system

Abstract

Provide a system. 【Solution means】 Data processing means for receiving inquiries from users, Function to record the received inquiries in a storage device, Artificial intelligence function for performing analysis based on the recorded history data and customer information, Reply generation function for generating a response based on the analysis result, System function for providing the generated response to the user, Function to refer to the business information of corporate customers and provide e-commerce information, Function to instantaneously record and process inquiries using a real-time communication function, A system including the above.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In customer support in a corporation, the person in charge often changes, and for this reason, the history of responses to customers is fragmented, which is a problem. This leads to a decrease in customer satisfaction and a lack of consistency in responses. Solving such problems and providing consistent customer support is an issue.

Means for Solving the Problems

[0005] This invention provides a system that receives inquiries from users and records them in a database. Furthermore, artificial intelligence performs analysis based on the recorded inquiry history and customer data. By generating responses based on the analysis results and providing them to the user, consistent customer service is achieved. In addition, by analyzing customer usage and proposing optimized plans, it is possible to always provide the best possible service. Furthermore, when the person in charge changes, handover documents are automatically created based on past history, supporting a smooth transition.

[0006] "Information processing means" refers to means that have the function of receiving inquiries from users and processing them appropriately.

[0007] "Means of recording in a database" refers to means that have the function of appropriately saving received queries and retaining them in a format that can be used as history as needed.

[0008] "Artificial intelligence means" refers to means that have the function of analyzing recorded inquiry history and customer data to generate appropriate responses and suggestions.

[0009] A "response generation means" is a means that has the function of generating a response to be provided to the user based on the analysis results.

[0010] "Means of providing to the user" refers to means that have the function of communicating the generated response to the user.

[0011] "Analysis tools" refer to tools that have the function of analyzing customer usage patterns and proposing optimized plans based on the results.

[0012] A "handover support system" is a system that automatically generates handover documents based on past history when the person in charge changes, and has the function of supporting the handover. [Brief explanation of the drawing]

[0013] [Figure 1]This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0014] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

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

[0016] In the following embodiments, the labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0017] In the following embodiments, the labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0018] In the following embodiments, the labeled storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0019] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor and 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), or Bluetooth (registered trademark), and the like.

[0020] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0021] [First Embodiment]

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

[0023] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0024] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0025] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0026] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[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 perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0028] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0030] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0031] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0032] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0033] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0034] The system of the present invention provides a mechanism for providing efficient and consistent services to corporate clients. This system is implemented in the following form.

[0035] First, the user accesses the corporate portal and enters customer information into the system via a terminal. This information includes company name, contact details, and contract details. The terminal sends this information to the server, which verifies it and then stores it in a database. Based on the stored information, the server assigns a dedicated AI agent to each customer.

[0036] When a user submits an inquiry, they enter their question through a chat window on their device. This inquiry is sent to the server, which receives it and records it in its database. During this process, the inquiry content and related history are all stored chronologically.

[0037] The recorded data is analyzed by the server via an AI agent. The AI ​​agent analyzes the accumulated inquiry history and customer data to generate appropriate responses. For example, if a user asks, "I would like information about the next contract renewal," the AI ​​agent will generate a specific suggestion such as, "The next renewal date is on XX day, and △△ is available as a benefit." This response is sent from the server to the terminal and displayed to the user.

[0038] The server also periodically analyzes customer usage and suggests optimized account plans. These suggestions, generated by the AI ​​agent, are delivered to the user's device by the server, allowing them to review and update them as needed.

[0039] Furthermore, the server automatically determines the need for a handover when the person in charge changes and uses an AI agent to create handover documents. These documents are generated based on past interaction history and can be viewed by the new person in charge on their terminal. This reduces the burden during the handover and ensures consistent service delivery.

[0040] By implementing this system, it becomes possible to consistently provide customers with high-quality and stable service, leading to improved customer satisfaction.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] The user accesses the corporate portal and enters the company name, contact information, and contract details as a new customer via their device.

[0044] Step 2:

[0045] The terminal sends the entered customer information to the server. The server receives this information and verifies its accuracy.

[0046] Step 3:

[0047] After verification by the server, customer information is saved to the database. At this point, a dedicated AI agent is assigned to the customer.

[0048] Step 4:

[0049] Users enter inquiries through the corporate portal's chat window. For example, they might ask, "I'd like to know about my current contract plan."

[0050] Step 5:

[0051] The terminal sends the user's query to the server. The server receives the query, adds a timestamp, and records it in the database.

[0052] Step 6:

[0053] The server provides the AI ​​agent with the inquiry details and past history, and instructs it to perform the analysis.

[0054] Step 7:

[0055] The AI ​​agent analyzes the provided information and generates an appropriate response. For example, specific information such as, "Your current contract is the XX plan."

[0056] Step 8:

[0057] The server sends the generated response to the terminal. The terminal receives it and displays it to the user.

[0058] Step 9:

[0059] The server periodically analyzes customer usage patterns and has an AI agent generate optimized plan suggestions.

[0060] Step 10:

[0061] The AI ​​agent generates a plan and returns it to the server, which then provides the suggestion to the user's device. The user reviews the suggestion and updates the plan as needed.

[0062] Step 11:

[0063] If a change in personnel occurs, the server will have the AI ​​agent create handover documents based on past interaction history.

[0064] Step 12:

[0065] The server sends the generated handover documents to the new person in charge's terminal. The terminal displays the documents and assists the person in charge in starting their work based on them.

[0066] (Example 1)

[0067] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0068] Customer management systems are required to improve the efficiency of handling inquiries, propose optimal plans based on customer usage, and provide consistent service. However, conventional systems have shortcomings in effectively managing inquiry history and automated handover procedures, resulting in insufficient customer satisfaction.

[0069] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0070] In this invention, the server includes information processing means for receiving inquiries from users, means for recording the received inquiries in a data set in chronological order, and artificial intelligence means utilizing a generative AI model that performs analysis based on the recorded inquiry history and customer data. This makes it possible to streamline inquiry handling and realize appropriate account proposals and automated handover procedures for each customer.

[0071] "Information processing means for receiving inquiries from users" refers to the functions of devices or software that receive inquiries entered by users into the system.

[0072] "Means of recording data in a time-series format" refers to devices or methods for sequentially saving received information in a database or similar format according to chronological order.

[0073] "Artificial intelligence methods using generative AI models" refer to systems and technologies that utilize machine learning models to perform data analysis and decision-making.

[0074] A "response generation means" refers to a process or device for creating a response to a user based on the results analyzed by artificial intelligence.

[0075] "Communication methods" refer to technologies and devices for sending and receiving data via computer networks.

[0076] "A means of sending customer information to a server and assigning a dedicated agent" refers to a function that delivers customer data to a central server, after which a specific program automatically assigns a dedicated artificial intelligence to each customer.

[0077] "Analytics that analyze usage history and generate optimized account suggestions" refers to technologies and systems that evaluate past usage data and propose service plans that are best suited to the user's needs.

[0078] A "handover document generation means for automatically generating handover documents" is a device or method for automatically creating documents for a new person to take over work based on past interaction history when a person in charge changes.

[0079] Modes for carrying out the invention

[0080] This invention is a system for providing efficient and consistent customer service to corporate clients. Specific embodiments are described below.

[0081] User

[0082] Users access a dedicated corporate portal using a browser and enter customer information such as company name, contact details, and contract details on their device. This allows users to directly provide their company information to service providers.

[0083] terminal

[0084] The terminal is a means of sending input data to the server. The terminal temporarily stores the data in memory and then transfers it to the server via the internet. In this process, the terminal also performs a simple format check to ensure that the data format is correct.

[0085] server

[0086] The server processes customer information and inquiries received from terminals and stores them in a database. Specifically, it uses PostgreSQL to manage the data. The server also utilizes widely used open-source models as generative AI models to analyze inquiry history and customer data. This enables highly accurate analysis based on historical information.

[0087] When a user submits an inquiry, the server receives it, stores it in a database, and then uses AI to analyze the content and generate an appropriate response. This generated response is then forwarded back to the terminal using a communication method, allowing the user to review it.

[0088] Specific example

[0089] For example, a user might enter an inquiry into the chat system such as, "Please tell me my next contract renewal date." This inquiry is sent to the server and analyzed by an AI agent. As a result, the server generates a response such as, "Your next renewal date is on XX day, and you can use △△ as a benefit," and sends this to the user's device.

[0090] Examples of prompt messages include, "Please provide detailed information regarding contract renewal." In this way, it is possible to respond quickly to customer needs and improve satisfaction. This system enables consistent customer service and efficient information management.

[0091] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0092] Step 1:

[0093] Users access the corporate portal and enter customer information such as company name, contact details, and contract details into the terminal. The entered data is temporarily stored in the terminal's memory. The input here consists of strings and numbers based on a form, and the terminal performs a simple verification to ensure the information format is correct.

[0094] Step 2:

[0095] The terminal sends verified customer information to the server. During transmission, the data is converted into packet format and transferred to the server using the Internet Protocol. This process enables real-time online data transmission.

[0096] Step 3:

[0097] The server receives data from the terminal and stores it in the database. The server converts the data to the correct format and saves it to the PostgreSQL database. This makes the data easily searchable and manageable later.

[0098] Step 4:

[0099] When a user submits an inquiry through the chat window on their device, that inquiry is sent to the server. For example, they might enter a prompt message such as, "Please tell me my next contract renewal date." This data is sent to the server in string format.

[0100] Step 5:

[0101] The server receives the query and records it in the database. Then, it analyzes the query content using a generating AI model. The AI ​​model refers to historical data and executes an algorithm to generate the optimal response to the input prompt.

[0102] Step 6:

[0103] The server constructs the generated response and sends it to the terminal via the communication means. This output is a text message formatted for user understanding. The terminal receives this data and prepares to display it to the user.

[0104] Step 7:

[0105] The server periodically analyzes customer usage history and generates optimized account plans. Based on past usage patterns, AI analyzes and generates recommendations, suggesting options that allow users to utilize the service more efficiently.

[0106] Step 8:

[0107] When a person in charge changes, the server automatically analyzes past history information to generate a handover document. This generated document can be easily viewed by the new person in charge on their terminal, helping to maintain consistency in operations.

[0108] (Application Example 1)

[0109] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0110] In services targeting corporate clients, there is a demand for efficient and consistent responses. However, existing systems often result in fragmented customer support and a lack of service consistency. Furthermore, the difficulty in handling inquiries in real time and analyzing large amounts of customer data hinders improvements in customer satisfaction. In addition, manual handover is required when personnel changes occur, leading to delays and errors in responses.

[0111] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0112] In this invention, the server includes data processing means for receiving inquiries from users, a function for recording received inquiries in a storage device, and a function for referencing business information of corporate customers and providing electronic transaction information. This makes it possible to provide efficient and consistent services to corporate customers, improve customer satisfaction, and reduce the effort required for handover.

[0113] A "user" is an entity that utilizes the system as a corporate customer.

[0114] An "inquiry" is a question or request that a user sends to a system seeking information or support.

[0115] "Data processing means" refers to functions and technologies for receiving inquiries from users and performing the necessary processing.

[0116] A "storage device" is a storage medium used to save received inquiries and historical data.

[0117] "Historical data" refers to records that include the user's past inquiries and transaction information.

[0118] "Artificial intelligence functionality" refers to technology that analyzes historical data and customer information, and uses the insights gained from this analysis to generate responses.

[0119] The "reply generation function" is a technology that constructs the optimal response to provide to the user based on the analysis results.

[0120] "System functionality" refers to a set of technologies and processes that enable the system to operate as a whole and provide services to users.

[0121] A "corporate customer" is a company or business entity that benefits from the service and uses the system.

[0122] "Business information" refers to data related to the activities and transactions of corporate clients.

[0123] "Electronic transaction information" refers to transaction-related information that is managed and provided in digital format.

[0124] "Real-time communication function" refers to communication technology that enables the instantaneous transmission and reception of information.

[0125] The system for realizing this invention utilizes specific technologies and programs to provide efficient and consistent services to corporate clients. The system receives data from users (corporate clients), and a server stores this information in a central database. Users can submit inquiries using a smartphone app, and the information is processed in real time. This app is developed using React Native and provides a user-friendly interface.

[0126] The server is based on Node.js and uses MongoDB to store all customer information and history in its database. This ensures efficient data management and quick access when needed. Socket.io technology is implemented for real-time communication, allowing user inquiries to be registered and processed instantly.

[0127] The AI ​​analysis uses an AI agent built in a Python environment. This agent analyzes the user's history and inquiries using the natural language processing libraries NLTK and spaCy. Based on the analysis results, the AI ​​agent generates the optimal response for the user and provides it to the user through the server.

[0128] For example, if a corporate client's representative wants to check the details of their next payment or discount information, the AI ​​agent will provide specific information such as, "The next payment date is △ / △, and the available discounts are ○○." This allows users to efficiently obtain information and also provides a quick response to their individual needs.

