] AI Engine: Using natural language processing algorithms and generative AI models, it analyzes incoming complaints and generates appropriate responses.

[0858] Server: Records the responses sent back by the AI ​​engine and adjusts the responses taking into account information from the sentiment analysis engine.

[0859] 4. A means of providing the generated response to the user

[0860] User: The operator sends a request for a response from the terminal to the server, for example by clicking "Generate response."

[0861] Terminal: Receives requests from users and sends them to the server.

[0862] Server: In response to a user request, it invokes the generated response and sends it to the terminal.

[0863] Terminal: The reply received from the server is displayed to the user, who then checks the reply and uses it to respond to the customer.

[0864] Specific examples

[0865] Operator A comes to work and logs in to the system. Operator A enters his / her current working status and past complaint history into the system. At this time, the emotion analysis engine analyzes Operator A's tone of voice and facial expressions and sends his / her emotional state to the server.

[0866] Next, operator A receives a complaint from customer B that the product has not arrived and enters the details into the system. The terminal then sends the details of the complaint and operator A's emotional information to the server.

[0867] The server sends the complaint details to the AI ​​engine, which uses a natural language processing algorithm and a generative AI model to generate an appropriate response. For example, a response such as, "We will check the delivery status of the product and contact you again." The response is adjusted based on information from the sentiment analysis engine.

[0868] This response is recorded on the server, and when Operator A sends a request for a response, the server sends the response to the terminal. Operator A checks the response on the terminal and uses it to respond to Customer B.

[0869] Prompt Sentence Examples

[0870] Operator: "Please enter your current work status and past claim history."

[0871] Terminal: "Please enter the details of the customer's complaint."

[0872] Server: "Generate an appropriate response based on the complaint."

[0873] Emotion Engine: "Provide emotional information by analyzing the tone of voice and facial expressions during input."

[0874] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0875] Step 1: Entering and receiving user context information

[0876] User: The operator enters information such as work status, job content, and past complaint history into the terminal.

[0877] How it works: At the start of a shift, an operator uses the system interface to fill out a form with their name, shift time, and complaint history for the past 24 hours.

[0878] Input: working status, work content, past complaint history

[0879] Terminal: Receives the status information entered by the user and sends it to the server.

[0880] Specific operation: The terminal converts the input data into JSON format and sends it to the server via the network.

[0881] Output: JSON format status information data

[0882] Step 2: Enter and receive your complaint

[0883] User: The operator inputs the specific details of the complaint received from the customer into the terminal.

[0884] Specific operation: The operator enters the details of the customer's complaint in real time while on the phone.

[0885] Input: Customer complaint details

[0886] Terminal: Receives the complaint content entered by the user and sends it to the server.

[0887] Specific operation: The terminal saves the content of the claim in real time, converts it into JSON format, and sends it to the server.

[0888] Output: Claim content in JSON format

[0889] Step 3: Sentiment analysis and information reception

[0890] User: The emotion engine analyzes the tone of voice, facial expressions, and input content when the operator enters the details of the complaint.

[0891] Specific operation: Data input by the operator through a microphone or camera is sent to the emotion engine.

[0892] Input: Operator tone of voice, facial expressions, input content

[0893] Emotion Engine: Analyzes the emotional state in real time based on the received data. This information is sent to the server.

[0894] Specific operation: The emotion engine uses a deep learning model to analyze the emotional state as numerical data and send it to the server.

[0895] Output: Numerical data of emotional information

[0896] Step 4: Generate and tailor an appropriate response

[0897] Server: Sends the received complaint to the AI ​​engine, which uses natural language processing algorithms and generative AI models to generate an appropriate response.

[0898] Specific operation: The server sends the claim content to the AI ​​engine as an API request.

[0899] Input: Complaint details

[0900] AI Engine: Generates responses using natural language processing algorithms and generative AI models.

[0901] Specific operation: The AI ​​engine analyzes the content of the complaint and generates an appropriate response.

[0902] Output: Generated response data

[0903] Step 5: Adjust your response based on emotional information

[0904] Server: Compares the response data returned from the AI ​​engine with emotional information and adjusts the response as necessary.

[0905] Specific operation: The server fine-tunes the response text based on information from the emotion engine.

[0906] Input: Generated response data, emotion information

[0907] Output: Adjusted response data

[0908] Step 6: Providing generated responses

[0909] User: Sends a request from the device to the server, asking for a response.

[0910] Specific action: The operator presses the button to generate a response.

[0911] Input: Reply request

[0912] Terminal: Receives requests from users and sends them to the server.

[0913] Specific operation: The terminal forwards the request to the server.

[0914] Output: Request data

[0915] Step 7: Capture and send response data

[0916] Server: In response to a user request, retrieves a pre-generated response from the database and sends it to the terminal.

[0917] Specific operation: The server retrieves the response from the database and sends it back to the terminal.

[0918] Input: Request data

[0919] Output: Response data

[0920] Step 8: Displaying the response on the terminal

[0921] Terminal: The reply received from the server is displayed to the user, who then checks the reply and uses it to respond to the customer.

[0922] Specific operation: The terminal displays the response data on the UI and the operator confirms it.

[0923] Input: Response data

[0924] Output: The response displayed in the UI

[0925] (Application example 2)

[0926] 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."

[0927] In conventional security services, employees face a heavy mental burden when dealing with threats or harassment, making it difficult for them to respond appropriately. Furthermore, there is a lack of systems that can grasp employees' emotional states in real time and provide appropriate countermeasures. Therefore, there is a need for a system that can reduce the mental burden on security operators and support more effective responses.

[0928] 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.

[0929] In this invention, the server includes means for receiving and recording user status information, means for receiving and recording complaint details, means for generating an appropriate response based on the received complaint details, means for providing the generated response to the user, means including an emotion engine for recognizing the user's emotions, and means for adjusting the response based on the user's emotion information. This reduces the mental burden on security operators and enables appropriate responses according to their emotional states in real time.

[0930] "User status information" refers to information such as an employee's working status, work content, and past complaint history.

[0931] "Complaint content" is information about specific dissatisfaction or problems reported by customers or users.

[0932] An "appropriate response" is a response that is generated based on the received complaint and is designed to solve the problem.

[0933] The "emotion engine" is a function that analyzes voice tone, facial expressions, input content, etc. to recognize the user's emotional state in real time.

[0934] A "natural language processing algorithm" is a technology that analyzes input text data, understands its meaning, and generates appropriate responses.

[0935] "Emotion information" is information about the user's emotional state analyzed by the emotion engine.

[0936] The "means for adjusting a response" is a function for optimizing the content of a response by taking into consideration the emotional information of the user.

[0937] System Program Overview

[0938] The system of this invention receives and records the user's situation information and complaint details, and generates appropriate responses using natural language processing algorithms. Furthermore, it can recognize the user's emotions in real time and adjust responses based on the emotional information. The system operates using a combination of smartphones, security robots, and a cloud server.

[0939] Hardware and software used

[0940] Hardware

[0941] Smartphone (iOS / Android)

[0942] Security robot (general-purpose robot)

[0943] software

[0944] Natural language processing engines (GPT-4, BERT, etc.)

[0945] Emotion recognition engine (Microsoft Azure Cognitive Services, IBM Watson, etc.)

[0946] Cloud server (Amazon AWS, Google Cloud)

[0947] Program processing overview

[0948] 1. Receiving and recording user status information

[0949] The server receives and records the work status, work content, and past complaint history sent from the smartphone or security robot, making it possible to understand what work an employee is in charge of and what problems they have faced in the past.

[0950] 2. Receiving and recording complaints

[0951] The server receives and records specific complaints sent by the smartphone or security robot, including specific information about the problem the user is facing.

[0952] 3. Generating appropriate responses

[0953] The server sends the received complaint to a natural language processing engine to generate a corresponding response. Using a natural language processing algorithm, a response that provides the best solution based on the input complaint content is created.

[0954] 4. Recognition of emotional information

[0955] The server uses an emotion recognition engine to analyze and record the user's emotional state based on their tone of voice, facial expressions, and input content, making it possible to respond according to the user's mental state.

[0956] 5. Tailor your response

[0957] The server adjusts the generated response based on the recognized emotion information, thereby providing a response that takes the user's emotions into consideration.

[0958] Specific examples

[0959] For example, a security operator may input a complaint to the system that he or she was "threatened by a suspicious individual." At this time, the emotion engine analyzes the operator's emotional state and sends it to the server. The server then takes the emotion recognition information into consideration and uses a natural language processing engine to generate an appropriate response, such as "We will inquire about the specific characteristics of the suspicious individual and take immediate action." This response also corresponds to the operator's emotional state.

[0960] Prompt Sentence Examples

[0961] Examples of prompts for this system include:

[0962] "Today's task: Patrol."

[0963] "I was threatened by a suspicious person"

[0964] This invention reduces the mental burden on security operators and enables them to respond effectively to various threats and harassment.

[0965] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0966] Step 1:

[0967] Receiving and recording user status information

[0968] The server receives and records employee work status, work content, and past complaint history sent from the device (smartphone or security robot). The server stores this information in a database and manages the information for each user. The data input here is the information entered by the user at the start of work, and the output is the recorded database entry.

[0969] Step 2:

[0970] Receiving and recording complaints

[0971] The server receives the specific complaint details (e.g., "I was threatened by a suspicious person") entered by the user (security operator) into the terminal and records this information in a database. The input data is the detailed complaint details, and the output is the recorded complaint data. This data is used for further processing.

[0972] Step 3:

[0973] Generating appropriate responses

[0974] The server sends the received complaint content to a natural language processing engine (such as GPT-4 or BERT) to generate an appropriate response. At this time, the text data of the complaint content is input. The natural language processing engine performs data analysis and calculations based on this input to generate an optimal response text. The generated response text is the output.

[0975] Step 4:

[0976] Recognition of emotional information

[0977] The server uses an emotion recognition engine (such as Microsoft Azure Cognitive Services or IBM Watson) to analyze the user's tone of voice, facial expression, and input content when the user enters a complaint, and recognizes the user's emotional state. The input is the user's voice and text data, and the output is analyzed emotional information. This information is used to tailor the response later.

[0978] Step 5:

[0979] Adjusting responses

[0980] The server adjusts the generated response based on the recognized emotion information. Emotion information is input, and the natural language processing engine optimizes the response text generated based on that information. The output is the adjusted response text.

[0981] Step 6:

[0982] Providing a response

[0983] The server responds to the user's request by sending a tailored response to the device (smartphone or security robot) and displaying it to the user. The user then checks the displayed response and responds to the customer based on it. The input is the response request, and the output is the response text provided to the user.

[0984] 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.

[0985] 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.

[0986] 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.

[0987] [Third embodiment]

[0988] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0989] 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.

[0990] 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).

[0991] 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.

[0992] 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.

[0993] 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).

[0994] 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.

[0995] 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.

[0996] 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.

[0997] 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.

[0998] 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.

[0999] 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."

[1000] The system of this invention utilizes AI's natural language processing capabilities to provide appropriate responses when complaint handling operators deal with excessive complaints or harassment from customers. Below, we will generate a program for the system and explain its processing in natural language.

[1001] System configuration

[1002] 1. A means of receiving and recording user status information

[1003] User: The operator inputs information about the working situation, work content, past complaint history, etc. into the terminal. This includes the operator's current physical condition and the types of complaints they have handled in the past.

[1004] Terminal: Receives status information entered by the user and sends it to the server.