[0129] An example of a prompt message would be, "Please provide the next payment date and available discount information for corporate customer XYZ." The AI ​​understands the user's needs and generates a response. This ensures that the information the user needs is provided in a timely manner.

[0130] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0131] Step 1:

[0132] Users enter their inquiries using a smartphone app. The entered data is sent from the device to the server via real-time communication. Socket.io is used for this process, and the data is compressed for efficient transmission.

[0133] Step 2:

[0134] The server decompresses the received query data and saves it to a MongoDB database using Node.js. This data is timestamped and stored as a query history. The database organizes information based on customer IDs and is managed for easy access.

[0135] Step 3:

[0136] The server detects that a query has been saved and invokes an AI agent running in a Python environment. The AI ​​agent takes the saved historical data and the current query data as input. The AI ​​agent analyzes the data using the natural language processing libraries NLTK and spaCy to understand the user's intent.

[0137] Step 4:

[0138] Based on the analysis results, the AI ​​agent generates the optimal response. For example, if the prompt "Please provide the next payment date and available discount information for corporate customer XYZ" is entered, the AI ​​agent will retrieve the necessary information from its internal data and generate a specific response such as "The next payment date is △ / △, and the available discounts are ○○."

[0139] Step 5:

[0140] The generated response is sent to the terminal via the server. The user receives this response in real time through a smartphone app, and it is displayed on the screen. This allows the user to obtain information quickly and accurately.

[0141] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0142] The system of this invention provides sophisticated responses that incorporate user sentiment analysis in order to deliver consistent service to corporate clients. This system is implemented in the following form.

[0143] First, the user accesses the corporate portal and enters an inquiry into the system from their terminal. This inquiry often includes questions or requests in natural language, and the terminal sends this information to the server. The server verifies the received information and records it in a database. This allows the inquiry history to be managed chronologically.

[0144] Next, the server uses an emotion engine to analyze the emotions expressed in the user's text and voice inquiries. The emotion engine identifies emotions such as joy, anger, and sadness, and records the emotional state in a database. This also allows for the management of the user's emotional history.

[0145] The server receives the analysis results from the emotion engine and provides the AI ​​agent with the analysis results and emotion data. Based on this information, the AI ​​agent generates the optimal response. For example, if a user expresses dissatisfaction with the contract terms, the AI ​​agent will generate an emotion-sensitive response such as, "We have prepared specific suggestions for improvement." Depending on the emotion, it may also adopt softer language or a more respectful communication style.

[0146] The generated response is sent from the server to the terminal and displayed to the user. This allows the user to have their emotions properly understood and receive a response accordingly.

[0147] Furthermore, the server regularly analyzes customer usage and sentiment history to suggest optimized plans. This allows for continuous service improvements aimed at increasing customer satisfaction.

[0148] When a change in personnel occurs, the server automatically generates handover documents based on past interaction history and emotional history. These documents are sent to the new person in charge's terminal, enabling an efficient handover.

[0149] Through this invention, the system can provide consistent and flexible services that respond to the user's emotions, thereby improving customer satisfaction.

[0150] The following describes the processing flow.

[0151] Step 1:

[0152] Users access the corporate portal and enter inquiries in natural language from their devices.

[0153] Step 2:

[0154] The terminal sends the entered query data to the server. The server receives the query and records the information in its database.

[0155] Step 3:

[0156] The server provides the received query data to the emotion engine. The emotion engine analyzes the user's emotions from the query content.

[0157] Step 4:

[0158] The emotion engine analyzes the emotion data and returns it to the server, which then records this information in a database as an emotion history.

[0159] Step 5:

[0160] The server passes the analyzed sentiment data and query content to the AI ​​agent and instructs it to generate an appropriate response.

[0161] Step 6:

[0162] The AI ​​agent generates user-optimized responses while taking into account the user's emotional state. For example, if the user expresses dissatisfaction, the response will be delivered in gentler language.

[0163] Step 7:

[0164] The server receives the response generated by the AI ​​agent and sends it to the terminal. The terminal then displays this to the user.

[0165] Step 8:

[0166] The server periodically analyzes customer usage and sentiment history, and uses analytical tools to generate new plan suggestions.

[0167] Step 9:

[0168] If the person in charge changes, the server automatically creates handover documents based on past interaction history and emotional history, and sends them to the new person in charge's terminal. The new person in charge reviews the documents on their terminal, enabling an efficient handover.

[0169] (Example 2)

[0170] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0171] In modern information systems, providing responses that take user emotions into consideration is difficult, leading to decreased customer satisfaction. Furthermore, a lack of efficient handover methods when personnel changes occur can impair operational efficiency. While it's necessary to consider user emotions and usage patterns to propose optimal service plans, analyzing these in real time presents a challenge.

[0172] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0173] In this invention, the server includes emotion analysis means for analyzing the user's emotions, response generation means for generating responses based on the emotion analysis results, and analysis means for analyzing the customer's usage trends and emotional history and proposing an optimized service plan. This makes it possible to provide consistent responses that correspond to the user's emotions, enable efficient handover even when the person in charge changes, and improve customer satisfaction.

[0174] "Information processing means" refers to technology for handling information such as inquiries received from users and processing it appropriately according to the purpose.

[0175] "Data aggregation" refers to the technology or method of accumulating received inquiries and information and systematically storing them for later analysis and reference.

[0176] "Artificial intelligence tools" refer to computer-based technologies that have the function of analyzing data and extracting useful information and patterns from it.

[0177] "Emotion analysis means" refers to technology that identifies emotional states from text and voice data entered by users and records them as data.

[0178] "Response generation means" refers to a technology or process for automatically generating the optimal response to a user based on analyzed data and emotional state.

[0179] "Usage trends" refer to information obtained by analyzing data on how customers use services over a long period, including frequency and behavioral patterns.

[0180] An "optimized service plan" refers to the best possible service delivery model for each individual customer, proposed based on their usage patterns and emotional state.

[0181] "Handover support methods" refer to technologies that automatically generate necessary documents based on past interaction history and emotional data in order to ensure efficient handover when the person in charge changes.

[0182] This section describes the embodiments for carrying out the invention. This invention involves a system that automatically generates and provides responses that take into account the user's emotions. The following details how this system is implemented.

[0183] Users access the corporate portal using their devices and input their inquiries in natural language. This inquiry data is sent to the server via the network. The server stores the received data in a database using specialized data aggregation technology. This process is in preparation for subsequent analysis and response generation.

[0184] Upon receiving information, the server activates its sentiment analysis system and begins processing the data. This sentiment analysis system typically uses software that identifies emotions from text content. For example, a commonly used sentiment analysis engine is "natural language understanding software." Through this analysis, emotions such as joy, anger, and sadness are extracted from the text data submitted by the user.

[0185] The results of the emotion analysis are analyzed by artificial intelligence tools, which then consider the optimal response based on the user's emotional state. This process utilizes a "generative AI model," and open-source language models can be used as an example. As a result, the server uses a response generation tool to create a response that takes the user's emotions into consideration and sends it to the terminal. The user then checks the latest information on their terminal and knows that the system understands their emotions and is providing optimal support.

[0186] For example, if a user inquires about a delay in product delivery, the server uses sentiment analysis to identify the user's anger. The generated response would then provide an apology and confirmation, such as, "We sincerely apologize for the delay. We are currently checking the delivery status and will contact you shortly."

[0187] To further support this invention, the following is an example of a relevant prompt: "A user has submitted an inquiry expressing anger about a delivery delay. Please generate an appropriate response to this inquiry." This prompt serves as a starting point for AI-generated responses and helps the system make accurate decisions.

[0188] As described above, this system realizes technology that analyzes user emotions and generates specific and user-friendly responses accordingly. This is expected to improve customer satisfaction.

[0189] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0190] Step 1:

[0191] The user accesses the corporate portal using a terminal and enters an inquiry in natural language. The entered data is sent from the terminal to the server. At this time, the input is natural language text data, and the output is that text data sent to the server. The terminal performs the action of accurately sending the entered inquiry through the user's actions.

[0192] Step 2:

[0193] The server receives queries sent from terminals and records them in its data repository. In this process, the server takes a text-formatted query as input and generates a record to be stored in the database as output. Specifically, the server performs data format validation and storage. This ensures that the query remains permanently accessible.

[0194] Step 3:

[0195] The server processes recorded queries using sentiment analysis tools. This analysis uses query text as input and outputs emotional state data. As a concrete example of sentiment analysis, natural language understanding software is used to identify emotions such as joy, anger, and sadness. The server sends text data to the analysis engine and extracts the emotional data.

[0196] Step 4:

[0197] The server generates a response using artificial intelligence based on the sentiment analysis results. The input in this step is the sentiment analysis result, and the output is the generated response text. This involves utilizing a generative AI model to select words that are sensitive to the user's emotions. For example, if anger is detected, the server constructs a response that includes an apology and suggestions for resolving the problem.

[0198] Step 5:

[0199] The server sends the AI-generated response to the terminal and provides it to the user. The input for this step is the response text, and the output is displayed on the user's terminal. The server performs the action of sending the generated response to the terminal over the network. The user confirms the response through the terminal screen and receives support from the system.

[0200] (Application Example 2)

[0201] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0202] In modern information processing systems, providing uniform responses without understanding user emotions is a factor that detracts from the customer experience. Furthermore, in commercial fields such as electronic payment services, there is a need to flexibly respond to the diverse emotions and needs of customers. Therefore, it is necessary to accurately analyze user emotions and provide appropriate and flexible responses based on that analysis.

[0203] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0204] In this invention, the server includes data processing means for receiving inquiries from users, means for recording the received inquiries in a storage device, artificial intelligence means for performing analysis based on the recorded inquiry time-series data and user information, response formation means for generating a response based on the analysis results, emotion analysis means for analyzing emotions from the language and voice data of the user's inquiry, and emotion response means for designing a flexible and consistent response based on the output of the emotion analysis means. This makes it possible to provide accurate services that are adapted to the user's emotions.

[0205] "Data processing means for receiving inquiries from users" refers to a device that has the function of appropriately receiving inquiries sent by users and inputting their contents into the system.

[0206] "Means for recording received inquiries in a storage device" refers to a device that performs the process of saving the content of inquiries received from users in digital format and systematizing it so that it can be referenced later.

[0207] "An artificial intelligence tool that performs analysis based on recorded inquiry time-series data and user information" refers to software that uses previously accumulated inquiry data and customer data to extract useful patterns and knowledge from that data.

[0208] A "response formation means for generating responses based on analysis results" is a device equipped with the function to automatically generate appropriate content to respond to the user based on analysis results provided by artificial intelligence.

[0209] "An emotion analysis device that analyzes emotions from the language and voice data of inquiries made by users" refers to a device that has the technology to detect and identify a user's emotional state from text and voice information and output the results as analysis data.

[0210] "An emotional response system that designs flexible and consistent responses based on the output of an emotional analysis system" refers to a system that uses the results of emotional analysis to design the most appropriate and emotionally resonant response for the user.

[0211] The system that realizes this invention allows a user to make an inquiry using a smartphone or other mobile device, and the data is immediately sent to a server. The server processes the received data using sentiment analysis engines such as Google® Cloud Natural Language or IBM Watson®, and identifies the emotional state from the user's language and voice data.

[0212] The server passes the analysis results to an AI agent such as OpenAI® GPT, which generates an appropriate response. The response is flexibly adjusted to the user's emotional state in terms of language and tone, and then returned to the terminal. For example, if a user expresses dissatisfaction with an electronic payment service, the system will offer an apology and a solution in a gentle tone.

[0213] The terminal can instantly display the response received from the server to the user. This gives the user a sense of satisfaction, knowing that their feelings are understood and that they receive considerate service. Furthermore, the system can accumulate customer data and usage logs and perform periodic analysis to propose plans optimized for each user.

[0214] For example, if a user expresses concern that "recent payments don't seem to be reflected," the system will quickly perform a verification and generate a response such as, "We have checked your payment information. It is currently being processed by the system, but there are no problems, so please rest assured." An example of a prompt message the system might use in this case is, "Based on the user's latest inquiry and sentiment analysis results, generate a gentle message that will alleviate the user's anxiety."

[0215] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0216] Step 1:

[0217] The user enters a query in natural language using a smartphone or other device and presses the submit button. The device sends this input as data to the server. The input can be text or audio data, which becomes the output to the server.

[0218] Step 2:

[0219] The server inputs the received data into an emotion analysis engine to identify the user's emotions. Using Google Cloud Natural Language and IBM Watson, it analyzes the input data to determine emotional states such as joy, anger, and sadness. The output is information about the user's emotional state.

[0220] Step 3:

[0221] Based on the sentiment analysis output, the server sends data to the AI ​​agent and requests it to generate a response. The AI ​​agent uses the analysis results and the prompt "Based on the user's latest inquiry and sentiment analysis results, please generate a gentle response that will alleviate the user's anxiety" to form an appropriate response. The generated response is then output.

[0222] Step 4:

[0223] The server returns the generated response to the terminal, providing it to the user. The terminal receives the response and displays it on the screen. This allows the user to receive a response that reflects their own emotions. The output is text information presented to the user.

[0224] Step 5:

[0225] The system records query data and sentiment analysis results in a database for later analysis. This recording process saves the data as a time series and is used for future service optimization. The output is the accumulated historical data.

[0226] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0227] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0228] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0229] [Second Embodiment]

[0230] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0231] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0232] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0233] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0234] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0235] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0236] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0237] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0238] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0239] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0240] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0241] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0242] The system of the present invention provides a mechanism for providing efficient and consistent services to corporate clients. This system is implemented in the following form.

[0243] First, the user accesses the corporate portal and enters customer information into the system via a terminal. This information includes company name, contact details, and contract details. The terminal sends this information to the server, which verifies it and then stores it in a database. Based on the stored information, the server assigns a dedicated AI agent to each customer.

[0244] When a user submits an inquiry, they enter their question through a chat window on their device. This inquiry is sent to the server, which receives it and records it in its database. During this process, the inquiry content and related history are all stored chronologically.

[0245] The recorded data is analyzed by the server via an AI agent. The AI ​​agent analyzes the accumulated inquiry history and customer data to generate appropriate responses. For example, if a user asks, "I would like information about the next contract renewal," the AI ​​agent will generate a specific suggestion such as, "The next renewal date is on XX day, and △△ is available as a benefit." This response is sent from the server to the terminal and displayed to the user.

[0246] The server also periodically analyzes customer usage and suggests optimized account plans. These suggestions, generated by the AI ​​agent, are delivered to the user's device by the server, allowing them to review and update them as needed.

[0247] Furthermore, the server automatically determines the need for a handover when the person in charge changes and uses an AI agent to create handover documents. These documents are generated based on past interaction history and can be viewed by the new person in charge on their terminal. This reduces the burden during the handover and ensures consistent service delivery.

[0248] By implementing this system, it becomes possible to consistently provide customers with high-quality and stable service, leading to improved customer satisfaction.

[0249] The following describes the processing flow.

[0250] Step 1:

[0251] The user accesses the corporate portal and enters the company name, contact information, and contract details as a new customer via their device.

[0252] Step 2:

[0253] The terminal sends the entered customer information to the server. The server receives this information and verifies its accuracy.

[0254] Step 3:

[0255] After verification by the server, customer information is saved to the database. At this point, a dedicated AI agent is assigned to the customer.

[0256] Step 4:

[0257] Users enter inquiries through the corporate portal's chat window. For example, they might ask, "I'd like to know about my current contract plan."

[0258] Step 5:

[0259] The terminal sends the user's query to the server. The server receives the query, adds a timestamp, and records it in the database.

[0260] Step 6:

[0261] The server provides the AI ​​agent with the inquiry details and past history, and instructs it to perform the analysis.

[0262] Step 7:

[0263] The AI ​​agent analyzes the provided information and generates an appropriate response. For example, specific information such as, "Your current contract is the XX plan."

[0264] Step 8:

[0265] The server sends the generated response to the terminal. The terminal receives it and displays it to the user.

[0266] Step 9:

[0267] The server periodically analyzes customer usage patterns and has an AI agent generate optimized plan suggestions.

[0268] Step 10:

[0269] The AI ​​agent generates a plan and returns it to the server, which then provides the suggestion to the user's device. The user reviews the suggestion and updates the plan as needed.

[0270] Step 11:

[0271] If a change in personnel occurs, the server will have the AI ​​agent create handover documents based on past interaction history.

[0272] Step 12:

[0273] The server sends the generated handover documents to the new person in charge's terminal. The terminal displays the documents and assists the person in charge in starting their work based on them.

[0274] (Example 1)

[0275] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0276] Customer management systems are required to improve the efficiency of handling inquiries, propose optimal plans based on customer usage, and provide consistent service. However, conventional systems have shortcomings in effectively managing inquiry history and automated handover procedures, resulting in insufficient customer satisfaction.

[0277] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0278] In this invention, the server includes information processing means for receiving inquiries from users, means for recording the received inquiries in a data aggregate in chronological order, and artificial intelligence means using a generative AI model for performing analysis based on the recorded inquiry history and customer data. As a result, it becomes possible to improve the efficiency of inquiry response and realize appropriate account proposals and automated handover procedures for each customer.

[0279] The "information processing means for receiving inquiries from users" is a function of a device or software for receiving inquiries input by users into the system.

[0280] The "means for recording in a data aggregate in chronological order" is a device or method for sequentially storing the received information in a database or the like along the order of time.

[0281] The "artificial intelligence means using a generative AI model" is a system and technology for performing data analysis and decision-making by utilizing a machine learning model.

[0282] The "response generation means" is a process or device for creating a response to the user based on the results analyzed by artificial intelligence.

[0283] The "communication means" is a technology or device for transmitting and receiving data via a computer network.

[0284] The "means for transmitting customer information to the server and assigning a dedicated agent" is a function for delivering customer data to the central server and then automatically arranging an artificial intelligence dedicated to each customer by a specific program.

[0285] The "analysis means for analyzing usage history and generating optimized account proposals" is a technology or system for evaluating past usage data and proposing a service plan optimal for the user's needs.

[0286] The "transfer document generation means for automatically generating transfer documents" is a device or method for automatically creating materials for a new person in charge to take over work based on the past response history when the person in charge changes.

[0287] Embodiments for Carrying Out the Invention

[0288] The present invention is a system for providing efficient and consistent customer service for corporations. Specific embodiments are shown below.

[0289] User

[0290] The user accesses the corporate dedicated portal using a browser and inputs customer information such as company name, contact information, and contract details on the terminal. Thereby, the user can directly provide the information of their own company to the service provider.

[0291] Terminal

[0292] The terminal is a means for transmitting the input data to the server. The terminal temporarily stores the data in the memory and then transfers it to the server via the Internet. In this process, the terminal also performs a simple verification of the format to correctly maintain the data format.

[0293] Server

[0294] The server processes the customer information and inquiries received from the terminal and stores them in the database. Specifically, it uses PostgreSQL to manage the data. Also, the server utilizes an open-source model widely used as a generative AI model to analyze the inquiry history and customer data. Thereby, highly accurate analysis based on the history information is possible.

[0295] When there is an inquiry from the user, the server receives it, stores it in the database, then analyzes the content using AI, and generates an appropriate response. This generated response is transferred back to the terminal using the communication means, and the user can confirm it.

[0296] Specific example

[0297] For example, a user might enter an inquiry into the chat system such as, "Please tell me my next contract renewal date." This inquiry is sent to the server and analyzed by an AI agent. As a result, the server generates a response such as, "Your next renewal date is on XX day, and you can use △△ as a benefit," and sends this to the user's device.

[0298] Examples of prompt messages include, "Please provide detailed information regarding contract renewal." In this way, it is possible to respond quickly to customer needs and improve satisfaction. This system enables consistent customer service and efficient information management.

[0299] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0300] Step 1:

[0301] Users access the corporate portal and enter customer information such as company name, contact details, and contract details into the terminal. The entered data is temporarily stored in the terminal's memory. The input here consists of strings and numbers based on a form, and the terminal performs a simple verification to ensure the information format is correct.

[0302] Step 2:

[0303] The terminal sends verified customer information to the server. During transmission, the data is converted into packet format and transferred to the server using the Internet Protocol. This process enables real-time online data transmission.

[0304] Step 3:

[0305] The server captures the data received from the terminal and stores it in the database. The server converts the data into the correct format and performs the save process to the PostgreSQL database. As a result, the data can be easily searched and managed later.

[0306] Step 4:

[0307] When the user makes an inquiry through the chat window of the terminal, the inquiry is sent to the server. For example, enter a prompt sentence such as "Please tell me the next contract renewal date". This data is sent to the server in string format.

[0308] Step 5:

[0309] The server receives the inquiry and records it in the database. Then, the server analyzes the inquiry content using a generative AI model. The AI model executes an algorithm that refers to past history data and generates an optimal response to the input prompt.

[0310] Step 6:

[0311] The server constructs the generated response and sends this response to the terminal via the communication means. This output is a text message formatted so that the user can easily understand it. The terminal receives this data and prepares to display it to the user.

[0312] Step 7:

[0313] The server periodically analyzes the customer's usage history and generates an optimized account plan. Using the past usage patterns as a reference, AI performs the analysis and generates the proposed content. This proposes options that allow the user to use the service more efficiently.

[0314] Step 8:

[0315] When a person in charge changes, the server automatically analyzes past history information to generate a handover document. This generated document can be easily viewed by the new person in charge on their terminal, helping to maintain consistency in operations.

[0316] (Application Example 1)

[0317] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0318] In services targeting corporate clients, there is a demand for efficient and consistent responses. However, existing systems often result in fragmented customer support and a lack of service consistency. Furthermore, the difficulty in handling inquiries in real time and analyzing large amounts of customer data hinders improvements in customer satisfaction. In addition, manual handover is required when personnel changes occur, leading to delays and errors in responses.

[0319] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0320] In this invention, the server includes data processing means for receiving inquiries from users, a function for recording received inquiries in a storage device, and a function for referencing business information of corporate customers and providing electronic transaction information. This makes it possible to provide efficient and consistent services to corporate customers, improve customer satisfaction, and reduce the effort required for handover.

[0321] A "user" is an entity that utilizes the system as a corporate customer.

[0322] An "inquiry" is a question or request that a user sends to a system seeking information or support.

[0323] "Data processing means" refers to functions and technologies for receiving inquiries from users and performing the necessary processing.

[0324] A "storage device" is a storage medium used to save received inquiries and historical data.

[0325] "Historical data" refers to records that include the user's past inquiries and transaction information.

[0326] "Artificial intelligence functionality" refers to technology that analyzes historical data and customer information, and uses the insights gained from this analysis to generate responses.

[0327] The "reply generation function" is a technology that constructs the optimal response to provide to the user based on the analysis results.

[0328] "System functionality" refers to a set of technologies and processes that enable the system to operate as a whole and provide services to users.

[0329] A "corporate customer" is a company or business entity that benefits from the service and uses the system.

[0330] "Business information" refers to data related to the activities and transactions of corporate clients.

[0331] "Electronic transaction information" refers to transaction-related information that is managed and provided in digital format.

[0332] "Real-time communication function" refers to communication technology that enables the instantaneous transmission and reception of information.

[0333] The system for realizing this invention utilizes specific technologies and programs to provide efficient and consistent services to corporate clients. The system receives data from users (corporate clients), and a server stores this information in a central database. Users can submit inquiries using a smartphone app, and the information is processed in real time. This app is developed using React Native and provides a user-friendly interface.

[0334] The server is based on Node.js and uses MongoDB to store all customer information and history in its database. This ensures efficient data management and quick access when needed. Socket.io technology is implemented for real-time communication, allowing user inquiries to be registered and processed instantly.

[0335] The AI ​​analysis uses an AI agent built in a Python environment. This agent analyzes the user's history and inquiries using the natural language processing libraries NLTK and spaCy. Based on the analysis results, the AI ​​agent generates the optimal response for the user and provides it to the user through the server.

[0336] For example, if a corporate client's representative wants to check the details of their next payment or discount information, the AI ​​agent will provide specific information such as, "The next payment date is △ / △, and the available discounts are ○○." This allows users to efficiently obtain information and also provides a quick response to their individual needs.

[0337] An example of a prompt message would be, "Please provide the next payment date and available discount information for corporate customer XYZ." The AI ​​understands the user's needs and generates a response. This ensures that the information the user needs is provided in a timely manner.

[0338] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0339] Step 1:

[0340] Users enter their inquiries using a smartphone app. The entered data is sent from the device to the server via real-time communication. Socket.io is used for this process, and the data is compressed for efficient transmission.

[0341] Step 2:

[0342] The server decompresses the received query data and saves it to a MongoDB database using Node.js. This data is timestamped and stored as a query history. The database organizes information based on customer IDs and is managed for easy access.

[0343] Step 3:

[0344] The server detects that a query has been saved and invokes an AI agent running in a Python environment. The AI ​​agent takes the saved historical data and the current query data as input. The AI ​​agent analyzes the data using the natural language processing libraries NLTK and spaCy to understand the user's intent.

[0345] Step 4:

[0346] Based on the analysis results, the AI ​​agent generates the optimal response. For example, if the prompt "Please provide the next payment date and available discount information for corporate customer XYZ" is entered, the AI ​​agent will retrieve the necessary information from its internal data and generate a specific response such as "The next payment date is △ / △, and the available discounts are ○○."

[0347] Step 5:

[0348] The generated response is sent to the terminal via the server. The user receives this response in real time through a smartphone app, and it is displayed on the screen. This allows the user to obtain information quickly and accurately.

[0349] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0350] The system of this invention provides sophisticated responses that incorporate user sentiment analysis in order to deliver consistent service to corporate clients. This system is implemented in the following form.