[1005] Server: Records the status information received from the terminal and manages information for each user.

[1006] 2. How complaints are received and recorded

[1007] User: The operator inputs the specific details of the customer's complaint into the terminal. For example, the operator inputs "The product has not arrived."

[1008] Terminal: Receives the complaint details entered by the user and sends them to the server.

[1009] Server: Records the complaint received from the terminal and prepares it for later processing.

[1010] 3. A means of generating appropriate responses based on the complaint received

[1011] Server: Sends the complaint details to the AI ​​engine, which uses natural language processing algorithms to generate an appropriate response based on the complaint details entered.

[1012] AI Engine: Using natural language processing algorithms, it analyzes incoming complaints and generates corresponding responses.

[1013] Server: Records the responses sent back from the AI ​​engine and manages them for each user.

[1014] 4. A means of providing the generated response to the user

[1015] User: Sends a request from the device to the server, asking for a response.

[1016] Terminal: Receives requests from users and sends them to the server.

[1017] Server: In response to a user request, it retrieves the generated response and sends it to the terminal.

[1018] Terminal: The reply received from the server is displayed to the user, who then checks the reply and uses it to respond to the customer.

[1019] Specific examples

[1020] For example, let's say Operator A comes to work and logs in to the system. Operator A enters his / her current work status and past complaint history, and registers the status information in the system.

[1021] Next, operator A receives a complaint from customer B that the product has not arrived and enters the details into the system. The terminal sends the details of the complaint to the server, which records them.

[1022] The server sends the complaint details to the AI ​​engine, which uses a natural language processing algorithm to generate an appropriate response, such as "We will check the delivery status of the product and contact you again."

[1023] This response is recorded on the server, and when Operator A sends a request for a response, the server generates a response and sends it to the terminal. Operator A checks the response on the terminal and uses it to respond to Customer B.

[1024] As mentioned above, this system is designed to reduce the mental burden on operators and improve the quality of their responses, enabling appropriate responses to excessive complaints and customer harassment.

[1025] The processing flow will be explained below.

[1026] Step 1:

[1027] The user inputs status information such as work status, work content, and past complaint history into the terminal.

[1028] Step 2:

[1029] The terminal receives the input status information and transmits it to the server.

[1030] Step 3:

[1031] The server records the received user status information and manages the information for each user.

[1032] Step 4:

[1033] The user inputs the specific content of the complaint received from the customer into the terminal.

[1034] Step 5:

[1035] The terminal receives the input complaint details and transmits them to the server.

[1036] Step 6:

[1037] The server records the complaint received for later processing.

[1038] Step 7:

[1039] The server sends the complaint details to the AI ​​engine.

[1040] Step 8:

[1041] The AI ​​engine uses natural language processing algorithms to analyze the inputted complaint content and generate an appropriate response.

[1042] Step 9:

[1043] The AI ​​engine sends the generated response back to the server.

[1044] Step 10:

[1045] The server records the responses it receives and manages them for each user.

[1046] Step 11:

[1047] The user sends a request from the terminal to the server, requesting a response.

[1048] Step 12:

[1049] The server responds to the user's request by recalling the generated response and sending it to the terminal.

[1050] Step 13:

[1051] The terminal displays the response received from the server to the user.

[1052] Step 14:

[1053] The user reviews the displayed response and uses it to respond to the customer.

[1054] Example 1

[1055] 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."

[1056] With conventional complaint handling systems, it was difficult for operators to quickly provide effective responses to excessive complaints or harassment from customers. This increased the mental burden on operators and raised concerns that the quality of responses would decline. Furthermore, operators needed time to think of an appropriate response on the spot, which could prolong response times.

[1057] 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.

[1058] In this invention, the server includes means for receiving and recording user status information, means for receiving and recording complaint details, means for transmitting the status information entered by the user to the server, means for transmitting the complaint details to the server, means for generating an appropriate response based on the received complaint details, means for saving the generated response, and means for providing the generated response to the user. This enables the operator to provide a prompt and appropriate response, reducing mental strain and improving the quality of the response.

[1059] "User status information" refers to information such as the user's (operator's) working status, work content, and past complaint history.

[1060] "Complaint details" refers to the details of specific complaints or claims received from customers.

[1061] "Server" refers to a central processing unit that receives and records user status information and complaints, generates appropriate responses, and stores them.

[1062] "Terminal" refers to a device through which a user inputs information and transmits that information to a server.

[1063] "Means for receiving and recording" refers to the function of receiving user status information and complaint details and storing them in a database or the like.

[1064] "Means for generating appropriate responses" refers to the ability to automatically generate responses based on the content of the complaint received using an AI engine or natural language processing algorithm.

[1065] "Means for storing generated responses" refers to the function of storing responses generated by the AI ​​engine in a database and managing them so that they can be retrieved later.

[1066] The "means for providing the generated response to the user" refers to a function for transmitting the response stored by the server to the terminal in response to a request from the user and providing it to the user.

[1067] "Natural language processing algorithms" refers to a set of algorithms that analyze text data, understand its meaning, and generate appropriate responses.

[1068] MODE FOR CARRYING OUT THE INVENTION

[1069] As an embodiment of the present invention, the following describes a system that utilizes the natural language processing capabilities of AI to provide appropriate responses to complaint handling operators in order to deal with excessive complaints and harassment from customers.

[1070] System configuration and hardware / software used

[1071] The system includes the following major components:

[1072] 1. A means of receiving and recording user status information

[1073] User: The operator inputs information such as working status, work content, and past complaint history into the terminal, including the operator's current physical condition and the types of complaints they have handled in the past.

[1074] Terminal: Receives the input status information and sends the data to the server. Terminals used include PCs and tablets.

[1075] Server: Records the status information received from the terminal and stores and manages information for each user in a database. The database can be MySQL or PostgreSQL.

[1076] 2. How complaints are received and recorded

[1077] User: The operator inputs the specific details of the customer's complaint into the terminal, such as "the product has not arrived."

[1078] Terminal: Receives the entered complaint details and sends them to the server.

[1079] Server: Records the complaint details received from the terminal and stores them in a database.

[1080] 3. A means of generating appropriate responses based on the complaint received

[1081] Server: Sends the saved claim details to an AI engine (e.g., OpenAI's GPT-3) in the form of an API request.

[1082] AI engine: Using a natural language processing algorithm, it generates an appropriate response based on the received complaint. For example, it generates a response such as, "We will check the delivery status of the product and contact you again."

[1083] 4. A means of saving the generated response

[1084] Server: The responses sent back from the AI ​​engine are stored in a database and managed for each user.

[1085] 5. A means of providing the generated response to the user

[1086] User: The operator sends a request from the terminal to the server, which asks for a response.

[1087] Terminal: Receives requests from users and sends them to the server.

[1088] Server: Retrieves the stored response and sends it to the device.

[1089] Terminal: Displays the response received from the server to the user. The operator checks the response and responds to the customer.

[1090] Specific examples

[1091] Specific examples of system use

[1092] For example, let's say Operator A comes to work and logs in to the system. Operator A enters his / her current work status and past complaint history, and registers the status information in the system.

[1093] Next, operator A receives a complaint from customer B that the product has not arrived and enters the details into the system. The terminal sends the details of the complaint to the server, which records them.

[1094] The server sends the complaint to an AI engine (e.g., OpenAI's GPT-3), which uses a natural language processing algorithm to generate an appropriate response, such as, "We will check the delivery status of your product and contact you again."

[1095] This response is recorded on the server, and when Operator A sends a request for a response, the server generates a response and sends it to the terminal. Operator A checks the response on the terminal and uses it to respond to Customer B.

[1096] Example prompts to input to the generative AI model

[1097] Example prompt sentence:

[1098] Customer complaint: "The product never arrived."

[1099] Generate an appropriate response for the operator.

[1100] keyword

[1101] Generative AI model, prompt sentence

[1102] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1103] System program processing flow

[1104] Step 1: User Enters Context Information

[1105] explanation

[1106] The user (operator) inputs information such as working conditions, work content, and past complaint history into the terminal, including the user's current physical condition and the types of complaints they have handled in the past.

[1107] Input and Output

[1108] Input: working status, work content, past complaint history, health information

[1109] Output: Situation information data

[1110] Specific actions

[1111] The operator enters the necessary information into the terminal form and sends it to the system.

[1112] Step 2: The device sends status information to the server

[1113] explanation

[1114] The device receives the status information entered by the user and sends the data to the server in a format such as JSON or XML.

[1115] Input and Output

[1116] Input: Situational information data

[1117] Output: Request sent to server

[1118] Specific actions

[1119] The input information is converted into a data format (e.g., JSON) within the terminal and sent to the specified API endpoint.

[1120] Step 3: The server records status information

[1121] explanation

[1122] The server records the status information received from the terminal and stores the information for each user in a database.

[1123] Input and Output

[1124] Input: Situational information data

[1125] Output: Save to database

[1126] Specific actions

[1127] The server parses the received JSON data and executes SQL queries to store the information in the corresponding database tables.

[1128] Step 4: User enters complaint details

[1129] explanation

[1130] The user (operator) inputs the specific details of the customer's complaint into the terminal. This information includes details of the complaint, such as "the product has not arrived."

[1131] Input and Output

[1132] Input: Complaint details

[1133] Output: Claim data

[1134] Specific actions

[1135] The operator enters the details of the complaint into a form on the terminal and sends it to the system.

[1136] Step 5: The device sends the complaint to the server

[1137] explanation

[1138] The terminal receives the complaint details entered by the user and transmits the data to the server.

[1139] Input and Output

[1140] Input: Claim data

[1141] Output: Request sent to server

[1142] Specific actions

[1143] The entered claim content is converted into a data format (e.g., JSON) within the terminal and sent to the specified API endpoint.

[1144] Step 6: The server records the complaint

[1145] explanation

[1146] The server records the complaint details received from the terminal and stores them in a database for later processing.

[1147] Input and Output

[1148] Input: Claim data

[1149] Output: Save to database

[1150] Specific actions

[1151] The server parses the received JSON data and executes SQL queries to store the information in the corresponding database tables.

[1152] Step 7: The server sends the claim to the AI ​​engine

[1153] explanation

[1154] The server retrieves the stored claim and sends it to an AI engine (e.g., OpenAI's GPT-3), usually in the form of an API request.

[1155] Input and Output

[1156] Input: Claim data

[1157] Output: Send request to AI engine

[1158] Specific actions

[1159] The server converts the claim data into API request format and sends it to the AI ​​engine's endpoint.

[1160] Step 8: The AI ​​engine generates a response

[1161] explanation

[1162] The AI ​​engine (generative AI model) analyzes the content of the received complaint and generates an appropriate response using a natural language processing algorithm.

[1163] Input and Output

[1164] Input: Claim data

[1165] Output: The generated response

[1166] Specific actions

[1167] The AI ​​engine runs the complaint through a natural language processing algorithm and generates a response in text format that corresponds to the complaint.

[1168] Step 9: The server stores the generated response

[1169] explanation

[1170] The server receives the response from the AI ​​engine and stores it in a database.

[1171] Input and Output

[1172] Input: Generated response

[1173] Output: Save to database

[1174] Specific actions

[1175] The server executes an SQL query to retrieve and store the received response data in a database.

[1176] Step 10: User submits request for response

[1177] explanation

[1178] The user (operator) sends a request for a response to the complaint from the terminal to the server.

[1179] Input and Output

[1180] Input: Reply Request

[1181] Output: Request sent to server

[1182] Specific actions

[1183] The operator operates the terminal interface to send a reply request to the server.