[0351] First, the user accesses the corporate portal and enters an inquiry into the system from their terminal. This inquiry often includes questions or requests in natural language, and the terminal sends this information to the server. The server verifies the received information and records it in a database. This allows the inquiry history to be managed chronologically.

[0352] Next, the server uses an emotion engine to analyze the emotions expressed in the user's text and voice inquiries. The emotion engine identifies emotions such as joy, anger, and sadness, and records the emotional state in a database. This also allows for the management of the user's emotional history.

[0353] The server receives the analysis results from the emotion engine and provides the AI ​​agent with the analysis results and emotion data. Based on this information, the AI ​​agent generates the optimal response. For example, if a user expresses dissatisfaction with the contract terms, the AI ​​agent will generate an emotion-sensitive response such as, "We have prepared specific suggestions for improvement." Depending on the emotion, it may also adopt softer language or a more respectful communication style.

[0354] The generated response is sent from the server to the terminal and displayed to the user. This allows the user to have their emotions properly understood and receive a response accordingly.

[0355] Furthermore, the server regularly analyzes customer usage and sentiment history to suggest optimized plans. This allows for continuous service improvements aimed at increasing customer satisfaction.

[0356] When a change in personnel occurs, the server automatically generates handover documents based on past interaction history and emotional history. These documents are sent to the new person in charge's terminal, enabling an efficient handover.

[0357] Through this invention, the system can provide consistent and flexible services that respond to the user's emotions, thereby improving customer satisfaction.

[0358] The following describes the processing flow.

[0359] Step 1:

[0360] Users access the corporate portal and enter inquiries in natural language from their devices.

[0361] Step 2:

[0362] The terminal sends the entered query data to the server. The server receives the query and records the information in its database.

[0363] Step 3:

[0364] The server provides the received query data to the emotion engine. The emotion engine analyzes the user's emotions from the query content.

[0365] Step 4:

[0366] The emotion engine analyzes the emotion data and returns it to the server, which then records this information in a database as an emotion history.

[0367] Step 5:

[0368] The server passes the analyzed sentiment data and query content to the AI ​​agent and instructs it to generate an appropriate response.

[0369] Step 6:

[0370] The AI ​​agent generates user-optimized responses while taking into account the user's emotional state. For example, if the user expresses dissatisfaction, the response will be delivered in gentler language.

[0371] Step 7:

[0372] The server receives the response generated by the AI ​​agent and sends it to the terminal. The terminal then displays this to the user.

[0373] Step 8:

[0374] The server periodically analyzes customer usage and sentiment history, and uses analytical tools to generate new plan suggestions.

[0375] Step 9:

[0376] If the person in charge changes, the server automatically creates handover documents based on past interaction history and emotional history, and sends them to the new person in charge's terminal. The new person in charge reviews the documents on their terminal, enabling an efficient handover.

[0377] (Example 2)

[0378] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0379] In modern information systems, providing responses that take user emotions into consideration is difficult, leading to decreased customer satisfaction. Furthermore, a lack of efficient handover methods when personnel changes occur can impair operational efficiency. While it's necessary to consider user emotions and usage patterns to propose optimal service plans, analyzing these in real time presents a challenge.

[0380] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0381] In this invention, the server includes emotion analysis means for analyzing the user's emotions, response generation means for generating responses based on the emotion analysis results, and analysis means for analyzing the customer's usage trends and emotional history and proposing an optimized service plan. This makes it possible to provide consistent responses that correspond to the user's emotions, enable efficient handover even when the person in charge changes, and improve customer satisfaction.

[0382] "Information processing means" refers to technology for handling information such as inquiries received from users and processing it appropriately according to the purpose.

[0383] "Data aggregation" refers to the technology or method of accumulating received inquiries and information and systematically storing them for later analysis and reference.

[0384] "Artificial intelligence tools" refer to computer-based technologies that have the function of analyzing data and extracting useful information and patterns from it.

[0385] "Emotion analysis means" refers to technology that identifies emotional states from text and voice data entered by users and records them as data.

[0386] "Response generation means" refers to a technology or process for automatically generating the optimal response to a user based on analyzed data and emotional state.

[0387] "Usage trends" refer to information obtained by analyzing data on how customers use services over a long period, including frequency and behavioral patterns.

[0388] An "optimized service plan" refers to the best possible service delivery model for each individual customer, proposed based on their usage patterns and emotional state.

[0389] "Handover support methods" refer to technologies that automatically generate necessary documents based on past interaction history and emotional data in order to ensure efficient handover when the person in charge changes.

[0390] This section describes the embodiments for carrying out the invention. This invention involves a system that automatically generates and provides responses that take into account the user's emotions. The following details how this system is implemented.

[0391] Users access the corporate portal using their devices and input their inquiries in natural language. This inquiry data is sent to the server via the network. The server stores the received data in a database using specialized data aggregation technology. This process is in preparation for subsequent analysis and response generation.

[0392] Upon receiving information, the server activates its sentiment analysis system and begins processing the data. This sentiment analysis system typically uses software that identifies emotions from text content. For example, a commonly used sentiment analysis engine is "natural language understanding software." Through this analysis, emotions such as joy, anger, and sadness are extracted from the text data submitted by the user.

[0393] The results of the emotion analysis are analyzed by artificial intelligence tools, which then consider the optimal response based on the user's emotional state. This process utilizes a "generative AI model," and open-source language models can be used as an example. As a result, the server uses a response generation tool to create a response that takes the user's emotions into consideration and sends it to the terminal. The user then checks the latest information on their terminal and knows that the system understands their emotions and is providing optimal support.

[0394] For example, if a user inquires about a delay in product delivery, the server uses sentiment analysis to identify the user's anger. The generated response would then provide an apology and confirmation, such as, "We sincerely apologize for the delay. We are currently checking the delivery status and will contact you shortly."

[0395] To further support this invention, the following is an example of a relevant prompt: "A user has submitted an inquiry expressing anger about a delivery delay. Please generate an appropriate response to this inquiry." This prompt serves as a starting point for AI-generated responses and helps the system make accurate decisions.

[0396] As described above, this system realizes technology that analyzes user emotions and generates specific and user-friendly responses accordingly. This is expected to improve customer satisfaction.

[0397] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0398] Step 1:

[0399] The user accesses the corporate portal using a terminal and enters an inquiry in natural language. The entered data is sent from the terminal to the server. At this time, the input is natural language text data, and the output is that text data sent to the server. The terminal performs the action of accurately sending the entered inquiry through the user's actions.

[0400] Step 2:

[0401] The server receives queries sent from terminals and records them in its data repository. In this process, the server takes a text-formatted query as input and generates a record to be stored in the database as output. Specifically, the server performs data format validation and storage. This ensures that the query remains permanently accessible.

[0402] Step 3:

[0403] The server processes recorded queries using sentiment analysis tools. This analysis uses query text as input and outputs emotional state data. As a concrete example of sentiment analysis, natural language understanding software is used to identify emotions such as joy, anger, and sadness. The server sends text data to the analysis engine and extracts the emotional data.

[0404] Step 4:

[0405] The server generates a response using artificial intelligence based on the sentiment analysis results. The input in this step is the sentiment analysis result, and the output is the generated response text. This involves utilizing a generative AI model to select words that are sensitive to the user's emotions. For example, if anger is detected, the server constructs a response that includes an apology and suggestions for resolving the problem.

[0406] Step 5:

[0407] The server sends the AI-generated response to the terminal and provides it to the user. The input for this step is the response text, and the output is displayed on the user's terminal. The server performs the action of sending the generated response to the terminal over the network. The user confirms the response through the terminal screen and receives support from the system.

[0408] (Application Example 2)

[0409] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0410] In modern information processing systems, providing uniform responses without understanding user emotions is a factor that detracts from the customer experience. Furthermore, in commercial fields such as electronic payment services, there is a need to flexibly respond to the diverse emotions and needs of customers. Therefore, it is necessary to accurately analyze user emotions and provide appropriate and flexible responses based on that analysis.

[0411] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0412] In this invention, the server includes data processing means for receiving inquiries from users, means for recording the received inquiries in a storage device, artificial intelligence means for performing analysis based on the recorded inquiry time-series data and user information, response formation means for generating a response based on the analysis results, emotion analysis means for analyzing emotions from the language and voice data of the user's inquiry, and emotion response means for designing a flexible and consistent response based on the output of the emotion analysis means. This makes it possible to provide accurate services that are adapted to the user's emotions.

[0413] "Data processing means for receiving inquiries from users" refers to a device that has the function of appropriately receiving inquiries sent by users and inputting their contents into the system.

[0414] "Means for recording received inquiries in a storage device" refers to a device that performs the process of saving the content of inquiries received from users in digital format and systematizing it so that it can be referenced later.

[0415] "An artificial intelligence tool that performs analysis based on recorded inquiry time-series data and user information" refers to software that uses previously accumulated inquiry data and customer data to extract useful patterns and knowledge from that data.

[0416] A "response formation means for generating responses based on analysis results" is a device equipped with the function to automatically generate appropriate content to respond to the user based on analysis results provided by artificial intelligence.

[0417] "An emotion analysis device that analyzes emotions from the language and voice data of inquiries made by users" refers to a device that has the technology to detect and identify a user's emotional state from text and voice information and output the results as analysis data.

[0418] "An emotional response system that designs flexible and consistent responses based on the output of an emotional analysis system" refers to a system that uses the results of emotional analysis to design the most appropriate and emotionally resonant response for the user.

[0419] The system that realizes this invention allows a user to make an inquiry using a smartphone or other mobile device, and that data is immediately sent to a server. The server processes the received data using sentiment analysis engines such as Google Cloud Natural Language or IBM Watson, and identifies the emotional state from the user's language and voice data.

[0420] The server passes the analysis results to an AI agent such as OpenAI GPT, which generates an appropriate response. The response is flexibly adjusted to the user's emotional state in terms of wording and tone, and then returned to the terminal. For example, if a user expresses dissatisfaction with an electronic payment service, the system will offer an apology and a solution in a gentle tone.

[0421] The terminal can instantly display the response received from the server to the user. This gives the user a sense of satisfaction, knowing that their feelings are understood and that they receive considerate service. Furthermore, the system can accumulate customer data and usage logs and perform periodic analysis to propose plans optimized for each user.

[0422] For example, if a user expresses concern that "recent payments don't seem to be reflected," the system will quickly perform a verification and generate a response such as, "We have checked your payment information. It is currently being processed by the system, but there are no problems, so please rest assured." An example of a prompt message the system might use in this case is, "Based on the user's latest inquiry and sentiment analysis results, generate a gentle message that will alleviate the user's anxiety."

[0423] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0424] Step 1:

[0425] The user enters a query in natural language using a smartphone or other device and presses the submit button. The device sends this input as data to the server. The input can be text or audio data, which becomes the output to the server.

[0426] Step 2:

[0427] The server inputs the received data into an emotion analysis engine to identify the user's emotions. Using Google Cloud Natural Language and IBM Watson, it analyzes the input data to determine emotional states such as joy, anger, and sadness. The output is information about the user's emotional state.

[0428] Step 3:

[0429] Based on the sentiment analysis output, the server sends data to the AI ​​agent and requests it to generate a response. The AI ​​agent uses the analysis results and the prompt "Based on the user's latest inquiry and sentiment analysis results, please generate a gentle response that will alleviate the user's anxiety" to form an appropriate response. The generated response is then output.

[0430] Step 4:

[0431] The server returns the generated response to the terminal, providing it to the user. The terminal receives the response and displays it on the screen. This allows the user to receive a response that reflects their own emotions. The output is text information presented to the user.

[0432] Step 5:

[0433] The system records query data and sentiment analysis results in a database for later analysis. This recording process saves the data as a time series and is used for future service optimization. The output is the accumulated historical data.

[0434] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0435] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0436] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0437] [Third Embodiment]

[0438] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0439] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0440] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0441] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0442] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0443] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0444] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0445] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0446] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0447] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0448] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0449] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0450] The system of the present invention provides a mechanism for providing efficient and consistent services to corporate clients. This system is implemented in the following form.

[0451] First, the user accesses the corporate portal and enters customer information into the system via a terminal. This information includes company name, contact details, and contract details. The terminal sends this information to the server, which verifies it and then stores it in a database. Based on the stored information, the server assigns a dedicated AI agent to each customer.

[0452] When a user submits an inquiry, they enter their question through a chat window on their device. This inquiry is sent to the server, which receives it and records it in its database. During this process, the inquiry content and related history are all stored chronologically.

[0453] The recorded data is analyzed by the server via an AI agent. The AI ​​agent analyzes the accumulated inquiry history and customer data to generate appropriate responses. For example, if a user asks, "I would like information about the next contract renewal," the AI ​​agent will generate a specific suggestion such as, "The next renewal date is on XX day, and △△ is available as a benefit." This response is sent from the server to the terminal and displayed to the user.

[0454] The server also periodically analyzes customer usage and suggests optimized account plans. These suggestions, generated by the AI ​​agent, are delivered to the user's device by the server, allowing them to review and update them as needed.