[1184] Step 11: The server sends the response to the device

[1185] explanation

[1186] The server receives a request from the user, retrieves the necessary response from the database, and sends it to the terminal.

[1187] Input and Output

[1188] Input: Reply Request

[1189] Output: Response data

[1190] Specific actions

[1191] The server retrieves the answer from the database and generates an API response to send to the device.

[1192] Step 12: The device displays the response to the user

[1193] explanation

[1194] The terminal displays the response received from the server to the user, who then checks the response and uses it to respond to the customer.

[1195] Input and Output

[1196] Input: Response data

[1197] Output: What is displayed to the user

[1198] Specific actions

[1199] The terminal displays the received response data on the screen and provides an interface that allows the operator to easily check it.

[1200] (Application example 1)

[1201] 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."

[1202] In recent years, excessive complaints and harassment (customer harassment) from customers and within companies have become an issue. Operators who respond to these complaints are often burdened with psychological stress, raising concerns that the quality of their responses may decline. In particular, there is a need to quickly provide appropriate countermeasures against harassment. However, conventional systems often fail to efficiently handle such responses. Therefore, the present invention aims to solve these issues.

[1203] 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.

[1204] In this invention, the server includes a means for receiving and recording user status information, a means for receiving and recording complaint details, and a means for generating an appropriate response based on the received complaint details, thereby enabling a means for proposing countermeasures using an AI engine.

[1205] "Means for receiving and recording user status information" refers to a device or function that receives and records the work status, work content, past complaint history, and other status information entered by the operator.

[1206] "Means for receiving and recording complaint details" refers to a device or function that receives and records the specific complaint details entered by the operator.

[1207] "Means for generating an appropriate response based on the received complaint content" refers to a device or function that automatically generates an appropriate response based on the received complaint content.

[1208] The "means for providing the generated response to the user" refers to a device or function that presents the generated response to the operator and enables the operator to take action based on the response.

[1209] "Means of proposing countermeasures using an AI engine" refers to devices or functions that use an AI engine to analyze the content of the complaint and the operator's situation information, and propose the most appropriate countermeasures.

[1210] A "natural language processing algorithm" is an algorithm for understanding and analyzing human language, and is a technology used to analyze the content of claims and generate appropriate responses.

[1211] A "generative AI model" is an AI model trained to perform a specific task (e.g., generating appropriate responses), and is a technology that uses this model to generate responses for complaints.

[1212] This invention provides a system that uses AI to generate and propose appropriate responses and countermeasures in response to excessive complaints and harassment (customer harassment) from customers or within a company.

[1213] The overall system configuration is as follows:

[1214] Means for receiving and recording user status information

[1215] The server receives and records status information entered by the operator through the terminal, such as work status, work content, and past complaint history. Specifically, the operator enters their current physical condition and the content of the complaint they are currently handling, and sends this to the server.

[1216] A means of receiving and recording complaints

[1217] The operator enters specific details of complaints received from customers or from within the company into the terminal, which then sends the information to the server, which records it and prepares it for analysis.

[1218] A means of generating appropriate responses based on the complaint received

[1219] The server sends the received complaint content to an existing AI engine, which uses a natural language processing algorithm to analyze the content of the complaint and generate an appropriate response. The server records the generated response and manages it so that it can be called up as needed.

[1220] A means of providing the generated response to the user

[1221] When an operator sends a request for a generated answer from a terminal, the server retrieves the corresponding answer from the answer database it manages and sends it to the terminal. The operator checks the answer on the terminal and uses it to respond to the customer.

[1222] A means of proposing countermeasures using an AI engine

[1223] The server sends the content of the complaint and the user's situation information to the AI ​​engine, which then uses the generative AI model to propose appropriate countermeasures and responses, enabling the operator to respond quickly and appropriately.

[1224] As a concrete example, consider the case where an operator receives a complaint that "I can't log in due to a system error." The operator enters this complaint into the terminal and sends it to the server. The server passes this complaint on to the AI ​​engine, which then generates a response such as, "Regarding the login problem, please first clear the cache and try again. If that doesn't resolve it, please contact your system administrator." This is recorded on the server, and when the operator calls it, it is immediately displayed on the terminal.

[1225] Here are some example prompts to input to the generative AI model:

[1226] Operator: Yamada Taro

[1227] Status: Feeling good today. Two complaints from yesterday have been dealt with.

[1228] Complaint: Unable to log in due to a system error.

[1229] Appropriate response:

[1230] By inputting this prompt into a generative AI model, an appropriate response is generated, thereby improving the quality of customer service and reducing the mental burden on operators.

[1231] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1232] Step 1:

[1233] The server receives and records information about the operator's status from the terminal. The operator enters information such as their work status, physical condition, and past complaint history into the terminal, which then sends this information to the server. The server stores the received data in a database and manages detailed status information for each user. The input data is work status, job content, and past complaint history, and the output is to save this information in an organized format in the database.

[1234] Step 2:

[1235] The user inputs specific complaints received from customers or from within the company into the terminal. The terminal then sends the complaints to the server. The server records the received complaints in a database for later processing. The input data is the specific complaints from customers or from within the company, and the output is to save these complaints in an organized format in the database.

[1236] Step 3:

[1237] The server sends the recorded complaint content to the AI ​​engine. The AI ​​engine uses a natural language processing algorithm to analyze the input complaint content and generate an appropriate response. The generated response is sent back to the server, which records it in a database. The input data is the complaint content, and the output is an appropriate response. The specific operation is the process of analyzing the complaint content and generating a response.

[1238] Step 4:

[1239] When a user sends a request for a reply from a terminal, the server retrieves the corresponding reply from the reply database it manages and sends it to the terminal. The terminal displays the reply to the user, who can then confirm it and use it to handle the complaint. The input data is the reply request, and the output is a display of the appropriate reply. The specific operation is to retrieve the reply data from the database and display it.

[1240] Step 5:

[1241] The server uses an AI engine to analyze the content of the complaint and the user's situation information, and then uses a generative AI model to propose appropriate countermeasures. This allows the user to respond quickly and appropriately. The input data is the content of the complaint and situation information, and the output is the proposed countermeasures. The specific operation involves analysis by the AI ​​engine and generation of countermeasures.

[1242] As a concrete example, if an operator receives a complaint that "I can't log in because of a system error," the complaint entered into the terminal is sent to the server, and the AI ​​engine generates a response such as, "Regarding the login problem, please first clear the cache and try again. If that doesn't resolve it, please contact your system administrator." This response is recorded on the server and displayed upon user request.

[1243] Here are some example prompts to input to a generative AI model:

[1244] Operator: Yamada Taro

[1245] Status: Feeling good today. Two complaints from yesterday have been dealt with.

[1246] Complaint: Unable to log in due to a system error.

[1247] Appropriate response:

[1248] By taking the above steps, it is possible to improve the quality of complaint handling and reduce the mental burden on operators.

[1249] 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.

[1250] The system of this invention utilizes AI's natural language processing capabilities and emotion recognition engine to provide appropriate responses when complaint handling operators deal with excessive complaints or harassment from customers. Below, we will generate a program for the system and explain its processing in natural language.

[1251] System configuration

[1252] 1. A means of receiving and recording user status information

[1253] User: The operator inputs status information such as work status, job content, and past complaint history into the terminal.

[1254] Terminal: Receives the status information entered by the user and sends it to the server.

[1255] Server: Records the status information received from the terminal and manages information for each user.

[1256] 2. How complaints are received and recorded

[1257] User: The operator inputs the specific details of the complaint received from the customer into the terminal.

[1258] Terminal: Receives the complaint content entered by the user and sends it to the server.

[1259] Server: Records the complaint received from the terminal and prepares it for later processing.

[1260] 3. A means of generating appropriate responses based on the complaint received

[1261] Server: Sends the complaint details to the AI ​​engine, which uses natural language processing algorithms to generate an appropriate response based on the complaint details entered.

[1262] AI Engine: Using natural language processing algorithms, it analyzes incoming complaints and generates corresponding responses.

[1263] Server: Records the responses sent back from the AI ​​engine and manages them for each user.

[1264] 4. A means of providing the generated response to the user

[1265] User: Sends a request from the device to the server, asking for a response.

[1266] Terminal: Receives requests from users and sends them to the server.

[1267] Server: In response to a user request, it invokes the generated response and sends it to the terminal.

[1268] Terminal: The reply received from the server is displayed to the user, who then checks the reply and uses it to respond to the customer.

[1269] 5. Includes an emotion engine that recognizes user emotions

[1270] User: When an operator inputs work status or complaint details, the tone of voice, facial expression, and input content are analyzed by the emotion engine.

[1271] Emotion engine: Analyzes voice tone, facial expressions, input content, etc. to recognize the user's emotional state in real time. This information is sent to the server.

[1272] Server: Based on the emotion information received from the emotion engine, the server adjusts the generated response and provides the most appropriate response for the user.

[1273] Specific examples

[1274] For example, let's say Operator A comes to work and logs in to the system. Operator A enters his / her current working status and past complaint history, and registers the status information in the system. At this time, the emotion engine analyzes Operator A's tone of voice and facial expressions, and sends the current emotional state to the server.

[1275] Next, operator A receives a complaint from customer B that the product has not arrived and enters the details into the system. The terminal sends the details of the complaint and operator A's emotional information at the time of input to the server.

[1276] The server sends the complaint details to the AI ​​engine, which uses a natural language processing algorithm to generate an appropriate response. For example, a response such as "We will check the delivery status of the product and contact you again." The response is then adjusted taking into account the emotional information from the emotion engine.

[1277] This response is recorded on the server, and when Operator A sends a request for a response, the server generates a response and sends it to the terminal. Operator A checks the response on the terminal and uses it to respond to Customer B.

[1278] This system reduces the mental burden on operators, improves the quality of customer service, and enables more appropriate and effective complaint handling.

[1279] The processing flow will be explained below.

[1280] Step 1:

[1281] The user inputs status information such as work status, work content, past complaint history, etc. into the terminal. The user logs in to the terminal at the start of work and inputs their physical condition and past complaint handling history.

[1282] Step 2:

[1283] The terminal receives the input status information and transmits it to the server. The terminal accurately records the information input by the user and transmits it to the server in real time.

[1284] Step 3:

[1285] The server records the received user status information and manages the information for each user. The server saves the received information in a database and makes it available for use.

[1286] Step 4:

[1287] The user inputs the specific complaint received from the customer into the terminal. To input the received complaint in detail, the user writes information along specific fields.

[1288] Step 5:

[1289] The terminal receives the complaint details entered and sends them to the server. The terminal accurately records the complaint details entered by the user and immediately sends them to the server.

[1290] Step 6:

[1291] The server records the received complaint for later processing. The received complaint is stored in a database along with other related information.

[1292] Step 7:

[1293] The server sends the content of the claim to the AI ​​engine. The received claim data is sent to the AI ​​engine for analysis.

[1294] Step 8:

[1295] The AI ​​engine uses natural language processing algorithms to analyze the content of the complaint entered and generate an appropriate response. The AI ​​engine understands the content of the complaint and compares it with past data to create the optimal response.

[1296] Step 9:

[1297] The AI ​​engine sends the generated response back to the server, where it is ready to be served to the user.

[1298] Step 10:

[1299] The server records the responses received and manages them for each user. Responses are saved with appropriate tags and metadata for easy access later.