[0455] Furthermore, the server automatically determines the need for a handover when the person in charge changes and uses an AI agent to create handover documents. These documents are generated based on past interaction history and can be viewed by the new person in charge on their terminal. This reduces the burden during the handover and ensures consistent service delivery.

[0456] By implementing this system, it becomes possible to consistently provide customers with high-quality and stable service, leading to improved customer satisfaction.

[0457] The following describes the processing flow.

[0458] Step 1:

[0459] The user accesses the corporate portal and enters the company name, contact information, and contract details as a new customer via their device.

[0460] Step 2:

[0461] The terminal sends the entered customer information to the server. The server receives this information and verifies its accuracy.

[0462] Step 3:

[0463] After verification by the server, customer information is saved to the database. At this point, a dedicated AI agent is assigned to the customer.

[0464] Step 4:

[0465] Users enter inquiries through the corporate portal's chat window. For example, they might ask, "I'd like to know about my current contract plan."

[0466] Step 5:

[0467] The terminal sends the user's query to the server. The server receives the query, adds a timestamp, and records it in the database.

[0468] Step 6:

[0469] The server provides the AI ​​agent with the inquiry details and past history, and instructs it to perform the analysis.

[0470] Step 7:

[0471] The AI ​​agent analyzes the provided information and generates an appropriate response. For example, specific information such as, "Your current contract is the XX plan."

[0472] Step 8:

[0473] The server sends the generated response to the terminal. The terminal receives it and displays it to the user.

[0474] Step 9:

[0475] The server periodically analyzes customer usage patterns and has an AI agent generate optimized plan suggestions.

[0476] Step 10:

[0477] The AI ​​agent generates a plan and returns it to the server, which then provides the suggestion to the user's device. The user reviews the suggestion and updates the plan as needed.

[0478] Step 11:

[0479] If a change in personnel occurs, the server will have the AI ​​agent create handover documents based on past interaction history.

[0480] Step 12:

[0481] The server sends the generated handover documents to the new person in charge's terminal. The terminal displays the documents and assists the person in charge in starting their work based on them.

[0482] (Example 1)

[0483] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0484] Customer management systems are required to improve the efficiency of handling inquiries, propose optimal plans based on customer usage, and provide consistent service. However, conventional systems have shortcomings in effectively managing inquiry history and automated handover procedures, resulting in insufficient customer satisfaction.

[0485] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0486] In this invention, the server includes information processing means for receiving inquiries from users, means for recording the received inquiries in a data set in chronological order, and artificial intelligence means utilizing a generative AI model that performs analysis based on the recorded inquiry history and customer data. This makes it possible to streamline inquiry handling and realize appropriate account proposals and automated handover procedures for each customer.

[0487] "Information processing means for receiving inquiries from users" refers to the functions of devices or software that receive inquiries entered by users into the system.

[0488] "Means of recording data in a time-series format" refers to devices or methods for sequentially saving received information in a database or similar format according to chronological order.

[0489] "Artificial intelligence methods using generative AI models" refer to systems and technologies that utilize machine learning models to perform data analysis and decision-making.

[0490] A "response generation means" refers to a process or device for creating a response to a user based on the results analyzed by artificial intelligence.

[0491] "Communication methods" refer to technologies and devices for sending and receiving data via computer networks.

[0492] "A means of sending customer information to a server and assigning a dedicated agent" refers to a function that delivers customer data to a central server, after which a specific program automatically assigns a dedicated artificial intelligence to each customer.

[0493] "Analytics that analyze usage history and generate optimized account suggestions" refers to technologies and systems that evaluate past usage data and propose service plans that are best suited to the user's needs.

[0494] A "handover document generation means for automatically generating handover documents" is a device or method for automatically creating documents for a new person to take over work based on past interaction history when a person in charge changes.

[0495] Modes for carrying out the invention

[0496] This invention is a system for providing efficient and consistent customer service to corporate clients. Specific embodiments are described below.

[0497] User

[0498] Users access a dedicated corporate portal using a browser and enter customer information such as company name, contact details, and contract details on their device. This allows users to directly provide their company information to service providers.

[0499] terminal

[0500] The terminal is a means of sending input data to the server. The terminal temporarily stores the data in memory and then transfers it to the server via the internet. In this process, the terminal also performs a simple format check to ensure that the data format is correct.

[0501] server

[0502] The server processes customer information and inquiries received from terminals and stores them in a database. Specifically, it uses PostgreSQL to manage the data. The server also utilizes widely used open-source models as generative AI models to analyze inquiry history and customer data. This enables highly accurate analysis based on historical information.

[0503] When a user submits an inquiry, the server receives it, stores it in a database, and then uses AI to analyze the content and generate an appropriate response. This generated response is then forwarded back to the terminal using a communication method, allowing the user to review it.

[0504] Specific example

[0505] For example, a user might enter an inquiry into the chat system such as, "Please tell me my next contract renewal date." This inquiry is sent to the server and analyzed by an AI agent. As a result, the server generates a response such as, "Your next renewal date is on XX day, and you can use △△ as a benefit," and sends this to the user's device.

[0506] Examples of prompt messages include, "Please provide detailed information regarding contract renewal." In this way, it is possible to respond quickly to customer needs and improve satisfaction. This system enables consistent customer service and efficient information management.

[0507] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0508] Step 1:

[0509] Users access the corporate portal and enter customer information such as company name, contact details, and contract details into the terminal. The entered data is temporarily stored in the terminal's memory. The input here consists of strings and numbers based on a form, and the terminal performs a simple verification to ensure the information format is correct.

[0510] Step 2:

[0511] The terminal sends verified customer information to the server. During transmission, the data is converted into packet format and transferred to the server using the Internet Protocol. This process enables real-time online data transmission.

[0512] Step 3:

[0513] The server receives data from the terminal and stores it in the database. The server converts the data to the correct format and saves it to the PostgreSQL database. This makes the data easily searchable and manageable later.

[0514] Step 4:

[0515] When a user submits an inquiry through the chat window on their device, that inquiry is sent to the server. For example, they might enter a prompt message such as, "Please tell me my next contract renewal date." This data is sent to the server in string format.

[0516] Step 5:

[0517] The server receives the query and records it in the database. Then, it analyzes the query content using a generating AI model. The AI ​​model refers to historical data and executes an algorithm to generate the optimal response to the input prompt.

[0518] Step 6:

[0519] The server constructs the generated response and sends it to the terminal via the communication means. This output is a text message formatted for user understanding. The terminal receives this data and prepares to display it to the user.

[0520] Step 7:

[0521] The server periodically analyzes customer usage history and generates optimized account plans. Based on past usage patterns, AI analyzes and generates recommendations, suggesting options that allow users to utilize the service more efficiently.

[0522] Step 8:

[0523] When a person in charge changes, the server automatically analyzes past history information to generate a handover document. This generated document can be easily viewed by the new person in charge on their terminal, helping to maintain consistency in operations.

[0524] (Application Example 1)

[0525] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0526] In services targeting corporate clients, there is a demand for efficient and consistent responses. However, existing systems often result in fragmented customer support and a lack of service consistency. Furthermore, the difficulty in handling inquiries in real time and analyzing large amounts of customer data hinders improvements in customer satisfaction. In addition, manual handover is required when personnel changes occur, leading to delays and errors in responses.

[0527] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0528] In this invention, the server includes data processing means for receiving inquiries from users, a function for recording received inquiries in a storage device, and a function for referencing business information of corporate customers and providing electronic transaction information. This makes it possible to provide efficient and consistent services to corporate customers, improve customer satisfaction, and reduce the effort required for handover.

[0529] A "user" is an entity that utilizes the system as a corporate customer.

[0530] An "inquiry" is a question or request that a user sends to a system seeking information or support.

[0531] "Data processing means" refers to functions and technologies for receiving inquiries from users and performing the necessary processing.

[0532] A "storage device" is a storage medium used to save received inquiries and historical data.

[0533] "Historical data" refers to records that include the user's past inquiries and transaction information.

[0534] "Artificial intelligence functionality" refers to technology that analyzes historical data and customer information, and uses the insights gained from this analysis to generate responses.

[0535] The "reply generation function" is a technology that constructs the optimal response to provide to the user based on the analysis results.

[0536] "System functionality" refers to a set of technologies and processes that enable the system to operate as a whole and provide services to users.

[0537] A "corporate customer" is a company or business entity that benefits from the service and uses the system.

[0538] "Business information" refers to data related to the activities and transactions of corporate clients.

[0539] "Electronic transaction information" refers to transaction-related information that is managed and provided in digital format.

[0540] "Real-time communication function" refers to communication technology that enables the instantaneous transmission and reception of information.

[0541] The system for realizing this invention utilizes specific technologies and programs to provide efficient and consistent services to corporate clients. The system receives data from users (corporate clients), and a server stores this information in a central database. Users can submit inquiries using a smartphone app, and the information is processed in real time. This app is developed using React Native and provides a user-friendly interface.

[0542] The server is based on Node.js and uses MongoDB to store all customer information and history in its database. This ensures efficient data management and quick access when needed. Socket.io technology is implemented for real-time communication, allowing user inquiries to be registered and processed instantly.

[0543] The AI ​​analysis uses an AI agent built in a Python environment. This agent analyzes the user's history and inquiries using the natural language processing libraries NLTK and spaCy. Based on the analysis results, the AI ​​agent generates the optimal response for the user and provides it to the user through the server.

[0544] For example, if a corporate client's representative wants to check the details of their next payment or discount information, the AI ​​agent will provide specific information such as, "The next payment date is △ / △, and the available discounts are ○○." This allows users to efficiently obtain information and also provides a quick response to their individual needs.

[0545] An example of a prompt message would be, "Please provide the next payment date and available discount information for corporate customer XYZ." The AI ​​understands the user's needs and generates a response. This ensures that the information the user needs is provided in a timely manner.

[0546] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0547] Step 1:

[0548] Users enter their inquiries using a smartphone app. The entered data is sent from the device to the server via real-time communication. Socket.io is used for this process, and the data is compressed for efficient transmission.

[0549] Step 2:

[0550] The server decompresses the received query data and saves it to a MongoDB database using Node.js. This data is timestamped and stored as a query history. The database organizes information based on customer IDs and is managed for easy access.

[0551] Step 3:

[0552] The server detects that a query has been saved and invokes an AI agent running in a Python environment. The AI ​​agent takes the saved historical data and the current query data as input. The AI ​​agent analyzes the data using the natural language processing libraries NLTK and spaCy to understand the user's intent.

[0553] Step 4:

[0554] Based on the analysis results, the AI ​​agent generates the optimal response. For example, if the prompt "Please provide the next payment date and available discount information for corporate customer XYZ" is entered, the AI ​​agent will retrieve the necessary information from its internal data and generate a specific response such as "The next payment date is △ / △, and the available discounts are ○○."

[0555] Step 5:

[0556] The generated response is sent to the terminal via the server. The user receives this response in real time through a smartphone app, and it is displayed on the screen. This allows the user to obtain information quickly and accurately.

[0557] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0558] The system of this invention provides sophisticated responses that incorporate user sentiment analysis in order to deliver consistent service to corporate clients. This system is implemented in the following form.

[0559] First, the user accesses the corporate portal and enters an inquiry into the system from their terminal. This inquiry often includes questions or requests in natural language, and the terminal sends this information to the server. The server verifies the received information and records it in a database. This allows the inquiry history to be managed chronologically.

[0560] Next, the server uses an emotion engine to analyze the emotions expressed in the user's text and voice inquiries. The emotion engine identifies emotions such as joy, anger, and sadness, and records the emotional state in a database. This also allows for the management of the user's emotional history.

[0561] The server receives the analysis results from the emotion engine and provides the AI ​​agent with the analysis results and emotion data. Based on this information, the AI ​​agent generates the optimal response. For example, if a user expresses dissatisfaction with the contract terms, the AI ​​agent will generate an emotion-sensitive response such as, "We have prepared specific suggestions for improvement." Depending on the emotion, it may also adopt softer language or a more respectful communication style.

[0562] The generated response is sent from the server to the terminal and displayed to the user. This allows the user to have their emotions properly understood and receive a response accordingly.

[0563] Furthermore, the server regularly analyzes customer usage and sentiment history to suggest optimized plans. This allows for continuous service improvements aimed at increasing customer satisfaction.

[0564] When a change in personnel occurs, the server automatically generates handover documents based on past interaction history and emotional history. These documents are sent to the new person in charge's terminal, enabling an efficient handover.

[0565] Through this invention, the system can provide consistent and flexible services that respond to the user's emotions, thereby improving customer satisfaction.

[0566] The following describes the processing flow.

[0567] Step 1:

[0568] Users access the corporate portal and enter inquiries in natural language from their devices.

[0569] Step 2:

[0570] The terminal sends the entered query data to the server. The server receives the query and records the information in its database.

[0571] Step 3:

[0572] The server provides the received query data to the emotion engine. The emotion engine analyzes the user's emotions from the query content.