[1300] Step 11:

[1301] The user sends a request for a response from the terminal to the server, and the user performs an operation to request a response generated by the system for a specific claim.

[1302] Step 12:

[1303] In response to a user request, the server retrieves a pre-generated response and sends it to the terminal. The server retrieves the appropriate response from the database and sends it to the user's terminal.

[1304] Step 13:

[1305] The terminal displays the response received from the server to the user. The terminal displays the response sent back from the server so that the user can check it.

[1306] Step 14:

[1307] The user checks the displayed response and responds to the customer using it. The user responds appropriately to the customer based on the confirmed response.

[1308] Step 15:

[1309] When a user inputs situation information or complaint details, the terminal sends the emotion to the emotion engine, which analyzes the user's tone of voice, facial expression, and input content.

[1310] Step 16:

[1311] The emotion engine sends the analysis results to the server to recognize the user's emotional state. The emotion engine sends the analysis results to the server to update the user's emotional state in real time.

[1312] Step 17:

[1313] The server adjusts the generated response based on the received emotional information to provide the most appropriate response for the user. The server incorporates the emotional information to adjust the tone and content of the response and display it to the user.

[1314] Example 2

[1315] 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."

[1316] In today's customer service environment, dealing with excessive customer complaints and malicious harassment (kasahara) places a heavy mental burden on operators. Responding appropriately and quickly requires high levels of judgment and emotional balance, but maintaining these skills is extremely difficult. Furthermore, the appropriate response must be considered for each complaint, making the process cumbersome. The present invention aims to provide a system that solves these problems, reduces the burden on operators, and improves the quality of customer service.

[1317] 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.

[1318] In this invention, the server includes means for receiving and recording user status information, means for receiving and recording complaint details, means for generating an appropriate response based on the received complaint details and adjusting the response based on emotion analysis, and means for providing the generated response to the user. This allows the operator to record their working status and work content in real time, and appropriate responses are generated accordingly, with adjustments made taking into account their emotional state, reducing their mental burden and enabling higher quality customer service.

[1319] "User" refers to the person who operates the system and inputs information. Specifically, this includes operators who handle customer complaints.

[1320] "Status information" refers to information including the current business status and past data, such as the user's working status, work content, and past complaint history.

[1321] "Complaint content" refers to the specific claims or dissatisfaction received from customers.

[1322] A "response" refers to an answer or response message that is generated based on the received complaint and that the user provides to the customer.

[1323] "Natural language processing algorithms" refer to computer programs that analyze text data and understand its meaning, allowing them to generate responses tailored to the content of a claim.

[1324] A "generative AI model" refers to an AI program that is trained on a huge amount of data and generates an appropriate output (response) for a specific input.

[1325] "Emotion analysis" refers to the process of analyzing a user's tone of voice, facial expression, input content, etc. to determine their emotional state.

[1326] "Server" refers to a computer system that receives and processes user input data, stores the data, executes algorithms, and generates and manages responses.

[1327] "Terminal" refers to a computer device that allows a user to input information and transmits the input data to a server.

[1328] The system of this invention utilizes AI's natural language processing capabilities and sentiment analysis engine to provide appropriate responses when a complaint handling operator deals with excessive complaints or harassment from customers. Specific embodiments of the system are described in detail below.

[1329] System configuration

[1330] 1. A means of receiving and recording user status information

[1331] User: The operator inputs status information such as work status, work content, past complaint history, etc. into the terminal. For example, when an operator starts a shift, they input their current work status and past complaint handling history.

[1332] Terminal: Receives status information entered by the user and sends it to the server. Specifically, it converts the data entered in the input form into JSON format and sends it to the server via the network.

[1333] Server: Records the status information received from the terminal and stores information for each user in a database.

[1334] 2. How complaints are received and recorded

[1335] User: The operator inputs the specific details of the complaint received from the customer into the terminal. For example, the operator inputs the details of the customer's complaint in real time while on the phone.

[1336] Terminal: Receives the complaint data entered by the user and sends it to the server. This data is also converted to JSON format.

[1337] Server: Records the complaint details received from the terminal and stores them in a database for later processing.

[1338] 3. A means to generate appropriate responses based on the complaints received and adjust them based on sentiment analysis

[1339] Server: Uses an API request to send the claim details to the AI ​​engine.

[1340] AI Engine: Using natural language processing algorithms and generative AI models, it analyzes incoming complaints and generates appropriate responses.

[1341] Server: Records the responses sent back by the AI ​​engine and adjusts the responses taking into account information from the sentiment analysis engine.

[1342] 4. A means of providing the generated response to the user

[1343] User: The operator sends a request for a response from the terminal to the server, for example by clicking "Generate response."

[1344] Terminal: Receives requests from users and sends them to the server.

[1345] Server: In response to a user request, it invokes the generated response and sends it to the terminal.

[1346] Terminal: The reply received from the server is displayed to the user, who then checks the reply and uses it to respond to the customer.

[1347] Specific examples

[1348] Operator A comes to work and logs in to the system. Operator A enters his / her current working status and past complaint history into the system. At this time, the emotion analysis engine analyzes Operator A's tone of voice and facial expressions and sends his / her emotional state to the server.

[1349] Next, operator A receives a complaint from customer B that the product has not arrived and enters the details into the system. The terminal then sends the details of the complaint and operator A's emotional information to the server.

[1350] The server sends the complaint details to the AI ​​engine, which uses a natural language processing algorithm and a generative AI model to generate an appropriate response. For example, a response such as, "We will check the delivery status of the product and contact you again." The response is adjusted based on information from the sentiment analysis engine.

[1351] This response is recorded on the server, and when Operator A sends a request for a response, the server sends the response to the terminal. Operator A checks the response on the terminal and uses it to respond to Customer B.

[1352] Prompt Sentence Examples

[1353] Operator: "Please enter your current work status and past claim history."

[1354] Terminal: "Please enter the details of the customer's complaint."

[1355] Server: "Generate an appropriate response based on the complaint."

[1356] Emotion Engine: "Provide emotional information by analyzing the tone of voice and facial expressions during input."

[1357] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1358] Step 1: Entering and receiving user context information

[1359] User: The operator enters information such as work status, job content, and past complaint history into the terminal.

[1360] How it works: At the start of a shift, an operator uses the system interface to fill out a form with their name, shift time, and complaint history for the past 24 hours.

[1361] Input: working status, work content, past complaint history

[1362] Terminal: Receives the status information entered by the user and sends it to the server.

[1363] Specific operation: The terminal converts the input data into JSON format and sends it to the server via the network.

[1364] Output: JSON format status information data

[1365] Step 2: Enter and receive your complaint

[1366] User: The operator inputs the specific details of the complaint received from the customer into the terminal.

[1367] Specific operation: The operator enters the details of the customer's complaint in real time while on the phone.

[1368] Input: Customer complaint details

[1369] Terminal: Receives the complaint content entered by the user and sends it to the server.

[1370] Specific operation: The terminal saves the content of the claim in real time, converts it into JSON format, and sends it to the server.

[1371] Output: Claim content in JSON format

[1372] Step 3: Sentiment analysis and information reception

[1373] User: The emotion engine analyzes the tone of voice, facial expressions, and input content when the operator enters the details of the complaint.

[1374] Specific operation: Data input by the operator through a microphone or camera is sent to the emotion engine.

[1375] Input: Operator tone of voice, facial expressions, input content

[1376] Emotion Engine: Analyzes the emotional state in real time based on the received data. This information is sent to the server.

[1377] Specific operation: The emotion engine uses a deep learning model to analyze the emotional state as numerical data and send it to the server.

[1378] Output: Numerical data of emotional information

[1379] Step 4: Generate and tailor an appropriate response

[1380] Server: Sends the received complaint to the AI ​​engine, which uses natural language processing algorithms and generative AI models to generate an appropriate response.

[1381] Specific operation: The server sends the claim content to the AI ​​engine as an API request.

[1382] Input: Complaint details

[1383] AI Engine: Generates responses using natural language processing algorithms and generative AI models.

[1384] Specific operation: The AI ​​engine analyzes the content of the complaint and generates an appropriate response.

[1385] Output: Generated response data

[1386] Step 5: Adjust your response based on emotional information

[1387] Server: Compares the response data returned from the AI ​​engine with emotional information and adjusts the response as necessary.

[1388] Specific operation: The server fine-tunes the response text based on information from the emotion engine.

[1389] Input: Generated response data, emotion information

[1390] Output: Adjusted response data

[1391] Step 6: Providing generated responses

[1392] User: Sends a request from the device to the server, asking for a response.

[1393] Specific action: The operator presses the button to generate a response.

[1394] Input: Reply request

[1395] Terminal: Receives requests from users and sends them to the server.

[1396] Specific operation: The terminal forwards the request to the server.

[1397] Output: Request data

[1398] Step 7: Capture and send response data

[1399] Server: In response to a user request, retrieves a pre-generated response from the database and sends it to the terminal.

[1400] Specific operation: The server retrieves the response from the database and sends it back to the terminal.

[1401] Input: Request data

[1402] Output: Response data

[1403] Step 8: Displaying the response on the terminal

[1404] Terminal: The reply received from the server is displayed to the user, who then checks the reply and uses it to respond to the customer.

[1405] Specific operation: The terminal displays the response data on the UI and the operator confirms it.

[1406] Input: Response data

[1407] Output: The response displayed in the UI

[1408] (Application example 2)

[1409] 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."

[1410] In conventional security services, employees face a heavy mental burden when dealing with threats or harassment, making it difficult for them to respond appropriately. Furthermore, there is a lack of systems that can grasp employees' emotional states in real time and provide appropriate countermeasures. Therefore, there is a need for a system that can reduce the mental burden on security operators and support more effective responses.

[1411] 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.

[1412] In this invention, the server includes means for receiving and recording user status information, means for receiving and recording complaint details, means for generating an appropriate response based on the received complaint details, means for providing the generated response to the user, means including an emotion engine for recognizing the user's emotions, and means for adjusting the response based on the user's emotion information. This reduces the mental burden on security operators and enables appropriate responses according to their emotional states in real time.

[1413] "User status information" refers to information such as an employee's working status, work content, and past complaint history.

[1414] "Complaint content" is information about specific dissatisfaction or problems reported by customers or users.

[1415] An "appropriate response" is a response that is generated based on the received complaint and is designed to solve the problem.

[1416] The "emotion engine" is a function that analyzes voice tone, facial expressions, input content, etc. to recognize the user's emotional state in real time.

[1417] A "natural language processing algorithm" is a technology that analyzes input text data, understands its meaning, and generates appropriate responses.

[1418] "Emotion information" is information about the user's emotional state analyzed by the emotion engine.

[1419] The "means for adjusting a response" is a function for optimizing the content of a response by taking into consideration the emotional information of the user.

[1420] System Program Overview

[1421] The system of this invention receives and records the user's situation information and complaint details, and generates appropriate responses using natural language processing algorithms. Furthermore, it can recognize the user's emotions in real time and adjust responses based on the emotional information. The system operates using a combination of smartphones, security robots, and a cloud server.

[1422] Hardware and software used

[1423] Hardware

[1424] Smartphone (iOS / Android)

[1425] Security robot (general-purpose robot)

[1426] software

[1427] Natural language processing engines (GPT-4, BERT, etc.)

[1428] Emotion recognition engine (Microsoft Azure Cognitive Services, IBM Watson, etc.)