[0573] Step 4:

[0574] The emotion engine analyzes the emotion data and returns it to the server, which then records this information in a database as an emotion history.

[0575] Step 5:

[0576] The server passes the analyzed sentiment data and query content to the AI ​​agent and instructs it to generate an appropriate response.

[0577] Step 6:

[0578] The AI ​​agent generates user-optimized responses while taking into account the user's emotional state. For example, if the user expresses dissatisfaction, the response will be delivered in gentler language.

[0579] Step 7:

[0580] The server receives the response generated by the AI ​​agent and sends it to the terminal. The terminal then displays this to the user.

[0581] Step 8:

[0582] The server periodically analyzes customer usage and sentiment history, and uses analytical tools to generate new plan suggestions.

[0583] Step 9:

[0584] If the person in charge changes, the server automatically creates handover documents based on past interaction history and emotional history, and sends them to the new person in charge's terminal. The new person in charge reviews the documents on their terminal, enabling an efficient handover.

[0585] (Example 2)

[0586] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0587] In modern information systems, providing responses that take user emotions into consideration is difficult, leading to decreased customer satisfaction. Furthermore, a lack of efficient handover methods when personnel changes occur can impair operational efficiency. While it's necessary to consider user emotions and usage patterns to propose optimal service plans, analyzing these in real time presents a challenge.

[0588] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0589] In this invention, the server includes emotion analysis means for analyzing the user's emotions, response generation means for generating responses based on the emotion analysis results, and analysis means for analyzing the customer's usage trends and emotional history and proposing an optimized service plan. This makes it possible to provide consistent responses that correspond to the user's emotions, enable efficient handover even when the person in charge changes, and improve customer satisfaction.

[0590] "Information processing means" refers to technology for handling information such as inquiries received from users and processing it appropriately according to the purpose.

[0591] "Data aggregation" refers to the technology or method of accumulating received inquiries and information and systematically storing them for later analysis and reference.

[0592] "Artificial intelligence tools" refer to computer-based technologies that have the function of analyzing data and extracting useful information and patterns from it.

[0593] "Emotion analysis means" refers to technology that identifies emotional states from text and voice data entered by users and records them as data.

[0594] "Response generation means" refers to a technology or process for automatically generating the optimal response to a user based on analyzed data and emotional state.

[0595] "Usage trends" refer to information obtained by analyzing data on how customers use services over a long period, including frequency and behavioral patterns.

[0596] An "optimized service plan" refers to the best possible service delivery model for each individual customer, proposed based on their usage patterns and emotional state.

[0597] "Handover support methods" refer to technologies that automatically generate necessary documents based on past interaction history and emotional data in order to ensure efficient handover when the person in charge changes.

[0598] This section describes the embodiments for carrying out the invention. This invention involves a system that automatically generates and provides responses that take into account the user's emotions. The following details how this system is implemented.

[0599] Users access the corporate portal using their devices and input their inquiries in natural language. This inquiry data is sent to the server via the network. The server stores the received data in a database using specialized data aggregation technology. This process is in preparation for subsequent analysis and response generation.

[0600] Upon receiving information, the server activates its sentiment analysis system and begins processing the data. This sentiment analysis system typically uses software that identifies emotions from text content. For example, a commonly used sentiment analysis engine is "natural language understanding software." Through this analysis, emotions such as joy, anger, and sadness are extracted from the text data submitted by the user.

[0601] The results of the emotion analysis are analyzed by artificial intelligence tools, which then consider the optimal response based on the user's emotional state. This process utilizes a "generative AI model," and open-source language models can be used as an example. As a result, the server uses a response generation tool to create a response that takes the user's emotions into consideration and sends it to the terminal. The user then checks the latest information on their terminal and knows that the system understands their emotions and is providing optimal support.

[0602] For example, if a user inquires about a delay in product delivery, the server uses sentiment analysis to identify the user's anger. The generated response would then provide an apology and confirmation, such as, "We sincerely apologize for the delay. We are currently checking the delivery status and will contact you shortly."

[0603] To further support this invention, the following is an example of a relevant prompt: "A user has submitted an inquiry expressing anger about a delivery delay. Please generate an appropriate response to this inquiry." This prompt serves as a starting point for AI-generated responses and helps the system make accurate decisions.

[0604] As described above, this system realizes technology that analyzes user emotions and generates specific and user-friendly responses accordingly. This is expected to improve customer satisfaction.

[0605] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0606] Step 1:

[0607] The user accesses the corporate portal using a terminal and enters an inquiry in natural language. The entered data is sent from the terminal to the server. At this time, the input is natural language text data, and the output is that text data sent to the server. The terminal performs the action of accurately sending the entered inquiry through the user's actions.

[0608] Step 2:

[0609] The server receives queries sent from terminals and records them in its data repository. In this process, the server takes a text-formatted query as input and generates a record to be stored in the database as output. Specifically, the server performs data format validation and storage. This ensures that the query remains permanently accessible.

[0610] Step 3:

[0611] The server processes recorded queries using sentiment analysis tools. This analysis uses query text as input and outputs emotional state data. As a concrete example of sentiment analysis, natural language understanding software is used to identify emotions such as joy, anger, and sadness. The server sends text data to the analysis engine and extracts the emotional data.

[0612] Step 4:

[0613] The server generates a response using artificial intelligence based on the sentiment analysis results. The input in this step is the sentiment analysis result, and the output is the generated response text. This involves utilizing a generative AI model to select words that are sensitive to the user's emotions. For example, if anger is detected, the server constructs a response that includes an apology and suggestions for resolving the problem.

[0614] Step 5:

[0615] The server sends the AI-generated response to the terminal and provides it to the user. The input for this step is the response text, and the output is displayed on the user's terminal. The server performs the action of sending the generated response to the terminal over the network. The user confirms the response through the terminal screen and receives support from the system.

[0616] (Application Example 2)

[0617] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0618] In modern information processing systems, providing uniform responses without understanding user emotions is a factor that detracts from the customer experience. Furthermore, in commercial fields such as electronic payment services, there is a need to flexibly respond to the diverse emotions and needs of customers. Therefore, it is necessary to accurately analyze user emotions and provide appropriate and flexible responses based on that analysis.

[0619] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0620] In this invention, the server includes data processing means for receiving inquiries from users, means for recording the received inquiries in a storage device, artificial intelligence means for performing analysis based on the recorded inquiry time-series data and user information, response formation means for generating a response based on the analysis results, emotion analysis means for analyzing emotions from the language and voice data of the user's inquiry, and emotion response means for designing a flexible and consistent response based on the output of the emotion analysis means. This makes it possible to provide accurate services that are adapted to the user's emotions.

[0621] "Data processing means for receiving inquiries from users" refers to a device that has the function of appropriately receiving inquiries sent by users and inputting their contents into the system.

[0622] "Means for recording received inquiries in a storage device" refers to a device that performs the process of saving the content of inquiries received from users in digital format and systematizing it so that it can be referenced later.

[0623] "An artificial intelligence tool that performs analysis based on recorded inquiry time-series data and user information" refers to software that uses previously accumulated inquiry data and customer data to extract useful patterns and knowledge from that data.

[0624] A "response formation means for generating responses based on analysis results" is a device equipped with the function to automatically generate appropriate content to respond to the user based on analysis results provided by artificial intelligence.

[0625] "An emotion analysis device that analyzes emotions from the language and voice data of inquiries made by users" refers to a device that has the technology to detect and identify a user's emotional state from text and voice information and output the results as analysis data.

[0626] "An emotional response system that designs flexible and consistent responses based on the output of an emotional analysis system" refers to a system that uses the results of emotional analysis to design the most appropriate and emotionally resonant response for the user.

[0627] The system that realizes this invention allows a user to make an inquiry using a smartphone or other mobile device, and that data is immediately sent to a server. The server processes the received data using sentiment analysis engines such as Google Cloud Natural Language or IBM Watson, and identifies the emotional state from the user's language and voice data.

[0628] The server passes the analysis results to an AI agent such as OpenAI GPT, which generates an appropriate response. The response is flexibly adjusted to the user's emotional state in terms of wording and tone, and then returned to the terminal. For example, if a user expresses dissatisfaction with an electronic payment service, the system will offer an apology and a solution in a gentle tone.

[0629] The terminal can instantly display the response received from the server to the user. This gives the user a sense of satisfaction, knowing that their feelings are understood and that they receive considerate service. Furthermore, the system can accumulate customer data and usage logs and perform periodic analysis to propose plans optimized for each user.

[0630] For example, if a user expresses concern that "recent payments don't seem to be reflected," the system will quickly perform a verification and generate a response such as, "We have checked your payment information. It is currently being processed by the system, but there are no problems, so please rest assured." An example of a prompt message the system might use in this case is, "Based on the user's latest inquiry and sentiment analysis results, generate a gentle message that will alleviate the user's anxiety."

[0631] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0632] Step 1:

[0633] The user enters a query in natural language using a smartphone or other device and presses the submit button. The device sends this input as data to the server. The input can be text or audio data, which becomes the output to the server.

[0634] Step 2:

[0635] The server inputs the received data into an emotion analysis engine to identify the user's emotions. Using Google Cloud Natural Language and IBM Watson, it analyzes the input data to determine emotional states such as joy, anger, and sadness. The output is information about the user's emotional state.

[0636] Step 3:

[0637] Based on the sentiment analysis output, the server sends data to the AI ​​agent and requests it to generate a response. The AI ​​agent uses the analysis results and the prompt "Based on the user's latest inquiry and sentiment analysis results, please generate a gentle response that will alleviate the user's anxiety" to form an appropriate response. The generated response is then output.

[0638] Step 4:

[0639] The server returns the generated response to the terminal, providing it to the user. The terminal receives the response and displays it on the screen. This allows the user to receive a response that reflects their own emotions. The output is text information presented to the user.

[0640] Step 5:

[0641] The system records query data and sentiment analysis results in a database for later analysis. This recording process saves the data as a time series and is used for future service optimization. The output is the accumulated historical data.

[0642] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0643] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0644] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0645] [Fourth Embodiment]

[0646] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0647] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0648] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0649] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0650] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0651] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0652] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0653] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0654] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0655] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0656] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0657] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0658] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0659] The system of the present invention provides a mechanism for providing efficient and consistent services to corporate clients. This system is implemented in the following form.

[0660] First, the user accesses the corporate portal and enters customer information into the system via a terminal. This information includes company name, contact details, and contract details. The terminal sends this information to the server, which verifies it and then stores it in a database. Based on the stored information, the server assigns a dedicated AI agent to each customer.

[0661] When a user submits an inquiry, they enter their question through a chat window on their device. This inquiry is sent to the server, which receives it and records it in its database. During this process, the inquiry content and related history are all stored chronologically.

[0662] The recorded data is analyzed by the server via an AI agent. The AI ​​agent analyzes the accumulated inquiry history and customer data to generate appropriate responses. For example, if a user asks, "I would like information about the next contract renewal," the AI ​​agent will generate a specific suggestion such as, "The next renewal date is on XX day, and △△ is available as a benefit." This response is sent from the server to the terminal and displayed to the user.

[0663] The server also periodically analyzes customer usage and suggests optimized account plans. These suggestions, generated by the AI ​​agent, are delivered to the user's device by the server, allowing them to review and update them as needed.

[0664] Furthermore, the server automatically determines the need for a handover when the person in charge changes and uses an AI agent to create handover documents. These documents are generated based on past interaction history and can be viewed by the new person in charge on their terminal. This reduces the burden during the handover and ensures consistent service delivery.

[0665] By implementing this system, it becomes possible to consistently provide customers with high-quality and stable service, leading to improved customer satisfaction.

[0666] The following describes the processing flow.

[0667] Step 1:

[0668] The user accesses the corporate portal and enters the company name, contact information, and contract details as a new customer via their device.

[0669] Step 2:

[0670] The terminal sends the entered customer information to the server. The server receives this information and verifies its accuracy.

[0671] Step 3:

[0672] After verification by the server, customer information is saved to the database. At this point, a dedicated AI agent is assigned to the customer.

[0673] Step 4:

[0674] Users enter inquiries through the corporate portal's chat window. For example, they might ask, "I'd like to know about my current contract plan."

[0675] Step 5:

[0676] The terminal sends the user's query to the server. The server receives the query, adds a timestamp, and records it in the database.

[0677] Step 6:

[0678] The server provides the AI ​​agent with the inquiry details and past history, and instructs it to perform the analysis.

[0679] Step 7:

[0680] The AI ​​agent analyzes the provided information and generates an appropriate response. For example, specific information such as, "Your current contract is the XX plan."

[0681] Step 8:

[0682] The server sends the generated response to the terminal. The terminal receives it and displays it to the user.

[0683] Step 9:

[0684] The server periodically analyzes customer usage patterns and has an AI agent generate optimized plan suggestions.

[0685] Step 10:

[0686] The AI ​​agent generates a plan and returns it to the server, which then provides the suggestion to the user's device. The user reviews the suggestion and updates the plan as needed.