[1429] Cloud server (Amazon AWS, Google Cloud)

[1430] Program processing overview

[1431] 1. Receiving and recording user status information

[1432] The server receives and records the work status, work content, and past complaint history sent from the smartphone or security robot, making it possible to understand what work an employee is in charge of and what problems they have faced in the past.

[1433] 2. Receiving and recording complaints

[1434] The server receives and records specific complaints sent by the smartphone or security robot, including specific information about the problem the user is facing.

[1435] 3. Generating appropriate responses

[1436] The server sends the received complaint to a natural language processing engine to generate a corresponding response. Using a natural language processing algorithm, a response that provides the best solution based on the input complaint content is created.

[1437] 4. Recognition of emotional information

[1438] The server uses an emotion recognition engine to analyze and record the user's emotional state based on their tone of voice, facial expressions, and input content, making it possible to respond according to the user's mental state.

[1439] 5. Tailor your response

[1440] The server adjusts the generated response based on the recognized emotion information, thereby providing a response that takes the user's emotions into consideration.

[1441] Specific examples

[1442] For example, a security operator may input a complaint to the system that he or she was "threatened by a suspicious individual." At this time, the emotion engine analyzes the operator's emotional state and sends it to the server. The server then takes the emotion recognition information into consideration and uses a natural language processing engine to generate an appropriate response, such as "We will inquire about the specific characteristics of the suspicious individual and take immediate action." This response also corresponds to the operator's emotional state.

[1443] Prompt Sentence Examples

[1444] Examples of prompts for this system include:

[1445] "Today's task: Patrol."

[1446] "I was threatened by a suspicious person"

[1447] This invention reduces the mental burden on security operators and enables them to respond effectively to various threats and harassment.

[1448] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1449] Step 1:

[1450] Receiving and recording user status information

[1451] The server receives and records employee work status, work content, and past complaint history sent from the device (smartphone or security robot). The server stores this information in a database and manages the information for each user. The data input here is the information entered by the user at the start of work, and the output is the recorded database entry.

[1452] Step 2:

[1453] Receiving and recording complaints

[1454] The server receives the specific complaint details (e.g., "I was threatened by a suspicious person") entered by the user (security operator) into the terminal and records this information in a database. The input data is the detailed complaint details, and the output is the recorded complaint data. This data is used for further processing.

[1455] Step 3:

[1456] Generating appropriate responses

[1457] The server sends the received complaint content to a natural language processing engine (such as GPT-4 or BERT) to generate an appropriate response. At this time, the text data of the complaint content is input. The natural language processing engine performs data analysis and calculations based on this input to generate an optimal response text. The generated response text is the output.

[1458] Step 4:

[1459] Recognition of emotional information

[1460] The server uses an emotion recognition engine (such as Microsoft Azure Cognitive Services or IBM Watson) to analyze the user's tone of voice, facial expression, and input content when the user enters a complaint, and recognizes the user's emotional state. The input is the user's voice and text data, and the output is analyzed emotional information. This information is used to tailor the response later.

[1461] Step 5:

[1462] Adjusting responses

[1463] The server adjusts the generated response based on the recognized emotion information. Emotion information is input, and the natural language processing engine optimizes the response text generated based on that information. The output is the adjusted response text.

[1464] Step 6:

[1465] Providing a response

[1466] The server responds to the user's request by sending a tailored response to the device (smartphone or security robot) and displaying it to the user. The user then checks the displayed response and responds to the customer based on it. The input is the response request, and the output is the response text provided to the user.

[1467] 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.

[1468] 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.

[1469] 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.

[1470] [Fourth embodiment]

[1471] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1472] 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.

[1473] 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).

[1474] 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.

[1475] 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.

[1476] 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).

[1477] 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.

[1478] 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.

[1479] 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.

[1480] 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.

[1481] 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.

[1482] 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.

[1483] 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."

[1484] The system of this invention utilizes AI's natural language processing capabilities to provide appropriate responses when complaint handling operators deal with excessive complaints or harassment from customers. Below, we will generate a program for the system and explain its processing in natural language.

[1485] System configuration

[1486] 1. A means of receiving and recording user status information

[1487] User: The operator inputs information about the working situation, work content, past complaint history, etc. into the terminal. This includes the operator's current physical condition and the types of complaints they have handled in the past.

[1488] Terminal: Receives status information entered by the user and sends it to the server.

[1489] Server: Records the status information received from the terminal and manages information for each user.

[1490] 2. How complaints are received and recorded

[1491] User: The operator inputs the specific details of the customer's complaint into the terminal. For example, the operator inputs "The product has not arrived."

[1492] Terminal: Receives the complaint details entered by the user and sends them to the server.

[1493] Server: Records the complaint received from the terminal and prepares it for later processing.

[1494] 3. A means of generating appropriate responses based on the complaint received

[1495] Server: Sends the complaint details to the AI ​​engine, which uses natural language processing algorithms to generate an appropriate response based on the complaint details entered.

[1496] AI Engine: Using natural language processing algorithms, it analyzes incoming complaints and generates corresponding responses.

[1497] Server: Records the responses sent back from the AI ​​engine and manages them for each user.

[1498] 4. A means of providing the generated response to the user

[1499] User: Sends a request from the device to the server, asking for a response.

[1500] Terminal: Receives requests from users and sends them to the server.

[1501] Server: In response to a user request, it retrieves the generated response and sends it to the terminal.

[1502] Terminal: The reply received from the server is displayed to the user, who then checks the reply and uses it to respond to the customer.

[1503] Specific examples

[1504] For example, let's say Operator A comes to work and logs in to the system. Operator A enters his / her current work status and past complaint history, and registers the status information in the system.

[1505] Next, operator A receives a complaint from customer B that the product has not arrived and enters the details into the system. The terminal sends the details of the complaint to the server, which records them.

[1506] The server sends the complaint details to the AI ​​engine, which uses a natural language processing algorithm to generate an appropriate response, such as "We will check the delivery status of the product and contact you again."

[1507] This response is recorded on the server, and when Operator A sends a request for a response, the server generates a response and sends it to the terminal. Operator A checks the response on the terminal and uses it to respond to Customer B.

[1508] As mentioned above, this system is designed to reduce the mental burden on operators and improve the quality of their responses, enabling appropriate responses to excessive complaints and customer harassment.

[1509] The processing flow will be explained below.

[1510] Step 1:

[1511] The user inputs status information such as work status, work content, and past complaint history into the terminal.

[1512] Step 2:

[1513] The terminal receives the input status information and transmits it to the server.

[1514] Step 3:

[1515] The server records the received user status information and manages the information for each user.

[1516] Step 4:

[1517] The user inputs the specific content of the complaint received from the customer into the terminal.

[1518] Step 5:

[1519] The terminal receives the input complaint details and transmits them to the server.

[1520] Step 6:

[1521] The server records the complaint received for later processing.

[1522] Step 7:

[1523] The server sends the complaint details to the AI ​​engine.

[1524] Step 8:

[1525] The AI ​​engine uses natural language processing algorithms to analyze the inputted complaint content and generate an appropriate response.

[1526] Step 9:

[1527] The AI ​​engine sends the generated response back to the server.

[1528] Step 10:

[1529] The server records the responses it receives and manages them for each user.

[1530] Step 11:

[1531] The user sends a request from the terminal to the server, requesting a response.

[1532] Step 12:

[1533] The server responds to the user's request by recalling the generated response and sending it to the terminal.

[1534] Step 13:

[1535] The terminal displays the response received from the server to the user.

[1536] Step 14:

[1537] The user reviews the displayed response and uses it to respond to the customer.

[1538] Example 1

[1539] 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."

[1540] With conventional complaint handling systems, it was difficult for operators to quickly provide effective responses to excessive complaints or harassment from customers. This increased the mental burden on operators and raised concerns that the quality of responses would decline. Furthermore, operators needed time to think of an appropriate response on the spot, which could prolong response times.

[1541] 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.

[1542] In this invention, the server includes means for receiving and recording user status information, means for receiving and recording complaint details, means for transmitting the status information entered by the user to the server, means for transmitting the complaint details to the server, means for generating an appropriate response based on the received complaint details, means for saving the generated response, and means for providing the generated response to the user. This enables the operator to provide a prompt and appropriate response, reducing mental strain and improving the quality of the response.

[1543] "User status information" refers to information such as the user's (operator's) working status, work content, and past complaint history.

[1544] "Complaint details" refers to the details of specific complaints or claims received from customers.

[1545] "Server" refers to a central processing unit that receives and records user status information and complaints, generates appropriate responses, and stores them.

[1546] "Terminal" refers to a device through which a user inputs information and transmits that information to a server.

[1547] "Means for receiving and recording" refers to the function of receiving user status information and complaint details and storing them in a database or the like.

[1548] "Means for generating appropriate responses" refers to the ability to automatically generate responses based on the content of the complaint received using an AI engine or natural language processing algorithm.

[1549] "Means for storing generated responses" refers to the function of storing responses generated by the AI ​​engine in a database and managing them so that they can be retrieved later.

[1550] The "means for providing the generated response to the user" refers to a function for transmitting the response stored by the server to the terminal in response to a request from the user and providing it to the user.

[1551] "Natural language processing algorithms" refers to a set of algorithms that analyze text data, understand its meaning, and generate appropriate responses.

[1552] MODE FOR CARRYING OUT THE INVENTION

[1553] As an embodiment of the present invention, the following describes a system that utilizes the natural language processing capabilities of AI to provide appropriate responses to complaint handling operators in order to deal with excessive complaints and harassment from customers.

[1554] System configuration and hardware / software used

[1555] The system includes the following major components:

[1556] 1. A means of receiving and recording user status information

[1557] User: The operator inputs information such as working status, work content, and past complaint history into the terminal, including the operator's current physical condition and the types of complaints they have handled in the past.

[1558] Terminal: Receives the input status information and sends the data to the server. Terminals used include PCs and tablets.

[1559] Server: Records the status information received from the terminal and stores and manages information for each user in a database. The database can be MySQL or PostgreSQL.

[1560] 2. How complaints are received and recorded

[1561] User: The operator inputs the specific details of the customer's complaint into the terminal, such as "the product has not arrived."

[1562] Terminal: Receives the entered complaint details and sends them to the server.

[1563] Server: Records the complaint details received from the terminal and stores them in a database.

[1564] 3. A means of generating appropriate responses based on the complaint received

[1565] Server: Sends the saved claim details to an AI engine (e.g., OpenAI's GPT-3) in the form of an API request.

[1566] AI engine: Using a natural language processing algorithm, it generates an appropriate response based on the received complaint. For example, it generates a response such as, "We will check the delivery status of the product and contact you again."

[1567] 4. A means of saving the generated response

[1568] Server: The responses sent back from the AI ​​engine are stored in a database and managed for each user.

[1569] 5. A means of providing the generated response to the user

[1570] User: The operator sends a request from the terminal to the server, which asks for a response.

[1571] Terminal: Receives requests from users and sends them to the server.

[1572] Server: Retrieves the stored response and sends it to the device.

[1573] Terminal: Displays the response received from the server to the user. The operator checks the response and responds to the customer.

[1574] Specific examples

[1575] Specific examples of system use

[1576] For example, let's say Operator A comes to work and logs in to the system. Operator A enters his / her current work status and past complaint history, and registers the status information in the system.

[1577] Next, operator A receives a complaint from customer B that the product has not arrived and enters the details into the system. The terminal sends the details of the complaint to the server, which records them.

[1578] The server sends the complaint to an AI engine (e.g., OpenAI's GPT-3), which uses a natural language processing algorithm to generate an appropriate response, such as, "We will check the delivery status of your product and contact you again."

[1579] This response is recorded on the server, and when Operator A sends a request for a response, the server generates a response and sends it to the terminal. Operator A checks the response on the terminal and uses it to respond to Customer B.

[1580] Example prompts to input to the generative AI model

[1581] Example prompt sentence:

[1582] Customer complaint: "The product never arrived."

[1583] Generate an appropriate response for the operator.

[1584] keyword

[1585] Generative AI model, prompt sentence

[1586] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1587] System program processing flow

[1588] Step 1: User Enters Context Information

[1589] explanation

[1590] The user (operator) inputs information such as working conditions, work content, and past complaint history into the terminal, including the user's current physical condition and the types of complaints they have handled in the past.

[1591] Input and Output

[1592] Input: working status, work content, past complaint history, health information

[1593] Output: Situation information data

[1594] Specific actions

[1595] The operator enters the necessary information into the terminal form and sends it to the system.

[1596] Step 2: The device sends status information to the server

[1597] explanation

[1598] The device receives the status information entered by the user and sends the data to the server in a format such as JSON or XML.

[1599] Input and Output

[1600] Input: Situational information data

[1601] Output: Request sent to server

[1602] Specific actions

[1603] The input information is converted into a data format (e.g., JSON) within the terminal and sent to the specified API endpoint.

[1604] Step 3: The server records status information

[1605] explanation

[1606] The server records the status information received from the terminal and stores the information for each user in a database.

[1607] Input and Output

[1608] Input: Situational information data

[1609] Output: Save to database

[1610] Specific actions

[1611] The server parses the received JSON data and executes SQL queries to store the information in the corresponding database tables.

[1612] Step 4: User enters complaint details

[1613] explanation

[1614] The user (operator) inputs the specific details of the customer's complaint into the terminal. This information includes details of the complaint, such as "the product has not arrived."

[1615] Input and Output

[1616] Input: Complaint details

[1617] Output: Claim data

[1618] Specific actions

[1619] The operator enters the details of the complaint into a form on the terminal and sends it to the system.

[1620] Step 5: The device sends the complaint to the server

[1621] explanation

[1622] The terminal receives the complaint details entered by the user and transmits the data to the server.

[1623] Input and Output

[1624] Input: Claim data

[1625] Output: Request sent to server

[1626] Specific actions

[1627] The entered claim content is converted into a data format (e.g., JSON) within the terminal and sent to the specified API endpoint.

[1628] Step 6: The server records the complaint

[1629] explanation

[1630] The server records the complaint details received from the terminal and stores them in a database for later processing.

[1631] Input and Output

[1632] Input: Claim data

[1633] Output: Save to database

[1634] Specific actions

[1635] The server parses the received JSON data and executes SQL queries to store the information in the corresponding database tables.

[1636] Step 7: The server sends the claim to the AI ​​engine

[1637] explanation

[1638] The server retrieves the stored claim and sends it to an AI engine (e.g., OpenAI's GPT-3), usually in the form of an API request.

[1639] Input and Output

[1640] Input: Claim data

[1641] Output: Send request to AI engine

[1642] Specific actions

[1643] The server converts the claim data into API request format and sends it to the AI ​​engine's endpoint.

[1644] Step 8: The AI ​​engine generates a response

[1645] explanation

[1646] The AI ​​engine (generative AI model) analyzes the content of the received complaint and generates an appropriate response using a natural language processing algorithm.

[1647] Input and Output

[1648] Input: Claim data

[1649] Output: The generated response

[1650] Specific actions

[1651] The AI ​​engine runs the complaint through a natural language processing algorithm and generates a response in text format that corresponds to the complaint.

[1652] Step 9: The server stores the generated response

[1653] explanation

[1654] The server receives the response from the AI ​​engine and stores it in a database.

[1655] Input and Output

[1656] Input: Generated response

[1657] Output: Save to database

[1658] Specific actions

[1659] The server executes an SQL query to retrieve and store the received response data in a database.

[1660] Step 10: User submits request for response

[1661] explanation

[1662] The user (operator) sends a request for a response to the complaint from the terminal to the server.

[1663] Input and Output

[1664] Input: Reply Request

[1665] Output: Request sent to server

[1666] Specific actions

[1667] The operator operates the terminal interface to send a reply request to the server.

[1668] Step 11: The server sends the response to the device

[1669] explanation

[1670] The server receives a request from the user, retrieves the necessary response from the database, and sends it to the terminal.

[1671] Input and Output

[1672] Input: Reply Request

[1673] Output: Response data

[1674] Specific actions

[1675] The server retrieves the answer from the database and generates an API response to send to the device.

[1676] Step 12: The device displays the response to the user

[1677] explanation

[1678] The terminal displays the response received from the server to the user, who then checks the response and uses it to respond to the customer.

[1679] Input and Output

[1680] Input: Response data

[1681] Output: What is displayed to the user

[1682] Specific actions

[1683] The terminal displays the received response data on the screen and provides an interface that allows the operator to easily check it.

[1684] (Application example 1)

[1685] 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."

[1686] In recent years, excessive complaints and harassment (customer harassment) from customers and within companies have become an issue. Operators who respond to these complaints are often burdened with psychological stress, raising concerns that the quality of their responses may decline. In particular, there is a need to quickly provide appropriate countermeasures against harassment. However, conventional systems often fail to efficiently handle such responses. Therefore, the present invention aims to solve these issues.

[1687] 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.

[1688] In this invention, the server includes a means for receiving and recording user status information, a means for receiving and recording complaint details, and a means for generating an appropriate response based on the received complaint details, thereby enabling a means for proposing countermeasures using an AI engine.

[1689] "Means for receiving and recording user status information" refers to a device or function that receives and records the work status, work content, past complaint history, and other status information entered by the operator.

[1690] "Means for receiving and recording complaint details" refers to a device or function that receives and records the specific complaint details entered by the operator.

[1691] "Means for generating an appropriate response based on the received complaint content" refers to a device or function that automatically generates an appropriate response based on the received complaint content.

[1692] The "means for providing the generated response to the user" refers to a device or function that presents the generated response to the operator and enables the operator to take action based on the response.

[1693] "Means of proposing countermeasures using an AI engine" refers to devices or functions that use an AI engine to analyze the content of the complaint and the operator's situation information, and propose the most appropriate countermeasures.

[1694] A "natural language processing algorithm" is an algorithm for understanding and analyzing human language, and is a technology used to analyze the content of claims and generate appropriate responses.

[1695] A "generative AI model" is an AI model trained to perform a specific task (e.g., generating appropriate responses), and is a technology that uses this model to generate responses for complaints.

[1696] This invention provides a system that uses AI to generate and propose appropriate responses and countermeasures in response to excessive complaints and harassment (customer harassment) from customers or within a company.

[1697] The overall system configuration is as follows:

[1698] Means for receiving and recording user status information

[1699] The server receives and records status information entered by the operator through the terminal, such as work status, work content, and past complaint history. Specifically, the operator enters their current physical condition and the content of the complaint they are currently handling, and sends this to the server.

[1700] A means of receiving and recording complaints

[1701] The operator enters specific details of complaints received from customers or from within the company into the terminal, which then sends the information to the server, which records it and prepares it for analysis.

[1702] A means of generating appropriate responses based on the complaint received

[1703] The server sends the received complaint content to an existing AI engine, which uses a natural language processing algorithm to analyze the content of the complaint and generate an appropriate response. The server records the generated response and manages it so that it can be called up as needed.

[1704] A means of providing the generated response to the user

[1705] When an operator sends a request for a generated answer from a terminal, the server retrieves the corresponding answer from the answer database it manages and sends it to the terminal. The operator checks the answer on the terminal and uses it to respond to the customer.

[1706] A means of proposing countermeasures using an AI engine

[1707] The server sends the content of the complaint and the user's situation information to the AI ​​engine, which then uses the generative AI model to propose appropriate countermeasures and responses, enabling the operator to respond quickly and appropriately.

[1708] As a concrete example, consider the case where an operator receives a complaint that "I can't log in due to a system error." The operator enters this complaint into the terminal and sends it to the server. The server passes this complaint on to the AI ​​engine, which then generates a response such as, "Regarding the login problem, please first clear the cache and try again. If that doesn't resolve it, please contact your system administrator." This is recorded on the server, and when the operator calls it, it is immediately displayed on the terminal.

[1709] Here are some example prompts to input to the generative AI model:

[1710] Operator: Yamada Taro

[1711] Status: Feeling good today. Two complaints from yesterday have been dealt with.

[1712] Complaint: Unable to log in due to a system error.

[1713] Appropriate response:

[1714] By inputting this prompt into a generative AI model, an appropriate response is generated, thereby improving the quality of customer service and reducing the mental burden on operators.

[1715] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1716] Step 1:

[1717] The server receives and records information about the operator's status from the terminal. The operator enters information such as their work status, physical condition, and past complaint history into the terminal, which then sends this information to the server. The server stores the received data in a database and manages detailed status information for each user. The input data is work status, job content, and past complaint history, and the output is to save this information in an organized format in the database.

[1718] Step 2:

[1719] The user inputs specific complaints received from customers or from within the company into the terminal. The terminal then sends the complaints to the server. The server records the received complaints in a database for later processing. The input data is the specific complaints from customers or from within the company, and the output is to save these complaints in an organized format in the database.

[1720] Step 3:

[1721] The server sends the recorded complaint content to the AI ​​engine. The AI ​​engine uses a natural language processing algorithm to analyze the input complaint content and generate an appropriate response. The generated response is sent back to the server, which records it in a database. The input data is the complaint content, and the output is an appropriate response. The specific operation is the process of analyzing the complaint content and generating a response.

[1722] Step 4:

[1723] When a user sends a request for a reply from a terminal, the server retrieves the corresponding reply from the reply database it manages and sends it to the terminal. The terminal displays the reply to the user, who can then confirm it and use it to handle the complaint. The input data is the reply request, and the output is a display of the appropriate reply. The specific operation is to retrieve the reply data from the database and display it.

[1724] Step 5:

[1725] The server uses an AI engine to analyze the content of the complaint and the user's situation information, and then uses a generative AI model to propose appropriate countermeasures. This allows the user to respond quickly and appropriately. The input data is the content of the complaint and situation information, and the output is the proposed countermeasures. The specific operation involves analysis by the AI ​​engine and generation of countermeasures.

[1726] As a concrete example, if an operator receives a complaint that "I can't log in because of a system error," the complaint entered into the terminal is sent to the server, and the AI ​​engine generates a response such as, "Regarding the login problem, please first clear the cache and try again. If that doesn't resolve it, please contact your system administrator." This response is recorded on the server and displayed upon user request.

[1727] Here are some example prompts to input to a generative AI model:

[1728] Operator: Yamada Taro

[1729] Status: Feeling good today. Two complaints from yesterday have been dealt with.

[1730] Complaint: Unable to log in due to a system error.

[1731] Appropriate response:

[1732] By taking the above steps, it is possible to improve the quality of complaint handling and reduce the mental burden on operators.

[1733] 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.

[1734] The system of this invention utilizes AI's natural language processing capabilities and emotion recognition engine to provide appropriate responses when complaint handling operators deal with excessive complaints or harassment from customers. Below, we will generate a program for the system and explain its processing in natural language.

[1735] System configuration

[1736] 1. A means of receiving and recording user status information

[1737] User: The operator inputs status information such as work status, job content, and past complaint history into the terminal.