[0687] Step 11:

[0688] If a change in personnel occurs, the server will have the AI ​​agent create handover documents based on past interaction history.

[0689] Step 12:

[0690] The server sends the generated handover documents to the new person in charge's terminal. The terminal displays the documents and assists the person in charge in starting their work based on them.

[0691] (Example 1)

[0692] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0693] Customer management systems are required to improve the efficiency of handling inquiries, propose optimal plans based on customer usage, and provide consistent service. However, conventional systems have shortcomings in effectively managing inquiry history and automated handover procedures, resulting in insufficient customer satisfaction.

[0694] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0695] In this invention, the server includes information processing means for receiving inquiries from users, means for recording the received inquiries in a data set in chronological order, and artificial intelligence means utilizing a generative AI model that performs analysis based on the recorded inquiry history and customer data. This makes it possible to streamline inquiry handling and realize appropriate account proposals and automated handover procedures for each customer.

[0696] "Information processing means for receiving inquiries from users" refers to the functions of devices or software that receive inquiries entered by users into the system.

[0697] "Means of recording data in a time-series format" refers to devices or methods for sequentially saving received information in a database or similar format according to chronological order.

[0698] "Artificial intelligence methods using generative AI models" refer to systems and technologies that utilize machine learning models to perform data analysis and decision-making.

[0699] A "response generation means" refers to a process or device for creating a response to a user based on the results analyzed by artificial intelligence.

[0700] "Communication methods" refer to technologies and devices for sending and receiving data via computer networks.

[0701] "A means of sending customer information to a server and assigning a dedicated agent" refers to a function that delivers customer data to a central server, after which a specific program automatically assigns a dedicated artificial intelligence to each customer.

[0702] "Analytics that analyze usage history and generate optimized account suggestions" refers to technologies and systems that evaluate past usage data and propose service plans that are best suited to the user's needs.

[0703] A "handover document generation means for automatically generating handover documents" is a device or method for automatically creating documents for a new person to take over work based on past interaction history when a person in charge changes.

[0704] Modes for carrying out the invention

[0705] This invention is a system for providing efficient and consistent customer service to corporate clients. Specific embodiments are described below.

[0706] User

[0707] Users access a dedicated corporate portal using a browser and enter customer information such as company name, contact details, and contract details on their device. This allows users to directly provide their company information to service providers.

[0708] terminal

[0709] The terminal is a means of sending input data to the server. The terminal temporarily stores the data in memory and then transfers it to the server via the internet. In this process, the terminal also performs a simple format check to ensure that the data format is correct.

[0710] server

[0711] The server processes customer information and inquiries received from terminals and stores them in a database. Specifically, it uses PostgreSQL to manage the data. The server also utilizes widely used open-source models as generative AI models to analyze inquiry history and customer data. This enables highly accurate analysis based on historical information.

[0712] When a user submits an inquiry, the server receives it, stores it in a database, and then uses AI to analyze the content and generate an appropriate response. This generated response is then forwarded back to the terminal using a communication method, allowing the user to review it.

[0713] Specific example

[0714] For example, a user might enter an inquiry into the chat system such as, "Please tell me my next contract renewal date." This inquiry is sent to the server and analyzed by an AI agent. As a result, the server generates a response such as, "Your next renewal date is on XX day, and you can use △△ as a benefit," and sends this to the user's device.

[0715] Examples of prompt messages include, "Please provide detailed information regarding contract renewal." In this way, it is possible to respond quickly to customer needs and improve satisfaction. This system enables consistent customer service and efficient information management.

[0716] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0717] Step 1:

[0718] Users access the corporate portal and enter customer information such as company name, contact details, and contract details into the terminal. The entered data is temporarily stored in the terminal's memory. The input here consists of strings and numbers based on a form, and the terminal performs a simple verification to ensure the information format is correct.

[0719] Step 2:

[0720] The terminal sends verified customer information to the server. During transmission, the data is converted into packet format and transferred to the server using the Internet Protocol. This process enables real-time online data transmission.

[0721] Step 3:

[0722] The server receives data from the terminal and stores it in the database. The server converts the data to the correct format and saves it to the PostgreSQL database. This makes the data easily searchable and manageable later.

[0723] Step 4:

[0724] When a user submits an inquiry through the chat window on their device, that inquiry is sent to the server. For example, they might enter a prompt message such as, "Please tell me my next contract renewal date." This data is sent to the server in string format.

[0725] Step 5:

[0726] The server receives the query and records it in the database. Then, it analyzes the query content using a generating AI model. The AI ​​model refers to historical data and executes an algorithm to generate the optimal response to the input prompt.

[0727] Step 6:

[0728] The server constructs the generated response and sends it to the terminal via the communication means. This output is a text message formatted for user understanding. The terminal receives this data and prepares to display it to the user.

[0729] Step 7:

[0730] The server periodically analyzes customer usage history and generates optimized account plans. Based on past usage patterns, AI analyzes and generates recommendations, suggesting options that allow users to utilize the service more efficiently.

[0731] Step 8:

[0732] When a person in charge changes, the server automatically analyzes past history information to generate a handover document. This generated document can be easily viewed by the new person in charge on their terminal, helping to maintain consistency in operations.

[0733] (Application Example 1)

[0734] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0735] In services targeting corporate clients, there is a demand for efficient and consistent responses. However, existing systems often result in fragmented customer support and a lack of service consistency. Furthermore, the difficulty in handling inquiries in real time and analyzing large amounts of customer data hinders improvements in customer satisfaction. In addition, manual handover is required when personnel changes occur, leading to delays and errors in responses.

[0736] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0737] In this invention, the server includes data processing means for receiving inquiries from users, a function for recording received inquiries in a storage device, and a function for referencing business information of corporate customers and providing electronic transaction information. This makes it possible to provide efficient and consistent services to corporate customers, improve customer satisfaction, and reduce the effort required for handover.

[0738] A "user" is an entity that utilizes the system as a corporate customer.

[0739] An "inquiry" is a question or request that a user sends to a system seeking information or support.

[0740] "Data processing means" refers to functions and technologies for receiving inquiries from users and performing the necessary processing.

[0741] A "storage device" is a storage medium used to save received inquiries and historical data.

[0742] "Historical data" refers to records that include the user's past inquiries and transaction information.

[0743] "Artificial intelligence functionality" refers to technology that analyzes historical data and customer information, and uses the insights gained from this analysis to generate responses.

[0744] The "reply generation function" is a technology that constructs the optimal response to provide to the user based on the analysis results.

[0745] "System functionality" refers to a set of technologies and processes that enable the system to operate as a whole and provide services to users.

[0746] A "corporate customer" is a company or business entity that benefits from the service and uses the system.

[0747] "Business information" refers to data related to the activities and transactions of corporate clients.

[0748] "Electronic transaction information" refers to transaction-related information that is managed and provided in digital format.

[0749] "Real-time communication function" refers to communication technology that enables the instantaneous transmission and reception of information.

[0750] The system for realizing this invention utilizes specific technologies and programs to provide efficient and consistent services to corporate clients. The system receives data from users (corporate clients), and a server stores this information in a central database. Users can submit inquiries using a smartphone app, and the information is processed in real time. This app is developed using React Native and provides a user-friendly interface.

[0751] The server is based on Node.js and uses MongoDB to store all customer information and history in its database. This ensures efficient data management and quick access when needed. Socket.io technology is implemented for real-time communication, allowing user inquiries to be registered and processed instantly.

[0752] The AI ​​analysis uses an AI agent built in a Python environment. This agent analyzes the user's history and inquiries using the natural language processing libraries NLTK and spaCy. Based on the analysis results, the AI ​​agent generates the optimal response for the user and provides it to the user through the server.

[0753] For example, if a corporate client's representative wants to check the details of their next payment or discount information, the AI ​​agent will provide specific information such as, "The next payment date is △ / △, and the available discounts are ○○." This allows users to efficiently obtain information and also provides a quick response to their individual needs.

[0754] An example of a prompt message would be, "Please provide the next payment date and available discount information for corporate customer XYZ." The AI ​​understands the user's needs and generates a response. This ensures that the information the user needs is provided in a timely manner.

[0755] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0756] Step 1:

[0757] Users enter their inquiries using a smartphone app. The entered data is sent from the device to the server via real-time communication. Socket.io is used for this process, and the data is compressed for efficient transmission.

[0758] Step 2:

[0759] The server decompresses the received query data and saves it to a MongoDB database using Node.js. This data is timestamped and stored as a query history. The database organizes information based on customer IDs and is managed for easy access.

[0760] Step 3:

[0761] The server detects that a query has been saved and invokes an AI agent running in a Python environment. The AI ​​agent takes the saved historical data and the current query data as input. The AI ​​agent analyzes the data using the natural language processing libraries NLTK and spaCy to understand the user's intent.

[0762] Step 4:

[0763] Based on the analysis results, the AI ​​agent generates the optimal response. For example, if the prompt "Please provide the next payment date and available discount information for corporate customer XYZ" is entered, the AI ​​agent will retrieve the necessary information from its internal data and generate a specific response such as "The next payment date is △ / △, and the available discounts are ○○."

[0764] Step 5:

[0765] The generated response is sent to the terminal via the server. The user receives this response in real time through a smartphone app, and it is displayed on the screen. This allows the user to obtain information quickly and accurately.

[0766] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0767] The system of this invention provides sophisticated responses that incorporate user sentiment analysis in order to deliver consistent service to corporate clients. This system is implemented in the following form.

[0768] First, the user accesses the corporate portal and enters an inquiry into the system from their terminal. This inquiry often includes questions or requests in natural language, and the terminal sends this information to the server. The server verifies the received information and records it in a database. This allows the inquiry history to be managed chronologically.

[0769] Next, the server uses an emotion engine to analyze the emotions expressed in the user's text and voice inquiries. The emotion engine identifies emotions such as joy, anger, and sadness, and records the emotional state in a database. This also allows for the management of the user's emotional history.

[0770] The server receives the analysis results from the emotion engine and provides the AI ​​agent with the analysis results and emotion data. Based on this information, the AI ​​agent generates the optimal response. For example, if a user expresses dissatisfaction with the contract terms, the AI ​​agent will generate an emotion-sensitive response such as, "We have prepared specific suggestions for improvement." Depending on the emotion, it may also adopt softer language or a more respectful communication style.

[0771] The generated response is sent from the server to the terminal and displayed to the user. This allows the user to have their emotions properly understood and receive a response accordingly.

[0772] Furthermore, the server regularly analyzes customer usage and sentiment history to suggest optimized plans. This allows for continuous service improvements aimed at increasing customer satisfaction.

[0773] When a change in personnel occurs, the server automatically generates handover documents based on past interaction history and emotional history. These documents are sent to the new person in charge's terminal, enabling an efficient handover.

[0774] Through this invention, the system can provide consistent and flexible services that respond to the user's emotions, thereby improving customer satisfaction.

[0775] The following describes the processing flow.

[0776] Step 1:

[0777] Users access the corporate portal and enter inquiries in natural language from their devices.

[0778] Step 2:

[0779] The terminal sends the entered query data to the server. The server receives the query and records the information in its database.

[0780] Step 3:

[0781] The server provides the received query data to the emotion engine. The emotion engine analyzes the user's emotions from the query content.

[0782] Step 4:

[0783] The emotion engine analyzes the emotion data and returns it to the server, which then records this information in a database as an emotion history.

[0784] Step 5:

[0785] The server passes the analyzed sentiment data and query content to the AI ​​agent and instructs it to generate an appropriate response.

[0786] Step 6:

[0787] The AI ​​agent generates user-optimized responses while taking into account the user's emotional state. For example, if the user expresses dissatisfaction, the response will be delivered in gentler language.

[0788] Step 7:

[0789] The server receives the response generated by the AI ​​agent and sends it to the terminal. The terminal then displays this to the user.

[0790] Step 8:

[0791] The server periodically analyzes customer usage and sentiment history, and uses analytical tools to generate new plan suggestions.

[0792] Step 9:

[0793] If the person in charge changes, the server automatically creates handover documents based on past interaction history and emotional history, and sends them to the new person in charge's terminal. The new person in charge reviews the documents on their terminal, enabling an efficient handover.

[0794] (Example 2)

[0795] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0796] In modern information systems, providing responses that take user emotions into consideration is difficult, leading to decreased customer satisfaction. Furthermore, a lack of efficient handover methods when personnel changes occur can impair operational efficiency. While it's necessary to consider user emotions and usage patterns to propose optimal service plans, analyzing these in real time presents a challenge.

[0797] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0798] In this invention, the server includes emotion analysis means for analyzing the user's emotions, response generation means for generating responses based on the emotion analysis results, and analysis means for analyzing the customer's usage trends and emotional history and proposing an optimized service plan. This makes it possible to provide consistent responses that correspond to the user's emotions, enable efficient handover even when the person in charge changes, and improve customer satisfaction.

[0799] "Information processing means" refers to technology for handling information such as inquiries received from users and processing it appropriately according to the purpose.