[1738] Terminal: Receives the status information entered by the user and sends it to the server.

[1739] Server: Records the status information received from the terminal and manages information for each user.

[1740] 2. How complaints are received and recorded

[1741] User: The operator inputs the specific details of the complaint received from the customer into the terminal.

[1742] Terminal: Receives the complaint content entered by the user and sends it to the server.

[1743] Server: Records the complaint received from the terminal and prepares it for later processing.

[1744] 3. A means of generating appropriate responses based on the complaint received

[1745] Server: Sends the complaint details to the AI ​​engine, which uses natural language processing algorithms to generate an appropriate response based on the complaint details entered.

[1746] AI Engine: Using natural language processing algorithms, it analyzes incoming complaints and generates corresponding responses.

[1747] Server: Records the responses sent back from the AI ​​engine and manages them for each user.

[1748] 4. A means of providing the generated response to the user

[1749] User: Sends a request from the device to the server, asking for a response.

[1750] Terminal: Receives requests from users and sends them to the server.

[1751] Server: In response to a user request, it invokes the generated response and sends it to the terminal.

[1752] Terminal: The reply received from the server is displayed to the user, who then checks the reply and uses it to respond to the customer.

[1753] 5. Includes an emotion engine that recognizes user emotions

[1754] User: When an operator inputs work status or complaint details, the tone of voice, facial expression, and input content are analyzed by the emotion engine.

[1755] Emotion engine: Analyzes voice tone, facial expressions, input content, etc. to recognize the user's emotional state in real time. This information is sent to the server.

[1756] Server: Based on the emotion information received from the emotion engine, the server adjusts the generated response and provides the most appropriate response for the user.

[1757] Specific examples

[1758] For example, let's say Operator A comes to work and logs in to the system. Operator A enters his / her current working status and past complaint history, and registers the status information in the system. At this time, the emotion engine analyzes Operator A's tone of voice and facial expressions, and sends the current emotional state to the server.

[1759] Next, operator A receives a complaint from customer B that the product has not arrived and enters the details into the system. The terminal sends the details of the complaint and operator A's emotional information at the time of input to the server.

[1760] The server sends the complaint details to the AI ​​engine, which uses a natural language processing algorithm to generate an appropriate response. For example, a response such as "We will check the delivery status of the product and contact you again." The response is then adjusted taking into account the emotional information from the emotion engine.

[1761] This response is recorded on the server, and when Operator A sends a request for a response, the server generates a response and sends it to the terminal. Operator A checks the response on the terminal and uses it to respond to Customer B.

[1762] This system reduces the mental burden on operators, improves the quality of customer service, and enables more appropriate and effective complaint handling.

[1763] The processing flow will be explained below.

[1764] Step 1:

[1765] The user inputs status information such as work status, work content, past complaint history, etc. into the terminal. The user logs in to the terminal at the start of work and inputs their physical condition and past complaint handling history.

[1766] Step 2:

[1767] The terminal receives the input status information and transmits it to the server. The terminal accurately records the information input by the user and transmits it to the server in real time.

[1768] Step 3:

[1769] The server records the received user status information and manages the information for each user. The server saves the received information in a database and makes it available for use.

[1770] Step 4:

[1771] The user inputs the specific complaint received from the customer into the terminal. To input the received complaint in detail, the user writes information along specific fields.

[1772] Step 5:

[1773] The terminal receives the complaint details entered and sends them to the server. The terminal accurately records the complaint details entered by the user and immediately sends them to the server.

[1774] Step 6:

[1775] The server records the received complaint for later processing. The received complaint is stored in a database along with other related information.

[1776] Step 7:

[1777] The server sends the content of the claim to the AI ​​engine. The received claim data is sent to the AI ​​engine for analysis.

[1778] Step 8:

[1779] The AI ​​engine uses natural language processing algorithms to analyze the content of the complaint entered and generate an appropriate response. The AI ​​engine understands the content of the complaint and compares it with past data to create the optimal response.

[1780] Step 9:

[1781] The AI ​​engine sends the generated response back to the server, where it is ready to be served to the user.

[1782] Step 10:

[1783] The server records the responses received and manages them for each user. Responses are saved with appropriate tags and metadata for easy access later.

[1784] Step 11:

[1785] The user sends a request for a response from the terminal to the server, and the user performs an operation to request a response generated by the system for a specific claim.

[1786] Step 12:

[1787] In response to a user request, the server retrieves a pre-generated response and sends it to the terminal. The server retrieves the appropriate response from the database and sends it to the user's terminal.

[1788] Step 13:

[1789] The terminal displays the response received from the server to the user. The terminal displays the response sent back from the server so that the user can check it.

[1790] Step 14:

[1791] The user checks the displayed response and responds to the customer using it. The user responds appropriately to the customer based on the confirmed response.

[1792] Step 15:

[1793] When a user inputs situation information or complaint details, the terminal sends the emotion to the emotion engine, which analyzes the user's tone of voice, facial expression, and input content.

[1794] Step 16:

[1795] The emotion engine sends the analysis results to the server to recognize the user's emotional state. The emotion engine sends the analysis results to the server to update the user's emotional state in real time.

[1796] Step 17:

[1797] The server adjusts the generated response based on the received emotional information to provide the most appropriate response for the user. The server incorporates the emotional information to adjust the tone and content of the response and display it to the user.

[1798] Example 2

[1799] 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."

[1800] In today's customer service environment, dealing with excessive customer complaints and malicious harassment (kasahara) places a heavy mental burden on operators. Responding appropriately and quickly requires high levels of judgment and emotional balance, but maintaining these skills is extremely difficult. Furthermore, the appropriate response must be considered for each complaint, making the process cumbersome. The present invention aims to provide a system that solves these problems, reduces the burden on operators, and improves the quality of customer service.

[1801] 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.

[1802] In this invention, the server includes means for receiving and recording user status information, means for receiving and recording complaint details, means for generating an appropriate response based on the received complaint details and adjusting the response based on emotion analysis, and means for providing the generated response to the user. This allows the operator to record their working status and work content in real time, and appropriate responses are generated accordingly, with adjustments made taking into account their emotional state, reducing their mental burden and enabling higher quality customer service.

[1803] "User" refers to the person who operates the system and inputs information. Specifically, this includes operators who handle customer complaints.

[1804] "Status information" refers to information including the current business status and past data, such as the user's working status, work content, and past complaint history.

[1805] "Complaint content" refers to the specific claims or dissatisfaction received from customers.

[1806] A "response" refers to an answer or response message that is generated based on the received complaint and that the user provides to the customer.

[1807] "Natural language processing algorithms" refer to computer programs that analyze text data and understand its meaning, allowing them to generate responses tailored to the content of a claim.

[1808] A "generative AI model" refers to an AI program that is trained on a huge amount of data and generates an appropriate output (response) for a specific input.

[1809] "Emotion analysis" refers to the process of analyzing a user's tone of voice, facial expression, input content, etc. to determine their emotional state.

[1810] "Server" refers to a computer system that receives and processes user input data, stores the data, executes algorithms, and generates and manages responses.

[1811] "Terminal" refers to a computer device that allows a user to input information and transmits the input data to a server.

[1812] The system of this invention utilizes AI's natural language processing capabilities and sentiment analysis engine to provide appropriate responses when a complaint handling operator deals with excessive complaints or harassment from customers. Specific embodiments of the system are described in detail below.

[1813] System configuration

[1814] 1. A means of receiving and recording user status information

[1815] User: The operator inputs status information such as work status, work content, past complaint history, etc. into the terminal. For example, when an operator starts a shift, they input their current work status and past complaint handling history.

[1816] Terminal: Receives status information entered by the user and sends it to the server. Specifically, it converts the data entered in the input form into JSON format and sends it to the server via the network.

[1817] Server: Records the status information received from the terminal and stores information for each user in a database.

[1818] 2. How complaints are received and recorded

[1819] User: The operator inputs the specific details of the complaint received from the customer into the terminal. For example, the operator inputs the details of the customer's complaint in real time while on the phone.

[1820] Terminal: Receives the complaint data entered by the user and sends it to the server. This data is also converted to JSON format.

[1821] Server: Records the complaint details received from the terminal and stores them in a database for later processing.

[1822] 3. A means to generate appropriate responses based on the complaints received and adjust them based on sentiment analysis

[1823] Server: Uses an API request to send the claim details to the AI ​​engine.

[1824] AI Engine: Using natural language processing algorithms and generative AI models, it analyzes incoming complaints and generates appropriate responses.

[1825] Server: Records the responses sent back by the AI ​​engine and adjusts the responses taking into account information from the sentiment analysis engine.

[1826] 4. A means of providing the generated response to the user

[1827] User: The operator sends a request for a response from the terminal to the server, for example by clicking "Generate response."

[1828] Terminal: Receives requests from users and sends them to the server.

[1829] Server: In response to a user request, it invokes the generated response and sends it to the terminal.

[1830] Terminal: The reply received from the server is displayed to the user, who then checks the reply and uses it to respond to the customer.

[1831] Specific examples

[1832] Operator A comes to work and logs in to the system. Operator A enters his / her current working status and past complaint history into the system. At this time, the emotion analysis engine analyzes Operator A's tone of voice and facial expressions and sends his / her emotional state to the server.

[1833] Next, operator A receives a complaint from customer B that the product has not arrived and enters the details into the system. The terminal then sends the details of the complaint and operator A's emotional information to the server.

[1834] The server sends the complaint details to the AI ​​engine, which uses a natural language processing algorithm and a generative AI model to generate an appropriate response. For example, a response such as, "We will check the delivery status of the product and contact you again." The response is adjusted based on information from the sentiment analysis engine.

[1835] This response is recorded on the server, and when Operator A sends a request for a response, the server sends the response to the terminal. Operator A checks the response on the terminal and uses it to respond to Customer B.

[1836] Prompt Sentence Examples

[1837] Operator: "Please enter your current work status and past claim history."

[1838] Terminal: "Please enter the details of the customer's complaint."

[1839] Server: "Generate an appropriate response based on the complaint."

[1840] Emotion Engine: "Provide emotional information by analyzing the tone of voice and facial expressions during input."

[1841] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1842] Step 1: Entering and receiving user context information

[1843] User: The operator enters information such as work status, job content, and past complaint history into the terminal.

[1844] How it works: At the start of a shift, an operator uses the system interface to fill out a form with their name, shift time, and complaint history for the past 24 hours.

[1845] Input: working status, work content, past complaint history

[1846] Terminal: Receives the status information entered by the user and sends it to the server.

[1847] Specific operation: The terminal converts the input data into JSON format and sends it to the server via the network.

[1848] Output: JSON format status information data

[1849] Step 2: Enter and receive your complaint

[1850] User: The operator inputs the specific details of the complaint received from the customer into the terminal.

[1851] Specific operation: The operator enters the details of the customer's complaint in real time while on the phone.

[1852] Input: Customer complaint details

[1853] Terminal: Receives the complaint content entered by the user and sends it to the server.

[1854] Specific operation: The terminal saves the content of the claim in real time, converts it into JSON format, and sends it to the server.

[1855] Output: Claim content in JSON format

[1856] Step 3: Sentiment analysis and information reception

[1857] User: The emotion engine analyzes the tone of voice, facial expressions, and input content when the operator enters the details of the complaint.

[1858] Specific operation: Data input by the operator through a microphone or camera is sent to the emotion engine.

[1859] Input: Operator tone of voice, facial expressions, input content

[1860] Emotion Engine: Analyzes the emotional state in real time based on the received data. This information is sent to the server.

[1861] Specific operation: The emotion engine uses a deep learning model to analyze the emotional state as numerical data and send it to the server.

[1862] Output: Numerical data of emotional information

[1863] Step 4: Generate and tailor an appropriate response

[1864] Server: Sends the received complaint to the AI ​​engine, which uses natural language processing algorithms and generative AI models to generate an appropriate response.

[1865] Specific operation: The server sends the claim content to the AI ​​engine as an API request.

[1866] Input: Complaint details

[1867] AI Engine: Generates responses using natural language processing algorithms and generative AI models.

[1868] Specific operation: The AI ​​engine analyzes the content of the complaint and generates an appropriate response.

[1869] Output: Generated response data

[1870] Step 5: Adjust your response based on emotional information

[1871] Server: Compares the response data returned from the AI ​​engine with emotional information and adjusts the response as necessary.

[1872] Specific operation: The server fine-tunes the response text based on information from the emotion engine.

[1873] Input: Generated response data, emotion information

[1874] Output: Adjusted response data

[1875] Step 6: Providing generated responses

[1876] User: Sends a request from the device to the server, asking for a response.

[1877] Specific action: The operator presses the button to generate a response.

[1878] Input: Reply request

[1879] Terminal: Receives requests from users and sends them to the server.

[1880] Specific operation: The terminal forwards the request to the server.

[1881] Output: Request data

[1882] Step 7: Capture and send response data

[1883] Server: In response to a user request, retrieves a pre-generated response from the database and sends it to the terminal.

[1884] Specific operation: The server retrieves the response from the database and sends it back to the terminal.

[1885] Input: Request data

[1886] Output: Response data

[1887] Step 8: Displaying the response on the terminal

[1888] Terminal: The reply received from the server is displayed to the user, who then checks the reply and uses it to respond to the customer.

[1889] Specific operation: The terminal displays the response data on the UI and the operator confirms it.

[1890] Input: Response data

[1891] Output: The response displayed in the UI

[1892] (Application example 2)

[1893] 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."

[1894] In conventional security services, employees face a heavy mental burden when dealing with threats or harassment, making it difficult for them to respond appropriately. Furthermore, there is a lack of systems that can grasp employees' emotional states in real time and provide appropriate countermeasures. Therefore, there is a need for a system that can reduce the mental burden on security operators and support more effective responses.

[1895] 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.

[1896] In this invention, the server includes means for receiving and recording user status information, means for receiving and recording complaint details, means for generating an appropriate response based on the received complaint details, means for providing the generated response to the user, means including an emotion engine for recognizing the user's emotions, and means for adjusting the response based on the user's emotion information. This reduces the mental burden on security operators and enables appropriate responses according to their emotional states in real time.

[1897] "User status information" refers to information such as an employee's working status, work content, and past complaint history.

[1898] "Complaint content" is information about specific dissatisfaction or problems reported by customers or users.

[1899] An "appropriate response" is a response that is generated based on the received complaint and is designed to solve the problem.

[1900] The "emotion engine" is a function that analyzes voice tone, facial expressions, input content, etc. to recognize the user's emotional state in real time.

[1901] A "natural language processing algorithm" is a technology that analyzes input text data, understands its meaning, and generates appropriate responses.

[1902] "Emotion information" is information about the user's emotional state analyzed by the emotion engine.

[1903] The "means for adjusting a response" is a function for optimizing the content of a response by taking into consideration the emotional information of the user.

[1904] System Program Overview

[1905] The system of this invention receives and records the user's situation information and complaint details, and generates appropriate responses using natural language processing algorithms. Furthermore, it can recognize the user's emotions in real time and adjust responses based on the emotional information. The system operates using a combination of smartphones, security robots, and a cloud server.

[1906] Hardware and software used

[1907] Hardware

[1908] Smartphone (iOS / Android)

[1909] Security robot (general-purpose robot)

[1910] software

[1911] Natural language processing engines (GPT-4, BERT, etc.)

[1912] Emotion recognition engine (Microsoft Azure Cognitive Services, IBM Watson, etc.)

[1913] Cloud server (Amazon AWS, Google Cloud)

[1914] Program processing overview

[1915] 1. Receiving and recording user status information

[1916] The server receives and records the work status, work content, and past complaint history sent from the smartphone or security robot, making it possible to understand what work an employee is in charge of and what problems they have faced in the past.

[1917] 2. Receiving and recording complaints

[1918] The server receives and records specific complaints sent by the smartphone or security robot, including specific information about the problem the user is facing.

[1919] 3. Generating appropriate responses

[1920] The server sends the received complaint to a natural language processing engine to generate a corresponding response. Using a natural language processing algorithm, a response that provides the best solution based on the input complaint content is created.

[1921] 4. Recognition of emotional information

[1922] The server uses an emotion recognition engine to analyze and record the user's emotional state based on their tone of voice, facial expressions, and input content, making it possible to respond according to the user's mental state.

[1923] 5. Tailor your response

[1924] The server adjusts the generated response based on the recognized emotion information, thereby providing a response that takes the user's emotions into consideration.

[1925] Specific examples

[1926] For example, a security operator may input a complaint to the system that he or she was "threatened by a suspicious individual." At this time, the emotion engine analyzes the operator's emotional state and sends it to the server. The server then takes the emotion recognition information into consideration and uses a natural language processing engine to generate an appropriate response, such as "We will inquire about the specific characteristics of the suspicious individual and take immediate action." This response also corresponds to the operator's emotional state.

[1927] Prompt Sentence Examples

[1928] Examples of prompts for this system include:

[1929] "Today's task: Patrol."

[1930] "I was threatened by a suspicious person"

[1931] This invention reduces the mental burden on security operators and enables them to respond effectively to various threats and harassment.

[1932] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1933] Step 1:

[1934] Receiving and recording user status information

[1935] The server receives and records employee work status, work content, and past complaint history sent from the device (smartphone or security robot). The server stores this information in a database and manages the information for each user. The data input here is the information entered by the user at the start of work, and the output is the recorded database entry.

[1936] Step 2:

[1937] Receiving and recording complaints

[1938] The server receives the specific complaint details (e.g., "I was threatened by a suspicious person") entered by the user (security operator) into the terminal and records this information in a database. The input data is the detailed complaint details, and the output is the recorded complaint data. This data is used for further processing.

[1939] Step 3:

[1940] Generating appropriate responses

[1941] The server sends the received complaint content to a natural language processing engine (such as GPT-4 or BERT) to generate an appropriate response. At this time, the text data of the complaint content is input. The natural language processing engine performs data analysis and calculations based on this input to generate an optimal response text. The generated response text is the output.

[1942] Step 4:

[1943] Recognition of emotional information

[1944] The server uses an emotion recognition engine (such as Microsoft Azure Cognitive Services or IBM Watson) to analyze the user's tone of voice, facial expression, and input content when the user enters a complaint, and recognizes the user's emotional state. The input is the user's voice and text data, and the output is analyzed emotional information. This information is used to tailor the response later.

[1945] Step 5:

[1946] Adjusting responses

[1947] The server adjusts the generated response based on the recognized emotion information. Emotion information is input, and the natural language processing engine optimizes the response text generated based on that information. The output is the adjusted response text.

[1948] Step 6:

[1949] Providing a response

[1950] The server responds to the user's request by sending a tailored response to the device (smartphone or security robot) and displaying it to the user. The user then checks the displayed response and responds to the customer based on it. The input is the response request, and the output is the response text provided to the user.

[1951] 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.

[1952] 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.

[1953] 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.

[1954] 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.

[1955] 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.

[1956] 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.

[1957] 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).

[1958] 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.

[1959] 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."

[1960] 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.

[1961] 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).

[1962] 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.

[1963] 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.

[1964] 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.

[1965] 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.

[1966] 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.

[1967] 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.

[1968] 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.

[1969] 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.

[1970] 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.

[1971] 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.

[1972] The following is further disclosed regarding the above embodiment.

[1973] (Claim 1)

[1974] means for receiving and recording user status information;

[1975] A means of receiving and recording complaints;

[1976] means for generating an appropriate response based on the received complaint;

[1977] means for providing the generated response to a user;

[1978] A system including:

[1979] (Claim 2)

[1980] 2. The system according to claim 1, wherein the user's status information includes work status, work content, and past complaint history.

[1981] (Claim 3)

[1982] 10. The system of claim 1, wherein the means for generating an appropriate response uses a natural language processing algorithm.

[1983] "Example 1"

[1984] (Claim 1)

[1985] means for receiving and recording user status information;

[1986] A means of receiving and recording complaints;

[1987] means for transmitting status information input by the user to a server;

[1988] means for transmitting the content of the complaint to a server;

[1989] means for generating an appropriate response based on the received complaint;

[1990] a means for storing the generated response;

[1991] means for providing the generated response to a user;

[1992] A system including:

[1993] (Claim 2)

[1994] 2. The system according to claim 1, wherein the user's status information includes work status, work content, and past complaint history.

[1995] (Claim 3)

[1996] 10. The system of claim 1, wherein the means for generating an appropriate response uses a natural language processing algorithm.

[1997] "Application Example 1"

[1998] (Claim 1)

[1999] means for receiving and recording user status information;

[2000] A means of receiving and recording complaints;

[2001] means for generating an appropriate response based on the received complaint;

[2002] means for providing the generated response to a user;

[2003] A means of proposing countermeasures using an AI engine,

[2004] A system including:

[2005] (Claim 2)

[2006] 2. The system according to claim 1, wherein the user's status information includes work status, work content, and past complaint history.

[2007] (Claim 3)

[2008] 10. The system of claim 1, wherein the means for generating an appropriate response uses a natural language processing algorithm and a generative AI model.

[2009] "Example 2: Combining Emotion Engines"

[2010] (Claim 1)

[2011] means for receiving and recording user status information;

[2012] A means of receiving and recording complaints;

[2013] means for generating an appropriate response based on the received complaint and adjusting the response based on sentiment analysis;

[2014] means for providing the generated response to a user;

[2015] A system including:

[2016] (Claim 2)

[2017] 2. The system of claim 1, wherein the user's situation information includes work status, work content, and past complaint history, and the emotion analysis is based on the user's voice tone, facial expression, and input content.

[2018] (Claim 3)

[2019] 2. The system of claim 1, wherein the means for generating an appropriate response uses a natural language processing algorithm and a generative AI model.

[2020] "Application example 2 when combining emotion engines"

[2021] (Claim 1)

[2022] means for receiving and recording user status information;

[2023] A means of receiving and recording complaints;

[2024] means for generating an appropriate response based on the received complaint;

[2025] means for providing the generated response to a user;

[2026] means including an emotion engine for recognizing an emotion of a user;

[2027] means for adjusting a response based on the user's emotional information;

[2028] A system including:

[2029] (Claim 2)

[2030] 2. The system according to claim 1, wherein the user's status information includes work status, work content, and past complaint history.

[2031] (Claim 3)

[2032] 10. The system of claim 1, wherein the means for generating an appropriate response uses a natural language processing algorithm. [Explanation of symbols]

[2033] 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. means for receiving and recording user status information; A means of receiving and recording complaints; means for generating an appropriate response based on the received complaint; means for providing the generated response to a user; A system including:

2. The system according to claim 1 , wherein the user's status information includes a work status, a job description, and a history of past complaints.

3. 2. The system of claim 1, wherein the means for generating an appropriate response uses a natural language processing algorithm.

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