[0800] "Data aggregation" refers to the technology or method of accumulating received inquiries and information and systematically storing them for later analysis and reference.

[0801] "Artificial intelligence tools" refer to computer-based technologies that have the function of analyzing data and extracting useful information and patterns from it.

[0802] "Emotion analysis means" refers to technology that identifies emotional states from text and voice data entered by users and records them as data.

[0803] "Response generation means" refers to a technology or process for automatically generating the optimal response to a user based on analyzed data and emotional state.

[0804] "Usage trends" refer to information obtained by analyzing data on how customers use services over a long period, including frequency and behavioral patterns.

[0805] An "optimized service plan" refers to the best possible service delivery model for each individual customer, proposed based on their usage patterns and emotional state.

[0806] "Handover support methods" refer to technologies that automatically generate necessary documents based on past interaction history and emotional data in order to ensure efficient handover when the person in charge changes.

[0807] This section describes the embodiments for carrying out the invention. This invention involves a system that automatically generates and provides responses that take into account the user's emotions. The following details how this system is implemented.

[0808] Users access the corporate portal using their devices and input their inquiries in natural language. This inquiry data is sent to the server via the network. The server stores the received data in a database using specialized data aggregation technology. This process is in preparation for subsequent analysis and response generation.

[0809] Upon receiving information, the server activates its sentiment analysis system and begins processing the data. This sentiment analysis system typically uses software that identifies emotions from text content. For example, a commonly used sentiment analysis engine is "natural language understanding software." Through this analysis, emotions such as joy, anger, and sadness are extracted from the text data submitted by the user.

[0810] The results of the emotion analysis are analyzed by artificial intelligence tools, which then consider the optimal response based on the user's emotional state. This process utilizes a "generative AI model," and open-source language models can be used as an example. As a result, the server uses a response generation tool to create a response that takes the user's emotions into consideration and sends it to the terminal. The user then checks the latest information on their terminal and knows that the system understands their emotions and is providing optimal support.

[0811] For example, if a user inquires about a delay in product delivery, the server uses sentiment analysis to identify the user's anger. The generated response would then provide an apology and confirmation, such as, "We sincerely apologize for the delay. We are currently checking the delivery status and will contact you shortly."

[0812] To further support this invention, the following is an example of a relevant prompt: "A user has submitted an inquiry expressing anger about a delivery delay. Please generate an appropriate response to this inquiry." This prompt serves as a starting point for AI-generated responses and helps the system make accurate decisions.

[0813] As described above, this system realizes technology that analyzes user emotions and generates specific and user-friendly responses accordingly. This is expected to improve customer satisfaction.

[0814] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0815] Step 1:

[0816] The user accesses the corporate portal using a terminal and enters an inquiry in natural language. The entered data is sent from the terminal to the server. At this time, the input is natural language text data, and the output is that text data sent to the server. The terminal performs the action of accurately sending the entered inquiry through the user's actions.

[0817] Step 2:

[0818] The server receives queries sent from terminals and records them in its data repository. In this process, the server takes a text-formatted query as input and generates a record to be stored in the database as output. Specifically, the server performs data format validation and storage. This ensures that the query remains permanently accessible.

[0819] Step 3:

[0820] The server processes recorded queries using sentiment analysis tools. This analysis uses query text as input and outputs emotional state data. As a concrete example of sentiment analysis, natural language understanding software is used to identify emotions such as joy, anger, and sadness. The server sends text data to the analysis engine and extracts the emotional data.

[0821] Step 4:

[0822] The server generates a response using artificial intelligence based on the sentiment analysis results. The input in this step is the sentiment analysis result, and the output is the generated response text. This involves utilizing a generative AI model to select words that are sensitive to the user's emotions. For example, if anger is detected, the server constructs a response that includes an apology and suggestions for resolving the problem.

[0823] Step 5:

[0824] The server sends the AI-generated response to the terminal and provides it to the user. The input for this step is the response text, and the output is displayed on the user's terminal. The server performs the action of sending the generated response to the terminal over the network. The user confirms the response through the terminal screen and receives support from the system.

[0825] (Application Example 2)

[0826] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0827] In modern information processing systems, providing uniform responses without understanding user emotions is a factor that detracts from the customer experience. Furthermore, in commercial fields such as electronic payment services, there is a need to flexibly respond to the diverse emotions and needs of customers. Therefore, it is necessary to accurately analyze user emotions and provide appropriate and flexible responses based on that analysis.

[0828] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0829] In this invention, the server includes data processing means for receiving inquiries from users, means for recording the received inquiries in a storage device, artificial intelligence means for performing analysis based on the recorded inquiry time-series data and user information, response formation means for generating a response based on the analysis results, emotion analysis means for analyzing emotions from the language and voice data of the user's inquiry, and emotion response means for designing a flexible and consistent response based on the output of the emotion analysis means. This makes it possible to provide accurate services that are adapted to the user's emotions.

[0830] "Data processing means for receiving inquiries from users" refers to a device that has the function of appropriately receiving inquiries sent by users and inputting their contents into the system.

[0831] "Means for recording received inquiries in a storage device" refers to a device that performs the process of saving the content of inquiries received from users in digital format and systematizing it so that it can be referenced later.

[0832] "An artificial intelligence tool that performs analysis based on recorded inquiry time-series data and user information" refers to software that uses previously accumulated inquiry data and customer data to extract useful patterns and knowledge from that data.

[0833] A "response formation means for generating responses based on analysis results" is a device equipped with the function to automatically generate appropriate content to respond to the user based on analysis results provided by artificial intelligence.

[0834] "An emotion analysis device that analyzes emotions from the language and voice data of inquiries made by users" refers to a device that has the technology to detect and identify a user's emotional state from text and voice information and output the results as analysis data.

[0835] "An emotional response system that designs flexible and consistent responses based on the output of an emotional analysis system" refers to a system that uses the results of emotional analysis to design the most appropriate and emotionally resonant response for the user.

[0836] The system that realizes this invention allows a user to make an inquiry using a smartphone or other mobile device, and that data is immediately sent to a server. The server processes the received data using sentiment analysis engines such as Google Cloud Natural Language or IBM Watson, and identifies the emotional state from the user's language and voice data.

[0837] The server passes the analysis results to an AI agent such as OpenAI GPT, which generates an appropriate response. The response is flexibly adjusted to the user's emotional state in terms of wording and tone, and then returned to the terminal. For example, if a user expresses dissatisfaction with an electronic payment service, the system will offer an apology and a solution in a gentle tone.

[0838] The terminal can instantly display the response received from the server to the user. This gives the user a sense of satisfaction, knowing that their feelings are understood and that they receive considerate service. Furthermore, the system can accumulate customer data and usage logs and perform periodic analysis to propose plans optimized for each user.

[0839] For example, if a user expresses concern that "recent payments don't seem to be reflected," the system will quickly perform a verification and generate a response such as, "We have checked your payment information. It is currently being processed by the system, but there are no problems, so please rest assured." An example of a prompt message the system might use in this case is, "Based on the user's latest inquiry and sentiment analysis results, generate a gentle message that will alleviate the user's anxiety."

[0840] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0841] Step 1:

[0842] The user enters a query in natural language using a smartphone or other device and presses the submit button. The device sends this input as data to the server. The input can be text or audio data, which becomes the output to the server.

[0843] Step 2:

[0844] The server inputs the received data into an emotion analysis engine to identify the user's emotions. Using Google Cloud Natural Language and IBM Watson, it analyzes the input data to determine emotional states such as joy, anger, and sadness. The output is information about the user's emotional state.

[0845] Step 3:

[0846] Based on the sentiment analysis output, the server sends data to the AI ​​agent and requests it to generate a response. The AI ​​agent uses the analysis results and the prompt "Based on the user's latest inquiry and sentiment analysis results, please generate a gentle response that will alleviate the user's anxiety" to form an appropriate response. The generated response is then output.

[0847] Step 4:

[0848] The server returns the generated response to the terminal, providing it to the user. The terminal receives the response and displays it on the screen. This allows the user to receive a response that reflects their own emotions. The output is text information presented to the user.

[0849] Step 5:

[0850] The system records query data and sentiment analysis results in a database for later analysis. This recording process saves the data as a time series and is used for future service optimization. The output is the accumulated historical data.

[0851] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0852] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0853] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0854] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0855] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0856] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0857] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0858] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0859] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0860] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0861] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0862] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0863] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0864] 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.

[0865] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0866] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0867] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0868] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0869] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0870] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0871] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0872] The following is further disclosed regarding the embodiments described above.

[0873] (Claim 1)

[0874] Information processing means for receiving inquiries from users,

[0875] A means of recording received queries in a database,

[0876] An artificial intelligence method that performs analysis based on recorded inquiry history and customer data,

[0877] A response generation means that generates a response based on the analysis results,

[0878] A means of providing the generated response to the user,

[0879] A system that includes this.

[0880] (Claim 2)

[0881] The system according to claim 1, further comprising analytical means for analyzing customer usage and proposing an optimized plan.

[0882] (Claim 3)

[0883] The system according to claim 1, further comprising a handover support means that automatically generates handover documents based on past history when the person in charge changes.

[0884] "Example 1"

[0885] (Claim 1)

[0886] Information processing means for receiving inquiries from users,

[0887] A means of recording received inquiries in a data set in chronological order,

[0888] An artificial intelligence method that uses a generative AI model to perform analysis based on recorded inquiry history and customer data,

[0889] A response generation means that generates a response based on the analysis results,

[0890] A communication means for providing the generated response to the user,

[0891] A means of sending customer information to a server and assigning a dedicated agent,

[0892] A system that includes this.

[0893] (Claim 2)

[0894] The system according to claim 1, further comprising analytical means for analyzing customer usage history and generating optimized account recommendations.

[0895] (Claim 3)

[0896] The system according to claim 1, further comprising a handover document generation means for automatically generating a handover document based on past history information when the person in charge changes.

[0897] "Application Example 1"

[0898] (Claim 1)

[0899] A data processing means for receiving user inquiries,

[0900] A function to record received inquiries in a storage device,

[0901] An artificial intelligence function that performs analysis based on recorded historical data and customer information,

[0902] A reply generation function that generates a response based on the analysis results,

[0903] A system function that provides the generated response to the user,

[0904] The function of referencing business information of corporate customers and providing electronic transaction information,

[0905] A function that instantly records and processes inquiries using real-time communication capabilities,

[0906] A system that includes this.

[0907] (Claim 2)

[0908] The system according to claim 1, further comprising an analytical function for analyzing customer usage patterns and proposing optimized service content.

[0909] (Claim 3)

[0910] The system according to claim 1, further comprising a handover support function that automatically generates handover information based on past support history when the person in charge changes.

[0911] "Example 2 of combining an emotion engine"

[0912] (Claim 1)

[0913] Information processing means for receiving inquiries from users,

[0914] A means of recording received inquiries in a data collection,

[0915] An artificial intelligence method that performs analysis based on recorded inquiry history and user data,

[0916] A means of analyzing user emotions,

[0917] A response generation means that generates a response based on the results of emotion analysis,

[0918] Means for providing the generated response to the user,

[0919] A system that includes this.

[0920] (Claim 2)

[0921] The system according to claim 1, further comprising analytical means for analyzing customer usage trends and emotional history and proposing an optimized service plan.

[0922] (Claim 3)

[0923] The system according to claim 1, further comprising a handover support means that automatically generates handover documents based on past interaction history and emotional history when the person in charge changes.

[0924] "Application example 2 when combining with an emotional engine"

[0925] (Claim 1)

[0926] A data processing means for receiving user inquiries,

[0927] A means for recording received queries in a storage device,

[0928] An artificial intelligence method that performs analysis based on recorded inquiry time-series data and user information,

[0929] A response formation means that generates a response based on the analysis results,

[0930] A means of presenting the generated response to the user,

[0931] A sentiment analysis method that analyzes emotions from the language and voice data of inquiries made by users,

[0932] An emotion response tool that designs flexible and consistent responses based on the output of an emotion analysis tool,

[0933] A system that includes this.

[0934] (Claim 2)

[0935] The system according to claim 1, further comprising analytical means for proposing an optimized service delivery model based on customer usage and the subject of analysis.

[0936] (Claim 3)

[0937] The system according to claim 1, further comprising a handover support function that automatically generates handover documents based on previously accumulated records when the person in charge changes. [Explanation of symbols]

[0938] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A data processing means for receiving user inquiries, A function to record received inquiries in a storage device, An artificial intelligence function that performs analysis based on recorded historical data and customer information, A reply generation function that generates a response based on the analysis results, A system function that provides the generated response to the user, The function of referencing business information of corporate customers and providing electronic transaction information, A function that instantly records and processes inquiries using real-time communication capabilities, A system that includes this.

2. The system according to claim 1, further comprising an analytical function for analyzing customer usage patterns and proposing optimized service content.

3. The system according to claim 1, further comprising a handover support function that automatically generates handover information based on past support history when the person in charge changes.

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